Introduction
Supply chain management has become the need of many organizations looking for a way to meet the hostile challenges of today’s business environment. Supply chain management is a broader perception of the business environment, as measure up to with more conventional approaches. Instead of administering a business as a group of virtually split functions, supply chain management views this role as closely associated links of a chain. The chain extends further than the boundaries of the organization to comprise suppliers and customers. Supply chain management engages the entire flow of manufactured goods from the purchase of raw materials from the merchant, all the way to the purchase made by the end-user.
The theory of supply chain management is based on numerous key beliefs. The key principle is that all strategy, judgment, and capacity are made considering their effect on the complete supply chain, not just divide functions or organizations. This progressive move towards is based on partnerships and the involvement of information between the links in the chain. (Sanders, 2001)
The objective of supply chain management is to meet up the needs of the final consumer by supplying the precise product at the right situate, time and price. The supply chain management advancement allows organizations to meet this goal while also accomplishing competitive advantages.
There are numerous success stories in which organizations have used supply chain management to “substitute inventory with information”, grip out costs, and improve competence and customer service. The outcomes of this development have also rendered into improved bottom lines. (Colleen, 2003) Success stories, attached with the push of an ever more demanding aggressive environment, and fresh technological progress, are the driving force following the fast-growing number of organizations put into practice the supply chain management theory.
As organizations employ supply chain management, they have to look at ways to bring the theory to each of the practice areas in their organization. This involves making the cultural and procedural changes that support the theory of supply chain management which will eventually lead them to the savings, competence, and customer service levels that they look for. The forecasting function is one area that must obtain priority in these functional check and development changes.
The demand from the final customer is the strength that steers the performance in the supply chain. Each of the links in the supply chain function in response to actual or anticipated demand from the consumer at the bottom line of the chain. The level of precision and competence with which this demand is corresponding up and down the chain is straightly associated with inventory and customer service levels. Forecasting and demand planning are consequently key features in the thriving performance of a supply chain management policy.
The density and improbability that continue living in the supply chain make the theory of precise and efficient forecasting an indefinable target. Many organizations are, conversely, making important improvements by using a move towards that supports and assists the theory of supply chain management. Collaborative forecasting is a way in which the complete supply chain is a contributor in results about the demand that will steer their activity. Collaborative forecasting accomplishes within and outwardly to assemble information that allows for the best and most appropriate predictions of demand. Recent technological developments are used to gather and bring the information collected as well as to spread forecasts back to chain members.
The number of achievement stories is escalating and includes organizations like Eastman Chemical, Reynolds Aluminium, Wal Mart, Ocean Spray, and Heineken. (Sanders, 2001) They have been able to radically decrease inventories and lead times while rising customer service and forecasting precision. Experts now imagine a day when forecasting, as we now know it, will not be essential. As the development improves, experts foretell the need for forecasts will reduce and be put back by the management of tangible demand information.
Demand forecasting has become an integral part of supply chain management and many sophisticated tools are now available to project demand either directly or indirectly through projecting its sources. In fact, demand forecasting has come to be used as a matter of routine rather than being focused on addressing any specific problems of supply chains it is applied to. The use of trends is an important part of demand forecasting and many tools and methods have been suggested to improve the accuracy of demand forecasts. Dirk (2003) trend forecasting as a discipline has often been viewed with reservation as it ignores its own impact on systems whose structure it often does not recognize, which creates much variability in its performance.
The use of demand forecasts in driving supply chains is also observed to increase what has come to be known as the bullwhip effect, which manifests in the amplification of inventory cycles as one moves farther from demand in the supply chain.
While it is widely recognized that distortions of information created by this effect can lead to tremendous inefficiencies in terms of inventory investment, poor customer service lost revenues, and misguided capacity planning, a clear guideline for intelligent use of forecasting does not exist and it is often used as an end rather than as a means with the expectation to improve the performance of the supply chain. Indeed, the importance of understanding complex production processes is quite widely advocated for effectively managing supply chains, but how these are influenced by the widespread use of forecasting is not well understood and, driven by widespread software support, trend forecasting continues to be used without focus on problem-solving. (Colleen, 2003)
Supply Chains And Forecasting
Although the terms supply chains and bullwhip effect entered operations management vocabulary in the 1990s, the structure of supply chain (called the production distribution system) was elaborated by Jay Forrester in the early 1960s. Forrester also was clearly concerned with the use of demand forecasts and how they could destabilize the production distribution system, but he called this amplification rather than the bullwhip effect. (Sanders, 2001)
Forrester pointed to the amplification of changing conditions created by the use of trend forecasting in policy in Appendix L of his seminal volume Industrial Dynamics as forecasts can be quite off the mark at and near turning points in a system. Dirk (2003) suggested that demand forecasts can also be self-fulfilling if used in production planning since demand is often affected by the delivery delay and the quality delivered in a production and distribution system.
He also investigated the destabilizing impact of the use of forecasts in decision-making. Sanders (2001) supplemented this work by developing a trend macro for the System Dynamics National Modeling Project at MIT and testing it with cyclical functions, which confirmed earlier propositions of Forrester and Lyneis about trend forecasting increasing instability while also adding to the evidence against the reliability of the use of forecasting in the policy. It is not surprising that trend forecasting has largely been viewed in the system dynamics community as a source of instability and as a dysfunctional basis for decisions, even though trend macros are available in most software used for system dynamics modeling.
Trend information has been used on the other hand with great reliability in engineering for enhancing the stability of servomechanisms, controllers, or governors. Originally formalized by Maxwell (1868) almost a century and a half ago, the concept underlying such applications is based on understanding the control channels in the system and using trend information to further reinforce them. Trend manifests in derivative control, which adds a correction in response to the rate of change of error, thus further speeding up correction when an error is rising. (Chaman, 2005)
Given that both trend forecasting and derivative control use the same basis for policy, it seems anomalous that the former cannot be applied with reliability while the latter can be used with complete certainty to improve performance. It should be noted, however, that while forecasting often entails the use of derivatives in policy without knowing other components of control in place and also without knowing which feedback loops it will create or reinforce, derivative control is fully cognizant of both these processes. Given that the control systems in engineering have inspired the application of feedback concepts in system dynamics, it is inappropriate to categorically reject trend forecasting as a dysfunctional process and not be guided by the engineering practice to use it for improving stability also in social systems.
It is furthermore observed that, while derivative control invariably uses the trend in what is called a tracking variable an entity residing within the control system as a basis for determining correction, trend forecasting may often be focused on a quantity in the market that a firm is trying to follow, which would be termed a tracked variable in control jargon. (Chaman, 2005) In supply chains, demand is often a tracked variable and supply a tracking variable.
Using a trend of demand in controlling the supply can create quite dysfunctional control processes, even though this trend may be very accurately determined. It is not surprising that the outcomes of forecasting demand in the management of supply chains remain uncertain depending on what control components might accidentally form, while the outcome of derivative control in servomechanisms is always certain based on the components deliberately created in designing the corrective regimes.
There seem to be similarities in policies created by the use of derivative control and forecasting. Both use trend information about the variable in the system to drive an error correction process. However, while the former explicitly creates a control process driven by the trend of error in a tracking stock, the latter may often use trends in tracked variables to create remedial processes with often dysfunctional consequences, since their underlying feedback structure may counter their intended goals. (Colleen, 2003) Using complex forecasts of many tracked variables may further complicate the process creating further unforeseen consequences, although demand forecasting software encourages this. It is also observed that while derivative control is cognizant of the feedback process it creates, forecasting in social systems may accidentally create a dysfunctional feedback structure since forecasting is done without any intent to create a control process.
As technology becomes faster and smarter and as the readiness of supply chains to distribute information increases, the forecasting function may become radically different than it is today. Replacing inventory with information is one of the essential principles of supply chain management. In the collaborative forecasting setting, companies’ effort to supplement statistical, chronological based information, the more conventional forecasting approach, with information collected from the customer, the market, the sales force, and other sources. (Dirk, 2003)
This supplemental information changes some of the improbability that exists in the forecast and therefore replaces the inventory created to cover that improbability. As supply chain partnerships develop and increase and the supporting technology also progresses, the supplemental information will enhance its quantity and quality. The forecaster’s role will instead be to organize and manage the information received so that the supply chain knows precisely what to produce when to produce it and where it will be distributed.
This perception is basically a shift from self-regulating demand to dependent demand. Traditionally, independent demands are consideration of as the demand for final product from a company’s external customers. Independent demand must be forecasted or calculate because of the related improbability. Dependent demand is a computation. Companies have prearranged bills of resources that set up exactly what is essential to build each manufactured goods.
Dependent demands are planned based on the demand at the next maximum level of demand. Each link of the supply chain shares information with the next link and passes on their necessities. Dependent demand is not forecasted because the requirements are definite. (Chaman, 2005) With dependent demands, each stage in the supply chain shares its needs with the next lower level, such as customer’s distribution future plans with their suppliers, or a manufacturer passing product plans to their mechanism. By relating each level in the supply chain, dependent demands produce better preparation.
The results are that material, labor, equipment, tooling, engineering specifications, space, transportation, and money all can be corresponding, make certain that the flow of products through the supply chain moves swiftly and inexpensively. (Mentzer, 2004)
There is, however, no reason why forecast-related information cannot be used to improve the performance of a policy since derivative control can use similar information to improve performance with great reliability. To accomplish this, we need to carefully identify the structure of the policy, the feedback loops it creates, and the variables forecast. Tracking variables seem to be better candidates for forecasting than the widely-used tracked variables.
Complex forecasts involving many variables might only increase the uncertainly of the outcomes. Derivative control in engineering seems to offer a good model for designing policies using trend information. Using this model, we can use forecasting reliably in improving system performance. Further research should aim at understanding control processes in complex supply chains involving cascaded delays and attempting to create a variety of control processes using the types of control widely used in servomechanisms. Other contexts in which the control model can be applied include financial and national planning and environmental remediation.
References
Sanders N. and Ritzman L., (2001) Judgmental adjustments of statistical forecasts. In: J.S. Armstrong, Editor, Principles of forecasting: a handbook for researchers and practitioners, Kluwer Academic Publishers, Norwell, MA.
Chaman L. Jain. (2005) Practical Guide to Business Forecasting. Graceway Publishing Company, Inc.; 2nd Edition.
Dirk Seifert. (2003) Collaborative Planning, Forecasting, and Replenishment: How to Create a Supply Chain Advantage. AMACOM.
Colleen Crum, George E. Palmatier. (2003) Demand Management Best Practices: Process, Principles, and Collaboration. J. Ross Publishing.
Mentzer John T, Mark A. Moon. (2004) Sales Forecasting Management: A Demand Management Approach. Sage Publications, Inc.