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Title:
Report on Information Sharing Among Supply Chain Partners
Course Code:
2050MGT
Course Name:
Logistics/Supply Chain Management Business Processes
Course Convenor:
Dr Ron Fisher
Due Date:
13 May 2008, 4pm
Weighting:
35 %
Student Name:
Raphael Wischnewsky
Student Number:
2642344
Word Count:
2157
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Executive Summary
This report aims to reveal the factors causing the demand fluctuations in Barilla’s supply
chain in the late 1980s. Further aim of this report is to discuss possible strategies to cope with
demand fluctuation. This includes discussion of the just-in-time distribution strategy (within
this report referred to as vendor-managed inventory strategy) proposed by Barilla’s logistics
management. The discussion of possible strategies is followed by examining the role of recent
technology within these strategies.
The findings reveal that the demand variability in Barilla’s supply chain can be attributed to
several causes which are summarised in a phenomenon called Bullwhip Effect. Main sources
of demand variability according to the bullwhip effect are lack of information, price
fluctuations, order batching, and inflated orders. It is found that main approach to cope with
the Bullwhip Effect is to share information. This approach with different levels of information
sharing hold benefits for both vendors and buyers. Recent technology contributes to these
strategies by enabling faster data transmission.
It is concluded that the discussed information sharing strategies provide the right approach to
diminish the Bullwhip Effect in Barilla’s supply chain. Although these strategies include
some limitations, the advantages of the strategies and vendor-managed inventory in particular
are regarded as viable.
This report recommends putting further effort in convincing distributors to agree on vendormanaged inventory co-operation. If the distributors are still unwilling to transfer inventory
responsibilities to Barilla, a less intensive information sharing strategy should be proposed. In
addition, it is recommended that Barilla counteracts single aspects of the Bullwhip effect.
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Table of Contents
Pg
Title Page
i
Executive Summary
ii
Table of Contents
iii
List of Figures
iv
1.0 Introduction
1
1.1 Background
1
1.2 Aims
1
1.3 Structure
2
2.0 Variability of Demand Among the Supply Chain
2
2.1 Reasons for Demand Variability and the Bullwhip Effect
2
2.2 Reasons for Demand Variability in the Case of Barilla
3
3.0 Strategies to Cope With Demand Variability
3
3.1 Information Sharing
5
3.2 Collaborative Replenishments Programs
5
3.3 Vendor-Managed Inventory
6
4.0 Technology as Facilitator of Data Sharing Strategies
7
5.0 Conclusions
8
6.0 Recommendations
8
Reference List
10
iv
List of Tables
pg
Figure 1:
Fig.1. Comparison of information and physical goods flows
among the baseline system, IS, CRP, and VMI.
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1
1.0 Introduction
1.1 Background
In the late 1980s, Barilla, the world’s largest pasta manufacturer (in 1990), faced rising
inventory of finished products due to fluctuating demand of their complex distribution system
in Italy. Barilla’s logistics management attributed this to the fact that Barilla had no
knowledge about demand data of retailers or even end consumers. They assumed that
consumer demand for pasta was relatively even. Therefore, it was concluded that the
fluctuation of incoming orders of their distributors referred to Barilla’s two-tiers-distribution
system and the lack of information flow between the different facilities and companies of the
distribution system. To reduce inventory by sharing demand among the supply chain, Brando
Vitali, Barilla’s Director of Logistics until 1988, proposed a just-in-time distribution (JITD)
strategy. The main idea of this strategy was that Barilla’s logistics organisation would be
supplied with demand data from the distributors’ customers (retailers) and to overtake the task
of determining the appropriate shipment sizes and dates. Finally, the strategy failed to be
implemented by Giorgio Maggiali, Vitali’s successor, due to reluctance of both Barilla’s sales
and marketing organisations and the distributors. The just-in-time-distribution strategy will be
discussed within this report, but will then be referred to as vendor-managed inventory.
1.2. Aims
The purpose of this report is to reveal the reasons for the fluctuating demand of Barilla’s
distributors and to discuss possible strategies that help organisations such as Barilla to cope
with variable demand by sharing demand data within the supply chain. The discussion will
include how technological progress contributes to these strategies and facilitates sharing of
information.
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1.3 Structure
This report introduces the case of the pasta manufacturer Barilla and their endeavour to cope
with demand variability by sharing demand and inventory data with their distributors. The
second section of this report evaluates how the reasons for the bullwhip effect according to
Lee et al. (1997a, p. 93) influence the demand variability Barilla is facing. This evaluation is
followed by a general discussion in section three on how certain concepts of information
sharing can help to diminish demand variability. This is supplemented by discussing
facilitation of these concepts by recent technology in section four of this report. This report
will conclude that the provided strategies can significantly reduce the bullwhip effect and that
efficiently applied technology contributes to this effect.
2.0 Variability in Demand Among the Supply Chain
2.1 Reasons for Demand Variability and the Bullwhip Effect
The phenomenon faced by Barilla that demand fluctuates more at upstream facilities than at
downstream organisations of the supply chain, although end consumer demand is relatively
even, is referred to as the bullwhip effect (Lee et al., 1997a, p. 93; Lee et al., 1997b, p. 1546;
Disney & Towill, 2003a, p. 630). According to Lee et al. (1997a, p. 95), the bullwhip effect is
caused by the four major reasons; demand forecast updating, order batching, price fluctuation,
and rationing and shortage gaming. As a result, companies facing the bullwhip effect have to
keep high inventory of finished goods to maintain their customer service (Lee et al., 1997a, p.
93).
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2.2 Reasons for Demand Variability in the Case of Barilla
The causes for the bullwhip effect provided by Lee et al. (1997a, p. 95) contribute all to the
demand variability of Barilla’s distributors, though not all up to the same extent. The least
important driver of demand variability in the case of Barilla is rationing and shortage gaming
(also referred to as inflated orders, Simchi-Levi et al., 2008, p. 156) due to the fact that the
actual demand for pasta is regarded as stable. More impact on demand variability has order
batching that is mainly driven by order processing and transportation cost (Lee et al. 1997a, p.
96). In case of Barilla this has to be regarded as main contributor to demand variability
because of the huge number of small distributors. Another important driver of demand
variability is the price fluctuation that is forced by Barilla’s promotion policy of offering
different “canvass” over the year. Within these “canvass” products are offered with
remarkable discounts. This leads to increasing demand from the distributors although the
actual consumer demand remains stable. The last critical issue that contributes to demand
variability is demand forecasting update. Lee et al. (1997a, p. 95) state that all orders received
are regarded as a demand signal. Thus, every incoming order affects future demand
forecasting. As these orders do not represent true consumer demand, the variability of demand
is increased. Chen et al. (2000, p. 42) support this argument by stating that even forecasts
based on periodically gathered demand data by retailers lead to demand variation. As a result
of all these factors affecting the demand variability Barilla has to keep enormous inventory
levels ass described in the case, to maintain customer service.
3.0 Strategies to Cope With Demand Variability
Lee et al. (1997a, p. 98; 2004, p. 1889) provide several guidelines how the impacts of the
reasons of demand variability can be diminished. For example, they recommend an “everyday
low price” policy to eliminate price fluctuation as contributor to demand variability. However
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their mainly suggested approach is to share demand information to cope with the bullwhip
effect. This point of view is shared by many different authors (Vigtil, 2007, p. 131; Chen et
al., 2000, p. 442; Yu et al., 2001, p. 118; McCarthy & Golicic, 2001, p. 434). Yao & Dresner
(2008, p. 362) categorise co-operations among supply chain partners based on their level of
information sharing. They relate to a partnership between a manufacturer and a retailer
without intermediary distributors and identify four levels of information sharing; baseline
system (refers to a situation of nil information sharing), information sharing (IS), continuous
replenishment programs (CRP), and vendor-managed inventory (VMI). These different levels
of information sharing and their information as well as their material flows are exemplified in
Fig. 1.
Fig.1. Comparison of information and physical goods flows among the baseline system, IS, CRP, and VMI.
Source: Yao & Dresner, 2008, p. 362.
The information that is shared includes not only demand data but also inventory data as these
are also important to plan shipments from the manufacturer to the retailer. Although Yao und
Dresner (2008, p. 362) employ the relationship between manufacturer and retailer to explain
levels of information sharing, the results can also be adapted to another relationship within a
supply chain (i.e. manufacturer and wholesaler, or wholesaler and retailer).
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3.1 Information Sharing
Information sharing is the provision of demand and inventory data by the retailer as soon as
they are gathered (Lee et al, 2000c, as cited in Yao & Dresner, 2008, p. 362). This gives the
manufacturer the opportunity to base demand forecast on actual data rather than on orders that
does not truly represent actual demand. Like all forms of information sharing, this reduces
inventory due to reduction of the bullwhip effect (Cachon & Zipkin, 1999; Xu et al., 2001, as
cited in Yao et al., 2007, p. 664). As the responsibility of replenishment of the retailer’s
inventory is not transferred to the manufacturer, this concept can be taken into consideration if
the retailer does not want to become too dependent of the manufacturer.
3.2 Collaborative Replenishment Programs
In addition to simple information sharing, CRP systems also include a collaboration regarding
replenishment of the retailer’s inventory (Yao & Dresner, 2008, p. 362). They state that this
additional responsibility “requires the manufacturer to implement a continuous replenishment
process with the retailer” (p. 362). McCarthy and Golicic refer to a similar approach as
“collaborative planning, forecasting, and replenishment (CPFR)” (2002, p. 432). They stress
that such collaborations require interaction and common goals (Citera et al., 1995, as cited in
McCarthy & Golicic, 2002, p. 433). Furthermore, they found that CPFR systems enhance not
only supply chain performance in terms of demand responsiveness, customer service level and
inventory, but also raise earning for both companies (p. 449). However, Yao & Dresner
(2008, p. 369) argue that profitability collaborative replenishment programs for the retailer
depends on the design of the replenishment process. Although the relationship is more
intensive, the retailer still maintains the responsibility for its inventory and places order to the
manufacturer.
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3.3 Vendor-Managed Inventory
The term vendor-managed inventory refers to a co-operation wherein the manufacturer takes
on the task of managing the retailer’s inventory by determining shipment size and date
(Disney & Towill, 2003b, p. 201). The manufacturer bases this management of inventory on
actual inventory and demand data. Therefore, the retailer does not place orders to the
manufacturer any longer (Yao & Dresner, 2008, p. 362). Recent literature specifies several
advantages of vendor-managed inventory co-operations for both manufacturer and retailer.
Sari lists advanced product availability, customer service and lower operating cost for
replenishment and order placing (Waller et al., 1999; Achabal et al., 2000, as cited in Sari,
2007, p. 530). The main advantage from vendor-managed inventory for the manufacturer is
identified as reduction of the bullwhip effect (Lee, et al., 1997a, as cited in Sari, 2007, p. 530).
Disney and Towill (2003b, p. 212) note that this reduction achieved by using vendor-managed
inventory systems is “typically halving the effect”. Despite these advantages attributed to
vendor-managed inventory strategies, there are limitations to these approaches. Sari (2008, p.
542) and also Yao and Dresner (2008, p. 369) found that profitability depends on the level of
demand uncertainty. Some authors agree on the disadvantage that benefits are not distributed
equally (Lee et al., 2000, Yu et al. 2001, 2002; as cited in Sari 2008, p. 532; Yao & Dresner,
2008, p. 369). They argue that benefit distribution is not only related to the system employed
but also to some other parameters such as inventory levels, replenishment frequency and end
consumer demand. However, Lee et al. (2000, as cited in Sari, 2008, p.532) and Yu et al.
(2001, 2002, as cited in Sari, 2008, p.532) argue that the benefits of the vendor usually exceed
the buyer’s benefits. In contrast, Yao and Dresner (2008, p. 369) argue that vendor-managed
inventory strategies often result in a relocation of inventory from the buyer to the vendor.
Despite the unequal benefit distribution under certain circumstances, it is found that both
manufacturer and retailer benefit from vendor-managed inventory concepts.
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4.0 Technology as Facilitator of Data Sharing Strategies
Technology has become a vital tool to support the co-operation of businesses and particularly
in supply chain environments (Green, 2001, p. 208). Especially Internet-based applications
have enabled data exchange and communication among supply chains and entire networks
(Green, 2001, p. 210; Sanders & Premus, 2002, p. 65). This technological progress is also
employed by the discussed data sharing strategies (Lee et al., 2004, p. 1891). Beside the
Internet, the technology that is most frequently referred to as a facilitator of these strategies is
electronic data interchange (EDI) (Yao & Dresner, 2008, p. 362; Yao et al., 2007, p. 663;
Gavirneni et al., 1999, as cited in Yu et al., 2001, p. 114). Another important enabler of data
sharing especially in retail industries is technology generating Point of Sales data. Scanning
checkouts in supermarkets are useful facilitators of data collection and real-time sharing of
demand data among the concerned supply chain partners (Vigtil, 2007, p. 134). These
technologies are regarded as enabler because there are also possible well-operating strategies
that employ traditional technologies such as fax or telephone to share information
(Holmström, 1998, as cited in Disney & Towill, 2003b, p. 201; McCarthy & Golicic, 2002, p.
439; Vigtil, 2007). McCarthy and Golicic (2002, p. 440) note that these forms of information
sharing hold the advantage that they do not require investment in sophisticated technology.
Nevertheless, they allow for the fact that these technologies are remarkably less efficient in
data transferring. Lee et al. (2004, p. 1892) add that there is another technique, radiofrequency identification (RFID), that is expected further facilitate information sharing. This
highlights the technologies’ supportive character for data sharing.
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5.0 Conclusions
It is concluded that the demand variability in the supply chain of Barilla is attributed to the
Bullwhip effect and its aspects provided by Lee et al. (1997a, 1997b). The possible actions
suggested by Lee et al. (1997a, 1997b) are regarded as useful to cope with certain aspects of
the bullwhip effect. The strategies discussed in section three of the report, especially the
vendor-managed inventory approach already proposed by Barilla, are considered even more
promising. Literature review shows that information sharing strategies in general are
considered as very effective to reduce variability of demand. The limitations to these
strategies have to be paid attention to. These include profitability depending on fluctuating
end consumer demand (Sari, 2008; Yao & Dresner, 2008) and the distribution of benefits
generated by operating an information sharing strategy (Lee et al., 2000, Yu et al. 2001, 2002;
as cited in Sari 2008; Yao & Dresner, 2008). It is further concluded that recent technology
such as the Internet and electronic data interchange facilitate information sharing within the
discussed strategies. Despite this improvement in data exchange, sharing of demand and
inventory data can still be performed by traditional communications technology (Holmström,
1998, as cited in Disney & Towill, 2003b; McCarthy & Golicic, 2002; Vigtil, 2007).
6.0 Recommendations
The findings of this report imply recommendations for Barilla’s logistics management. Due to
the highlighted advantages of vendor-managed inventory strategies, it should be further tried
to convince their distributors of the just-in-time distribution strategy. If this still encounters
distributors’ resistance, it should be proposed to engage in one of the less intensive forms of
co-operation. Although this means cutting back the initial proposal, it would still provide
Barilla with more accurate data to base their forecasting on. If there is an agreement on
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information sharing co-operation made, Barilla should invest in appropriate technology to
allow for faster transmission of data. If no agreement on co-operation can be made, it is
highly recommended that management sticks to the actions suggested by Lee et al (1997a,
1997b) to counteract the single reason of the bullwhip effect. In particular, logistics and
marketing management should collaboratively consider alternative promotion strategies as the
current “canvass” strategy is mainly responsible for the demand variability the company is
facing.
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Reference List
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in a Simple Supply Chain: The Impact of Forecasting, Lead Times, and Information,
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Lee, H.L., Padmanabhan, V., & Whang, S. (2004). Information Distortion in a Supply Chain:
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Supply Chain (3rd ed.). New York: McGraw-Hill.
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