Hockey stick phenomenon: supply chain management challenge in Brazil.
| Autor | Sanches, Lars Meyer |
Introduction
Companies must consider particular characteristics if they wish to be successful in their ventures in emerging markets (Lorentz, Wong, & Hilmola, 2007). The hockey stick sales or hockey stick phenomenon, a sales spike at the end of the sales period, is an important characteristics of many markets in Brazil but not exclusive to this country. There are references in the literature to companies in highly diverse industries and countries suffering from the effects of the hockey stick phenomenon (Bradley & Arntzen, 1999; Chen, 2000; Hines, Holweg, & Sullivan, 2000; Hoole, 2005; Lee, Padmanabhan, & Whang, 1997a; Neale & Willems, 2009; Nyaga, Closs, Rodrigues, & Calantone, 2007; Oyer, 1998; Shaw, 1996; Singer, Donoso, & Konstantinidis, 2009; Slone, Mentzer, & Dittmann, 2007; Sohoni, Bassamboo, Chopra, Mohan, & Sendil, 2010; Stank, Dittmann, & Autry, 2011; Sterman, 2006; Umble & Srikanth, 1990; Villegas & Smith, 2006; Zotteri, 2013).
As a research topic, the sales spike at the end of the sales period is still under-investigated in the literature (Singer et al., 2009). Agency theory (Chen, 2000), non-cooperative game theory (Singer et al., 2009) and dynamic stochastic (Sohoni et al., 2010) theoretical models have been employed to study this phenomenon. One of our goals is to evaluate the adequacy, with an empirical study, of the policies proposed by previous authors to eliminate the hockey stick phenomenon and also to test new alternatives. Our study expands the scope of previous work and considers, in addition to competition between retailers, the actions and reactions of competing suppliers and consumers, and quantifies the impacts the hockey stick phenomenon has on involved agents' financial performance.
The objective of this study will not be limited to evaluating alternative policies to eliminate the spike in demand at the end of a sales period. This analysis will encompass the causes as well as the impacts of the hockey stick phenomenon. The system dynamics method (Forrester, 1961; Sterman, 2000) is used for the study.
Background
The vast literature regarding changes in the demand along the supply chain is an important source for this research. Jay Forrester's research using difference equation modeling (Forrester, 1961) demonstrated what he called the demand amplification. According to Potter and Lalwani (2008, p. 835) "demand amplification refers to the increase in variability of orders across an echelon within a supply chain". The bullwhip effect was coined by Procter and Gamble executives to describe the problems that they were facing in the diapers market (Lee, Padmanabhan, & Whang, 1997b). The work of Lee, Padmanabhan and Whang (1997b) was responsible for disseminating this new term. According to Lee et al. (1997b, p. 546) "the bullwhip effect or whiplash effect refers to the phenomenon where orders to the supplier tend to have larger variance than sales to the buyer (i. e., demand distortion), and the distortion propagates upstream in an amplified form (i.e., variance amplification)". Some authors point that demand amplification and bullwhip effect referred to the same phenomenon (D. H. Taylor, 1999; Towill, Zhou, & Disney, 2007) but according with Lee et al. (1997b) their work differs from Forrester (1961) regarding members' behavior. According to Lee et al. (1997b, p. 548) changes in demand is "an outcome of the strategic interactions among rational supply chain members". Demand amplification, also known as Forrester Effect (Disney & Towill, 2003), is related demand signal processing and non-zero lead times (Forrester, 1961).
Excessive inventory, inadequate distribution and production capacity, low product availability and high transportation costs are some of the impacts of the bullwhip effect (Lee et al., 1997b). Potter and Lalwani (2008) use a simulation model to quantify the impacts demand amplification has on transportation costs. They stated that demand amplification will affect transport costs due to premium transport rates and inefficient scheduling. Towill and McCullen (1999) report impacts in inventory turns and customer service level as well.
Many causes for the amplification of demand variability along the supply chain have been pointed out: information and physical delays (Forrester, 1961); agents' irrational behavior (Sterman, 1989); demand forecast updating, order batching, price variations and rationing gaming (Lee et al., 1997b). Paik and Bagchi (2007) used a computer simulation model to suggest that among many of the causes for the bullwhip effect the most significant ones are demand forecasting updating, echelon levels and price variations.
Lee et al. (1997b) mentioned the hockey stick phenomenon and they attribute this phenomenon to order batching. They pointed out that in the case that when customers are assembly plants, the end of the month peak is due to the habit of monthly production planning cycle runs. According to Lee et al. (1997b) retailers' order batching is related to gain economies in pricing (i.e., volume discounts), transportation, periodic review of ordering processes and transaction costs of processing purchase orders. Lee et al. (1997b) propose a serious of actions to mitigate the effects of order batching: sell-though and inventory data collaboration; reduce transaction costs through electronic data interchange (EDI), less-than-truck-load (LTL) and coordination of delivery schedules. We refer to Bhattacharya and Bandyopadhyay (2011) for a comprehensive review of bullwhip effect literature and order batching in particular.
Lee et al. (1997b, p. 554) also cited that another source of positive order correlation was that "salespeople tend to rush and close deals towards the end of the quarter to meet their quarterly sales target". They demonstrated that when rational behavior agents are submitted to high-low prices policies the bullwhip effect is generated. Lee, Padmanabhan and Whang (1997a) mentioned the impacts of forward buying, a practice where products are purchased prior to the actual demand, on the bullwhip. Forward buying was due to manufacturers' price and quantity discounts that resulted in price fluctuations. They also refer to push ordering when orders are pushed to customers due to salespeople incentives. Ozelkan and Cakanyildirim (2009) coined the term reverse bullwhip effect in pricing (RBP) to the impact of upstream supply chain price changes into retail prices. In order to reduce price fluctuations Lee et al. (1997b) propose the use of Every Day Low Price (EDLP) strategies along with Vendor Management Inventory (VMI) arrangements.
According to Lorentz, Wong and Hilmola (2007) actions to reduce the bullwhip effect are difficult to implement in emerging markets. Handfield and Withers (1993) point out that emerging market executives have gaps in management competencies. Boubekri (2001) proposes that the implementation of supply chain management concepts depends upon market characteristics. Foreign companies can either try to apply their home country supplier-customer relationships policies in emerging markets or adapt them to market environments (Canning & Hanmer-Lloyd, 2002). Studying the environment in East Europe, Lorentz et al. (2007) points out that general economic development and diverse environmental variables play important roles in setting the structure of the distribution systems and play a crucial role in determining the level of demand amplification. Moori, Perera, and Mangini (2011) concluded that price changes and replenishment policies are the main variables that caused the bullwhip effect in a study conducted in the food supply chain in Brazil. They claim that price changes are due to the habit of end customers searching for promotional prices at retailers. These results can help explain the problems that Wal-Mart had while trying to introduce its EDLP policy in Brazil (Rocha & Dib, 2002). Wal-Mart tried to apply their home country policies but struggled with the impacts of this decision.
The hockey stick sales phenomenon is also associated with other effects in accounting and economics literature, such as the end of fiscal year effect, channel stuffing, sales manipulation and forward selling (Cohen, Dey, & Lys, 2008). Forward selling is marked by an attempt by sales personnel to anticipate future requests to reach sales period quotas (Dodd & Favaro, 2006). Authors of the marketing channels school have associated forward selling and forward buying as a result of the use of temporary discounts (Desai, Koenigsberg, & Purohit, 2010). Many retailers manage discounts as a profit center that results in forward buying being very important to financial results (Blattberg, Briesch, & Fox, 1995). According to Buzzell, Quelch and Salmon (1990) the use of EDLPP (every day low purchase price) models similar to that practiced by Wal-Mart allows reducing the negative effects of the fluctuation in demand. Poddar and Donthu (2013) propose a virtual forwarding strategy where the goods are pre-paid and delivered to the retailers in later periods. Anily and Hassin (2013) shows that customers respond to high and low price strategies by forward buying products during discount periods. Su and Geunes (2012) demonstrate that in some cases increased revenues generated by trade promotions may be offset by the increase in operation costs.
Umble and Srikanth (1990) mention that the hockey-stick effect occurs in different industries and that the main factor that influences its shape is the length of the reporting period. Bradley and Arntzen (1999) examined hockey stick sales occurring in an electronics company. The authors developed an optimization algorithm in order to define, based on the demand peak at the end of the quarter, the optimum combination of productive capacity and stocks (Bradley & Arntzen, 1999). Chen (2000) applied agency theory to construct a dynamic theoretical model in which a company that had no competition sold its...
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