The bullwhip effect in retail supply chains

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).
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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.
DMPPIM
.70*
14.736
DM ICT
.47*
5.411
PPIMICT
-.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.
DMPPIM
.53*
4.256
DMICT
.50*
4.687
PPIMICT
.39*
3.699
ICTBWE
.48**
2.876
PPIMBWE
.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. The comparative study brings in the fact that LSC
managers’ demand management needs further improvement compared to TNC
managers though both are affected equally from their independent suppliers and
convenience stores who need to upgrade their ICT systems within their limitations. On
methodology side, given the sample limitations, inclusion of more partners from other
regions of Indonesia could help in generalising the result. Future research with
longitudinal data will enable the researcher to follow a product and its ordering
information that can be used to estimate the mean and standard deviation of demand
amplification as used in literature study.
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