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inflation and increase overall channel efficiency when capacity cost is high. In our research, we also examine the credibility of forecast sharing, and examine external and internal complexity factors that might affect the credibility of forecasts, and, hence impact distributor’s performance. However, unlike the literature mentioned above, we focus on the forecast sharing between semiconductor distributor and its customers instead of between retailers and manufacturers. Our research examines both external and internal complexity factors that affect the credibility of forecasts, which are forecast fluctuation and order diversification.
CHAPTER 3: RESEARCH FRAMEWORK
Information sharing such as inventory level, sales data, sales forecast, and production schedule, is beneficial for supply chain (Lee & Whang, 2000). As we know, forecasts are considered essential to the supply chains decision making and planning processes. Therefore, among different types of information shared between supply chain members, we focus on examining the effect of forecasts on distributors performance.
For a semiconductor distributor, not only sales but inventory management is important to its profitability. Better forecasting not only serves as advanced notification for future orders and sales but contributes to better inventory management (Yue, & Liu, 2006; Lee & Whang, 2000). Therefore, our research examines how forecast impact sales and inventories in semiconductor distributor. We not only want to confirm forecast sharing is related and essential to distributor’s sales and inventory management, but also aim to find out what factors impact the forecast signal. In previous research (Bozarth, Warsing & Flynn, 2009), they categorized supply chain complexity into internal complexity arising from within the plant and external complexity resulting from connections with downstream and upstream partners, when
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examining the impact of supply chain complexity on manufacturing plant performance.
In our research, we focus on two moderators: forecast fluctuation and order diversification. The former is external complexity resulted from forecast sharing activities with downstream partners, which are electronic device manufacturers in our research. The latter is internal complexity arising from hubs’ order structures.
When we cooperate with a semiconductor distributor, we find out that customers update their forecasts from 13 to 5 weeks prior to actual demand date. Previous literature has shown that customers’ bad forecasting behaviors, categorized as forecast inflation and forecast volatility, make suppliers discount the forecasts (Terwiesch, Ren
& Cohen, 2005). Therefore, we want to examine how forecast fluctuation, the variance of forecasts provided by customers, moderates the relationships among forecast sharing, inventories and sales in a semiconductor distributor.
We add order diversification as a moderator because we find out that the hubs of the semiconductor distributor have different order structures. Some hubs have orders spread across various items with low volume of each one. Some hubs have orders concentrated on few items with high volume. An extreme case is that only one item accounts for the total orders of a hub. Prior literature has suggested that manufacturing complexity increase as the number of supported parts or products increases and production volumes are spread across more distinct items (Bozarth, Warsing, Flynn &
Flynn,2009). Following the logic, we argue that order diversification, how orders distributed among items, creates complexity to the distributor’s hub management, and hence moderates the relationship between forecast sharing and semiconductor distributors’ performance. The research framework is shown in Figure 3-1.
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Figure 3-1. Research Model
3-1 Forecast sharing, inventory and sales.
Forecast information sharing is essential to the supply chains decision making and planning processes and has been recognized as a key element in supply chain coordination (Cachon, 2001). For example, Texas Instruments share their forecasts with suppliers as part of their quantity-flexible contracts (Lee & Whang, 2000). And suppliers use the forecasts to develop their production plan. Forecast sharing is shown beneficial for supply chain members. For example, a 5%-20% reduction in inventory costs and an 2%-12% increase in off-the-shelf availability have been reported by GlobalNetXchange, a consortium consisting of more than 30 trade partners including Sears, Kroger, Unilever, Procter & Gamble, and Kimberly Clark, after engaging in CPFR program (VICS CPFR Committee,2002; Terwiesch, Ren & Cohen, 2005).
Because forecast sharing contributes to supply chain performance, we assume that forecast sharing is relevant to inventories and sales, the performance of a semiconductor distributor. In addition, prior literature suggested that demand forecast updating induced the supplier to produce more or less than the manufacturer’s uncommitted quantity request (Angulo, Nachtmann & Waller, 2004), so we assume that demand forecast also induces a semiconductor distributor to replenish more or less than the
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customers’ uncommitted quantity request, which is related to inventory management.
Also, forecasts are informative for the distributor to make replenishment decisions. We argue that forecast sharing is a signal for inventory replenishment and might somewhat reflect the volume of inventories. We argue that when the total forecast demands shared by customers are higher, the total inventories are intuitively higher since the distributor must assure sufficient supply. Also, because forecast is shared by customers and serves a notification of future orders, we argue that forecast sharing is relevant to sales for a semiconductor distributor. And as the demand forecast increases, the future sales accordingly increase. Thus:
H1a: Forecast sharing has positive relationship with inventories.
H1b: Forecast sharing has positive relationship with sales.
3-2 The moderating effect of forecast fluctuation on forecast sharing.
Although the value of sharing demand forecast within a supply chain has been investigated, much of the literature assumes truthful information is exchanged (Li 2002, Zhang 2002; Özer and Wei 2006; Oh & Özer, 2013). However, forecasts are not always shared truthfully between supply chain members (Terwiesch, Ren & Cohen, 2005;
Ebrahim‐Khanjari, Hopp & Iravani, 2012; Fu, Dong, Liu, & Han, 2016; Spiliotopoulou, Donohue & Gürbüz, 2016). The credibility and trust of forecast sharing is gaining attention in supply chain management (Ozer and Wei, 2006; Ozer, Zheng, Chen, 2011;
Inderfurth, Sadrieh, Voigt, 2013; Spiliotopoulou, Donohue & Gürbüz, 2016). To earn more profit, the agent has an incentive to inflate her forecast to the retailer who seeks demand forecast information from the agent before purchasing products (Fu, Dong, Liu,
& Han, 2016). Sometimes customers inflate their forecast to guarantee sufficient
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products provided from suppliers, and manufacturers inflate her forecast to induce the supplier to build more capacity. Sometimes customers continually revise their forecasts as they receive new information about market demand. The bad forecasting behaviors, referred to as forecast inflation and volatility, prevent optimal supply chain performance (Terwiesch, Ren & Cohen, 2005), and make forecast less informative. It was shown that the suppliers may not trust the manufacturer's forecast when they are aware of forecast bias, in turn harming supply chain performance (Cachon & Lariviere, 2001).
Also, Cattani and Hausman (2000) show that demand forecasts do not necessarily become more accurate as they are updated, in turn causing inefficiencies if the firm reacts to the wrong forecast update. Therefore, suppliers might delay acting on the forecast. Because of bad forecasting behaviors, suppliers may not take forecast seriously and discount forecast, in turn decrease the impact of forecast signal. In our research, we define forecast fluctuation as the variance of forecasts shared by customers due to market uncertainty they face. Because of market uncertainty, customers change and update forecasts frequently, and inflates demands. We argue that forecast fluctuation decreases the forecast signal to inventories because a semiconductor distributor might consider forecasts with higher variance as unreliable, in turn discounting the forecasts. Therefore, when forecast fluctuation is higher, the forecasts are less informative for the distributor to make replenishment decisions. In addition, because of high market uncertainty, the actual demand and future sales are difficult to understand and forecast. We therefore argue that forecast fluctuation decreases the impact of forecast sharing on sales for a semiconductor distributor. Thus:
H2a: Forecast fluctuation decrease the impact of forecast sharing on inventories.
H2b: Forecast fluctuation decrease the impact of forecast sharing on sales.
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3-3 The moderating effect of order diversification on forecast sharing.
Previous literature categorized supply chain complexity into internal complexity from the manufacturing plant itself and external complexity resulted from interactions with supply chain members (Bozarth, Warsing & Flynn, 2009). While customers’
forecast fluctuations resemble external complexity, we explore order diversification of a hub as the internal complexity. Some hubs in our research have orders concentrated on few items with high volume, which is alike high volumes of standardized products in manufacturing literature (Hayes and Wheelwright, 1979; Hill, 1994; Safizadeh et al., 1996; Duray et al., 2000). While some hubs in our research has orders spreading across multiple distinct items with relatively low volume, which is similar to the concept of producing customized, or very low volume products in manufacturing literature.
At the manufacturing planning level, greater numbers of products and parts, and higher level of customization will increase the size and scope of manufacturing operations. An unstable master production schedule also makes it more difficult for plants to effectively balance demands against capacity and identify feasible production schedules. Also, One-of-a-kind and low volume batch production requires more complex interactions between different areas of the plant and higher levels of decentralized decision making (Hill, 1994). Internal manufacturing complexity was shown to have negative impact on manufacturing performance such as schedule attainment and manufacturing costs (Bozarth, Warsing & Flynn, 2009). Although our research context is in a semiconductor distributor, we assume that internal hub complexity, referred to as order diversification in our research, also has negative impacts on distributor performance: sales and inventories. We argue that with
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higher order diversification, indicating that orders in the hub spread across many distinct items with lower volume, distributors’ planning and replenishment tasks are more complex. Such complexity makes it difficult for a distributor to interpret forecasts provided by customers. Therefore, we argue that higher order diversification decreases the impact of forecast signal on inventories. In addition, because orders of customized and very low volume products are with high erratic and discontinuous demand, their actual demands of market are accordingly more difficult to forecast than orders of standardized and large volume products. Therefore, we argue that higher order diversification decreases the impact of forecast signal on sales. Thus, the hypotheses related to the moderating effect of order diversification are as below:
H3a: Order diversification decreases the impact of forecast sharing on inventories.
H3b: Order diversification decreases the impact of forecast sharing on sales.