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Executive Summary
The current challenge with Nestlé’s operations management system is its inability to effectively function at expected efficiency level for every bundle of inputs and outputs. Unfortunately, the linear efficiency feedback tracker of the company is not helping the situation since it generalizes the entire product line as a single unit. As a result wastes in terms of factors and cost of production are on the rise. The paper proposes full integration of a six-sigma tool to improve on production level efficiency by streamlining the decision process for every bundle of inputs against expected output units. The results indicate that integration of six-sigma would result in improved productivity level by up to 78% against an earlier expectation of 45%. Implementation of the proposed six-sigma tool will ensure that there is a balance in the operation efficiency cost elements such as dependability, speed, cost, and sustainability to create a self-sustaining decision process. Therefore, Nestlé will be in a better productivity level when the proposed recommendations are integrated within its linear production efficiency decision tracker.
Background Information
Operational Problem
The Nestlé Company has a stable operational management framework characterized by efficiency in the quality certification system from a centralized production network. However, the company is currently grappling with the challenge of sustaining its quality management matrix due to the constantly shifting production dynamics and poor forecasting strategies. For instance, in the last three financial years, the company has not been able to meet the production efficiency targets as the costs continue to expand. Nestlé depends primarily on macro production tools for tracking different quality elements in the production sustainability matrix. However, considering the dynamics in the mass production sector, this approach has not been effective since it cannot accurately pinpoint any inefficiency within a unit of the centralized production design. Thus, it is difficult to monitor the efficiency strategies in different segments since the macro production tracking approach has a generalized module for managing productivity in all divisions using similar tools.
Company Background
Nestlé Incorporation is a global company in the food service industry. The corporation has more than 400 offices spread across the globe. The company is one of the most successful global brands with a series of food-based products such as baby foods, cereals, confectionery, chocolate, bottled water, healthcare nutrition, and pet care among others. Headquartered in Vevey, Switzerland, the company has been in operation for more than a hundred years. At present, Nestlé has an active market presence in all the continents with special concentration in the US and Europe. The company has successfully employed different business expansion strategies such as acquisitions, mergers, and franchising to spread across the globe.
Problem Description
Nestlé has not been able to settle on an effective production efficiency tracking instrument since its current approach has generalized operations management across all the production segments. This is making it difficult to track efficiency in each unit of operation. Specifically, the overreliance on the linear operations efficiency feedback tracker is not replicable in a scenario with different production matrices. For instance, the system has been effective in the healthcare nutrition department, but ineffective in the other production line. Despite this challenge, the company has not been able to modify the linear efficiency tracker to accommodate the dynamics in different production fronts. As a result, the company continues to struggle with the consequences of poor decision-making from the linear model as evidence in the last financial year as Nestlé was forced to abandon the dairy production plant in the UK due to inability to balance the efficiency efforts in each unit.
Unfortunately, the imbalance created by a linear productivity matrix might have serious long-term impacts on the general operations management. For instance, in the last four years, the company has been reporting dwindling net revenue, despite rolling out several production expansion strategies. The linear efficiency production feedback scanner has not been effective in balancing the elements of the effective production decision-making process. The current system does not integrate efficiency variables such as dependability, speed, cost, and sustainability of the production decision-making process. The element of speed guarantees faster customer response while dependability is necessary for on-time deliveries. Flexibility and quality ensure that there is variation in production customization and minimal errors in the entire cycle as part of cost management.
Although the current system has resulted in improved efficiency in the production line, it attracts higher costs that make productivity unsustainable in the long-term. For instance, the system cannot balance the capacity and process design for different production units and sections. Despite the fact that the production decision process is consultative, the linear model has failed to balance the dynamics of productivity and organizational environmental realities such as increased cost of labor and competition. Despite the high skills set in innovation and creativity in the labor force, the linear efficiency tracker has not accomplished the intention of production sustainability. Therefore, this paper considers the problem of determining the best strategies to transform the current linear production efficiency feedback tracker into a dynamic, multifaceted and a replicable alternative. At present, the main problem of the firm is how to create an effective production efficiency tracker that can sustain different operations management strategies.
Recommended OM Concepts and Tools to Address the Problem
On the basis of the problem statement, the operational efficiency tracker within the production section should be reorganized to guarantee a sustainable and quality management matrix. This means that Nestlé should create small-scale efficiency inspection units to support the decision-making process as opposed to a generalized approach. For instance, application of the full scale production model would empower production supervisors to execute procedures while tracking their impacts at the same time. However, the success of such strategy is subject to the ability to balance delicate production variables. For instance, balancing correspondence and creativity in efficiency tracking requires credibility and approval from the top management. Moreover, controlling the aspects of cost and speed in the decision-making process should be done at the epicenter of a unit-per-unit production strategy. Through this approach it would be possible to track the decisions and their impacts in employing bundles of inputs to accomplish the expected output. The main operational management tools that could be used to solve the current problem include adopting the flexible monitoring system (FMS) and six-sigma.
The FMS is an ideal tool for balancing the variables of cost, speed, reliability, and dependability to track production sustainability. For instance, integration of the FMS within the current production decision-making system would enable Nestlé to effectively manage the cost variables in the production sections of each product line. The FMS is capable of reducing the costs, thus, increasing the revenue streams from each production unit through optimal productivity for every bundle of inputs. In addition, the FMS will ensure that the logistics involved in the decision-making process are balanced for short and long-term gains in a concurrent manner. Fundamentally, adjustments as a result of the FMS might catalyze the achievement of a sustainable operations management process. As a result, the increased productivity will necessitate continued development of other production lines that are independently controlled without physical expansion. The expanded outputs will place Nestlé in a competitive advantage position to resume its current physical expansion strategies in a sustainable manner.
The six-sigma could be integrated in the current linear productivity tracker to minimize wastages and stabilize the production cycle through quality control. In this case, the process of managing quality is used through designing a hybrid decision-making process that should be informed by scientific evidence. In the case of Nestlé, the proposed six-sigma tool could incorporate the factors of production such as labor and time for self-appraisal in productivity performance. The six-sigma is used in planning, synchronization, and handling diverse production needs in a streamlined manner. When the six-sigma tool is actualized within Nestlé’s production efficiency feedback framework, it will be possible for this company to accurately forecast the productivity dynamics such as obstacles to take timely measures for effective solutions.
Application of OM Concept: Six-Sigma
Since the current challenge is inability of Nestlé to effectively manage the element of efficiency in production feedback tracking, there is a need to modify the current decision management matrix. The proposed modification is aimed at achieving quality assurance, innovation, and improved productivity in the production lines of each product bundle. The modifications suggested are significant in the administration of the decision-making process when selecting the bundle of inputs that would result in optimal output at the least possible cost. In addition, the current efficiency system should be tailored to integrate the component of micro management of each production unit as opposed to a generalized approach. Therefore, the company should consider integration of the six-sigma tool into the current linear operation efficiency feedback tracker.
Since productivity is measured by the bundles of inputs producing a certain level of output, integration of the six-sigma into the current decision-making process is estimated to improve productivity efficiency by at least 45%. For instance, considering a scenario where producing a an output unit was costing $1, 116,000 to serve 400 customers, the productivity level is 400 customers /$1, 116,000 which is equal to 0.000369344 customers /per dollar cost of production. When the six-sigma tool is incorporated, it would be possible to reduce the production cost to $952,000 for the same bundle that can serve at most 600 customers. Therefore, productivity with the new tool in place will be 600 customers /$952,000, which is equal to 0.000661 customers/$. The difference in productivity before and after integration of the six-sigma tool will be 0.000661 subtracted from 0.000369344, which is equivalent to 0.000272. In terms of percentage change, it will be 0.000291 / 0.000369344 * 100= 75.84% (see table 1).
Table 1. Productivity before and after integration of the six-sigma tool.
The estimations above indicate that implementation of the six-sigma tool, valued at $40,000, resulted in increased productivity by 75.84%. The new tool resulted in an increase in the number of customers a single productivity bundle can serve by 200. The cost of labor dropped by $216,000 after the incorporation of six-sigma. The resulted exceeded the expected change of 45% when the six-sigma is incorporated.
Analysis of Expected Results
As captured in the estimated calculations, incorporation of six-sigma has the potential of doubling the current output bundle for every unit of input. The best case scenario would be an increase in productivity levels by almost 80%, while the worst case scenario would be a plus or minus 10% of the 45%. When six-sigma is implemented, it would be easy to bundle all elements of productions to ensure that the costing is controlled and managed below almost half the expected output. The reduced cost of production translates into improvement of operational performance in terms of time, rate, and inventory elements (see table 1). The six-sigma tool is an ideal productivity tracker for a multifaceted production line such as that of Nestlé. This tool utilizes different efficiency variables to create an effective and a self-regulating production cycle. Six-sigma is an operations tracking tool that conveys optimal returns at the least possible cost of production. For instance, the variable of dependability in the production efficiency is easily realized through occasional audits from the values generated against targets through this tool. In application, the six-sigma ensures that the production chain is effective in balancing the inputs against outputs for each production unit of one or more divisions. The generated production functions are then enhanced through creating a standardized decision matrix that incorporates factors of production and efficiency timeline.
Conclusion
Through enhancements in the production efficiency schedule, six-sigma could internalize a systematic quality tracking matrix that surpasses the current trends of competitors. It was estimated that the length and time in production will reduce by about 45% if the system is fully integrated. As a result, Nestlé will be positioned to gain from the economies of scale due to expanded ability to disseminate and create a consistent production timeframe for different units and sections. Proper integration of six-sigma system has the potential of improving the general decision-making framework since it balances the aspects of production and efficiency through a single control unit. For instance, when the six-sigma is integrated within Nestlé’s production decision process, it was possible to cut the cost of labor and increase productivity level by 78.45%, which is 30% above the expected results. The key to an effective application of six-sigma is the integration of a quality framework for transforming the short, mid, and long-term decisions into positive and efficient results. In addition, the business procedure is likely to be more dynamic, self-sustaining and properly positioned through six-sigma’s trackers that can anticipate the expected results in a complete production cycle.
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