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Book ChapterDOI

A Consumer Centric VMI Methodology for a Collaborative Supply Chain Model – An Answer to Demand Volatility

02 Jul 2018-pp 136-146

TL;DR: A next generation VMI methodology which is based on a cloud environment which can integrate the customer demand in the entire value chain is proposed which would eventually result in higher customer service levels and inventory on-shelf availability.

AbstractThe increase in the market competitiveness and demand volatility is urging organizations to be more responsive to the customer needs. An out of stock scenario is a huge loss in the consumer products sector as it is a loss of revenue and a loss of brand loyalty which cannot be afforded. This raises the need for a consumer centric collaborative supply chain model. A Vendor Managed Inventory (VMI) practice is a collaborative inventory replenishment technique engaging the suppliers and the retailers. This paper proposes a next generation VMI methodology which is based on a cloud environment which can integrate the customer demand in the entire value chain. This would eventually result in higher customer service levels and inventory on-shelf availability. This proposed methodology has been implemented as a case study with one of the major clients, various parameters such as customer satisfaction and demand fluctuations were studied and compared with the present scenario. The methodology proposed here will benefit both sides of the supply chain (suppliers and retailers) by improving the visibility ultimately resulting in an improved collaborative supply chain model. This paper also intends to highlight the importance of collaboration of all role players in the entire end to end chain.

Topics: Supply chain (66%), Vendor-managed inventory (60%), Demand forecasting (57%), Customer satisfaction (56%), Brand loyalty (53%)

Summary (4 min read)

1 Introduction

  • In today’s complex business structure, managing order-delivery processes has been a major issue in supply chain management.
  • With the advancement in information technology, a supply chain has progressed a lot through information sharing.
  • VMI is a way to cut costs and keep inventory levels low in the entire supply chain and evidences has shown the it has significantly improved the supply chain performance [2].
  • This enables to do have better production planning, delivery schedules and eventually managed order volumes and having a control over the inventory levels.
  • VMI also play a substantial role in diminishing the bull whip effect as there is elimination of decision making and elimination of time delays in information flows.

2.1 Overview of a traditional Supply Chain

  • A supply chain is a system consisting of material suppliers, production facilities, distribution centers and customers who are all linked together via the downstream feed-forward flow of materials and the upstream feedback flow of information , as shown in Fig. 1 [4].
  • In a traditional supply chain, each player is responsible for his own inventory control and production or distribution ordering activities.
  • According to Axsater [5] the purpose of an inventory control system is to transform incomplete information about the market place into coordinated plans for production and replenishment of raw materials.
  • In the real world, the ordering process is frequently biased according to who is perceived as the most important customer.
  • This does not allow suppliers to gain any insight into what their customers are ordering to cover their own inventory-based customer service level and cost requirements and what the customers are ordering to satisfy immediate customer demand [6].

2.2 Overview of VMI Supply Chain

  • In reacting to this scenario, many companies have been compelled to improve their supply chain operations by sharing demand and inventory information with their suppliers and customers.
  • VMI is a supply chain strategy where the vendor or supplier is given the responsibility of managing the customer’s stock.
  • VMI has become more popular in the grocery sector in the last 15 years due to the success of retailers such as Wal-Mart [7] [8].
  • Additionally, it is only relatively recently that the necessary information and communication technology has become economically available to enable the strategy.
  • Familiar names are quick response (QR) [11], synchronized consumer response (SCR), continuous replenishment (CR), efficient consumer response (ECR) [12], forecasting and replenishment (CPFR) [13] depending on sector application, ownership issues and scope of implementation.

2.3 Information Sharing and Integration

  • The implementation of VMI requires both the sharing of information and the coordination and integration of processes between buyers and suppliers.
  • The bullwhip effect is the phenomenon whereby the size of inventory overages and shortages increases the further a firm is from final consumer demand in a supply chain.
  • Much of this literature has shown that the bullwhip effect can be minimized through information sharing in the supply chain [14,15].
  • The ability to smooth supply and demand, and thus reduce the possibility of inventory overages or shortages, has been suggested as a key benefit of systems like VMI, Just in Time, quick response, and efficient consumer response that integrate the operations of supply chain members [18].
  • A cloud-based service-oriented Demand Driven Supply Network could eventually increase business interoperability thus giving a global visibility of the entire cycle [21].

2.4 VMI Model Functionality

  • The working of the VMI Model is being explained by using the working model of Procter & Gamble and its service provider Datalliance [22] which has a SaaS working model thus forming a link between P&G and its VMI Client.
  • This order is transmitted to the client for addition of prices and eventually it returns to P&G thus completing the entire cycle of the information flow.

2.5 Synthesis

  • The sections above discuss about the existing methods and the critical analysis shows there are quite a few gaps.
  • The research gaps have been well mentioned out in the recent extensive literature study [23].
  • Therefore, the authors have focussed on every individual player of the supply chain and have attempted in reducing the existing gaps by their proposed methodology discussed in Section 3.
  • The work also focuses on the importance of having an entire end to end view to the methodology eventually leading in a more synchronised Supply Chain.

3 Proposal of a methodology

  • This article proposes VMI 2.0 – Smart Replenishment which is the next step in the domain of vendor managed inventory and thus would be trying to close the gaps in the existing process.
  • The authors have defined the major segments which is the customers/shoppers, customer interface, distribution and manufacturing.
  • The authors have touched each of the four segments aiming to have an end to end synchronized supply chain.
  • The fourth stage enables us to know the economic order quantity balancing to transportation and inventory costs.
  • The coming sub sections in the paper deals with all the methodology phases which were deployed and tested at P&G and the results were analyzed.

4 Case Study - Implementation

  • The proposed methodology has been explained in further details in this section and mode of implementation and the expected outcomes.
  • The realized results have been discussed later.

4.1 Store Order Forecasting

  • The idea here is to leverage store order forecasting data thus allowing us to have a more precise demand forecasts eventually enabling to be proactive rather than reactive.
  • The key role player is the collaboration which and then finally using the real-time data at store level to optimize the replenishment of the Promotional only products.
  • And for the shelf products which are played in promotion to have no impact on the on-shelf availability.
  • The authors took two years history to create predict the sales but till now ae have not achieved in do exact prediction for all type which clearly means there are other parameters which are affecting the sales.
  • This is still under development for higher precision but at this present stage the authors can estimate, and this is heling the sales team to push for more volumes and thus helping in business intelligence.

4.2 Product Segmentation

  • Categorization is very important in the retail business decision making process and thus product segmentation today is an integral part in the business strategy.
  • The idea behind is to segment the product range based on three major business factors which are Volume, Profitability and Volatility.
  • The second segment are the products which has high volumes as well as profits and are very less volatile products.
  • Then the authors would be having certain products which are medium in volume and profitability.
  • The authors can very well say that the percentage volume covered by the Strategic, Priority and Agile segments is around eighty and thus saying the focus should be in these segments and they should not have the best customer service levels and should have no out of stock scenarios.

4.3 Smart Ordering

  • This forms the next step of the proposed methodology which would be making smart orders and would allow us to be more intelligent in creating the orders thus optimizing the whole delivery logistics.
  • The existing process is to deliver the total promotional quantity at once (by completing full trucks) and then the rest demand of the shelf products would be delivered differently.
  • The real need of shelf products at the DC is 63 palettes and but since the authors must complete the 4th truck they fill it with more shelf stock, this increases the stock of the DC.
  • The outcome would be no peaks in deliveries, reduction in inventory and reduction in the number of trucks making the system more sustainable.
  • The realized results were increase in service by 0.5 % as the frequency of the shelf products were more regular thus reducing the out of stock scenarios.

4.4 Economic Order Quantity

  • A synchronized Supply Chain will source, produce and ship daily what the consumers require and the flow this need seamlessly through the network.
  • For more than a century, the act of determining order quantity (or lot sizing) for a firm's requirements has been a primary consideration.
  • The authors emphasize on another factor which is Days Between Next Shipment.
  • The preparation of layers and cases were costly as they involved human intervention which would increase the price.
  • The implementation of EOQ based on the DBNS resulted in cost saving both for P&G and the client’s distribution centers too.

4.5 Live Availability Check

  • Not knowing about the availability of the product in the plant had huge impacts on the vehicle fill rates (VFR).
  • The authors would have ordered certain products which might not be available and thus while the order is being loaded in the truck they would miss out certain quantities leading to the truck being partially filled and impacting VFR.
  • This new functionality would not allow any unavailable product to be put on order and alert the user about certain products which might be at risk of being unavailable at realtime which resulted in increased VFR.
  • This now would be implemented in remaining all distribution centers.

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Distributed under a Creative Commons Attribution| 4.0 International License
A Consumer Centric VMI Methodology for a
Collaborative Supply Chain Model An Answer to
Demand Volatility
Abhro Choudhury, Nicolas Maranzana, Frédéric Segonds, Sandrine Gautier
To cite this version:
Abhro Choudhury, Nicolas Maranzana, Frédéric Segonds, Sandrine Gautier. A Consumer Centric VMI
Methodology for a Collaborative Supply Chain Model An Answer to Demand Volatility. 15th IFIP
International Conference on Product Lifecycle Management (PLM), Jul 2018, Turin, Italy. pp.136-146,
�10.1007/978-3-030-01614-2_13�. �hal-02075568�

A consumer centric VMI methodology for a collaborative
Supply Chain Model An answer to demand volatility
Abhro Choudhury
1, 2
, Nicolas Maranzana
1
, Frederic Segonds
1
Sandrine Gautier
2
1
Arts et Métiers, ParisTech, LCPI, 151 Boulevard de l’Hôpital, 75013 Paris, France
2
Procter & Gamble, 163 Quai Aulagnier, 92600 Asnières-sur-Seine, France
abhro.choudhury@ensam.eu
Abstract.
The increase in the market competitiveness and demand volatility is urging or-
ganizations to be more responsive to the customer needs. An out of stock scenario
is a huge loss in the consumer products sector as it is a loss of revenue and a loss
of brand loyalty which cannot be afforded. This raises the need for a consumer
centric collaborative supply chain model. A Vendor Managed Inventory (VMI)
practice is a collaborative inventory replenishment technique engaging the sup-
pliers and the retailers. This paper proposes a next generation VMI methodology
which is based on a cloud environment which can integrate the customer demand
in the entire value chain. This would eventually result in higher customer service
levels and inventory on-shelf availability. This proposed methodology has been
implemented as a case study with one of the major clients, various parameters
such as customer satisfaction and demand fluctuations were studied and com-
pared with the present scenario. The methodology proposed here will benefit both
sides of the supply chain (suppliers and retailers) by improving the visibility ul-
timately resulting in an improved collaborative supply chain model. This paper
also intends to highlight the importance of collaboration of all role players in the
entire end to end chain.
Keywords Collaborative Supply Chain, Vendor Managed Inventory, Cloud, De-
mand Forecast
1 Introduction
In today’s complex business structure, managing order-delivery processes has been a
major issue in supply chain management. With the advancement in information tech-
nology, a supply chain has progressed a lot through information sharing. Despite the
advancement and enablement of having higher visibility in the entire supply chain,
there a lot of areas which are still left unattended and thus having a scope of improve-
ment.

Vendor Managed Inventory (VMI) is one of the most widely discussed alternative for
order replenishments for improving multi-firm supply chain efficiency. It is a replen-
ishment strategy where the traditional ordering process is eliminated, and the supplier
has the right and responsibility to make stock replenishment decisions based on regular
automatic inventory and / or sales data from buyer (ECR handbook) [1]. It was popu-
larized by Procter & Gamble and Wal-Mart in 1980s and since then the use of VMI has
grown in various industries. VMI is a way to cut costs and keep inventory levels low in
the entire supply chain and evidences has shown the it has significantly improved the
supply chain performance [2]. VMI partnership is a collaborative method where the
suppliers are authorized to manage the inventory and make inventory replenishment
decisions for the buyer. The integration of operations between suppliers and buyers is
done through information sharing using technologies such as Electronic Data Exchange
(EDI) or other internet-based protocols which is on a real-time basis. One of the major
benefits of the supplier controlling the stocks is that using this information the whole
chain can be more organized. This enables to do have better production planning, de-
livery schedules and eventually managed order volumes and having a control over the
inventory levels. The reason why VMI is getting popular among industries is reduced
inventory costs for the supplier and the buyer, improved customer service levels and
higher vehicle fill rates [3]. More accurate sales forecasting and improved inventory
distribution helps in achieving the better efficiency. VMI also play a substantial role in
diminishing the bull whip effect as there is elimination of decision making and elimi-
nation of time delays in information flows. The research question which is studied in
this work is what the future of Vendor Managed Inventory would be and how can this
next version of VMI would help an enterprise to improve and synchronize the entire
end to end supply chain. The article has been divided in sections and we start with a
detailed state of the art analysis in Section 2, the proposed methodology in Section 3,
followed by a case study implementation in Section 4 and then in Section 5, the con-
clusion and future work.
2 State of The Art
An in-depth review of the existing literature has been done to understand the difference
in functionality of a VMI based supply chain over a traditional one and eventually to
identify the gaps in the system.
2.1 Overview of a traditional Supply Chain
A supply chain is a system consisting of material suppliers, production facilities, distri-
bution centers and customers who are all linked together via the downstream feed-for-
ward flow of materials (deliveries) and the upstream feedback flow of information (or-
ders), as shown in Fig. 1 [4]. In a traditional supply chain, each player is responsible
for his own inventory control and production or distribution ordering activities. One
fundamental characteristic and problem that all players in a traditional supply chain
face is the decision making of the quantity to be ordered to the suppliers, to enable a

supply chain which can satisfy the customers demands which leads to inventory con-
trol issues.
According to Axsater [5] the purpose of an inventory control system is to transform
incomplete information about the market place into coordinated plans for production
and replenishment of raw materials. Normally users tackle the inventory control prob-
lem by inspecting data relating to demands, inventory levels and orders in the pipeline
and either, in a structured, mathematical way (for example, by using a decision support
system and a designed replenishment rule), or in a less formal way (by using their own
experience and judgement), place orders up the supply chain. In the real world, the
ordering process is frequently biased according to who is perceived as the most im-
portant customer. The traditional supply chain is characterized by each player in the
supply chain basing his production orders or delivery orders solely on his sales to his
customer, on his inventory levels. Each player in the supply chain only has information
about what their immediate customers want and not on what the end customer wants.
This does not allow suppliers to gain any insight into what their customers are ordering
to cover their own inventory-based customer service level and cost requirements and
what the customers are ordering to satisfy immediate customer demand [6]. This lack
of visibility of real demand can and does cause several problems in a supply chain if it
is not properly designed and even then, fluctuations cannot be eliminated.
Fig. 1. Overview of a traditional Supply Chain
2.2 Overview of VMI Supply Chain
In reacting to this scenario, many companies have been compelled to improve their
supply chain operations by sharing demand and inventory information with their sup-
pliers and customers. VMI is a supply chain strategy where the vendor or supplier is
given the responsibility of managing the customer’s stock.
VMI has become more popular in the grocery sector in the last 15 years due to the
success of retailers such as Wal-Mart [7] [8]. Additionally, it is only relatively recently
that the necessary information and communication technology has become economi-
cally available to enable the strategy. A research work has implemented VMI in a sup-
ply chain using data available from a popular ERP system and a spreadsheet-based de-
cision support system [9]. Moreover, VMI is not a new strategy; it was eloquently dis-
cussed in a presentation of a conceptual framework for designing a production control
system [10].

VMI comes in many different forms. Familiar names are quick response (QR) [11],
synchronized consumer response (SCR), continuous replenishment (CR), efficient con-
sumer response (ECR) [12], forecasting and replenishment (CPFR) [13] depending on
sector application, ownership issues and scope of implementation. However, they are
all specific as applications of VMI, as summarized conceptually in Fig. 2.
Fig. 2. Overview of a VMI Supply Chain
2.3 Information Sharing and Integration
The implementation of VMI requires both the sharing of information and the coor-
dination and integration of processes between buyers and suppliers. In general, buyers
share demand and inventory status information with their suppliers (information shar-
ing) so that suppliers can take over the inventory control and purchasing function from
the buyers (process integration).
A stream of research has quantitatively studied the value of information sharing in
supply chains, especially the causes and consequences of the bullwhip effect. The bull-
whip effect is the phenomenon whereby the size of inventory overages and shortages
increases the further a firm is from final consumer demand in a supply chain. Much of
this literature has shown that the bullwhip effect can be minimized through information
sharing in the supply chain [14,15]. The result of a decrease in the bullwhip effect is an
improvement in supply chain performance (e.g., the lowering of inventory levels and
the reduction in cycle times) [16]. Another work presented an analytical model for co-
ordinating inventory and transportation decisions in VMI systems and found that the
vendor’s actual inventory requirement is partly determined by the parameters of the
shipment-release policy in use [17]. This result holds because vendors have the auton-
omy to retain orders until an agreeable dispatch time is reached, with the expectation
that an economical consolidated dispatch quantity will accumulate before an order is
dispatched.

References
More filters

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Abstract: (This article originally appeared in Management Science, April 1997, Volume 43, Number 4, pp. 546-558, published by The Institute of Management Sciences.) Consider a series of companies in a supply chain, each of whom orders from its immediate upstream member. In this setting, inbound orders from a downstream member serve as a valuable informational input to upstream production and inventory decisions. This paper claims that the information transferred in the form of "orders" tends to be distorted and can misguide upstream members in their inventory and production decisions. In particular, the variance of orders may be larger than that of sales, and distortion tends to increase as one moves upstream-a phenomenon termed "bullwhip effect." This paper analyzes four sources of the bullwhip effect: demand signal processing, rationing game, order batching, and price variations. Actions that can be taken to mitigate the detrimental impact of this distortion are also discussed.

3,929 citations


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Abstract: Many companies have embarked on initiatives that enable more demand information sharing between retailers and their upstream suppliers. While the literature on such initiatives in the business press is proliferating, it is not clear how one can quantify the benefits of these initiatives and how one can identify the drivers of the magnitudes of these benefits. Using analytical models, this paper aims at addressing these questions for a simple two-level supply chain with nonstationary end demands. Our analysis suggests that the value of demand information sharing can be quite high, especially when demands are significantly correlated over time.

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Frequently Asked Questions (2)
Q1. What have the authors contributed in "A consumer centric vmi methodology for a collaborative supply chain model – an answer to demand volatility" ?

This paper proposes a next generation VMI methodology which is based on a cloud environment which can integrate the customer demand in the entire value chain. This proposed methodology has been implemented as a case study with one of the major clients, various parameters such as customer satisfaction and demand fluctuations were studied and compared with the present scenario. This paper also intends to highlight the importance of collaboration of all role players in the entire end to end chain. 

This opens an area of future work.