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CHAPTER6: CONCLUSION
Forecast sharing has been considered essential for supply chain improvements.
However, when we cooperated with a semiconductor distributor, we surprisingly found out that they do not make replenishment decisions by forecast information shared by their customers. Instead, they use past purchased orders to decide the volume of components to be replenished. We therefore, aim to investigate whether forecast sharing is a signal to semiconductor distributor’s inventories and sales management. Also, we examine if the signal of forecast sharing becomes more or less valuable to the semiconductor distributor’s inventories and sales with external factor referred to as forecast fluctuation and internal factor referred to as order diversification considered in the study. Our results show that forecast sharing is a signal to the semiconductor distributor’s sales and inventories. As we show with respect to Hypothesis 1a and Hypothesis 1b, forecast sharing positively related to the distributor’s inventories and sales. When the forecasts of the electronic item are higher, the sales and the inventories of the item are correspondingly higher. Our results also show that forecast fluctuation and order diversification both negatively moderate the relationships among forecast sharing, inventories and sales. They both decrease the signal of forecast sharing on the semiconductor distributor’s inventories and sales. Hypothesis 2a and Hypothesis 2b demonstrate that forecast fluctuation, resulted from external complexity such as market uncertainty, discounts the signal of forecast sharing on the distributor’s sales and inventories. When the forecast fluctuation is higher, the positive relationships among forecast sharing, inventories and sales decrease. In addition, Hypothesis 3a and Hypothesis 3b show that order diversification, arising from internal complexity of hubs’
order structure, decreases the signal of forecast sharing on the distributor’s sales and inventories. We further examine moderation effects at different levels of forecast
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fluctuations and order diversification (see Figure 5-2.1 and Figure 5-2.2). Interestingly, we find that the moderation effect of order diversification on inventories are only observable when a hub has high diversified orders whereas such a moderation effect is not shown for hubs with medium and low levels of order diversification. We also find out that order diversification has more significant moderation effect on sales than forecast fluctuation does.
In practice, our finding gives W company, a semiconductor distributor in a dynamic environment, some management implications. Forecast information is signal to items’ sales and inventories. Therefore, in addition to making replenishment decisions only by past purchasing orders, W company may also adopt forecasts shared by their customers to better make replenishment. In addition, they can have better understanding of sales of each item by forecast information and realize which item can contribute more to profits. Furthermore, we give W company insights of conditions when the forecast information is less informative. When forecast fluctuation is higher, which indicates that forecast information fluctuates more extensively, the forecast information is less reliable for W company to adopt when managing sales and inventories. The forecast information is also less informative when the item’s hub has high order diversification, the orders of the hub spread across multiple items. Therefore, W company can decide to what degree they manage inventories and sales by forecast information according to different order structure of hubs. In other words, when the hub has higher order diversification than other hubs, the forecasts of items in this hub is less reliable for W company to use. As our research focus on the impact of forecast sharing on a semiconductor distributor’s inventory and sales management in the hubs, our finding may be applied to other semiconductor distributors when referring to hubs’
inventory and sales management.
In supply chain literature, forecast sharing has been shown to have benefits for
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supply chain members. Although the credibility of forecast information is growing in literature, it is most discussed in retailing industry or in manufacturing, and it is not well discussed in semiconductor distributor. Therefore, our research contributes to literature more insights of forecast sharing and its credibility on semiconductor distributors, who especially face dynamic environment. While most studies suggest beneficial effect of forecast sharing (Lee et al., 2000; Yue and Liu, 2006; Spiliotopoulou et al., 2016), we contribute to the literature mechanisms that might influence forecasting signal for a semiconductor distributor. Our research highlights both external and internal complexity can decrease the forecasts’ credibility. We specify external complexity as forecast fluctuation resulted from market uncertainty, and internal complexity as order diversification arising from hubs’ order structure. We investigate the relationships among forecast sharing, sales and inventory management by considering external and internal complexity at the same time, which contributes to literature on forecasting in supply chain management.
Though our study shows interesting implications, we also recognize several limitations. First, our data is only from one customer of the distributor. Therefore, for future research, more data from different customers can be collected to better understand forecast signal on a semiconductor distributor’s performance. Additional analysis can be extended to distributors of other industries to examine the applicability of the research findings. We also acknowledge that there are characteristics other than forecast fluctuations and order diversification that can be used to moderate forecast signals. Future studies are suggested to explore other complexity factors that might also have impacts on forecast credibility. Besides, we focus on inventories and sales of the distributor in the study. Future researchers can investigate other performance indicators that are also relevant to forecast sharing. Despite these limitations, our study extends the literature on forecasting to the industry of semiconductor distribution, and goes
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beyond direct effect to specify contextual factors where forecasting sharing is not so beneficial. We expect researchers to follow the suggested research directions and further enhance the understanding of role of forecasting.
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