Introduction to Management Science in the Context of Supply Chain Management

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Table of Contents

Introduction to Management Science
The Vendor Chain Suppliers
Stochastic Model: Lead Time Confidence Interval
Importance of this Model
Current Trends and Future Innovations
Conclusion
References

Introduction to Management Science

Management science is described as the use of scientific approaches to decipher and solve managerial problems. The concept is widely used in a variety of contemporary organizations. It entails the use of logical mathematical strategies in solving problems encountered in the running of contemporary organizations.

Management science originated from management information systems (herein referred to as MISs) and operational research. The two concepts (management information systems and operational research) are analytical processes that require one to formulate a model that is used in solving a problem.

The main characteristics of modeling techniques associated with management science include, among others, linear programming, graphical analysis, and decision analysis. As such, management science is widely used in many processes taking place in contemporary organizations.

The processes include, among others, forecasting, creating a capital budget, managing a project, creating a schedule, capacity planning, planning for production, managing inventory, and analyzing investment portfolio.

In this paper, the author examines the use of management science techniques in vendor supply chain management (Coronado, Lyons, Kehoe & Coleman, 2004).

In addition, the author addresses various aspects that bring about uncertainties as far as the use of vendor supplies is concerned. Furthermore, the author advises on how to deal with such uncertainties as they occur. The author will also look at statistical methods that can help in reducing these uncertainties.

The Vendor Chain Suppliers

According to Welborn (2008), vendor chain suppliers are beneficial to contemporary business organizations. For example, they provide the organization with low cost supplies because of increased competition. They offer high quality products at flexible lead times. Each vendor has unique properties that characterize their operations. The properties set them apart from other vendors.

For example, different vendors have different lead times, which are brought about by uncontrolled operation situations. The variation in the lead time largely depends on the type of vendor. To this end, a one tier vendor has a manageable short lead time compared to a two tier vendor, who is characterized by a complex lead time (Welborn, 2008).

A two tier vendor scenario is observed when one vendor sends a partially completed item to a second vendor. It is the second vendor who is charged with the responsibility of completing the supply chain (Welborn, 2008)

To help them remain relevant in the market, vendors carry out regular statistical analysis of their business. The aim of such analyses is to estimate the ‘worst case scenario’ lead time. To achieve this, they combine statistical techniques with confidence intervals. Most of the vendors quantify the resulting lead time in terms of percentages, such as 98%.

The ‘worst case scenarios’ are brought about by several factors. The factors include, among others, material inconsistency, machine breakdowns, quality errors, priority conflict, work force attendance, and process bottlenecks.

Stochastic Model: Lead Time Confidence Interval

It is one of the most important models in supply chain management. It is analyzed in terms of average, coefficient variance, standard deviation, and variation. The model is computed from a sample of lead time values. It is possible to use the model for one tier and two tier vendor supply systems. It is computed in situations where there is late delivery brought about by the factors discussed earlier.

In cases of a two tier system, the lead time may be longer because of the partiality observed in handling the item (Lowson, 2001). In addition to the variation in lead time, there is a ‘cushion time’ to allow for the unexpected delay in the delivery process. The lead and cushion time are actually determined from the experience with a vendor. As a result of this, the variation differs from one vendor to the other.

In this model, the analyst makes use of coefficient variance to determine the lead time. The coefficient variance formula is stage- managed to make it possible to express the standard deviation in relation to the coefficient value and the average of the same. As a result of this, the upper limit of the confidence interval is manipulated to contain the coefficient variance together with the average value for the lead time (Hess & Lucasa, 2004).

The expression of this simple multiplier (in relation to the average lead time) is then estimated as upper or lower confidence limits. The upper confidence ‘interval limit’ represents the ‘worst case’ expected lead time. As a result of this observation, the upper confidence interval limit is the value of interest to the customer.

Furthermore, the upper limit shows the level at which the customers will be disappointed as a result of the failure of the manufacturer to avail the product in the market (Randall & Theodre, 2009). There are various reasons why the manufacturer may fail to avail the product in the market. One of the reasons is the lack of timely supplies, which is largely caused by unreliable suppliers.

Importance of this Model

According to Boyle, Humphreys & McIvor (2008), a good inventory model will help in dealing with backorders brought about by the lead time. A new reorder point inventory model is formulated, which in effect shortens the lead time. At the end of the day, the model enhances the quality of the products, in addition to eliminating extra costs brought about by tier vendors.

To this end, the marketer strives to develop an algorithm for the normal distribution channel. The algorithm provides the most optimal solution to the problem encountered. As a result, the whole process translates to an application of management science techniques in inventory management.

An example of such an algorithm is the Just-In-Time inventory model. Through the use of statistical data, the model shortens the lead time. In addition, the model reduces setup cost, and most importantly, improves the quality of the process.

Boyle et al. (2008) are of the view that some of the longest lead times are caused by organizational problems. The scholars have come up with an e- intermediation premise where all possible ways of reducing these problems are sought. They argue that the environmental uncertainties impacting negatively on the supply chain can be reduced through the use of e- intermediation.

The technique involves appropriate analysis of stochastic models and managerial techniques, as well as evaluation of relevant case studies. The technique makes use of these strategies to build the premises it is based on (Boyle et al., 2008).

Hess & Lucasa (2004) are of the view that business organizations should come up with new strategies to address these problems. The two advocate for the matching of production with sales to help the organization survive in the volatile market.

To this end, management scientists advocate for production planning and prudent allocation of resources. They appreciate the fact that this can only be achieved if the lead times are shortened and vendors honor their part of the bargain, which is supplying items at the right time.

Current Trends and Future Innovations

Globally, most firms are embracing qualitative management techniques. As a result of this, the demand for professionals with experience in operations management and management science has increased. The result of this is increased efficiency at all managerial levels, especially when it comes to supply chain management (Chen, Paulraj & Lado, 2004).

Consequently, many firms have improved their operations and a huge number of customers are pleased with the services they are offered. In addition, the firms have drastically reduced the cost of running their business, helping them expand their operations. The expansion is significant considering the level of competition in the global market.

Analysts are today talking of future innovations in the supply management sector. The innovations include, among others, the increased use of computerized supply management systems. Computerization will lead to the emergence or development of software that help in detecting changes in the supply chain.

The changes will be communicated to the supplier on time (Coronado et al., 2004). Consequently, the lead time for tier vendors will be significantly reduced, and delays in the supply chain will be avoided where possible.

Conclusion

The use of operations research and management science has revolutionized supply chain management. Managers have combined operations research and management science in running the organization. The combination has helped the managers in determining the suitable lead time that will not unnecessarily inconvenience production or delivery of services.

To this end, firms have resorted to the formulation of different models aimed at improving operations. The models are formulated using fairly accurate statistical data. As a result, the operations of supply departments have become more efficient and more cost effective.

References

Boyle, E., Humphreys, P., & McIvor, R. (2008). Reducing supply chain environmental uncertainty through e‐intermediation: An organization theory perspective. International Journal of Production Economics. 114(3), 347‐360.

Chen, I., Paulraj, A., & Lado, A. (2004). Strategic purchasing, supply management and firm performance. International Journal of Production Economics, 22(5), 505-523.

Coronado, E., Lyons, C., Kehoe, F., & Coleman, J. (2004). Enabling mass customization: Extending build-to-order concepts to supply chains. Production Planning and Control, 15(4), 298-411.

Hess, D., & Lucasa, T. (2004). Doing the right thing or doing the thing right: Allocating resources between marketing research and manufacturing. Management Science, 50(1), 521-526.

Lowson, R. (2001). Analyzing the effectiveness of European retail sourcing strategies. European Management Journal, 19(5), 543‐551.

Randall, W., & Theodre, F. (2009). Supply chain financing: Using cash-to-cash variables to strengthen the supply chain. International Journal of Physical Distribution & Logistic Management, 39(8), 669-689.

Welborn, C. (2008). Strengthening supply chains. Institute for Operations Management and Managements Science, 35(4), 351-397.

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