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CHAPTER 1: INTRODUCTION
Sharing demand forecast information has been recognized as a key element in supply chain coordination (Cachon 2001). Forecasts are critical for decision making and planning processes in the supply chain, so companies have engaged in Collaborative Planning, Forecasting, and Replenishment (CPFR) to facilitate sharing of demand forecasts among the supply chain members. It is also commonly believed that forecasts sharing within a supply chain improves the forecast accuracy and results in higher profitability (Yue, & Liu, 2006). In addition, better forecasting can contribute to better inventory management (Yue, & Liu, 2006). Much literature has suggested that information sharing is beneficial in lowering supply chain costs (Aviv and Federgruen, 1998; Chen ,1998; Aviv, 2001). Aviv (2001) has suggested that each member of supply chain incorporates the forecast updates into the replenishment process, so updated information is relevant to future demand (Aviv, 2001). Also, it is found out that sharing forecasts of items with nonstationary demand rates has significantly improved the outlet fill rate and effectively reduced costs resulted from stockout (Angulo, Nachtmann &
Waller, 2004). Downstream data sharing between manufacturer and distributor is found to benefit distributor in reduced inventory and stock-outs (Dong, Dresner & Yao, 2014).
Demand forecasts from multiple retailers are somewhat informative for the central planner to make aggregate inventory decision (Spiliotopoulou, Donohue & Gürbüz, 2016). In much research and practice, forecast sharing has been considered beneficial for supply chain performance.
In this study, we cooperate with W company, a semiconductor distributor which manages sourcing of electronic components from manufacturers such as Intel and TSMC, and offers a comprehensive range of components to fulfill diverse demand for customers which are manufacturers of consumer electronics, such as Quanta Computer
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and Foxconn. W company implements vendor management inventory (VMI), helping their customers manage inventories of electronic components in customers’ plants.
Interestingly, we found out that the company makes replenishment decisions not on forecast information provided by customers, but on the real call off information eight weeks ago. Forecast sharing seemed less valuable in this context. W company encounters two major problems. First, due to suppliers’ lead time so long as twelve weeks, W company needs to place orders to suppliers three months before customers need the components. Although customers do share forecast information three months prior to the demand date, they adjust forecasts along the way as they receive new information of market demands. In W company, forecast information shared five to thirteen weeks prior to the demand date is referred as soft orders that can reflect customers’ intent of purchasing but are not legally binding. Therefore, it is difficult for W company to make replenishment decisions based on the frequently-adjusted forecast information. The other problem is that customers often inflate the forecasts to assure sufficient supply. It is common for W company to get inflated forecasts because W company might not be the only supplier of the customers. Customers place soft orders to W company and other distributors at the same time to adverse the risk of insufficient supply of components. Therefore, the demand forecasts may not be as reliable as the actual purchased record for W company to make replenishment decisions. Some studies have examined the issues of forecast sharing and mentioned that forecast sharing might not result in potential benefits for supply chains. For example, Spiliotopoulou et al.
(2016) examine the issue of unreliable forecasting in the retailing industry. The authors observes the existence of misreporting forecasts and mistrusting forecast information, and discuss the magnitude and causes between multiple retailers and a central planner in retailing supply chain.
In sight of the inconsistency of the role of forecast sharing in the main stream of
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the literature and in the case company, we therefore aim for the role of forecast sharing in the semiconductor industry. Our research questions are two folds. First, we aim to examine, under the uncertainty demand of semiconductor industry, whether forecast is trustful and meaningful to distributors’ inventory and sales management (RQ1). In addition, we would like to examine what contingency factors may make forecast information more credible or less valuable. We include two contextual factors: forecast fluctuations and order diversification in the study. Forecast fluctuation is one contextual factor reflecting external forces. Because of market uncertainty, it is difficult for manufacturers of consumer electronics, i.e. W company’s customers, to capture actual demands. Therefore, W company’s customers not only provide forecasts, but also frequently update forecasts. We then are interested in whether effects of forecast signals vary by different levels of forecast fluctuations (RQ2). Order diversification is another contextual factor related to internal structures of W company’s storage hubs. W company implements VMI, and helps each customer manage their inventory stored in storage hubs which are set near customers’ production plants. Some hubs have orders distributed among multiple items with low volume of each one. Others have orders that are concentrated on certain items with high volume. According to previous literature, manufacturing processes of high customized and lower volume production experience higher levels of complexity than processes that produce high volumes of standardized products (Bozarth, Warsing, Flynn & Flynn,2009). Following the logic, we argue that hubs with different order structure may have different level of complexity in managing inventory and sales. Thus, hubs with various order structures may react to forecast signals differently (RQ3). The following research questions are thus proposed:
1. Is forecast information relevant and helpful to inventory and sale management?
2. How forecast fluctuation plays a role in the relationships among forecast, inventory and sales?
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3. What is the impact of hub’s order composition on the relationships among forecast, inventory and sales?
To answer the above research questions, we collect empirical data from W company, the leading semiconductor distributor in Asia. Specially, we obtain orders and forecasts of one main customer of W company, and the associated inventory records prepared by W company. Such data are span from 2016/10 to 2017/10 for one year, and shows a hierarchical relationship: orders of a component belonged to a storage hub.
Using the dataset, we expect to empirically discover the role forecast sharing in the semiconductor industry, and identify how contextual factors affect achievable outcomes.
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