The main purpose of a project management maturity model is to provide a plan and a framework for achieving excellence in program management (Hartwig and Smith, 2000, p. 3). Likewise, the 5 level project management process maturity model aims at transforming an organization from being functionally-drive to project-driven (Kwak and Ibbs, 2000). Level 4 of the 5 level project management process maturity model combines and controls a number of projects to attain precision (Kwak and Ibbs, 2000). For that reason, this level closely resembles Six Sigma. Six Sigma is a method of measuring quality that attempts to achieve results that are near perfection. This essay uses the 5 level project management process maturity model to discus how Honeywell can move from Level 3 to Level 4.
This essay assumes that Honeywell has already gone through level 3 of the 5 level project management process maturity model and now wants to proceed to level 4. For that reason, Honeywells project manager has already introduced professionalism in project management. In addition, problems affecting project management have already been indentified and documented, formal methods applied to collect data and a cross-functional team integrated to form a project management team. Nonetheless, in level 3, Honeywell cannot effectively integrate and manage multiple projects (Kwak and Ibbs, 2000).
To do so, Honeywell must move from level 3 to level 4. According to Kwak and Ibbs (2000), an organization at level 4 is able to define, quantitatively measure, understand and implement project management processes. Additionally, the project management team has the ability to use data collected to predict and prevent occurrences that may adversely affect productivity or quality of products (Kwak and Ibbs, 2000). Therefore, in this level, decision making is based on facts. At the same time, an organization has a strong project management team and trainings are well planned and provided to all employees.
Level 4 is sometimes synonymous to the application of the Six Sigma (Gack, 2010). Therefore, Six Sigma and project management have so much in common. For instance, both processes indentify and communicate with stakeholders and conduct continuous monitoring and evaluation (Gack, 2010). In addition, both processes aim at minimizing failures and managing costs and schedules. Furthermore, the two processes seek avoid defects and control risks. Six Sigma, therefore, complements project management, but does not replace it (Rever, n.d.). In addition, Six Sigma provides additional features to project management (Rever, n.d.).
For instance, Six Sigma provides project managers with a number of process enhancement steps and tools. Secondly, Six Sigma enables a project manager to comprehend and improve outcomes through statistical and other methods. In addition, a project manager is able to understand variations in the variables being measured. Therefore, when the Six Sigma is applied, key decisions are based on factual data.
At level 4 of the 5 level project management process maturity model, there is full implementation of integrated project management processes. Therefore, Honeywell must be able to successfully plan and control multiple projects in a professional manner if it is to reach level 4. Consequently, Honeywell must use maturity level results and the lessons learned from the assessments to reach level 4. Honeywell main aim to improve its project management processes (Hartwig and Smith, 2000). However, a project manager can only be able to improve processes after improving major performance indices (Rever, n.d.). According to Rever (n.d.), Six Sigmas tools and methods are the best ways of improving processes in project management. If you cant describe what you are doing as a process, you dont know what youre doing (Rever, n.d., Para 4). Therefore, in order to move to level 4, the management of Honeywell must focus on a strategy that focuses on key project management variables.
Applications of the Six Sigma will, hence, help Honeywell use a logical and a fact based approach to indentify root causes of problems within its projects (Rever, n.d.). Subsequently, Six Sigma will help Honeywells management device ways of preventing the reoccurrence of these problems. Six Sigma defines measures, analyzes, improves and manages business results (Rever, n.d.). These are the five steps in which Six Sigma can help Honeywells project manager improve results (Rever, n.d.).
Therefore, Honeywells management must undertake extensive planning to ensure that its projects are set up correctly. Secondly, Honeywells management must ensure that available data is enough and accurate.Thirdly, Honeywells management must be able to use available data to improve project management processes. Various statistical tools can be used to present, analyze and interpret available data. Fourthly, Honeywells management must ensure that final recommendations make a sustainable difference in its project management processes. Therefore, all recommendations must be verified and validated. Finally, all improved processes must be handed over to the project managers and other specialists. This act helps maintain long-term gains and improvements.
In conclusion, Level 4 of the 5 level project management process maturity model combines and controls a number of projects to attain precision (Kwak and Ibbs, 2000). Likewise, Six Sigma ensures that all measurements done are almost perfect (Gack, 2010). Therefore, Six Sigma ensures that project management processes are improved and deviations are reduced. For that reason, in order to reach level 4, Honeywell must compliment its project management with Six Sigma.
Digitalization and the rapid development of emerging technologies have undoubtedly influenced numerous industries throughout the world. The recent studies in this sphere claim that such output enhancing techniques as Lean Six Sigma have become tremendously easier to conduct with the development of digital solutions. Although the Lean Six Sigma approach is extremely versatile and easily adaptable, it is recommended that companies and other organizations that utilize digital methods should always seek modification opportunities. As such, it is often claimed that Industry 4.0 includes the most advanced technological equipment and methods. Furthermore, it seems that the Industry 4.0 technologies might be the most prominent latest advancement in this sphere, as it promotes the simplification of evidence gathering and operational deficits prevention. The current paper focuses on the impact of digital technologies on the Lean Six Sigma usage, defining Industry.
Introduction
With the emergence of the digital era, various companies began to implement advanced technologies into their operations, leading to the introduction of progressive methods into the corporations activities. Such involvements typically yield higher production rates and better efficiency levels, improving the overall economic standing of the enterprise and allowing it to enhance its performance in the market (Saad & Khamkham, 2018). However, these developments have not only impacted the general organizational processes but also reshaped the traditional approaches to elevating a companys productivity, leading to the advancements in the previously utilized principles of corporation improvement (Saad & Khamkham, 2016). As such, a well-recognized method of preventing organizational failures and enhancing conducted enterprise activities, Lean Six Sigma, was changed remarkably since the beginning of the digital era.
The benefits of utilizing Lean Six Sigma for organizational improvement have been frequently recognized in the recent decade. Based on data analysis and work standardization, Lean Six Sigma combines the methods of lean process improvement and the six Sigma strategies, integrating these principles into a united approach to company productivity (Saad & Khamkham, 2018). However, after the onset of the digital era, additional methods aimed at assessing clientele interests and reducing excessive resource consumption became available, rapidly transforming the typical Lean Six Sigma implementations (Purwanto et al., 2020). As the fourth industrial revolution grows closer, with more Industry 4.0 technologies becoming implemented into the daily activities of various corporations, the approaches to enhancing industrial outputs are also reshaped (Park, Dahlgaard-Park, & Kim, 2020). As a result, the Lean Six Sigma methodology, based on thorough data collection and analysis, benefits by becoming more versatile, adaptable, and efficient in locating and resolving production processes defects.
Literature Review
The incorporation of digital solutions to allow multiple companies to achieve exceptional growth in their performance levels is the core purpose of numerous scholarly investigations. Traditionally, Lean Six Sigma techniques were implemented to attain a continuous increase in growth and ensure that the relevant customer values are understood and considered during product development (Gupta, Modgil, & Gunasekaran, 2020). After that, any wasteful activities would be eliminated and incorporate novel solutions that would enhance the outputs of the procedure, creating a different approach to the established production process (Gupta et al., 2020). Nevertheless, as more advanced technological methods of conducting such practices emerge, it becomes easier for the professionals to analyze the efficiency of organizational procedures, as well as introduce strategies that enhance such activities.
Prominent research by Lameijer, Pereira, and Antony (2021) suggests that digital technologies and their adaptations are highly useful for the utilization of Lean Six Sigma processes in the company environment. As such, it is reported that adhering to the novel developments in the digital industry and applying them in accordance with the organizational context is more effective. Even though the authors note that several previous studies regarded the strict utilization of the Lean Six Sigma structure as highly beneficial, the current findings corroborate the idea that more agile practices can also be advantageous (Gupta et al., 2020). The authors conclude that Lean Six Sigma in the DE-TECH companies requires evaluating the organizational environment, necessitating the station of necessary adjustments for improving the productivity of the intervention.
Another valuable benefit of introducing emerging technologies together with Lean Six Sigma is the possibility to combine these methods with Digital Curation. Arcidiacono, De Luca, Fallucchi, and Alessandra (2016) report that the necessity to analyze vast amounts of data is a crucial task for many organizations that desire to improve their efficiency and secure additional output benefits by utilizing Lean Six Sigma. Nonetheless, to thoroughly use this technique, it is crucial to use the most advanced strategies that allow conducting numerous assessments of large scopes of information. Such methods have been impossible to implement before the digital era due to the lack of computerization and the devices capacity to examine such data (Gabriele Arcidiacono et al., 2016). Therefore, the analysis and resolution possibilities were limited to the exploration of less complicated and vast evidence.
Relying on the results of previous studies and carefully evaluating recent reports from scientific research and organizational settings, the authors investigate how the digital era developments have contributed to the establishment of such practices. Digital Curation methods, which are typically defined as the processes of establishing and developing long-term repositories of digital assets for research issues, have been shown to be extremely beneficial for productivity improvement. As such, rather than continuously collecting organization-specific data or having to fund tedious research activities that are aimed at gathering such evidence, companies can store this information using Digital Curation (Gabriele Arcidiacono et al., 2016). In the long term, these practices simplify data extraction and allow quick access to gathered knowledge.
After that, the authors also propose that Big Data usage is a critical advancement introduced with the onset of the digital era. The tendency to utilize Big Data in the Lean Six Sigma processes is claimed to be a vital advantage that allows the companies to progress their data analysis methods and promote innovation within the enterprises structure (Gabriele Arcidiacono et al., 2016). While traditionally corporations used a limited scope of information derived from their own business processs or other sources, in the contemporary age, it is possible to access evidence available due to the development of the Internet. Indeed, numerous recent studies report the connection between Big Data and Lean Six Sigma, explaining how a larger amount of data, as well as the use of wireless devices, have contributed to the advancements in Lean Six Sigma strategies (Chiarini & Kumar, 2020; Park et al., 2020; Valamede & Akkari, 2020). From this perspective, technological developments have significantly influenced the implementation of the described methodology, clearly enhancing its potential for company executives.
Research Method
The current study researches contemporary literature, gathering and evaluating recent scientific data on the impact of digitalization on Lean Six Sigma methods in various corporations. This method is especially beneficial for determining the core tendencies within the scientific community, ascertaining the overall scholarly perspective towards combining Lean Six Sigma and prominent technological developments of the present age (Lameijer et al., 2021). In addition, this methodology allows establishing the significance of this combination, outlining its core benefits according to evidence-based practices, and proposing how the latest digitalization developments might contribute to the growth of Lean Six Sigma.
In order to conduct the research, recent scientific articles no older than five years since their publication were collected and examined. The keywords used for the search included Lean Six Sigma, digitalization, digital age, lean management, and emerging technologies. The scope of the chosen articles was restricted to materials that were published incredible academic journals and peer-reviewed by a number of scholars possessing the necessary qualifications for conducting the assessment. Similarly, the articles authors credentials were examined prior tool, including the publication into the research scope. Articles that did not correspond to the described criteria were eliminated from further assessment. A total of 15 studies were included in the final evaluation, and the core tendencies highlighted by the authors were explored. On the basis of the highlighted ideas, the technology claimed to be most prominent and frequently referred to as emerging was selected, resulting in the definition of one latest development. The chosen development was selected based on its recent introduction and the declared impact on Lean Six Sigma and Six Sigma.
The Latest Development
Following the research conducted, the technologies related to Industry 4.0 can be considered the latest technological developments that impacted the sphere of Lean Six Sigma implementation. The term Industry 4.0 refers to the upcoming 4th industrial revolution (Park et al., 2020). It is commonly defined as the process where cyber-physical and digital systems are massively incorporated into the production industry and services aimed at satisfying basic human needs (G. Arcidiacono & Pieroni, 2018; Park et al., 2020). Although the world is currently in the final stage of the third industrial revolution and the beginning of the 4th revolution is yet to be encountered, some of the digital technologies that herald the beginning of this era already exist. As such, a predominant majority of production companies and some services providers heavily rely on digital systems as a means of improving their productivity (Ganjavi & Fazlollahtabar, 2021). Thus, Industry 4.0 is a crucial concept for investigating technological developments in a variety of areas, including Lean Six Sigma.
The Impact of Digitalisation on the Six Sigma Implementation
Relying on the aforementioned evidence, it is evident that Industry 4.0 has significantly impacted the traditional processes of Lean Six Sigma. Scholars suggest that the fusion between Lean Six Sigma and Industry 4.0 technologies could be the latest advancement incorporated into the Lean Six Sigma methodology (G. Arcidiacono & Pieroni, 2018). For instance, Sordan, Oprime, Pimenta, Silva, and González (2021) delineate the points of contact between the two phenomena. The scholars propose a conceptual framework that considers what enhancements were implemented into the Lean Six Sigma practices and how they impacted the methodology. According to the research findings, there are critical correlations that include process mapping, performance measurement system updates, machine condition monitoring, optimization of handling and storage, real-time production control enhancements, and more (Sordan et al., 2021). As Lean Six Sigma methods hinge on improving business performance and elevating customer satisfaction, successfully conducting these procedures requires a variety of technologies that can easily process data and establish relevant connections.
Outlining the most prominent tendencies in customer values and statistically supporting the benefits of potential resolutions is critical for implementing Lean Six Sigma. The Industry 4.0 technologies allow for strong analysis possibilities even when a multitude of data is present (Rejikumar, Asokan, & Sreedharan, 2018). In comparison with other latest developments in the digital world, such as information transferability and robotics, it seems that proper analysis of information is most advantageous for enhancing the Lean Six Sigma procedures (Rejikumar et al., 2018). By including Industry 4.0 technologies into the working activities, an enterprise might improve its process mapping techniques, namely value stream mapping or swimlane chart, by using the novel development to monitor task execution in real-time (Sordan et al., 2021). The established algorithms will be able to drive data from the manufacturing execution systems, assess various factors that promote or decrease efficiency, and provide a comprehensive report for further examination (Tay & Loh, 2021). Overall, it is clear that Industry 4.0 technologies can be used as a means of improving a companys output by gathering relevant data and providing an analysis of the evident trends.
Lessons Learned
Conducting this research allowed me to gain some vital ideas about using Lean Six Sigma together with emerging digital solutions. The various advantages of digitalization, from Big Data to the development of more efficient technologies, allowed numerous companies to analyze the procedural flow faster, saving invaluable time and resources. Furthermore, in the contemporary world, such concepts as artificial intelligence, the Internet of Things, and 3D printing are becoming more common and are frequently utilized not only by large-scale manufacturers but even by small businesses (Jayaram, 2016). In the future, as the fourth industrial revolution commences, it is possible that further collaboration between Lean Six Sigma and digital technologies will occur, resulting in even more productive data analysis approaches.
Conclusion
To conclude, the onset of the digital era has tremendously impacted Lean Six Sigma methods for numerous industries, offering corporations a possibility to increase the outputs through incorporating novel technologies. Although Lean Six Sigma is a highly versatile approach for improving organizational processes in numerous working settings and industries, it also requires adaptation to the recent technological developments. Numerous scholarly studies report that Lean Six Sigma and the emerging digital solutions can be especially beneficial for the enterprises economic standing and productivity. By relying on such strategies as Big Data or Digital Curation, it is possible to use a diverse set of knowledge for subsequent analysis and evaluation.
References
Arcidiacono, G., & Pieroni, A. (2018). The revolution Lean Six Sigma 4.0. International Journal on Advanced Science, Engineering and Information Technology, 8(1).
Arcidiacono, Gabriele, De Luca, E., Fallucchi, F., & Alessandra, P. (2016). The use of Lean Six Sigma methodology in digital curation. 1st Workshop on Digital Humanities and Digital Curation, DHC.
Purwanto, A., Wirawati, S. M., Arthawati, S. N., Radyawanto, A. S., Rusdianto, B., Haris, M., & Kartika, H. (2020). Lean Six Sigma model for pharmacy manufacturing: Yesterday, today and tomorrow. Systematic Reviews in Pharmacy, 11(8).
Saad, S., & Khamkham, M. (2016). Development of Lean Six-Sigma conceptual implementation model for manufacturing organisations. In: Yee Mey Goh and Keith Case (Eds.): Advances in manufacturing technology XXX:Proceedings of the 14th International Conference on Manufacturing Research, incorporating the 31st National Conference on Manufacturing Research, September 6-8, 2016 Loughborough University, UK (pp. 497-502). Amsterdam: IOS Press.
The papers primary purpose is to demonstrate the impact of the digital era on the implementation of Lean Six Sigma. Moreover, it aims at defining the concepts of Lean Six Sigma and Industry 4.0 by raising the preliminary research question How the Digital Era impacts the implementation of Lean Six Sigma? Hence, the study uses secondary data analysis and the systematic literature review to collect and narrate the data. The sources were obtained from reliable databases for scientific research, namely ScienceDirect and Scopus. Therefore, peer-reviewed articles, proceedings, and online resources were analyzed and compared to reveal the significance of digitalization for the Six Sigma methodology. The Industry 4.0 technologies were illustrated and explained how they are used in Lean Six Sigma DMAIC. As a result, Lean Six Sigma is an essential foundation for efficiently combining Industry 4.0 technologies with business processes for organization success.
Introduction
The Lean Six Sigma methodology is currently successfully used by the worlds dominant firms in all industries. Singh and Rakhi (2018) emphasize that Lean Six Sigma is an essential business strategy to enhance the quality and productivity of companies. Nonetheless, the continuing and emerging technological trends are reshaping business processes and presenting new development opportunities. Titmarsh et al. (2020) highlight that Industry 4.0 offers expanding accessibility, data availability, and growing IT capabilities. Thus, the paper identifies the Lean Six Sigma and Industry 4.0 concepts and discusses how organizations can combine them. Consequently, the latest development and impact of digitalization on the Six Sigma implementation are investigated.
Literature Review
Lean Six Sigma
Notably, Lean Six Sigma manages the blueprint and advancement of products and processes. Lean Six Sigma originated from two terms, Lean and Six Sigma (Six Sigma Daily, 2020). Consequently, Lean refers to a methodology developed by Toyota for any standard process enhancement via determining and removing waste in the industry (Silantyev et al., 2019). Hence, Six Sigma was created by Motorola; this method is applied for recognizing and extracting the root causes of faults and errors employing variation reduction in business manufacturing and processes.
Thus, the combination of two methods helps firms to refine business processes. Prasad et al. (2020) highlight that Lean Six Sigma focuses on operations and activities which make products or services more valuable; the remaining is examined as waste. Singh and Rakhi (2018) justify that Lean Six Sigma can be used in various industries, such as manufacturing, finance, education, health care, and human resources. Lean Six Sigma contributes to increasing organizational knowledge by addressing crucial issues in order to allow managers to consider better decisions (Juliani & de Oliveira, 2020). Based on Yuen et al. (2016) and Timans et al. (2016), the considerable advantages of Lean Six Sigma include reduction in inventory, error-free processes development, productivity enhancement, and customer satisfaction growth. According to Silantyev et al. (2019), the Lean Six Sigma term is associated with Continuous Improvement steps, namely Define, Measure, Analyze, Improve, and Control, as shown in Figure 1.
Figure 1. Lean Six Sigma: The Continuous Improvement (Silantyev et al., 2019).
Consequently, the Define step is characterized by specifying the projects problem statement and objectives, obtaining customer information and understanding their needs, establishing team responsibilities, and alleviating project risks. The second step, Measure, refers to determining cause and effect connections between process inputs and outputs, calculating performance metrics, and conducting a cost-benefit analysis (Silantyev et al., 2019). Thus, during the Analyze phase, companies identify and verify potential root causes through investigational analyses. The fourth initiative, Improve, is associated with planning, designating, and applying tools to eliminate waste. Finally, Control Roadmap is created to enable outstanding success, reveal opportunities, and complete the business case.
Thus, through using DMAIC initiatives, a company can remarkably improve processes and increase value for customers. Antony et al. (2017) indicate that Lean Six Sigma should be seen as one of the most superior and reliable business process enhancement methodologies. Nowadays, many companies and organizations around the globe use Lean Six Sigma to achieve success. The future of Lean Six Sigma depends on Industry 4.0 and innovative technologies.
Industry 4.0
Industry 4.0 refers to the evolving technologies which offer new business opportunities. The critical characteristic of Industry 4.0 is that it integrates innovative technologies and changes the traditional ways of business operating (Six Sigma Daily, 2020). Moreover, Industry 4.0 technologies provide the real potential to increase the influence of Lean Six Sigma in the manufacturing field (The Manufacturer, 2018). Silantyev et al. (2019) acknowledge the fundamental technologies that formulate Industry 4.0 are Internet of Things, 3D Printing, Drones, Artificial Intelligence, Virtual Reality, among others, as illustrated in Figure 2 below.
Figure 2. Fundamental elements of Industry 4.0 (Silantyev et al., 2019).
Additionally, companies can use these technologies in business, manufacturing, and transactional processes to introduce improvements. The digital evolution, namely Industry 4.0, is modifying the way organizations operate. For instance, Industry 4.0 upgrades process integration and product connectivity and assist firms in achieving better performance (Dalenogare et al., 2018; Ghobakhloo, 2020). Moreover, new technologies are changing industries focus from mass to custom-tailored production. Essentially, the digitalization of business processes is the driving force behind a companys success.
Research Methods
To answer the preliminary research question How the Digital Era impacts the implementation of Lean Six Sigma? the secondary data analysis and the systematic literature review were deployed as research methods to collect and narrate the data. The research goal was to evaluate peer-reviewed articles published in the last five years and credible online resources to ensure credibility. Engin et al. (2020) claim that a systematic literature review is a process of searching articles in various databases and evaluating them to obtain necessary information. The sources were extracted from trustworthy sources for scientific research, such as ScienceDirect and Scopus, to verify quality assurance and data reliability. Consequently, the thirty-five peer-reviewed articles, proceedings, and online resources were analyzed and compared to understand the impact of digitalization on the Six Sigma methodology.
The Latest Development and the Impact of Digitalization on the Six Sigma Implementation
With the evolution of digitalization, traditional methodologies are often combined with new technologies. Lean Six Sigma and Industry 4.0 offer possibilities to companies for better data analysis and processes optimization (Bhat et al., 2021; Arcidiacono & Pieroni, 2018). Sanders et al. (2016) argue that Industry 4.0 provides high-end technology solutions to facilitate Lean Six Sigmas enhancement. For instance, Analytics and robust data mining in the Measure and Analyze phase of Lean Six Sigma combined with technologies will result in a well-founded decision, improved product quality, and the Turn-Around-Time (TAT) reduction (Bhat et al., 2021; Dogan & Gurcan, 2018; Júnior et al., 2018). Sodhi (2020) states that future processes will embed more technology and may become smarter, but they will remain processes (p. 5). The deployment of both Industry 4.0 and Lean Six Sigma can be implemented by any organization, regardless of its size (Tortorella & Fettermann, 2018). Thus, Industry 4.0 would not replace the Lean Six Sigma methodology, however, rather dramatically improve it.
Consequently, the new digital technologies can be used to intensify DMAIC initiatives. Hofmann (2021) claims that Six Sigma methods should become more flexible due to digitalization to provide a faster response to customers needs and requirements. Notably, there is a necessity for Lean Six Sigma to move towards digitalization. Javaid et al. (2021) inform that digitalization is suitable for Six Sigma methods to accelerate uninterrupted improvement. Digitalization affects Six Sigma significantly; for instance, paper-based processes are replaced by various mobile solutions (Rio, 2019). Martinez (2019) emphasizes that digitalization and new technologies are tools and drivers for processes brilliance. The Industry 4.0 technologies can be combined with the Lean Six Sigma methodology to improve business processes, as shown in Figure 3.
Therefore, data accumulation and measurement are completed with the assistance of the Internet of Things, Artificial Intelligence, and robotics. Cui et al. (2020) inform that the Internet of Things is crucial for operational excellence. Basios and Loucopoulos (2017) argue that the fundamental idea behind Industry 4.0 is to connect virtual and real realities into the Internet of Things (IoT). Thus, the Six Sigma DMAIC will be strengthened by IoT, which allows collecting, measuring, analyzing, and controlling data, inputs, and outputs more efficiently.
Additionally, 3D printing can be utilized in the Improve phase of DMAIC. Hence, 3D printing is used to create prototypes and present new products and ideas faster (Chiarini & Kumar, 2020). Therefore, companies can use 3D printing instead of traditional prototyping methods to increase the value for customers. Goffnett et al. (2019) present a theoretical study illustrating that drones can be used successfully in the Lean Six Sigma DMAIC in a logistics field. In addition, drones have the capacity of scanning places and taking photos; therefore, they can be used in Measure and Control steps.
Figure 3. The Industry 4.0 technologies combines with Lean Six Sigma (Silantyev et al., 2019).
Essentially, blockchain technology is a brilliant solution for the Improve phase because it ensures traceability and reliability. Blockchain technology improves the management of data obtaining processes and makes them accurate and intelligent (Giacalone et al., 2021). Augmented reality assists in refining and controlling business processes. Rifqi et al. (2021) suggest that the interaction of Lean Six Sigma with augmented and virtual realities makes processes easy to implement. Sordan et al. (2021) determined that virtual reality and augmented reality can be applied in training activities. Moreover, augmented reality and virtual reality may be used to visualize 3D graphs in the Analyze, Improve, and Control phases.
Robotics is another effective mechanism that can be combined with Lean Six Sigma. Companies usually employ robotics to achieve quality improvement (Yadav et al., 2021). According to Silantyev et al. (2019), robotics accelerates repeating operations of data reformatting with the support of Machine Vision and Natural Language processing. Therefore, the concept relates to the DMAICs Measure, Improve, and Control.
Moreover, Artificial Intelligence assists in investigating patterns in the data and defining the connection between inputs and outputs. Companies may apply Artificial Intelligence, namely Machine Learning, to synthesize and boost the success elements of Lean Six Sigma (Perera et al., 2021). Szedlak et al. (2020) argue that Industrial AI applications are vital for organizations to achieve success. Therefore, AI ensures that data is processed intelligently and accurately.
Lessons Learned and Conclusions
Lessons
As a result, Industry 4.0 definitely influence the Six Sigma implementation. Essentially, Industry 4.0, together with Lean Six Sigma, enhances customer engagement through better and faster connectivity (The Manufacturer, 2018). Chiarini & Kumar (2020) acknowledge that Lean Six Sigma provides a good base to remove waste and minimize variation before embarking on automation and use of cyber technologies (p. 9). To conclude, Lean Six Sigma can be a crucial base for successfully combining Industry 4.0 technologies with internal and external processes (Chiarini & Kumar, 2020). Haartman et al. (2021) justify that Industry 4.0 facilitates Six Sigma and Lean methods. Various Industry 4.0 techniques and Lean Six Sigma methodology can work in practice to add more value to offerings, processes, and products.
Conclusions
In the era of digitalization, organizations are forced to adapt their processes and old models to rapid technological change. Digitalization is a modification of a business using the latest digital technologies; for example, Industry 4.0 is characterized by technological trends such as artificial intelligence, blockchain, virtual reality, robotics, among others. Lean Six Sigma is a proven method based on systematic waste disposal and business process optimization and includes the following steps: Define, Measure, Analyze, Improve, and Control, briefly, DMAIC. Consequently, the Industry 4.0 technologies should be applied in Lean Six Sigma DMAIC to improve company productivity, add additional value to products and services, and enhance customer experience. To conclude, the Digital Era provides excellent opportunities and efficient tools for implementing Lean Six Sigma.
Basios, A. and Loucopoulos, P. (2017). Six Sigma DMAIC enhanced with capability modelling. 2017 IEEE 19th Conference on Business Informatics (CBI), 2017, pp. 55-62, doi: 10.1109/CBI.2017.70
Engin, B. E., Khajeh, E., & Paksoy, T. (2020). Lean Manufacturing and Industry 4.0: A framework to integrate the two paradigms. In Logistics 4.0 (pp. 350-360). CRC Press.
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The main purpose of a project management maturity model is to “provide a plan and a framework for achieving excellence in program management” (Hartwig and Smith, 2000, p. 3). Likewise, the 5 level project management process maturity model aims at transforming an organization from being functionally-drive to project-driven (Kwak and Ibbs, 2000). Level 4 of the 5 level project management process maturity model combines and controls a number of projects to attain precision (Kwak and Ibbs, 2000). For that reason, this level closely resembles Six Sigma. Six Sigma is a method of measuring quality that attempts to achieve results that are near perfection. This essay uses the 5 level project management process maturity model to discus how Honeywell can move from Level 3 to Level 4.
This essay assumes that Honeywell has already gone through level 3 of the 5 level project management process maturity model and now wants to proceed to level 4. For that reason, Honeywell’s project manager has already introduced professionalism in project management. In addition, problems affecting project management have already been indentified and documented, formal methods applied to collect data and a cross-functional team integrated to form a project management team. Nonetheless, in level 3, Honeywell cannot effectively integrate and manage multiple projects (Kwak and Ibbs, 2000).
To do so, Honeywell must move from level 3 to level 4. According to Kwak and Ibbs (2000), an organization at level 4 is able to define, quantitatively measure, understand and implement project management processes. Additionally, the project management team has the ability to use data collected to predict and prevent occurrences that may adversely affect productivity or quality of products (Kwak and Ibbs, 2000). Therefore, in this level, decision making is based on facts. At the same time, an organization has a strong project management team and trainings are well planned and provided to all employees.
Level 4 is sometimes synonymous to the application of the Six Sigma (Gack, 2010). Therefore, Six Sigma and project management have so much in common. For instance, both processes indentify and communicate with stakeholders and conduct continuous monitoring and evaluation (Gack, 2010). In addition, both processes aim at minimizing failures and managing costs and schedules. Furthermore, the two processes seek avoid defects and control risks. Six Sigma, therefore, complements project management, but does not replace it (Rever, n.d.). In addition, Six Sigma provides additional features to project management (Rever, n.d.).
For instance, Six Sigma provides project managers with a number of process’ enhancement steps and tools. Secondly, Six Sigma enables a project manager to comprehend and improve outcomes through statistical and other methods. In addition, a project manager is able to understand variations in the variables being measured. Therefore, when the Six Sigma is applied, key decisions are based on factual data.
At level 4 of the 5 level project management process maturity model, there is full implementation of integrated project management processes. Therefore, Honeywell must be able to successfully plan and control multiple projects in a professional manner if it is to reach level 4. Consequently, Honeywell must use maturity level results and the lessons learned from the assessments to reach level 4. Honeywell main aim to improve its project management processes (Hartwig and Smith, 2000). However, a project manager can only be able to improve processes after improving major performance indices (Rever, n.d.). According to Rever (n.d.), Six Sigma’s tools and methods are the best ways of improving processes in project management. “If you can’t describe what you are doing as a process, you don’t know what you’re doing” (Rever, n.d., Para 4). Therefore, in order to move to level 4, the management of Honeywell must focus on a strategy that focuses on key project management variables.
Applications of the Six Sigma will, hence, help Honeywell use a logical and a fact based approach to indentify root causes of problems within its projects (Rever, n.d.). Subsequently, Six Sigma will help Honeywell’s management device ways of preventing the reoccurrence of these problems. Six Sigma defines measures, analyzes, improves and manages business results (Rever, n.d.). These are the five steps in which Six Sigma can help Honeywell’s project manager improve results (Rever, n.d.).
Therefore, Honeywell’s management must undertake extensive planning to ensure that its projects are set up correctly. Secondly, Honeywell’s management must ensure that available data is enough and accurate.Thirdly, Honeywell’s management must be able to use available data to improve project management processes. Various statistical tools can be used to present, analyze and interpret available data. Fourthly, Honeywell’s management must ensure that final recommendations make a sustainable difference in its project management processes. Therefore, all recommendations must be verified and validated. Finally, all improved processes must be handed over to the project managers and other specialists. This act helps maintain long-term gains and improvements.
In conclusion, Level 4 of the 5 level project management process maturity model combines and controls a number of projects to attain precision (Kwak and Ibbs, 2000). Likewise, Six Sigma ensures that all measurements done are almost perfect (Gack, 2010). Therefore, Six Sigma ensures that project management processes are improved and deviations are reduced. For that reason, in order to reach level 4, Honeywell must compliment its project management with Six Sigma.
The problem of the nurse-to-patient ratio (NPR) has been growing out of proportions over the past few years. Because of the increasing demands for quality and the lack of opportunities provided to nurses, including the chances to grow professionally, build a career, benefit financially, etc., nursing facilities have been experiencing a shortage of nurses (Aiken et al., 2016). As a result, the number of nurses has been reducing, whereas the number of inpatients has been experiencing a rapid growth thus, leading to a problem (Griffiths et al., 2014). The lack of competent nurses triggers the need to create a tighter schedule, therefore, putting a significant strain on nurses and building premises for the development of health issues, including depression, workplace burnouts, etc. (Aiken et al., 2016). Furthermore, the quality of the provided services has been dropping as a result of the specified phenomenon. The introduction of an improved policy for managing nurses’ needs, including the one for sensible workload and the one for acquiring new competencies, must be combined with the focus on continuous improvement, which can be launched with the help of the Six Sigma model, particularly, the DMAIC tool.
The incorporation of the Six Sigma principles into the environment of the nursing facility will allow studying the nature of the problem, exploring the available solutions, and introducing organizational change into the target environment by combining efficient leadership with a well-designed framework for organizational processes coordination (Stanton et al., 2014). Particularly, the adoption of the DMAIC framework will require that the situation should be defined, measured, analyzed, improved, and controlled successfully (Stanton et al., 2014). Seeing that the issue has already been identified, the approach in question will help select an appropriate measurement tool (e.g., a quantitative analysis of the PNR changes over the past few days compared to the changes in the patient results and the overall quality of the service delivery).
The analysis that must follow the measurement stage will require comparing the pretest results to the post-test ones so that the efficacy of using a different leadership strategy and a redesign of the nurses’ schedule could be determined. The outcomes of the evaluation will inform the further choice of strategies for enhancing the quality of the nursing services. Furthermore, efficient control tools, such as regular reports and occasional audits, will have to be viewed as the means of keeping the situation under control. Thus, the instances of mismanagement of certain processes, be it the case of poor service delivery or a misconception occurring due to a poor information management process, will be revealed and addressed immediately. It is expected that the DMAIC framework as a part of the Six Sigma philosophy will lead to a massive rise in the quality of the services and the following improvement in patient outcomes.
However, to make sure that the suggested framework could be successfully integrated into the context of the nursing services in question, one must design an elaborate assessment tool that will provide a deep insight into the effects of the DMAIC strategy as the basis for introducing the nursing staff to the context of continuous improvement and providing them with the amount of tasks that they can handle. The first and most important the issues associated with the workplace burnout rates among nurses will have to be checked. For this purpose, the surveys measuring the nurses’ satisfaction rates needs to be deployed. The identified instrument will help shed light on the effects that the new workload will have on nurses. Moreover, apart from addressing the needs of the personnel, one will also have to evaluate the quality of patient outcomes, thus, defining the efficacy of the nurse’ work. The specified task can be accomplished by carrying out a statistical analysis of the current and past patient recovery rates. The application of a t-test as the means of measuring the effects that the suggested strategy will have on the current PNR will have to be considered.
Because of the threats to which both nurses and patients are exposed with the drop in the NPR, there is a need to incorporate the principles of the Six Sigma framework to make sure that the suggested alterations to HR policies should remain part and parcel of the contemporary nursing environment. The application of the identified frameworks will serve as the tool for introducing the principles of meeting the needs of all stakeholders into the environment of the nursing facility and at the same time create the foundation for a faster acquisition of the relevant skills and knowledge by the staff members. As a result, the course for a continuous improvement will be set successfully. Furthermore, the proposed framework may contribute to a massive improvement in patient outcomes since nurses will have more opportunities for engaging in the active communication with the target population and, thus, adders their culture-specific needs in a more efficient and careful manner. In other words, it is expected that the introduction of the Six Sigma tool into the setting of the target nursing facility will set the course for continuous improvements as the corporate philosophy and will allow meeting the needs of all stakeholders involved (Pyzdek & Keller, 2014).
References
Aiken, L. H., Sloane, D., Griffiths, P., Rafferty, A. M., Bruyneel, L., McHugh, M.,… & Sermeus, W. (2017). Nursing skill mix in European hospitals: Cross-sectional study of the association with mortality, patient ratings, and quality of care. BMJ Quality & Safety, 26(7), 559-568.
Griffiths, P., Dall’Ora, C., Simon, M., Ball, J., Lindqvist, R., Rafferty, A. M., … Aiken, L. H. (2014). Nurses’ shift length and overtime working in 12 European countries: The association with perceived quality of care and patient safety. Medical Care, 52(11), 975.
Pyzdek, T., & Keller, P. (2014). The Six Sigma handbook (4th ed.). New York, NY: McGraw-Hill.
Stanton, P., Gough, R., Ballardie, R., Bartram, T., Bamber, G. J., & Sohal, A. (2014). Implementing lean management/Six Sigma in hospitals: Beyond empowerment or work intensification? The International Journal of Human Resource Management, 25(21), 2926-2940.
The main problem in many institutions is losses due to a redundant management system. Practical examples include ineffective resource-intensive control systems, unnecessary reporting systems, and excessive control over performers and details. Thus, it is necessary to create a system of work for the institution aimed at minimizing all possible losses of material and intangible resources, standardizing processes, eliminating errors, and creating the most patient-centered organization of medical services.
For this purpose, it seems necessary to introduce a KPI system and Six Sigma. The management of the clinic through the management accounting system allows an organization to achieve efficiency without much expenditure of human and material resources (Shohet & Nobili, 2017). One of the main components of this accounting is key performance indicators or KPIs. It is a system of assessment that allows one to determine the effectiveness in achieving strategic and tactical goals. The use of the key indicators allows us to assess the state of the clinic at the moment to help correctly assess the implementation of the strategy.
The KPI system in medicine is the monitoring of the activities of the staff of a clinic or medical center, namely the business activity of its managers, as well as other employees of the clinic as a whole, in an online mode.
The Six Sigma methodology focuses on improving management performance, and process efficiency and selecting appropriate criteria for evaluating production processes and projects for their improvement (Henrique & Godinho-Filho, 2020). The emphasis, in this case, is necessary on change management. This process is focused on changes in the corporate culture when medical organizations abandon decision-making based on subjective opinions and redundant infrastructure in favor of analysis based on factual information and the use of statistical methods. For the Six Sigma methodology to become an integral part of the management system, management must apply unique methods and tools to control the implementation and operation of six-sigma projects.
References
Henrique, D. B., Godinho-Filho, M. (2020). A systematic literature review of empirical research in Lean and Six Sigma in healthcare. Total Quality Management & Business Excellence, 31(3-4), 429-449.
Shohet, I. M., & Nobili, L. (2017). Application of key performance indicators for maintenance management of clinic facilities. International Journal of Strategic Property Management, 21(1), 58-71.
The clinical environment is fraught with many threats and challenges that can impede effective care and the overall quality of health care in the community. One such threat is long waiting times in the queue, which causes a drop in clinical productivity. The long waiting time for patients in an actual queue to see a doctor is also dangerous in terms of the spread of hospital-acquired infections because the queue is close contact between different people (Sikora & Zahra, 2021). This paper uses waiting time in the queue in terms of the variable under study; it is recognized that the threat of long waiting time is disruptive to clinical performance, so the work is aimed at improving the current agenda. The paper uses the LEAN SIX SIGMA model as a tool to explore and identify potential improvements.
DMAIC
Define
The current problem is an essential threat to clinical performance and public health in general, undermining trust in healthcare providers and inhibiting positive treatment outcomes. Fixing the current agenda involves improving the organizational well-being of clinical organizations and investing in a more comfortable and enjoyable patient-facility interaction experience. From the client’s perspective, the improvements presented are expected to help resolve dissatisfaction with waiting times and improve communication between the patient and providers; in the longer term, this means creating a more welcoming atmosphere within the clinic and the opportunity to focus on solving health-related problems. Thus, working to reduce waiting times at clinical site queues is the first priority addressed in this paper.
Measure
In terms of the measurement method, a stopwatch is used to examine the behavior of three different queues: the reception area where patients check in for their appointments, the doctor’s office, and the pharmacy branch of the clinic. The waiting time in each of the queues is carefully measured for each person. It is noteworthy that sample sizes were unequal, which is natural for different sites-the pharmacy branch was more popular with people than the therapist’s office. More specifically, the distribution of the number of participants in each sample was as shown in Table 1; the largest sample size — but not the maximum average waiting time — was specific to the pharmacy.
Table 1. Descriptive Statistics for the Three Samples
Groups
Count
Sum
Average
Variance
Reception
29
85.8
2.96
1.44
Doctor’s office
19
125.1
6.58
6.84
Pharmacy at the Clinic
35
93.9
2.68
1.00
Figure 1. Distribution of the Waiting Time of each Sample with the Trend Line
Analyse
Analyzing the results, it is well seen that the waiting time at the pharmacy is the shortest, while in contrast, the waiting time for patients near the therapist’s office was the longest. In terms of group behavior, Figure 1 shows that over time, the average waiting time for the receptionist and the pharmacy branch slowly fell (slope of the trend line), whereas, for the physician’s office, this value grew. The detected difference between the mean values should be evaluated with ANOVA because there is no guarantee that such differences are statistically significant for samples of varied sizes. The results of the one-way ANOVA test (Table 2) showed that the p-value was below the threshold value, which means that the statistical difference between the means is valid. To put it another way, the mean values between the three samples were indeed different. Based on these data, it can be concluded that the critical problem is centered around the therapist’s office – this clinical site took the longest wait time, so improvements should be targeted there.
Table 2. Results of the One-way ANOVA test
Source of Variation
SS
df
MS
F
P-value
F crit
Between Groups
210.187875
2
105.093938
42.5294567
2.6184E-13
3.11076617
Within Groups
197.686866
80
2.47108582
Total
407.874741
82
Improve
The fishbone model was used to analyze the current problem; since it was found that the queue near the doctor’s office was the longest, this was the sample used for analysis. Four key factors were identified, as shown in Figure 2. The main aspects of the problem were low client orientation, potential technical failures, lack of adequate shift scheduling, and ineffective patient appointment schedules. The aspects detected demonstrate opportunities for improvement; specifically, this refers to work to improve the technical equipment of the clinic, namely regular checks of equipment operability and prompt replacement possibilities. In addition, HR specialists are recommended to review shift schedules to ensure that at least two therapists are always active. From a client-centered perspective, re-training the therapists would be beneficial in order to perform their professional tasks competently. Finally, a revision of the patient appointment schedule with potentially shorter appointment times per client would be proper.
Figure 2. Fishbone Model for Improvement
Control
Based on the proposed improvements, it can be assumed that wait times in line at the therapist’s office will be reduced if operational changes are implemented. It is recommended that an added round of observation of wait times with the control variables intact after the improvements are implemented — it is assumed that the results of such observation will show a reduction in average wait time in the queue. However, additional research is required to test this assumption. Improvement control can also be carried out with SOP, in which routine processes are critically examined. SOP implies the introduction of practices that allow standardization of quality and smooth operation. Strictly speaking, the use of SOP in a clinic should help set new standards of performance, modify the management of resources in the clinic, and avoid any interruptions. To introduce SOP, it is recommended that the clinic use known practice models of operational change, including problem analysis, finding proper resources, and planning for change.
Conclusion
It must be recognized that waiting time in the queue is a severe problem for the operations of a clinical organization. The current study observed three different sites within the same clinic and found that the average wait time to the therapist’s office was the highest. In other words, the critical problem of long wait times is centered on this particular site, so improvements and changes should focus on this aspect of the overall problem. Among the critical improvements suggested were revisions to current regulations and physician training, as well as changes in shift schedules and improvements in the technical equipment of the clinics.
Reference
Sikora, A., & Zahra, F. (2021). Nosocomial infections. NCBI. Web.
The issue under focus is the insufficient level of commitment of the nursing staff to the delivery of patient care services. The lack of motivation results in the perpetuating cycle of mistakes and negligence. In the best-case scenario, the clients are left unsatisfied with the quality of healthcare. In the worst case, the well-being of the patients is jeopardized, which may even result in a negative patient care outcome.
The most effective way to resolve the commitment problem is to utilize the Six Sigma quality model. It is a strategic tool designed to ascertain and eliminate the causes of defects in the work process. At the core of the model lie five essential steps: define, measure, analyze, improve, and control (Chugani et al., 2017). The define phase involves identifying the needed result and the problem which prevents the achievement of the desired outcome. The measuring step encompasses the collection of critical-to-quality characteristics. Then, the root causes of the defects are analyzed. The next stage is comprised of the efforts directed at mitigating the causative factors. Finally, the control phase ensures the maintenance of the improvements.
The first step in working with Six Sigma is defining the problem. When applied to nursing, the end result is patient satisfaction with nursing care. There are two primary factors that adversely affect the process – clinical incompetence and duty negligence (Karami et al., 2017). A nurse who does not have sufficient knowledge or is not able to perform their duties jeopardizes the clients’ well-being. At the same time, a nurse who neglects the clients’ perspective or does not respect their wishes is equally damaging because they harm the reputation of their healthcare organization. Both causes stem from the lack of commitment to the profession. Establishing the fact of commitment issues requires collecting data from anonymous online employee and patient questionnaires. Patients and nurses would be requested to answer a set of questions, targeting their satisfaction with job conditions, healthcare delivery, and the impression patient care makes on them. The more negative results are gathered, the stronger the argument that nursing staff has commitment issues.
The second stage is the identification of critical-to-quality characteristics. As it should be evident from the previous assessment, accomplishing patient satisfaction requires competent personnel who are motivated to sharpen their skills while maintaining a welcoming and professional attitude towards the patients. Nursing competence manifests itself in the set of skills necessary for the execution of all clinical procedures (Bing-Jonsson et al., 2016). Attitude is expressed through respect for the patients’ needs and requests. Ultimately, professional enthusiasm and determination is the primary driving factor behind both these characteristics. The subsequent question is understanding why there are commitment issues among the nursing staff.
The third phase is the analysis of the defects. There are three reasons for the lack of nursing commitment. First, an insufficient level of actual education prevents nurses from understanding the health risks and the measures essential for mitigating them (Bing-Jonsson et al., 2016). Second, nurses may lack the conception of cultural sensitivity, which demands a heavier emphasis on communication and interaction with the patients (Karami et al., 2017). When people neglect the value of proper communication, they precipitate cultural misunderstandings and social apprehension. In the nursing setting, it may mean a prejudiced attitude against religious patients, race-based hostility, or any other form of cultural insensitivity. Finally, nurses who have been in the healthcare facility for a long period of time may suffer occupational burnout, which leaves them indifferent to the patients’ well-being (Karami et al., 2017). Combined together, these factors determine how committed a person is to their job.
The fourth stage is selecting the ways of resolving the problem. Inadequate education can be resolved by organizing training courses for those nurses who do not meet the necessary requirements. Another way is to institute additional hiring criteria, which would require the nurses to pass a practice-based test aimed at evaluating their factual skill level. The problem of cultural sensitivity of the nursing staff can be addressed with obligatory training of the personnel. The administration can explain that communicative deficiencies backfire in the form of negative client feedback, which impacts the hospitals’ reputations, which will, in its turn, result in the nurses’ salaries. The administration can also offer financial incentives to nurses whose clients were not upset or left dissatisfied with services. The data about the clients’ level of satisfaction can be gathered during short surveys done upon the discharge of the patients. As for occupational burnout, it is essential to understand that it is a mental issue caused by stress, fatigue, and insufficient rest (Mudallal et al., 2017). The administration can rotate nurses, making sure that the most loaded shifts are distributed between the entire staff evenly.
The final step is making sure that the changes are constant. Therefore, the administration should take the test during the hiring stage as a continuous rather than temporary practice. At the same time, every six months, the nursing staff will be required to attend competence and cultural sensitivity training courses to ensure the continuation of nurse-patient interaction. As for the rotating shifts, the administration can implement a rule dictating that a nurse cannot follow the same shift more than three times in a row. It is possible to use the same anonymous online questionnaires to collect data on the fact of commitment issues.
As a result, Six Sigma can identify two primary reasons behind the lack of commitment – inadequate education and cultural insensitivity. Both can be resolved with proper courses, financial incentives, and rules regulating shift rotation. The data about the quality of the healthcare changes can be gathered through client surveys upon discharge. These surveys will comprise quantitative evidence for the success of the implemented improvements. Anonymous questionnaires will provide additional data, which will either corroborate the fact of commitment issues or refute it.
For any building project, an electrical layout for building (ELB) is required to give the construction engineers the map of the house. However, the approvals make the construction project to delay as they take extensive time to implement. The main problem is majority of organization consultants have defected ELBs, that do not match the authority prerequisites. The research aims at improving the Consultant’s Electrical Drawing for the building by implementing Lean Six Sigma (DMAIC) for the Authority in UAE to reduce the approval time. The acquisition of approvals ensures honest contractors, who ensure the utilization of safe and standard methods, including electrical layouts. The methods used in the research include interviews and field observations together with literature reviews from journals, conference papers, and databases, including SCOPUS, EBSCO, Proquest, and Google Scholar. The key research objectives include identifying the root causes of the electrical layout for building (ELB) drawings defects, identifying the root causes of the electrical layout drawings defects and to investigate the application of DMAIC methodology in improving the technical drawing process. The expected outcome includes reducing the ELB errors, thus enhancing the immediate construction process.
KEYWORDS: Consultants, electrical drawing, approval process; Construction projects delay, DMAIC, Lean Six Sigma
Introduction
All the structural buildings require to be accomplished in a specified time and schedule. However, there are many people involved in such project works including authorities, contractors, designers, clients, and suppliers, who share the same goal of reducing costs and delivery time. The construction project delay has many consequences such as cost and overrun, stress among the contractors, disputes for all involved parties, and poor-quality work due to rush. In order to eliminate the ramifications and derive the benefits, there is need to minimize the construction project delays (Rachid et al., 2019). Many building clients frequently become frustrated with the electrical outlet’s locations or anticipate changing the switch position in the building. To a greater extent, that is why electrical structural engineers develop an electrical plan (electrical drawing), which is the rough layout of what a building’s electrical system resembles before installation to avoid construction delays. The electrical drawing is not only essential to the electrical engineer but also the customer (Adelakun et al., 2020). Not acquiring the electrical layout for building (ELB) causes project delay, which negatively affects the success of the entire project.
Significantly, a technical drawing refers to a visual representation of where all the building’s electrical points need to be situated. Therefore, it describes the number of switches, sockets, circuits, and outlet locations. Despite that, it showcases entire electrical appliances and lighting fixtures. Different symbols become utilized during the electrical plan to highlight which materials need to be utilized at a given point, these symbols arrangement in the buildings massively determines the electrical drawing. A study by (Adelakun et al., 2020) shows electrical drawing is essential as it ensures building safety because it enables the electrical system to run smoothly and safely. Having an electrical diagram enables all the crucial electrical preparation processes to complete before moving to another step of constructing the building.
However, most of the ELB that is done by the consultants contain massive errors and defects, both in the design itself and the electrical calculations, which can cause building electrical unsafety to the occupants of the buildings. The consultants are responsible for designing the building hole electricity, which is to be submitted to the authority. In that process, the electrical engineer conducts an electrical drawing audit to ensure that the layout meets the authority requirements for approval. Otherwise, the electrical engineer who’s working in the authority gives feedback regarding the electrical design and calculation for the electrical drawing consultants, for example, calculating the proper breaker (CB) and type of CB, among other calculations, including capacitor bank. Such errors give the electrical engineer much time to revise the design, deterring their approvals and may result in financial losses before sending the consultant to fix the technical drawings (Kamal et al., 2020). The delays in getting approvals also lead to setbacks, including construction process delays. The defects in electrical drawings can be attributed to inexperienced consultant engineers, who have higher job ratings and have not familiarized themselves with large building projects.
The building construction companies are currently operating in an increasingly competitive marketplace. Regardless of their size and experience, they are forced daily to offer the highest quality electrical diagrams with no defects. Companies failing to enhance productivity, quality, and client satisfaction encounter a bleak future whereby business rivals overtake their market share, resulting in huge financial losses. The business contracting consultants need to offer them the best guidelines and measures to compete effectively in this construction-changing environment. In our case, for the consultants to fix the errors in their electrical drawings, they require to execute a fixed methodology, such as Lean Six Sigma (DMAIC), to attain vast improvements in productivity, quality, and client satisfaction (Sreedharan et al., 2018). One of the biggest concerns with the consultants working in Construction company is to eliminate defects, like electrical drawing calculation and design errors, which delay the project approvals. In that case, the firm consultants waste their resources and time reworking the ELB process and lose the client’s trust and satisfaction due to the delayed construction process (National Academies of Sciences, Engineering, and Medicine, 2017). The companies and consultants need to improve the quality of their technical drawings processes to create a strong business strategic advantage and introduce themselves as an international firm for further prospects. This study examines electrical drawings quality issues and offers a solution to minimize the most critical errors. In order to attain this, the study proposal advocates for the use of the most effective quality management and improvement methodology, that is, Lean Six Sigma, in particular DMAIC (Define, Measure, Analyze, Improve, Control) as a problem-solving and improvement model.
The study undertakes a literature review of DMAIC, indicating the positive impact and benefits of the theory on the electrical drawing on eliminating defects for its quick approval. Therefore, the study aims at enhancing the electrical technical drawings undertaken by the consultants. By integrating and executing lean Sigma (DMAIC) to the ELBs, the study outcomes will have reduced ELB auditing for the electrical engineers in the authority, which will improve the construction process due to approving it in a shorter time.
The research report for the study will involve the literature review, which will discuss the major accomplishments in the ELBs in buildings, the use of DMAIC tool, causes of project delays, research gap, and provide critical discussion. The report will have research methods, the study aims and objectives, and provide a feasible case study directed towards improving the electrical drawing for the buildings to reduce the approval time and minimize construction project delays.
Literature Review
Major Accomplishments in Electrical Layout for Buildings
According to (Sreedharan et al., 2018) over the several years, the electrical drawings have improved from manual designing and calculations to specialized software, which aids in minimizing the layout errors. The availability of the maps has made the electricians and other construction workers have full detail on how to install and repair the electrical systems. Over the years, several building approval agencies have been developed to ensure that the building adheres to the standards to ensure the safety of the occupants. In that case, most companies have highly invested in having highly trained and qualified engineer consultants to develop the best electrical layout for building (ELBs) (Saldarriaga et al., 2019). The literature pinpointed the role of approval authorities in designing the best ELBS, but it did not showcase how the approvals assist in reducing layout errors.
A study done by (Zarei et al., 2018) shows that with internal and external consultations, the errors and defects associated with electrical drawings are effectively tackled to enable approval for beginning the building construction process. The availability of computers and computation software has increasingly led to effective technical drawings. In that case, the technical diagrams contain extensive database regarding the site plan, including external wiring, floor plans, and structural location. A study by (Sreedharan et al., 2018) shows that wiring diagrams have a blueprint of electrical circuits and their physical connection. The literature identified defects for ELBs for buildings, but it did not offer recommendations to improve or eliminate the errors.
According to the statistical reports, the entire ELBs must embed comprehensive details, including light fixtures, power transformers, the main and fused switches, and the tiebreakers. Other information necessary to integrate in the technical drawings includes the interconnection and switching of the electrical system parts, such as electrical wires and calculating size and voltage of equipment, like generators, batteries, and solar panels (Madsen & Madsen, 2016). Such measures have been implemented to eliminate design errors associated with electrical drawings (Saldarriaga et al., 2019). The literature identified the primary components of ELBs, but it did show how their absence causes delay in construction projects.
Different researches have been done on how to improve electrical drawings. However, the available literature does not indicate how much electrical drawing errors cause delays in approval leading to increased construction process wait-time (Sreedharan et al., 2018). All the components of wiring diagrams should be designed error-free to ensure the quickest approval of the projects (Madsen & Madsen, 2016). Multiple research advocates for interscapular knowledge exchange, whereby the qualified engineers of the client company and the consultants work together to attain non-defective technical drawings during the design. The study does not provide data on how technical diagram errors cause construction project delays, but it advocates for interdisciplinary fields working together.
The Lean Six Sigma (DMAIC) Tool Use in Construction Field
Literature has advocated using the DMAIC tool and technique to improve the wiring diagrams, which deploys other quality and improved performance models, including control plan, measurement system analysis, Pareto chart, fishbone diagram, and capability analysis. The DMAIC approach usage helps analyze a process before implementation, which enables the fixation of a wrong issue that can impact the whole project (Wogan et al., 2017). The use of DMAIC process during the ELB approval by authorities can minimize construction project delays leading to their success. The phenomenon results in general performance improvement and ultimately filtering such through to happier clients. Through rectifying the electrical drawing defects, the literature suggested methods, including DMAIC, ensure improvement of the process, minimizing the approval delays and recurrent reworking (Newbery et al., 2021). The literature gives statistics on importance of using DMAIC tool.
Despite improving the design quality, the Lean Six Sigma (DMAIC) acts as a management strategy and philosophy applied to each process, like electrical drawing, to abolish the error root causes. To a greater extent, several authors argue that the primary advantages to the building electrical design development companies from applying the Six Sigma. They include defects elimination, cycle time improvements, cost reduction, rise in profits, and improved customer experience (Wogan et al., 2017). The literature suggests that the DMAIC methodology becomes utilized in design process enhancement. It can be expanded to improve other business elements, including legal and purchasing, attached with electrical drawings. Incorporating DMAIC with other techniques helps in encouraging employee participation in the project, increases the process knowledge of the less qualified and inexperienced building specialists, and engendering problem-solving by using statistical thinking concepts (Bhawika et al., 2019). The literature does not give the names of other techniques to be incorporated with DMAIC to improve construction delays despite identifying the root causes of ELB failure.
According to diverse research undertaken, using DMAIC in this research project will enable all the individuals involved in the electrical diagram to enhance their skills and knowledge, thus effectively solving problems through gained statistical expertise (Hsu et al., 2017). Therefore, all the consultant engineers receiving high stars despite having decreased ratings will have improved experience in improving the electrical drawings, making them error-free. The study will benefit from the five interlinked phases: define, measure, analyze, improve, and control. The literature identifies DMAIC stages, but it does not show how they will reduce the project construction delays.
Regarding the definition stage, this phase entails determining the project team’s responsibilities, customer requirements, and expectations, elaborating the project scope and limits and establishing the project’s identified goals using the project charter (Wogan et al., 2017). The other step involves mapping the electrical drawing process to help comprehend the defects in the implemented design. The ‘measure’ DMAIC stage involves choosing the measurement factors to be enhanced, offering a structure to assess pre-existing performance, and evaluating, contrasting, and monitoring subsequent improvements and capability. The frequent revision of electricity drawings shows that the operations failure costs exceed the target of escalated design defects over time due to denied approvals (Newbery et al., 2021).
The study done by (Riva et al., 2019) indicates that it is essential to prioritize the causes of frequent electricity design process failures. This can be achieved by integrating the Pareto chart to showcase the highest triggers of the wiring diagram errors, which the design quality inspector might not detect. The ‘analyze’ phase focuses on determining the problem’s root causes, comprehending why defects have occurred, and comparing and prioritizing chances for better advancement. The study encourages the incorporation of a fish-borne (cause-effect) diagram to pinpoint the root causes of the electrical drawing failure. Improper design maintenance and poor process release procedure can attribute to such defects. The ‘improve’ stage concentrates on deploying. statistical techniques to create future improvements to minimize the number of quality issues Last is the control phase, which focuses on sustainability enhancements and monitoring the ongoing performance. The stage enables institutionalization and documentation of ELB improvements through training wiring operators on the newly used tools and the current modified processes and updating the control plan, which is essential for this project. The literature gives a vivid description of each DMAIC process phase and the attached advantages in minimizing the construction delays of the ELB for building.
Technical Drawing Problem Improvement by other Sources
Several study sources have developed ways to improve the problem of quality electricity diagrams, which have resulted in quicker approvals. Literature suggests using Deming’s continuous learning and process enhancement model plan-do-check-act (PDCA). Despite PDCA being a learning model intended for executing advancement activities, it also envisions data collection and analysis directed towards finding the problem associated with electricity layout (Realyvásquez et al., 2018). The literature fails to compare PDCA with DMAIC to showcase which is more effective in reducing construction delays despite giving its benefits to improve ELB.
According to literature, such a phenomenon enables project team members to take decisions and courses of action grounded on actual and scientific statistics instead of relying on personal knowledge and experience, as with many consultation wiring companies. The literature does not highlight the effective decisions to be undertaken by the project team despite advocating for the same.
Causes of Construction Project Delays
According to (Ren et al., 2021), There are many causes of construction project delays. The first one is budget inaccuracies. A building project’s budget might suffer greatly if an incorrect estimate is provided. Several positions are temporarily or permanently axed. In order to avoid overspending, estimates must be accurate. Software streamlining bids, estimates, and financial project planning makes the process less risky. As a result, contractors need a simple method to enter the job into their construction management platform and start to work. Real-time access to data is essential after the work is started to assess how progress is compared to expenses. The literature provides the causes of project delay but fails to show how they should be effectively implemented to realize results.
The study done by (Madsen & Madsen, 2016), shows that the data reporting between the field and the office is typically delayed due to outdated spreadsheets or on-premise software solutions. People responsible for monitoring project productivity sometimes work with days, weeks, or even months old data. Automating procedures and giving sophisticated data and analysis tools in real-time is the answer provided by a cloud-based, integrated construction management system
According to (Umar, 2018), Labor is a major cause of building delays in the modern-day. While many skilled individuals laid off during the crisis could find employment in other areas, the downturn impacted this business. Construction jobs have seen a decline in popularity among newer employees entering the market. That dynamic is shifting due to the use of technology to streamline human resources and labor management processes in construction companies. Building a construction schedule may be made or broken by correctly assigning a team.
Approvals also cause construction delays. The government authorities require buildings, particularly commercial, industrial, and residential buildings, to have permits. Lack of compliance with the requirements may cause approval denial; thus, construction increases wait time (Ren et al., 2021). The project obstruction is frequently caused by overreliance of experts in each stage due to many paperwork involved and their reluctance in executing their duties and responsibilities (Bhawika et al., 2019). When owners are in charge of a project, they have to make choices that keep it going forward. Keeping construction projects on schedule and within budget may be achieved by swiftly coordinating and approving tasks. The literature indicates how approvals causes project delay and their benefits but fails to highlight the attached consequences if not acquired.
A study done by (Wogan et al., 2017), indicate that every following task on the construction site might be delayed if a subcontractor is overworked or uninformed of the timeframe for the bigger project. There is an increased possibility of legal challenges or disputes if a contractor is not in compliance with their bonding, license, or other contractual requirements. In order to ensure that initiatives go off without a hitch, open communication is crucial. The literature indicates how a subcontractor causes project delay but fails to highlight the attached consequences.
According to (Ren et al., 2021), Projects might be delayed or even done wrong if the right and left hands aren’t communicating. It does not matter whether it’s an owner, customer, in the field, or the office; everyone should be kept up to speed on any new information that comes to light. In a disagreement, the general contractor may rely on the communication and cooperation audit trails to ensure that teams have all the information they need to address problems before they become a project problem. The literature shows how lack of communication causes building delay but it fails to show how it affects the success of the whole construction project.
Data Collection Methods
The tools and techniques for collecting and analyzing data include interviews, case studies, and observations. They will help preserve the information used in the future interpretation of the research findings (Prashar, 2020). According to a study done by (Sarstedt et al., 2019) the data collection helps in identifying the actual information regarding on what the researcher wants to examine in the study problem. The data collection methods help in spotting any errors committed during calculating and designing ELBs. The literature shows how the use of different data collection methods assists in identifying ELBs defects but it does not indicate the weaknesses of using them.
Research Gap
The research gap identified is that there are no studies on electrical drawings failure and the impact on permit approval acquisition. The topic requires future research as electricity diagram defects make the company not obtain approval, thus delaying the construction process (Tariq et al., 2020). In modern society, the acquisition of construction wiring permits is crucial to ensure the future safety of residents and enable countering electricity fire hazards adequately in case they occur.
Research Purpose and Objectives
Research Aim
The research aims to improve electrical drawings for buildings via Lean Six Sigma (DMAIC) implementation and reduce the average approval time in the construction project to avoid delay.
Research Objectives
There exist several research objectives that will guide in attaining the study aim. They include:
Identify the main reasons for delays in the construction projects.
Identify the root causes of the electrical layout drawings defects.
Investigate the application of DMAIC methodology in improving the technical drawing process.
Methods
Tools and Techniques
The research will be conducted by reviewing the literature and undertaking interviews. This will ensure the results obtained from the research indicate that utilization of the DMAIC is essential in improving the ELB to avoid construction project delays. The tools and techniques for collecting and analyzing data will include interviews, case studies, and observations. They will help preserve the information used in the future interpretation of the research findings (Prashar, 2020). According to a study done by (Sarstedt et al., 2019) the research methodology diagram is essential as it guides the researcher on pinpointing the links between the study topics and earlier deployed methodologies by other investigators. In becomes easy to identify the research gap by using it. The data collection carrying regression analysis helps in spotting any errors committed during calculating and designing ELBs. This is the research methodology diagram that will be follow:
Figure 1: Research Methodology Diagram
The data will be gathered through both primary and secondary data collection methods. After that, regression analysis model will be used to establish the actual variations in the ELB process. In the authority case study, the DMAIC method will be used and each phase will use a different tool to ensure whether they will improve the ELB errors made by the consultants.
Primary Data Collection Method
Primary method involves data obtaining directly from the first-hand source by the researcher (Sarstedt et al., 2019). primary data collection method will include interviews and field observations. The interviews will be conducted with the electrical domain experts, including the consultant and engineers, to collect data. Moreover, discussing the most frequent errors in auditing ELBs. Secondly, through field observations of the technical drawings of ELBs, the data will be assembled regarding frequent errors. A schedule will be prepared indicating the number of consultants committing the most mistakes and the period for each case. In that case, the data will assist in enhancing the ELB auditing process and adopting them quickly.
Secondary Data Collection Method
The secondary method involves acquiring information by someone instead of actual user, thus being readily available for use (Prashar, 2020). Regarding the secondary data collection method, the literature review from journals, conference papers, and databases, including SCOPUS, EBSCO, Proquest, and Google Scholar, will be used to collect gather by reviewing twenty sources. The DEWA Circular & Regulation Book as well as, SEWA Rules and Regulation Book will be used to gather data as it gives massive information regarding the company in the study question. The checklist that include the main rules and regulation that related to electrical supplies in the building
Data Analysis
In data analysis, the statistical tool that will be used is regression. This will help establish the real causes of variations in the ELB process. In that case, the impact of one variable over the other will be measured and prove or disapprove available hypotheses on what is causing the frequent electrical drawing defects (Sarstedt et al., 2019). The solutions obtained will be formed and tested.
Case Study
In order to get a tangible, contextual, and in-depth understanding of an actual issue, a case study is the ideal research design. It gives one a chance to delve further into the case’s essential traits, meanings, and ramifications (Umar, 2018). The authority will be used as the case study. It is located in the United Arabs Emirates (UAE), and it is a government facility providing residents with water and electricity. It has over 4500 employees and over 35 branches countrywide. The authority is a state-owned enterprise type of business (Umar, 2018). The use of the DMAIC methodology will lower the approval time when reviewing and auditing ELBs from consultants to ensure they match the requirements.
The ‘define’ phase will establish the project scope and limits and give team members duties. The process map tool will be used in this initial DMAIC stage. The second phase is ‘measure, ‘which will identify the highest triggers of the ELB defects. The data gathering plan tool will be deployed in the second process (Newbery et al., 2021). In the ‘analyze’ phase, the root causes of the ELB’s defects and failures will be established and adopting strategies for improving them. The correlation analysis and graphing tool will be used to identify cause-effect relationships (Wogan et al., 2017). The ‘improve’ stage will be geared towards minimizing the quality issues of the ELBs. The management tools, including risk assessment, will be used to create future improvements. The ‘control’ phase will concentrate on sustainability and checking the ongoing performance (Riva et al., 2019). The control plan tool will be used in this phase to ensure documentation and institutionalization of ELB enhancements via training all wiring operators.
Initial Findings
Define Phase
Figure 2: Define Phase Process Map
From the process map above, the primary problems of the case study are that there are many revisions of the ELBs approvals from the consultant to the authority and back to which causes time wastage. The case study shows that due to frequent ELB revisions, the consultants may not be developing the good electrical diagrams for the clients, which may cause fire and can access the plan in case of emergency. However, after such rectifications the customer’s receive safety enhanced building free from electrical faults. The problem can be identified by undertaking auditing of the ELBs and determining whether they require revisions. When a revision occurs, it shows that the issue is present and it requires to be corrected.
Measure Phase
In the measure phase, the available data was collected by a worker having the direct information flow at the authority and the employee obtained SEWA book database surrounding the number of ELBs revisions undertaken. The worker was able to determine the time taken to approve the ELBs and the number of faults in each ELB committed by individual consultant in the authority. However, the interviews will be done in the next semester, whereby the data will be extracted from the ELB auditors and the consultants to determine the rationale causing ELB errors.
Figure 3: Collected Data on Faults in ELBSFigure 4: Collected Data on ELB Faults per Consultant
Analyze phase
On the analyze phase, the correlation analysis of two variable involving the ELB errors and the time required to undertake the revisions will be graphed. Another correlation analysis will include the number of auditing ELB revisions by each consultant and the time used by the consultant to rectify them. In the measure phase, the current approval duration ranges between 6 days to 46 days depending on the design and calculation error revisions of each ELB.
Figure 5: A Graph of Number of Days Against Number of ELB Revisions
From the graph above, it shows that the is a positive correlation between number of days taken against the number of ELB revisions. Therefore, reducing the number of the ELB revisions by the consultants can significantly lower the approval time taken as it takes lower period to rectify them.
Figure 6: A Graph of Number of Lead Days Against the Number of Errors in Each ELB
From the graph above, it shows that the is a positive correlation between number of lead days taken by the consultants against the number of errors in each ELB. Therefore, reducing the number of the ELB errors by the consultants can significantly lower the approval time taken as it takes lower period to rectify them. The data was collected by an employee working at the authority and tells the investigator that the delay in approvals is due to many ELBs revisions in rectifying the design and calculation errors committed by the consultants.
Conclusion and Future Plan
To a greater extent, the ELB errors committed by the authority consultants tend to cause many revisions to obtain the approvals. The key findings established is that there is a stronger correlation between the number of ELB revisions undertaken by the consultant and the days taken. Some of the design and the calculation error committed by the consultant may take different lead times to rectify. In the measure stage, the duration required for the approval of the revised ELBs will be datamined. More data will be collected during the analyze phase to ensure the examined data reflects the actual root causes of the ELB errors. In the next semester, during the improve phase, the researcher will construct likely solutions to the research problem, select and modify the best solutions, undertake the validation testing, and document the key findings. Lastly, during the control phase, the obtained solutions will be validated and the all related changes be documented. After that, the investigator will write a comprehensive action plan, including the recommendations on what consultants need to do to avoid ELB defects for quick and direct approvals. The Gantt chart will illustrate a project timeline and progress for Project II. See Table 1.
Table 1. Gantt chart
Months
September
October
November
December
Weeks
1
2
3
4
1
2
3
4
1
2
3
4
1
2
3
4
Title
Literature Review
Pinpointing keywords of ELB themes
Searching the research articles’ sources
Evaluating and structuring research articles
Writing a literature review
Data Collection
Identify the aim and objective of the interview
Search for the expert
Prepare a list of questions
Conduct the interview
Data analysis
Literature data analysis
Interview data analysis
Survey data analysis
Improve Phase
Construct likely solutions to the research problem
Selection and modification of the best solutions
Undertake validation testing
Document the findings
Control Phase
Validation of solutions
Documenting all related changes
Writing an action plan
Writing the Research
Write the research
Review whether it to all the study requirements
Submit
References
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This paper evaluates the implementation of the lean six-sigma culture in DHL, which operates in the logistics industry. The paper illustrates the company’s history and growth since its inception in a bid to provide the background information on the firm’s operations and success.
The critical event that motivated the firm to integrate the lean six-sigma culture in its organizational culture is identified. Moreover, the paper identifies the problems facing the firm and the decisions taken in order to resolve the issues by evaluating the current state and the future state.
The effects of adopting lean six-sigma on the firm’s organizational culture and corporate governance are identified. Furthermore, the paper outlines the lean methodology adopted and the process of implementing the lean six-sigma culture in the organization.
Company history and growth
DHL is a private limited company that was founded in 1969 in Bonn, Germany. The firm operates in the Express Logistics industry and it deals with the provision of logistics and communication services. Initially, the firm operated in Europe, the Asia Pacific, and the Americas. However, the company has entered and developed market recognition in other markets over the past few decades.
The firm has four main divisions, which include global forwarding, supply chain, mail express, and freight. The mail division’s operations entail press distribution, philately, franking, delivery of written communications, cross-border parcel and mail delivery, mailroom and printing, mail advertisement, document management, and home delivery.
The express division provides “express services coupled with local and international courier services to individual and corporate customers, while the operations of the global forwarding and freight divisions entail transportation of goods through air, sea, road, and rail” (DHL, 2014, par. 5).
On the other hand, the supply chain division offers, “diverse logistic solutions such as managed transport, warehousing, supply chain management, and business process outsourcing” (DHL, 2014, par. 6). Moreover, the firm also offers end-to-end solutions with regard to corporate communications. The firm also offers marketing services such as packaging, customer correspondence solutions, and lead logistic services amongst other services.
Since its inception, the firm has undergone remarkable growth emanating from integration of effective corporate, business, functional, and enterprise level strategies. The firm has been committed towards exploiting the prevailing market demand in an effort to achieve its profit maximization objectives. The company operates in over 252 countries (DHL, 2014).
The firm has effectively defined its business operations in addition to integrating the concept of strategic partnership in an effort to penetrate the global market. In 2013, DHL announced its desire to collaborate with two oil and energy companies, viz. OEMs and EPC Contractors. This move will further enhance the company’s growth. The firm’s growth has also emanated from its commitment to position itself as the global market leader with regard to provision of logistics.
Critical events that motivated adoption of lean six-sigma
Goldsby and Martichenko (2005) define six-sigma as a “management methodology that attempts to understand and eliminate the negative effects of variation in processes” (p.57). On the other hand, the term lean is concerned with elimination of waste and enhancing flow and speed.
In this light, the concept of lean six-sigma is “the process of eliminating waste by integrating disciplined efforts that contribute to the development of comprehensive understanding on how to reduce variation and enhance speed within an organization’s supply chain” (Goldsby & Martichenko, 2005, p. 63) .
It is critical to logistician as it aids in reducing variations. Customer satisfaction is one of the major challenges facing service companies. The decision of DHL to integrate lean six-sigma was motivated by growth in customer expectations. The firm’s core strengths relate to its ability to position itself as the market leader amongst its employees and clients. This objective is in line with its corporate growth strategy.
In 2007, the firm conducted a customer survey in an effort to understand its touch points. The aim of the survey was to identify the prevailing operational gaps and hence develop insight on the necessary adjustments in order to improve its performance. The survey revealed that customers attach high value on adherence to predetermined specifications on a product or service.
Subsequently, the firm identified adherence to specification as a major factor in the determination of company’s success. The above analysis shows that DHL has managed to nurture a strong competitive advantage in the global logistics industry. The firm has successfully penetrated different markets around the world.
Problem definition
Ismail (2008) contends that customers are increasingly demanding a high level of efficiency in the delivery of their products and services. Subsequently, it has become critical for firms in the logistics industry to improve their operational performance in order to meet customers’ expectations.
Ismail (2008) argues that improving performance-cycle expectations and meeting customers’ logistical expectations is one of the ways through which logistic firms can build strong customer relationships. Consistency and flexibility are the other critical factors that motivated DHL to consider integrating lean six-sigma in its operations. Customers are increasingly demanding a high level of consistency with regard to delivery of products and services.
Firms in the logistics industry have an obligation to ensure that they are consistent in their delivery services. With regard to operational flexibility, it has become imperative for courier firms to improve their ability to deal with extraordinary customer service requests. It has become critical for courier firms to improve their logistical competency.
Current state
Prior to the adoption of the lean six-sigma quality management concept, DHL experienced challenges in its effort to satisfy and meet customers’ expectations. The firm’s service delivery processes had a low level of flexibility and inconsistency hence limiting the firm’s competitiveness in the global logistics industry.
Future state
The firm recognized the important of adopting customer focus as its core goal in its lean management processes. In a bid to integrate lean six-sigma in its management processes effectively, DHL conducted a comprehensive value stream mapping, which entailed analyzing the current level of performance with regard to delivery of services.
The firm evaluated its value delivery system based on two main parameters, which include time and quality. Moreover, the firm evaluated the change in consumer behavior with regard to delivery of products and services.
From the analysis, DHL identified a trend whereby customers increasingly prefer quick and reliable levels of service delivery. Subsequently, the firm identified the need to adjust its operations in order to address the changing customer needs. Moreover, the firm identified areas that hindered the firm’s delivery efficiency. One of the aspects identified related to lack of sufficient knowledge on how to deal with dynamic customer needs and demands.
Effects of lean six-sigma on organizational culture and corporate governance
Organizational culture is comprised of the beliefs, assumptions, values, and norms shared by an organization’s employees. The shared assumptions, beliefs, norms, and values bind employees to an organization is such a way that their activities become inclined to achievement of the set organizational goals. Organizational culture has a strong influence on an organization’s ability to implement quality management.
The high rate of globalization coupled with change in consumer behavior is a major motivation for firms to consider when developing a high level of organizational sustainability. Nurturing the lean six-sigma culture is one of the strategies through which an organization can attain long-term growth. In its pursuit to attain a high competitive advantage through integration of the lean six-sigma concepts, DHL experienced a significant change in its culture.
According to Aruleswaran (2009), organizations should integrate the concept of corporate governance in order to improve their performance. The concept of corporate governance entails different ideas some of which include the framework designed by an organization in order to improve utilization of resources. Moreover, corporate governance entails the processes undertaken by an organization in order to address the stakeholders’ desires.
In the course of implementing the lean six-sigma, DHL took into account a number of issues, which in turn affected the organization’s culture and corporate governance. The change in organizational culture emanated from the resulting ‘disruptive change’. DHL’s management team developed and implemented the First Choice Program.
The program aimed at enabling the firm to improve how it conducts its business operations, hence providing customers with sustainable and excellent services. Moreover, the First Choice Program aimed at transforming the employees’ behavior so that they can be more customer-focused and provide unbeatable level of customer service.
In a bid to implement the program successfully, DHL formulated a comprehensive employee-training program. The training focused on ensuring that employees understand the organization’s core competencies (Deutsche Post, 2013). In its pursuit to improve its business processes through the First Choice Program, DHL faced a major challenge emanating from its size.
Currently, the firm operates in over 252 countries and it employs over 500,000 employees with its operations touching over 5% of the global trade. The firm interacts with over 1 million customers every hour. Satisfying such a large customer base is a major challenge. Moreover, the firm ensures that its large workforce understands the significance of the First Choice Program presented a major challenge to DHL.
Despite these challenges, DHL was committed towards ensuring the successful implementation of the lean six-sigma program [First Choice Program]. Subsequently, the firm formulated a continuous training program. The training program focused on ensuring that employees understand the logic behind the First Choice Program.
Lean methodology
Prior to implementing the First Choice Program, DHL formulated a project charter. The charter aimed at developing a comprehensive understanding of the project, its scope, participants, and objectives. Aruleswaran (2009) contends that a methodology is paramount for successfully integration of the lean six-sigma paradigm in an organization.
An organization can adopt various methodologies. One of these methodologies includes the DMAIC methodology. This methodology provides organizations with strategies that they should adopt in order to undertake continuous improvement in an organization’s operational routine.
Aruleswaran (2009) further argues that the DMAIC methodology produces rapid results. Therefore, one can assert that the success of lean six-sigma in an organization depends on the thoroughness applied in adopting the DMAIC methodology. DHL adopted the DMAIC methodology. The various issues considered in each step are evaluated herein.
Define-First, the firm ensured that the problem faced was defined effectively. This stage involved evaluating various customer issues in order to identify the areas in which customers are not satisfied with the firm’s operations. During this phase, the firm evaluated how it would improve its operations in order to deliver optimal satisfaction to its customers.
Measure- In this phase, DHL established a comprehensive baseline data in order to determine the Critical-To-Customer’s Quality (CTQ) accurately. In the course of measuring its operations, DHL quantified the problem faced by assessing the level of customer satisfaction and evaluating the trend of growth in the size of its customer base. Moreover, the firm mapped out the process through which the problems that hinder its effectiveness in attaining flexibility, consistency, and meeting the customers’ expectations occur. The mapping out process also entailed an evaluation of the firm’s value stream. By assessing the CTQ, DHL was in a position to understand the critical inputs and outputs in its operation.
Analyze– This phase is concerned with developing a comprehensive understanding of the cause of the problem faced. A thorough analysis is undertaken during this phase in order to determine the real reasons for the challenge faced (Aruleswaran, 2009).
Improve –During this step, DHL evaluated how the identified issues would be adjusted in order to achieve optimal performance and meet the customers’ expectations. The firm conducted a focus group meeting amongst its top executives. The meeting led to the development of the First Choice Program.
Control- The control phase entails documenting the plans identified in order to ensure that no issue is left out. All the necessary information is gathered and consolidated in order to ensure that the necessary steps are undertaken in order of priority.
Implementation
The success of a particular project is dependent on the effectiveness with which it is implemented and controlled. In its pursuit to implement the First Choice Program successfully, DHL sought the support of one of its departments, viz. the Market Research Service Centre. The MRSC enabled DHL to pinpoint the customers’ needs effectively.
Moreover, the firm utilized a wide range of data collection tools such as surveys, questionnaires, and outsourcing in order to understand and analyze the customers’ experiences and expectations effectively (Deutsche Post, 2013).
The firm also undertook brainstorming session in which different support teams were involved. DHL was cognizant of the view that the success of the lean six-sigma culture in the organization was dependent on the extent to which all employees supported the program.
Therefore, the firm ensured that the program was introduced systematically and gradually. In a bid to ensure that the program is implemented successfully in all its subsidiary firms distributed in different parts of the world, the firm developed a toolkit, which outlined the necessary aspects. This move led to minimization of variations and enhanced transparency and continuity of the lean six-sigma concept.
Conclusion
Developing a strong organizational culture is critical in an organization’s pursuit for long-term competitive advantage. DHL’s decision to incorporate the lean six-sigma concepts in its organizational culture has played a critical role in enhancing the firm’s success in the global market.
Formulation and implementation of the First Choice Program, as one of its lean six-sigma management practices, has improved the effectiveness with which the firm offers logistic services to customers. DHL has been in a position to enhance the effectiveness and efficiency with which it offers delivery services to its customers.
Moreover, the program has enhanced the degree of customer focus amongst its employees. Employees ensure that their activities contribute towards a high level of customer satisfaction. Therefore, one can assert that development of the lean six-sigma culture has stimulated DHL’s ability to deliver value to customers as illustrated by the high rate at which the firm is growing. The firm has managed to establish its operation in over 252 countries in addition to increasing its human resource base to over 500,000 employees.
Reference List
Aruleswaran, A. (2009). Changing with lean six-sigma. Selangar, Malaysia: LSS Academy.
Deutsche Post: International award for the first choice program of Deutsche Post DHL. (2013). Web.
