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The effect of forecasting and information sharing in SCM for multi-generation products

https://doi.org/10.1016/j.ejor.2007.01.034Get rights and content

Abstract

Proper selection of information sharing policy and forecasting method has a significant impact on supply chain performance, especially in the high-tech industry where the product life cycle is short and multiple generations of products coexist. This paper evaluates the value of information sharing with various forecasting methods where two generations of high-tech products compete with each other in the same market. We consider two market environmental factors and two supply chain factors for the Monte Carlo Simulation and find out the most ideal combination of information sharing policy and forecasting method producing the maximum profits and service level.

Introduction

For many companies, supply chain management has become an important element of strategic advantage to gain a competitive edge over their competitors. However, managing modern supply chain effectively, especially in the high-tech industry, is becoming more complex and challenging due to the current business trends of expanding product variety, shrinking product life cycle and business globalization. With the prediction of over 50% of the worldwide semiconductor memory production rate will be held by Asia by 2009 (In-stat Report, 2006), it is no surprise that many high-tech companies’ supply chain challenges are global in nature. Besides, high-tech companies today, such as a computer processor or a semiconductor memory manufacturer, are facing ever-changing customer demand which constantly shrinks the product life cycle. As a result, the importance of supply chain in the high-tech industry has extremely increased over the past decade. Many companies developed and deployed their globalized and complex supply chain systems in order to control and optimize the balance between inventory and customer service levels. Yet the fact is, many high-tech companies do not have an effective demand planning process – the key to achieving the ideal balance – and as a result, they can face dire consequences.

One of the major difficulties of managing the supply chain in the high-tech industry is to face the volatility of the market. In high-tech industry, the new generation technology constantly emerges to take over the previous technology, drastically dropping the salvage cost of the older generation products. This makes the inventory cost of high-tech products extremely higher than other industry products, therefore the high-tech companies are making enormous investments to improve the accuracy of their forecasting.

However, it is often observed in the supply chain that a small forecasting error can be amplified while being passed to its supplier to create an order distortion. In high-tech industry, the consequence of this phenomenon, which is called bullwhip effect (Lee et al., 1997), can be extremely magnified into a major disaster. In modern supply chain literatures, the information sharing between the manufacturer, supplier, distributor and retailer has been viewed as one of the major strategy to counter this bullwhip effect (Gavirneni et al., 1999, Lee et al., 2000, Cachon and Fisher, 2000, Raghunathan, 2003). With successful information sharing, the suppliers can effectively plan their operations without blindly depending on their previous channels to satisfy higher customer service levels with less inventory cost. Zhao et al. (2002b) claimed that the cost savings through the proper forecasting and information sharing are substantial enough to motivate other parties in the supply chain to share the information.

Although many studies confirmed the value and the importance of information sharing and forecasting, they have not considered the effect of multi-generation products which coexist in the market to deflate the price of the older generation products. Since, newer technologies continuously replace older ones faster than ever, appropriate forecasting model is required to evaluate the future demand accurately.

The main objective of this study is to find out the proper information sharing policy and the forecasting method for which maximizes the net profit and customer satisfaction for the multi-generation products in the high-tech industry. Monte Carlo simulation is performed to simulate various conditions of market environment in a supply chain.

The organization of the rest of the paper is as follows. Section 2 deals with the review of related literature for information sharing and forecasting methods along with the high-tech industry context. In Section 3, we introduce our research hypotheses along with necessary Monte Carlo simulation. In section 4, experimental results are summarized. The discussion and the direction for the further research are given in Section 5.

Section snippets

Literature review

In this section we review two important research streams to progress further in our study; information sharing and forecasting models in supply chain.

The bullwhip effect, first addressed by Lee et al. (1997), is a phenomenon which the demand variation is amplified as the supply chain moves up from the retailer to the supplier. Thereafter, Lee et al., 2000, Gavirneni et al., 1999, Gavirneni, 2001, Cachon and Fisher, 2000, Raghunathan, 2003 analyzed the value of real-time information sharing to

Supply chain with multi-generation products

The main purpose of this study is to suggest the proper information policy and forecasting method under various conditions for multi-generation products of high-tech industry. To identify the most profitable information sharing policy and forecasting method, we consider the factors such as seasonality, price sensitivity of multi-generation products and supplier’s capacity based on our literature review.

We set the following research hypotheses:

Hypothesis 1

Environmental factors (seasonality, price

Results

Results of our Monte Carlo study are summarized in terms of the four response variables in Table 5. Since our main objective was to find the best combination of forecasting methods and information sharing, we will mainly deal with the results which contain both information sharing and forecasting method factors.

The ANOVA result shown in Table 5 indicates that the five-way interaction effect (FM  IS  η  sea  CP) does not significantly influence the profit or the service level at the significance

Discussion

In this study, we verified the relationships between the information sharing and the forecasting model with the various conditions of market environment and supply chain. Pre-investigation of market condition and supply chain can surely be helpful for selecting the proper information sharing policy and forecasting model. From the computational experiments, we derived following results.

Generally, the Winters’ model showed the great performance with the centralized policy. However, in the aspect

Acknowledgement

Jehyun Wu contributed to this research by conduction necessary computation.

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