The bullwhip effect in retail supply chains: an analysis of stock ordering policy and ICT logistics systems Kaswi Surahman College of Business, Victoria University, City Flinders Campus, Politeknik Negeri Samarinda, Indonesia PO Box 14428, Vic-8001, Melbourne, [email protected] Himanshu K. Shee College of Business, Victoria University, City Flinders Campus, PO Box 14428, Vic-8001, Melbourne, [email protected] Abstract This study found a significant positive relationship between demand management and the bullwhip effect (BWE) via purchasing policy in stock ordering and ICT logistics systems. SEM with variance analysis was employed to analyse cross-sectional survey data from suppliers, wholesale DCs and convenience stores in both TNCs and LSCs in Indonesia. The results confirmed the presence of demand variability yet exists in both type of chains. Thus demand variability significantly influences inventory stock policy and ICT logistics systems leading to a significant predictor of the bullwhip effect. The paper concludes with implications and limitation of the study. Keywords: bullwhip effect, retail supply chain, stock ordering policy, ICT logistics systems Topic: Supply chains Methodology: Theory and/or research framework Introduction The phenomenon of bullwhip effect (BWE) in forecast-driven distribution channel refers to progressively larger swings in inventory with any change in downstream demand (Lee and Lim, 2005). The BWE is a situation that often occurs due to customers’ varying demand resulting in a series of effects that disturb operations. The effect is also triggered by lack of coordination in the exchange of information among supply chain partners. In fact, an uncontrolled external market and customer demand might worsen the variability. Forrester (1958) traced the theoretical foundation of the BWE from “industrial dynamic” principle and his work was subsequently extended to Burbidge’s (1961) economic order quantity (EOQ). Extant literature describes the BWE phenomenon as happening in several business practices. This is not surprising given that the academic literature is replete with the BWE in supply chains using several analysis methods such as analytical models and game theory simulation. Scholars have simulated the situation that causes the effect and prescribed cure to mitigate it. For example, Sterman (1989) and Chen et al., (2000) used the well-known ‘Beer Distribution Game’ theory to classify several causes of demand amplification. At the same time, other literature has highlighted the BWE in relation to 1 production planning problems, inventory stock, customer services and operating costs (Chen et al., 2000; Eggert and Fassott, 2003; Li et al., 2011; Richards and Laughlin, 1980). In the past decade, the BWE issues were also studied by many authors such as Anderson and Morrice (2000), Chen and Samroengraja (2000), Disney and Towill (2003), and Machuca and Barajas (2004). These studies have attracted considerable attention from researchers and practitioners in the management of grocery supply chains. However, there is a need to further investigate the demand variation in retail chain operations. The research looks at demand management practices and stock ordering policy in two types of retail chains in Indonesia and explores whether they likely to affect the BWE. ICT logistics systems can develop an inter-organisational information system (IOIS) connecting supply chain partners, thus reducing the network lead-time (Valera, Lagace, & Bergeron, 2010). Integration of supply chain partners through ICT can increase productivity and lower costs (Huang, Lau, & Mak, 2003; Siau & Tian, 2004). Internet utilisation for decision support in supply chain operation has become a means through which companies can collect and forecast customer demand precisely. Authors have shown that the Internet is relevant in assisting users in decision-making and forecasting of customer demand (Cachon and Lariviere 2005; Lariviere and Porteus 1999; Crum and Palmatier 2003). Some firms have excellent experience in sharing information. For example, Seven-Eleven Japan, the largest Japanese convenience store, detects thieves by using the POS data and detects customer buying behaviour (Lee & Whang, 2000). This study investigates Indonesian TNCs and LSCs, the extent to which they use ICT systems to communicate with chain partners. This paper commences with a review of earlier studies in relation to how firms practice stock ordering and use ICT systems while estimating demand that ultimately influences the BWE. It follows by a conceptual framework and hypotheses development. The methodology with descriptive statistics on sample is then discussed. The results of SEM analysis along with path diagram are presented. Finally, discussion and conclusion of the research are presented. Literature review Bullwhip effect The objective of the BWE mitigation is to improve the effectiveness and efficiencies of product distribution along the supply chain through accurate demand forecast. This might imply the need to synchronise the demand forecast method more precisely in addressing customer demands. Demand forecast thus is a key point to balance between customer needs and availability of inventory. Inventory management refers to an activity which manages an idle stock of physical materials processed through to customers (Wild, 2002 p.4). It combines with purchasing and distributing the product to meet customer demands. The bullwhip effect is caused by the way demand is managed and can be a real threat to a supply chain. When a retailer is suffering from the bullwhip effect, it could end up as carrying excess inventory or out-of-stock situation; poor customer service resulting in non-fulfilment of orders; uncertainty in distribution planning resulting in high-cost of transportation leading to decreased profitability (Chatfield et al., 2004; Croson and Donohue, 2005; Lee and Lim, 2005; Lee et al., 1997a). While the BWE results from demand variation, forecast of an accurate demand is crucial. Authors believe that accurate demand estimation can be achieved through right purchasing policy and 2 inventory stocking issues. Further, precise estimation of forecast is closely associated with and dependent on the robustness of information and communication system that the partners use in a chain operation. Both are discussed in detail. Purchasing policy in stock ordering Purchasing policy in stock ordering aims to meet customer demand while keeping inventory costs at a reasonable level (Mercado, 2008). The objective is not simply to reduce costs of ordering and holding inventory, but is to just meeting customer demands. According to Mercado (2008), these two objectives are clearly opposed to each other when considered by themselves. Balancing inventory cost with a level of safety stock should be defined in a rational way in order to achieve customer satisfaction and potentially increase the profit margin. Wild (2002) suggest that in order to meet the required customer demand at a minimum cost, firm should display excellent customer service. Thus, leading firms are not only successful in their distribution of goods as customers demand, but also provide a high level of customer service in a manageable and effective way. The balanced and accurate management of the inventory is critical for high service level. When a firm decides to reduce the cost on inventory, it might simply hold limited stock and restock when customers place an order, thereby resulting in low inventory cost. However, shortage in the inventory level will probably lead to negative consequences on lead time and potentially result in a greater proportion of unsatisfied customers. On the other hand, when the company decides to guarantee the customers’ demand requirements against a shortage of product and service levels, they will add safety stock to their inventory. With respect to inventory management, the most critical approach is to determining how to balance supply and demand (Bolarin et al., 2009; Chandra and Grabis, 2006). Four factors need to be implemented to improve inventory management in a more agile manner: inventory accuracy, inventory replenishment policies, inventory planning process and vendor managed inventory (VMI) (Chandra and Grabis, 2006). Information and communication technology (ICT) logistics systems While addressing retail global market competitiveness, the implementation of logistics ICT system is the most beneficial in retail supply chains. ICT logistics facilitates the distribution of products from upstream manufacturers and supplier to downstream retailers and vice versa. To assist ICT logistics systems exchange among the supply chain players, some firms have applied inter-organisational information systems such as EDI (Sterman, 1989). Several studies have shown that EDI shares accurate information (Stank et al., 1999), helps in integration (Lee and Lim, 2005), enhances data integrity, responsiveness and product quality (Craighead et al., 2006), and reduces information distortion (Tan et al., 2010). EDI thus generates benefits of increasing response time, enables value and unique services, and adds capabilities of product and service delivery to customers. Time reduction in product distribution is crucial to ensuring a competitive advantage. In this context, supply chain players have embraced radio-frequency identification (RFID) in their ICT logistics systems. RFID is an automatic identification and data capturing system combining three components: a tag formed by a chip connected to an antenna; a reader that emits a radio signal; and middleware connected to the hardware and other applications devices (Mercado, 2008). RFID technology provides real-time 3 data communication through radio waves to several data or objects at the same time and at a distance without touch. This helps improving product visibility and traceability between supply chain partners. Further, RFID might give some potential benefit in increasing the efficiency, speed of processes, reduction of inventory losses and improvement of information accuracy (Mercado, 2008; Son et al., 2005). In addition, it can decrease storage, product handling and distribution costs as well as decrease out of stock products (Leung et al., 2007). One of the most frequently used systems for reducing demand amplification in the retail supply chain is implementation of a VMI, which is an operating model that suppliers use to manage an inventory for customers. Disney and Towill (2003) indicate that shortages and rationing gaming as well as order batching might be reduced by implementing VMI in a supply chain. They also explored how the VMI system reduces price variation in the promotion of products. Through a VMI system, demand signalling processes are much lower than in traditional supply chains (Disney et al., 2004; Forrester, 1958; Gavirneni, 2006). To anticipate the increasing number of product sales and manage the inventory, retailers should develop a better replenishment system. One of the suggested systems is a vendor managed category management (VMCM), an innovative system developed from VMI (Lee and Lim, 2005). VMCM helps minimizing problems in replenishment practices, in particular for managing out-of-stock. The concept of VMCM is a combination between VMI, efficient customer response (ECR) and outsourcing in the manufacturing industry. The benefit of VMCM is that it positively contributes to both suppliers and retailers. The conceptual framework and hypotheses development This study has proposed a conceptual framework to investigate how better demand management practices are perceived to reducing the BWE. The framework is developed using the ‘double-bell’ model suggested by Sadler (2007). The investigation is focused on retail chains, TNCs and LSCs, as unit of analysis (Edwards & Lambert, 2007). The framework is illustrated in Figure 1. The relationship between demand management, purchasing policy (PPSO) and the bullwhip effect Lambert and Cooper (2000) argue that demand variability is inevitable. The management of inventory is crucial in this context and it is based on supply and demand (Bolarin et al, 2009; Chandra and Grabis, 2005). Chandra and Grabis (2005) have identified issues in inventory management such as ordering frequency, supply source, demand type and lead time. They argue that the inventory should be regulated periodically, and attempt be made to reduce the volume, and if necessary reorder, as the situation arises. This is linked to purchasing policy of an individual firm. Xie (2009) claims that accuracy in the demand forecast will generate less bullwhip effect. Therefore, it is crucial for all supply chain partners to have accurate forecasts to minimize error and uncertainty. Chen et al. (2000) suggested that exponential smoothing forecasting potentially reduces the bullwhip effect. Unless addressed adequately, The BWE creates inefficiency and triggers the system towards high-cost of product distribution and decreased profitability. Stock ordering in inventory is driven by customer demand variability, distribution and replenishment lead time (Mercado, 2008). Stock ordering relates to products maketo-stock and make-to-order environment. Make-to-stock firm produces in batches based 4 on forecast, carrying finished goods inventories for end items. While, make-to-order firm is characterised by an almost limitless number of possible end item configurations (Vollmann, et al. 2005). It is essential to create an agile and responsive supply chain. Suppliers ideally join the network with retailers in order to increase material flows, making smaller and more frequent transportation, more frequent distribution, and over stock replenishment from retailers. Any deviation from this approach would lead to the BWE (Mercado, 2008). Hence it can be hypothesized that: Hypothesis 1 (H1a): Better demand management is positively associated with purchasing policy in stock ordering in inventory management in both retail TNCs and LSCs. Hypothesis 1 (H1b): Purchasing policy in stock ordering in inventory management is positively associated with the extent of BWE in both retail TNCs and LSCs. The relationship of demand management, ICT logistics systems and the bullwhip effect ICT brings in benefits to the demand management process. According to Narayaman et al., (2009), application of EDI has some benefits to firms. Further, RFID, VMI and other technologies assist firms to achieving real-time, transparent data visibility thereby accessing data accuracy in demand management. Demand estimation in an ICT enabled environment can lead to reduce inventory levels, improve customer services, increase productivity & data accuracy, reduce paperwork and respond quickly to market demand (Aiken and West, 1996; Byrne, 2010; Gavirneni, 2006). Non-adoption of these supply chain technologies may impact on demand variation, which then may lead to the BWE (Byrne, 2010; Chandra and Grabis, 2005; Gimenez et al., 2005; Tabachnick and Fidell, 2001). Demand signal processing may impact order batching, which in turn potentially influences the BWE (Aiken and West, 1996; Candra and Grabis, 2005; Chen et al., 2000; Lynda, 1998; Wangphanich et al., 2010). Hence it can be hypothesized that: Hypothesis 2 (H2a): Better demand management is positively associated with ICT logistics systems in both retail TNCs and LSCs. Hypothesis 2 (H2b): ICT logistics systems is positively associated with the extent of BWE in both retail TNCs and LSCs. Product • < 3 months • 3-12 months • > 12 months H1a Purchasing policy in stock ordering H1b Bullwhip effect (BWE) Demand management (DM) Firm’s • Organisation structure, • Team work • Work force composition • Target market H2a+ ICT logistics Systems (ICT) Figure 1: Conceptual framework 5 H2b+ Measures Demand management and ICT logistics systems questionnaires were adapted from current literature and measured with well-validated multiple items using five point Likert scales, 1 being ‘strongly disagreed’ to 5 being the ‘strongly agreed’. A total of 88 items of survey questionnaire was taken around 30 minutes to complete. As one of the purposes of this study was to analyse the perceptual data on demand management that relate to inventory and ICT logistics systems in the retail supply chain, the choice of 5point scales are considered appropriate for eliciting levels of agreement. Demand management scale was adapted from Cachon (1999) and Robertson (2006). This essentially deals with demand estimation, automation of ordering procedure, batch size and lead time issues. Purchasing policy in stock ordering items were adapted from studies by Robertson (2006) and Hsiao (2006). ICT in logistics systems items were adapted from studies by Fliedner (2008), Stank et al. (1999) and Closs and Cliton (1997). The BWE items were adapted from studies by Mason (2002) and Lynda (1998). The data collection was limited to products that were categorised on the basis of their shelf life. Product-1 with a shelf life between 0 to 3 months; (e.g. dairy product: bread, milk, etc.), product-2 with a shelf life between 3 to12 months (e.g. non-food items etc.) and product-3 with a shelf life more than 12 months, (e.g. electronic, garment product, etc.) are considered in the study. The respondents were fully aware of this categorisation. Table 1 Characteristics of firms No Firms 1. Suppliers 2. Food industry Soft drink Ice-cream Seasoning products Fruit canning Cosmetics Dairy products Water minerals Plastic products Clothing and garments Electronic products Household & furniture products Stationery Total Wholesalers and wholesale DCs Carrefour Lotte Mart Giant Subtotal Matahari Indomaret Alfamart Subtotal Total Convenience stores/minimarkets Indomaret minimarkets Alfamart minimarkets Other independent stores Total 3 Characteristics 6 Count Column N% Owner 13 4 3 2 3 8 8 3 6 4 7 5 7 73 18% 5% 4% 3% 4% 11% 11% 4% 8% 5% 10% 7% 10% 100% TNC & LSC TNC & LSC TNC & LSC LSC TNC & LSC TNC & LSC TNC & LSC LSC LSC LSC TNC & LSC TNC & LSC LSC 9 12 9 30 10 4 7 21 51 18% 24% 18% 63.41 20% 8% 14% 36.59 100 TNC TNC TNC 25 28 47 100 25% 25% 47% 100 LSC LSC LSC LSC LSC LSC Methods Population and sampling The sample size of 510 firms representing retailers, suppliers/manufacturers and convenience stores in Indonesia participated in the cross-sectional survey that used convenient sampling. The local retailers and convenience stores are almost dominated by three leading players such as Matahari supermarkets (310 stores), Indomaret stores (6,161 stores and 15 DCs) and Alfamart stores (6,585 stores and 19 DCs) [11]. The local retailers were conveniently chosen with a total of 15 stores: Matahari Hypermart (8 stores), Indomaret (2 DCs) and Alfamart (5 DCs). As Indonesia opens up its economy for globalization of modern grocery, TNC retailers enter Indonesia’s retail market. This research thus involves three international retailers (e.g. Carrefour, Lotte Mart and Giant) at 26 different store locations. Connected to these retailers are 73 local SME manufacturers/ suppliers and 100 convenience stores were randomly chosen as well. The survey was initiated at retailers’ level involving logistics and supply chain managers, marketing managers and inventory managers or owners. The suppliers and convenience stores contact details were collected from a retailers’ database before the researcher moved to collect data from each of them. The three echelon supply chain represents the unit of analysis. Demographic statistics A total of 224 valid responses were returned from 73 suppliers, 51 wholesale and DCs and 100 convenience stores representing a response rate of 44%. The survey was represented by the logistics or supply chain manager (17%), marketing manager (40%), and owner of convenience stores (43%). The gender distribution of respondents was male (68%) and female (32%) with work experience 5-10 years (35%) and 11-15 years (47%). Most respondents held a Bachelor degree (42%), Master degree (13.4%) and Diploma or Certificate (14.7%) and others (13%). As usual the retail industry is dominated by male members: 68 per cent. The data were collected in May – July 2011. Most firms are domestic-owned (74.6%) and the others are foreign owned stores, such as Carrefour, Lotte Mart and Giant. More than half of the participating firms (62.9%) have less than 100 employees. The large modern retail formats such as hypermarkets (16.4%) have 3,000 employees (see Table 2). The participating TNC suppliers comprised of 17 large suppliers that included food products (e.g. Nestle, Arnott and Friesland Campina), and soft drink products (e. q. Coca Cola). There were three foreign retailers participated in the survey (e.g. Carrefour, Lotte Mart and Giant) with a total 30 stores and DCs. The local firms were represented by 60 suppliers, 36 wholesalers, 15 DCs and 100 convenience stores or minimarkets (see Table 1). 7 Table 2 Respondent demographic characteristics (valid N = 224) Categories Firm category Position Gender Age Education Work experience Number of employees Type of firm Number of customers Number of suppliers Convenience stores Wholesale DCs Suppliers Logistics/Supply Chain Manager Marketing Manager Head of Logistics Department Head of Marketing Other/owner of convenience/retail store Male Female 20 years and below 21-30 years 31-40 years 41-50 years 51 years and above Secondary school and below Diploma/Certificate Undergraduate Masters/PhD Less than 5 years 5 – 10 years 11- 15 years 16 – 20 years More than 20 years Less than 100 employees 101 – 500 employees 501 – 1000 employees 1001 – 3000 employees More than 3000 employees Foreign owned and sells its products to international market Foreign owned and sells its products to Indonesian market Locally owned and sells its products to Indonesian market Less than 5 customers 5 – 10 customers 16 – 20 customers More than 20 customers Less than 5 suppliers 5 – 10 supplier 11 – 15 suppliers 16 – 20 suppliers More than 20 suppliers Frequency Percent 101 50 73 39 67 12 9 96 152 72 3 39 139 31 21 68 33 93 30 22 79 106 16 1 141 51 11 17 4 2 45.1 22.3 32.6 17.4 39.9 5.8 4.0 42.9 67.9 32.1 1.3 17.4 62.1 13.8 5.4 30.4 14.7 41.5 13.4 9.8 35.3 47.3 7.1 .4 62.9 22.8 4.9 7.6 1.8 0.9 Cumulative percent 45.1 67.4 100.0 17.4 47.3 53.1 57.1 100.0 67.9 100.0 1.3 18.8 80.8 94.6 100.0 30.4 45.1 86.6 100.0 9.8 45.1 92.4 99.6 100.0 62.9 85.7 90.6 98.2 100.0 0.9 55 24.6 25.4 167 74.6 100.0 2 2 4 216 54 28 18 30 94 0.9 0.9 1.8 96.4 24.1 12.5 8.0 13.4 42.0 0.9 1.8 3.6 100.0 24.1 36.6 44.6 58.0 100.0 Results The cross-sectional data collected from the surveys were screened for any errors and checked for completeness before being entered into the statistical package PASW. Initial estimation of non-response bias, multicollinearity, and internal consistency (Cronbach alpha) was checked for the data set. Exploratory factor analysis (EFA) was carried out to check the independent factor loading for all of the constructs. Confirmatory factors analysis (CFA) was then used to provide a confirmatory test of 8 measurement model ensuring that all the variables logically and systematically represent constructs involved in the theoretical model (Hair et al., 2010). In order to test the statistical model and hypothesis, SEM with variance-covariance analysis was used with AMOS 20 software to figure out the data fit. The mean, standard deviation (SD) and correlation coefficients are presented in Table 3 considering the whole sample. The value of Pearson’s r range from r = .100 to r = .35, suggesting the existence of a moderate and significant relationship. The Cronbach alpha varies from .72 to .87 indicating a good internal consistency. Table 3 Mean and SD and Correlation coefficient (N=224) Std. DM PPSO ICT BWE Variable Mean Deviation DM 2.92 .92 .87 PPSO 4.24 .51 .16* .72 * ICT 3.69 .60 .17 .30** .74 ** ** BWE 4.59 .48 .35 .29 .10 .78 *. p< .05 **. p< .01 level Diagonal italicised values are Cronbach alpha We evaluated the path model separately for TNCs and LSCs supply chain to check their data validity. The whole sample 224 was split for TNCs (101) and that for LSC (123). Figure 2 shows a path model for TNC supply chain and the goodness-of-fit indices are found significant. The results indicate that better demand management (DM) has significant positive influence (.70, p<.001) on inventory policy. The inventory policy is also a positive significant (.24, p<.001) predictor of the BWE. Similarly, in relation to ICT logistics system, the results show positive significant effect of demand management on it (.47, p<.001, and a significant predictor of the BWE (.29, p<.001). In addition, though we did not state formal hypothesis, the result shows significant but negative relationship between PPIM and ICT (-.48,p<.001). It suggests that stock ordering policy has negative effect on ICT logistics system which is true to some extent as TNC managers perceived ICT application not helping in inventory efficiency. The results support the hypotheses H1a,b and H2a,b in relation to TNC supply chain (Figure 2). Table 5 provides the standardised loading. 9 Figure 2: Path Diagram of TNC Retails Table 4 Standardised loading for TNC supply chain Estimate C.R. DMPPIM .70* 14.736 DM ICT .47* 5.411 PPIMICT -.48* -5.531 ICT BWE .29* 4.617 PPIM BEW .24* 3.779 *p<.001 The path analysis on LSC supports this model as well. The path diagram and goodness-of-indices are presented in Figure 3. The result shows that demand management (DM) has significant effect on stock ordering policies (.53, p<.001). However, the BWE is not significantly predicted by the stock ordering policy (.02, p>.05) but indirectly influenced through ICT (.39x.48=.19, p<.001). Further, demand management has a positive effect on ICT logistics system (.53, p<.001), that acts as a significant positive predictor of the BWE (.48, p<.01). In addition, though we did not state formal hypothesis, the result shows significant positive relationship between PPIM and ICT (.39, p<.001). It suggests that stock ordering policy has positive effect on ICT system application. It means the policy decision on frequency of inventory ordering, safety stock, supplier information on lead time etc. are affecting the extent of ICT system application in local supply chain. The LSC managers perceive ICT application as affected by the inventory policy decision which is in contrast to what TNC managers perceive. Both hypotheses H1a,b and H2a,b are supported in relation to LSC. Table 5 provides the standardised loading. 10 Figure 3: Path Diagram of LSC Table 5 Standardised loading for LSC supply chain Estimate C.R. DMPPIM .53* 4.256 DMICT .50* 4.687 PPIMICT .39* 3.699 ICTBWE .48** 2.876 PPIMBWE .017 .104 *p<.001,**p<.01 Discussion The demand management is critical in managing the BWE. This research characterises DM as quantitative and qualitative way of estimation, and extra quantities involved while ordering upstream in a situation of demand variability. Any wrong estimation of customer demand will end up with higher BWE leading to chain inefficiency. In addition, ordering frequency and safety stock holding policy are always influenced by market demand estimation and its variability within that. Inevitable demand variation causes managers to frequent intervention while ordering upstream. Further, decision on application of EDI, RFID, POS access, Internet and VMI facility is further dependent on demand estimation practices of a firm. Customer demand estimation and the way it undergoes variation substantially depend on how the partners communicate with each other in reality and what extent they use the ICT in a chain. Obviously ICT is seen as communication backbone helping in seamless flow of order information. We hypothesised that DM would positively affect a firm’s inventory policy and ICT systems. Further, inventory policy and ICT systems were hypothesised as positive 11 predictor of the BWE. We tested these two sets of hypotheses in TNCs and LSC supply chain in Indonesia retail context. Cross-sectional data collected from three-echelon supply chains was analysed by SEM path analysis. We conducted two separate path analyses to test the hypotheses. This approach facilitates us to carry out a comparative study between two categories of chains in Indonesia retail sector. The path analysis results of TNC supply chain show that DM significantly affects inventory policy and ICT systems. Managers of TNC Indonesia perceived customer demand as challenging in retail sector given that the merchandise procurement and distribution along the chain get affected because of demand volatility. Further, they perceive inventory policy as a significant positive predictor of the BWE. Because TNCs seem to have their own ordering quantities what is stated in policies. They perceive the policy as competent and efficient and adhere to its application that does not allow the BWE to occur. Also, ICT systems came out as significant positive predictor of the BWE. This appears logical as modern TNC retailers adopt a comprehensive ICT logistics system which remains the most essential aspect in real-time solutions. The retailers interact with real-time devices to network with other chain players. However, a contrasting approach is observed with small suppliers and independent convenience stores. Limited ICT support is available for information gathering and demand forecasting at small local supplier level. The small suppliers continue to deal with market demand using manual methods and tend to not use any ICT application because they see it as not beneficial rather a costly proposition. In addition, lack of ICT applications at traditional stores or independent convenience stores means that TNC DCs or retail managers have to use their initiative to combine the use of both ICT application and manual methods of demand forecasting. For example, to generate a forecast they might have to use the historical sales data mixed with their sales experience. This, however, takes a lot of effort, time and experience to deliver reliable results. Ustundag (2010) and Narayaman et al., (2009) argue that application of EDI will reduce inventory levels, improve customer services, increase productivity, data accuracy, reduce paperwork, and respond quickly to market trends. Thus, better ICT systems can help reduce the BWE. The path analysis results of LSC supply chain show that DM significantly affects inventory policy and ICT systems. This finding is particularly evident in the context of local supply chains. That means the demand estimation, its variation and decision on any extra quantities influence the inventory policy decision on how many, and what to buy considering the inventory cost and safety stock. Further, demand variability can be reduced through ICT system applications. For example, the leading local convenience store Indomaret and Alfamaret have aimed at reducing lead time variability, maximising profit and sales targets. The digital picking systems (DPS) in their convenience stores and tail gate system (TGS) in their DCs record real-time demand that increases the efficiency of goods distribution to their convenience stores. Application of POS has also been used to improve their sales, inventory accuracy and receiving goods. As an effort towards improving order accuracy and related transactions, they also used check out systems with scanners at each counter. Further, LSC results show stock ordering policy is not a significant predictor of the BWE. However, it influences the BWE indirectly through ICT systems. The reason for not being a direct predictor may be attributed to the managers who perceived the firm’s inventory policy as a guiding principle and they have nothing to do with any change in stock ordering that would affect the BWE. However, they perceive the stock ordering policy as a likely predictor of ICT system where inventory decision significantly affect 12 the way firm’s application of information communication with other chain partners. Further, result shows ICT systems are a significant predictor of the BWE. The LSC managers are of the opinion that better communication technology can help reducing the BWE. Better ICT systems can effectively monitor and help in visibility of products and information as they flow from source through complex supply chain to customers and vice versa. This enables the managers to organize inventory just to meet the customer requirement with minimum bullwhip effect. Conclusion The research investigated the relationship between demand management, stock ordering policy, ICT systems and the BWE in Indonesia retail supply chain context. The study considered TNC and LSC as two separate chains with supplier-DC-convenience store making up a three echelon chain for each one. The chain was considered as a unit of analysis. The TNC chain found all variables significantly associated with each other where LSC chain revealed stock ordering as not a direct predictor of the BWE. Demand estimation is complex considering the variability that might emerge from internal and external sources. This research has used cross-sectional survey to capture managers’ perception of demand estimation given its variability caused by uncertainties. The variability per say is measured in this research using a survey questionnaire that directly received the managers’ perception on extra order that they add every time they order upstream. This extra order itself serves as a variation that this research has used to represent the BWE. Literature defined the BWE as a measure of further amplification of demand information, this cross-sectional study has adequately captured the amplification and analysed further for its elimination. In what way TNCs differ from LSCs, the research found ICT systems perceived to be more important in LSC. It appears that TNCs have already more or less ICT systems in place helping to managing the communication between upstream suppliers and downstream convenience stores. Thus LSC managers realised old ways of information sharing is inefficient to meet today’s hyper-dynamic markets. The traditional independent convenience stores need to upgrade their communication systems if the BWE has to come down. While modern stores make up 95 per cent of retail outlets in Australia and New Zealand, Japan, Singapore, but only five per cent in Indonesia. The TNC firms can often act as role models to proven demand management and stock ordering policy. The results of this study have significant implications. On theoretical side, the main contribution of this study is in describing the role of demand management in reducing the BWE with stock ordering policy and ICT logistics systems as mediating variables. The use of cross-sectional data collection in this research is novice as previous research used either a longitudinal research or simulation technique to measure the BWE. It attempted to collect managers’ perception on extra inventory while ordering upstream and used for further explanation. This contribution emphasises that accurate demand management considers the strategic value of stock ordering policy and role of ICT in logistics. All supply chain managers, especially convenience stores and suppliers, need to become more aware of managing extra orders that directly affect the level of their safety stock which is the root cause of the BWE. Thus, the study provides strategies for improving networking collaboration to combat the BWE. On practical side, the study looked at retail TNCs and LSCs practices on order management in Indonesia. While both the chains have experienced the existence of the BWE, TNCs managers do practice a robust demand management then LSC managers as 13 evident from the causal relationships. 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