internet enabled collaborative store ordering: the case of

Internet Enabled Collaborative Store Ordering: Veropoulos Spar Retailer (A)

09/2005-5306

This case was written by Katerina Pramatari, Lecturer, George Doukidis, Professor, both at the Athens University of Economics and Business, and Theodoros Evgeniou, Assistant Professor of Technology Management at INSEAD. It is intended to be used as a basis for class discussion rather than to illustrate either effective or ineffective handling of an administrative situation.

Copyright © 2005 INSEAD, Fontainebleau, France.

N.B. PLEASE NOTE THAT DETAILS OF ORDERING INSEAD CASES ARE FOUND ON THE BACK COVER. COPIES MAY NOT BE MADE WITHOUT

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“The system now works. The organization cannot use it, yet.”

Haris Kirlis, Commercial Director

“We’d better freeze this system.”

Store Manager, Company Conference, October 2002

Two years after initiating the first attempt to put in place an internet-enabled system for ordering directly from suppliers, Vassilis Spiliotis, chief purchasing officer of Veropoulos Spar, the €770 million retailer in Greece and the Balkan region, had to go back to square one. From spring 2001, he had led the company’s adoption of a major new system for the implementation of a process for collaborative store ordering (PCSO) with suppliers. It had stopped short in early 2003 when the software platform provider had to abandon its IT support and development due to financial problems. Even before this occurred, employees in the Veropoulos stores were complaining about the system, and the company’s suppliers had not fully embraced it.

For the first six months of 2003 implementation of the order-support IT and the PCSO initiative stalled. However rumor had it that the initiative would start again – and the store ordering and replenishment processes would eventually be performed directly via internet as planned – although memories of the first failure were still fresh and painful. Nick Veropoulos, the company’s owner and CEO, had not forgotten the moment when, in a company-wide conference in October 2002, a store manager had interrupted his presentation with the warning: “We’d better freeze this system.”

The event was brought up when Nick called Spiliotis into his office in June 2003 to discuss how to proceed. They had to decide whether or not to restart the engines. If they agreed to go ahead, how could they overcome employee resistance due to the low morale and bad memories from the first attempt? What could they learn from the experience and what mistakes could they avoid second time around? How could they convince the store employees to get involved, and make sure that suppliers joined the initiative and used the system? Or, should they simply set the project aside and restart at some time in the future when the technology and the organization were more ready?

Company Background

In the competitive environment of the Greek grocery retail business, where two international and three national chains account for approximately 60% of the total retail market, Veropoulos is ranked in fourth place (see Exhibit 1). With 196 stores around Greece, Veropoulos covers almost the whole country, with sales estimated at around €770 million for 2005. Recent expansion outside Greece has added 9 new large stores in the Balkan region, 7 in FYROM (with an eighth currently under construction) and 2 in Serbia (currently building a third).

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Its top management is young, second generation, and has the ownership of the retail chain. Nick Veropoulos, CEO and son of one of the two brothers who founded the company, started his career in the company as an employee in a store – despite his university qualifications – and assumed full responsibility as CEO in the mid 90s. The company has an organizational structure with three main directives: commercial, logistics, and financial (see Exhibit 2).

Nick is open to new ideas and a great supporter of Efficient Consumer Response1 (ECR) global industry activities, participating in both local and European ECR groups. In 2001 Veropoulos became the first and only supermarket chain in Greece to offer online store services to its customers.

In terms of product distribution, the company owns a central warehouse serving all the supermarkets around the country with its own fleet of vans. Almost 50% of the products in large stores are replenished through the central warehouse, whereas for the smaller store the figure is around 60-70%. The remainder is replenished by non-centralized suppliers.

IT at the Company

The Veropoulos IT department resembled what one would find in any other Greek retail chain: a small centralized group of around 15 people, playing a support role to the stores’ and offices’ operations and systems, focusing largely on point-of-sale (POS) software and ERP systems. The group was responsible for maintaining and running all internally-developed central information systems. Veropoulos was unique in that it operated an AS-400 ERP system developed in-house which supported all major operations including retail management. The central warehouse ran a separate warehouse management system integrated with the central information system via the exchange of data files.

Although relatively small for a large organization, the IT group was flexible and efficient and had successfully spearheaded two major IT projects without disrupting the company’s operations: the Y2K problem and the euro conversion issue in 2002. The group would race to develop new capabilities that invariably needed to be updated to support the ever-changing processes and practices of the organization, its suppliers, or partners. As Diamantaras, the company’s chief technology officer, put it: “The question is…how to think proactively what the suppliers will do and prepare our IT?” For example, if a supplier offered a discount on a kilo of cheese, consumers who bought less than a kilo would expect the price reduction to be adjusted proportionately. However, this was not initially supported by the ERP system, which accordingly had to be modified very fast.

Veropoulos’ aim was to operate a standardized IT solution in all its stores, with the goal of reducing the complexity of managing the IT infrastructure. Even when it acquired other retail chains it would replace any existing systems with the standardized IT solution. Each store was equipped with point-of-sale scanning facilities at the checkout counters and ran its own information system, which was connected via a Virtual Private Network (VPN) to the central offices. At the store level, an office employee would use the main PC to update invoices, and the store manager would also use it occasionally to monitor sales and other figures. Like

1 See www.ecrnet.org for more information.

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almost every other retailer in Greece, retailers did not use store sales data (POS data) to support the ordering process, nor were stock data captured in the store’s systems.

The Store Ordering Processes

A typical large store carries an assortment of more than 10,000 products. Deciding what to order every day at the store level is, naturally, a daunting task. There are two main ways to order and replenish products in a supermarket: either via the retailer’s distribution centre, (i.e., a central warehouse where suppliers deliver their goods), or by the suppliers directly to the stores. Variations exist, such as orders between stores or predetermined orders dictated centrally by the organization’s buyers (not involving the store managers), but to a lesser extent. In Veropoulos’ case, these accounted for a very small proportion of the total ordering volume.

Centralized Deliveries: Orders to the central warehouse are placed by the supermarket store personnel to the central warehouse, either daily or on certain days of the week. The store manager has the main responsibility for the final order (products and quantities), but more than one person is usually involved in the large stores. The store personnel physically check the store shelves and back-room inventory and determine which products and quantities to order, either using a hand-held scanner, or by marking a print-out of the full product assortment. Deciding what to order and how much is left to the intuition of the store personnel, based on past experience, product knowledge, current shelf image, etc. In addition to “intuition failures” by decision makers about what, when, and how much to order, other unintended errors often occur at this stage as a result of the growing number of products to be handled (more than 5,000 products in the active assortment of an average supermarket were ordered from the central warehouse).

The company started the move from manual to computerized ordering processes around 1995. This led to changes in the roles of the store personnel. “The store manager was more involved in the ordering process pre-1995,” noted Mr. Karavidas, one area store manager. “Each large store has a manager and a deputy-manager. Before 1995, one of the two was preparing the order. When computers were introduced, e.g., when they stopped sending orders via telephone or fax and instead were typing the orders in a computer and sending them electronically, this job passed from the store manager to the people working in the store – those responsible for a certain section of the store – for checking and preparing the order, and to an office employee (one in each store) operating the computer. Only in small stores, where a hand-scanner is used for ordering, this job was still done by the store manager.”

Direct from Supplier Deliveries: Products that are delivered to the store directly by the supplier are ordered through a different process, although not fully standardized, as Mr. Kolias, head of the company’s central warehouse, noted: “Suppliers’ sales people would not come regularly; there is no really standardized process.” In such cases the supplier’s salesperson visits the store at regular intervals (usually weekly or bi-weekly), checks the shelves and back-room inventory, and prepares the order. As the salesperson makes less frequent shelf-checks than for centralized orders by the store personnel, in cases where an out-of-stock situation is observed it is harder to understand for how long the product has been out of stock. On the other hand, the salesperson places more emphasis on a much smaller number

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of products than the store personnel, and knows more about the behavior of his/her products across the market and the marketing plan for each one. Studies have shown that when they pass this product knowledge on to the store personnel, hence playing more the role of a sales consultant instead than an order taker, this can have a positive impact on sales, as Spiliotis illustrated:

“One supplier, Mega, during a new launch, agreed to act as sales consultants to the stores. They thus organised presentations to all the store managers/ deputy-managers per region and presented the products in order to get them involved in the new product launch. This resulted in a 45% sales increase compared to the 3% sales increase of the total retail chain and 15% sales increase of the supplier in the total market.”

One issue for the direct-store-delivery process, however, is that, depending on the sales strategy of supplier companies, salesmen are often driven by motives other than avoiding out-of-stock situations, such as achieving personal sales bonus targets, which may result in overstocking situations. As Kolias noted: “Suppliers need to feel they are controlled, otherwise they tend to overstock the store.”

Information Needed for Ordering Decisions

What affects the effectiveness of the order decision-making process is the information available to the decision maker. The order decisions taken by store managers and employees or recommended by suppliers’ salesmen are based on many different pieces of information which may indicate future consumer demand or affect the product replenishment cycle. Relevant information includes product sales during the last replenishment cycle, average weekly sales, stock available in store, shelf-space capacity, back-orders that were not delivered on previous occasions, as well as promotional and marketing information about the product (see Exhibit 3 for more details on the information used to make ordering decisions).

The richness of the relevant information makes the use of an IT system to automate or support ordering decisions more complicated as not only does this require the integration of information from many sources, it also makes the data quality issue in a dynamic environment very challenging. Inaccuracy in the data can increase complexity ten-fold. For example, the existence of non-active product codes can result in storing information about bigger product assortments than actually exist in the stores. This problem occurs in grocery retail due to the huge number of new product codes introduced or becoming obsolete each month. In order to allow information to be updated, the system needs to be electronically linked to multiple data sources, which immediately enlarges the scope of any IT initiative.

The Out-Of-Shelf (OOS) Problem

The main objective of the store replenishment process is to ensure that the products that consumers want are always on the supermarket’s shelves, i.e. avoid stock-outs and maintain optimum levels of stock in the store. These are performance trade-off, as increasing the stock minimizes the possibility for stock-outs and vice versa.

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The overall level of stock-outs – referred to as out-of-shelf (OOS) – is a critical issue for both suppliers and retailers. Studies2 show that low OOS improves consumer value, builds consumer loyalty to the brand and shopper loyalty to the store, increases sales, and boosts category profitability. It is therefore important for both retailers and suppliers to reduce OOS levels.

Before the first pilot started in spring 2001, Veropoulos suffered an average out-of-shelf rate of 8.4%, which translated into a yearly turnover loss of more than €20 million (leaving aside the negative impact on shopper loyalty and long-term profitability). More than 70% of these cases could be attributed to either wrong order quantity (the quantity ordered was not enough to fulfill consumer demand till the next replenishment cycle), or to no-order at all (the product had not been ordered at the last replenishment cycle even though it was not in the store). The remainder could be traced to combined upstream causes due to out-of-stock situations or other problems with the retailer’s distribution center or the supplier. The situation was similar both for products delivered through the central warehouse and those delivered directly by the supplier. There was a compelling need to improve the store ordering and replenishment practices, as Nick Veropoulos affirmed: “Even a small reduction in out-of-shelf can bring significant results in turnover increase and improved consumer satisfaction … clearly this is not a problem we can ignore and the suppliers have to get involved as well. It’s not only ours but also their problem.”

The New Way: Order Decision Support Technology and Collaborative Processes

The idea of using IT and involving the suppliers in a collaborative process in order to decrease the OOS rate and improve the ordering process has gained traction in the retail industry.3 User-friendly order-decision support systems (O-DSS) that gather on one single screen or piece of paper all the data characterizing a product, can enable new ordering processes. (See Exhibit 4 for a sample screen snapshot of such a system, the one Veropoulos wanted to implement in 2001).

Various key decisions need to be made for such a system. Deciding what information to display is, itself, an important issue. The choices are many (see Exhibit 3), as Kirlis, the recently appointed chief sales manager, pointed out: “Should we include in-store stock information in the system? One issue is that if this information is available, it may lead to not physically checking what is on the shelves and relying only on the system.”

In fact, during the initial implementation in 2001 the system did not include in-store stock information. The O-DSS can also flag individual products to indicate promotions, undelivered products, new products, etc.

2 Roland Berger (2002), “Full-Shelf Satisfaction. Reducing out-of-stocks in the grocery channel”, Grocery

Manufacturers of America (GMA). 3 Accenture (2002) European CPFR Insights. ECR Europe Publications. Available at: www.ecrnet.org,

Available at: www.ecr-europe.com

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Another important question is for how many products should the system provide information or order recommendations. For example, should it provide all information for all products, (which can be around 10,000 in a single store) and allow the user to make the final decision? Or should it only provide information for products deemed necessary, e.g., those that have had some sales since the last replenishment, (typically around 5,000 products)? Alternatively the system could use some internal “intelligence” to provide information and recommendations for only a few products in the order proposal (see Exhibit 5) – a feature that Veropoulos added in the later stages of the IT implementation (see below).

The O-DSS information available to the store manager and employees can also be made available to the suppliers’ salespeople for those products ordered directly from the suppliers (see Exhibit 6). This way the latter have all the information they need which they then simply confirm with the store managers. An internet-enabled collaboration platform can also support the process transactions, from order preparation by the supplier and order confirmation by the store manager, up to order send-out, enabling the supplier to perform the ordering process from a distance. It can also reduce logistics costs, facilitate the management of remote stores, facilitate analysis of all sales per store, and allow more time for the suppliers’ salespeople to consult store managers about their products instead of just counting missing units for the purpose of taking orders. Moreover, such a collaborative platform can provide real-time information of sales and stock to suppliers who do not replenish directly to the stores – even though they do not visit the stores often. Suppliers could also provide order proposals to the stores this way.

The First Attempt: 2001-2003

“A key strategy is to make the store director believe in the system and then communicate it to the store employees.”

Karavidas, Area Stores Manager.

The promising capabilities of an internet-enabled collaborative platform were presented to the top management of Veropoulos by a software service provider, a start-up called OniaNet, in spring 2001.

OniaNet was a new company acting as an intermediary between supermarkets and their suppliers, supporting their business transactions and exchange of information via its electronic marketplace as an Application Service Provider.4 As it was a new company, the agreement was that it would build and operate the internet-based collaborative platform based on a concept and requirements to be developed in collaboration with Veropoulos and three pilot suppliers. OniaNet wanted to establish its business plan at the time and Veropoulos was its first large customer. The plan was to develop a collaborative platform for ordering that would also include point-of-sale data direct from the stores to get the full benefit of such a platform.

4 ASP’s offer and manage outsourcing application services to many organizations via the Internet, while

organizations outsource applications to ASP’s to reduce upgrade and maintenance costs and to focus their efforts on core competencies.

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The positive attitude of Nick, the CEO, towards ECR-related supply chain initiatives, the presentation of the value proposition by OniaNet, and the full support of a key supplier who was also an investor in OniaNet, spurred Nick’s enthusiasm for the project and triggered the decision of Veropoulos to run a pilot test in the spring of 2001. Moreover, no financial costs were incurred by Veropoulos at that time.

The plan was to start the pilot project with just a few suppliers. This is common practice for major transformations of the supply chain – retailers and suppliers around the world, such as Wal-Mart or Henkel, have tested similar initiatives such as collaborative planning, forecasting and replenishment (CPFR) in small pilots.

Three suppliers got on board for the pilot project: Elgeka, a domestic company with an international clientele, Procter & Gamble, and Unilever. Elgeka was a direct-store-delivery supplier operating its own logistics fleet, and was also a shareholder in OniaNet. P&G and Unilever, on the other hand, delivered to the stores through Veropoulos’ central warehouse. Five representative stores were selected to participate in the pilot program.

Responsibility for overseeing the project was assigned by the CEO to Spiliotis, the chief purchasing officer. The sales director at the time, whose organization would be the most affected, was informed and seemed to like the overall idea, but his involvement was limited to assigning an area stores manager, one of the most senior in the organization, Mr. Fragoulis, to represent the sales department and keep an eye on the project. The main responsibility for the full project management lay with the service provider, OniaNet, which was putting all its emphasis on this project since the result of the pilot would influence their backers’ decisions to further invest in them.

Building the First Version

The implementation of the pilot started in spring 2001 with the definition of requirements. The objective of the requirements-gathering phase was to understand how to customize an electronic procurement platform to work in the context of grocery retailing by enhancing it with the data (e.g., daily POS data, store assortment data etc.) and functionality (e.g., order suggestions) required to support work processes in this context.

A demo of the system was initially built which was shared with people from the retail and supplier organizations. On the retail side, those involved in the requirements meetings were from IT, the central warehouse, the procurement department, and the stores’ management. Other potential users from the stores – personnel preparing the orders – were not actively involved in the requirements gathering process at the beginning, but only at latter stages. Nobody felt they were important at that stage: with the new system it was planned that the roles of the personnel in the stores would change again – like in the pre-1995 pre-PC era, the store managers would be the main intended users of the new IT system in the stores taking simplified orders. On the supplier side, interdepartmental project teams attended the requirements meetings, with active participation from the salespeople.

Development and testing of the system took place during the summer and was completed in September 2001. The system ran centrally on the platform of the software service provider. It was integrated with the retailer’s and the suppliers’ information systems. The following data

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from the retailer’s central information system were used: a) daily POS sales data from the stores; b) the product assortment of each store; c) the mapping between the product EAN barcode with the retailer’s internal product code (retailer SKU); d) promotional activities running in the stores. At this stage, no information regarding the product stock in the stores was used.

A lot of effort was invested in ensuring that the right products were loaded and appeared for each user. This was a difficult task as the data from the retailer’s and the suppliers’ information systems had to be aligned. For example, the data in all the retailer’s information systems were maintained at the level of the retailer’s internal product code, the “tax-ID”, which was different from the barcode used for scanning a product through the cashier. The reason for this is that the relationship between two codes is not necessarily one-to-one. (A jar of Nutella, for example, is not well defined, as shown in Exhibit 7). Such multiple product definitions make the ordering process more complicated. For example, if the order decision maker sees the information at the barcode level, it will be more accurate because sales of ‘product X’ and ‘product X with promotion’ are shown separately. Otherwise s/he would see one figure for ‘product X’ and not know if this included the promotion so be unable to judge the effect of the promotion on sales. The situation gets even worse when the retailer wants to exchange data with its suppliers, as the suppliers have their own product codes.

Going Live: Positive Business Results and the Trouble Starts…

The pilot went live on 1 October 2001 and ran in the five stores for six weeks, from the first week of October to the second week of November 2001. At this stage, the five pilot stores used the internet-based platform for their collaboration with the direct-delivery supplier Elgeka, and for ordering the products of the two centralized suppliers, P&G and Unilever, from Veropoulos’ central warehouse. For the remaining centralized products they followed the traditional processes; likewise for the remaining suppliers the processes went unchanged.

Systematic out-of-shelf and stock measurements were carried out to evaluate the results. The improvements were significant: in the six-week period, OOS was reduced by 62% in the pilot stores but only by 23% in the five control stores.5 In particular, OOS that could be attributed to wrong order quantity or no-order at all saw an even bigger reduction in the pilot stores (see Exhibit 8). Simultaneous inventory measurements showed that OOS reduction was not due to any inventory increase.

Notwithstanding these positive business results, the pilot went far from smoothly. Right from the start, various technical problems frustrated the users of the system, namely the store managers. For example:

• During the first two weeks, several frustrating data-validation issues arose, e.g., products that were missing from the platform and thus could not be ordered when required, leading to OSS situations.

5 The explanation of the OOS reduction in the control stores has to do with the fact that the subjects taking

part in the experiment (the order decision makers in the stores) are aware of the fact there is a measurement and this influences their behaviour, a phenomenon referred to as the Hawthorne effect.

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• Connecting to the system through a dial-up internet connection from the stores took time. In many cases the modems used were very slow (less than 19Kbps). To resolve this, modems were upgraded to 52Kbps.

• The printing out of product lists from the system was not user-friendly. Due to delays in the system response, as perceived by users, products in the system were not presented all at once but on separate pages. This, combined with the low transfer rate, meant that users wasted time waiting for pages to download. Printers were subsequently replaced in all stores.

• Worst of all, there were now four different processes for ordering: one for confirming orders to Elgeka via the new system, one for ordering products from the two centralized suppliers to the central warehouse, one for confirming orders in person to the rest of the direct-store-delivery suppliers, and one for ordering remaining products to the central warehouse via the old system. As Nick Veropoulos’ insisted: “Make it possible that the people in the stores can use one single system for all their ordering and then we’ll continue”

• Moreover, the planned input of “intelligence” to the system for making order recommendations to simplify the task of the employees was very challenging from a technical perspective and these requirements had to be met in a narrow timeframe of about three months, as set out by the service provider, who was under funding pressure.

The Fall Out…

Despite all the trouble, the measurable positive business results encouraged Veropoulos to continue. The system was subsequently rolled-out to 20 more stores under the control of the Fragoulis store area manager. But the data quality issues persisted, to the frustration of the users, the service provider, and to Spiliotis, who maintained his role as the main project manager from the Veropoulos side. Problems arose as products were “intelligently” proposed by the system when, from the users’ perspective, this didn’t make any sense. This further undermined the users’ trust in the system and the quality of the order proposal. For another three months the service provider worked intensively to overcome the data quality issues while the 20 stores kept using the system.

More important, the actual users in the stores lacked training. Although it was initially intended that the store managers would use the system to evaluate the information and place orders (as had been done pre-1995), due to their frustration with the new IT they eventually pushed the responsibility back to other store personnel who were not appropriately trained to use the new system.

In June 2002, Veropoulos finally succumbed to the time pressure of the service provider and agreed to roll out the system to the remaining stores in the rest of the country, signing a three year contract with OniaNet. The roll-out phase lasted two months in Attica (July-August 2002) and a further three months in the rest of the country (September-November 2002). Unfortunately, once all the stores were on line, users experienced even more system delays and frustration with the technology. On a positive note, however, in September 2002 the OOS measurements confirmed the initial reductions (see Exhibit 9).

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For supplier Elgeka the story was more positive. Apart from being a shareholder in OniaNet, Elgeka, as a direct supplier, had the human resources to invest in the new system and process, and was enjoying the benefits that it had to offer: access to daily POS information, possibility to order from a distance, etc. With a single salesman it could manage orders for 20 of the Veropoulos stores through the collaboration platform.

The outlook was less bright for P&G and Unilever, the centralized suppliers. Soon after the roll-out they decided that the time their salesforce had to invest in placing suggestive orders to the stores directly didn’t fit with their centralization strategy. More specifically, a prior decision to move to centralized deliveries had allowed for reductions in their salesforce: Unilever had only one salesman to cover all 72 Veropoulos stores in the Athens region. One implication of joining the pilot was that they would have to now increase their salesforce in order to support their customer in the ordering process, but it was not clear from the pilot results whether such a change was worth it for them. Both companies discontinued their participation in the daily ordering process, limiting their involvement to simply maintaining their updated product catalogue on the collaboration platform in order to support the Veropoulos internal ordering process to the central warehouse.

A Last Attempt to Save it …

In October 2002, Mr. Karavidas, an area stores manager with strong communication skills who was close to the end users, was assigned the full responsibility for the project on the Veropoulos side. His task was to communicate with all the users of the new system (store managers and personnel), gather their feedback, and address every single issue and problem expressed by the stores individually. A user survey conducted by Karavidas among 80 stores showed that there was plenty of room for improvement, the major issue being the long order-update time on the computer screens (see Exhibit 10).

His involvement soon made a difference. For example, by December 2002 all communication and internet connection delays had been resolved, and the old printers had been replaced in all the stores. New requirements regarding the usability of the system, expected to significantly reduce the time the users had to spend on the computer, also came out of this work.

The worst, however, was to come. In early 2003, OniaNet ceased operations due to financial problems. While Nick Veropoulos was caught by surprise, most of the people in the stores felt relieved – the frustrating new system would never come to life!

Ready for a Fresh Start? Dilemmas and Challenges

From the ashes of OniaNet, a new service provider, with a number of key personnel from the old OniaNet team, decided to invest in the OniaNet business model in the summer of 2003. The new company, Retail@Link, approached Veropoulos to recommend a resurrection of the ambitious collaborative ordering initiative. Their argument was that the benefits of the system were clear – if they could succeed in implementing it – and second time around they knew much more about how to succeed as they had already done their homework.

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Nick Veropoulos called Spiliotis in his office to discuss how to proceed. He knew that internet-enabled collaborative commerce was the future, but was it the right time to go for it? Was the company mature enough for such an initiative? And even if that was the case, would it be possible to overcome the negative vibes that had accumulated during the first two years of attempting to implement it? Even if they could do so, would they succeed this time? What mistakes must they avoid this time around? What about the suppliers: would they remain loyal to the initiative this time, or should they simply not count on them as much? And would the new service provider survive?

Nick and Spiliotis were pondering how to respond to the new proposal of the new service provider. Should they now re-start the engines of the initiative they launched in 2001, or leave it for the future? How should they respond to the new service provider’s proposal?

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Exhibit 1 The Retail Market in Greece

Rank Retailer Sales 2003 (1000s Euro) Market Share 2003

1 Carrefour 1.727.331 22,5% 2 AB Vassilopoulos 1.074.442 14,0% 3 Sklavenitis 764.808 10,0% 4 Veropoulos 631.654 8,2% 5 Atlantic 523.370 6,8% 6 Masoutis 481.604 6,3% 7 Metro 422.752 5,5% 8 Pente 319.077 4,2% 9 Lidl (estimated) 235.000 3,1% 10 Arvanitidis 196.450 2,6% 11 ΙΝ.ΚΑ Chanion 100.480 1,3% Total of top 11 retailers 6.476.968 84,4% Remaining retailers 1.197.435 15,6% Total of all 83 Retailers 7.674.403 100,0%

Sales of the 11 largest retailers in Greece.

Source: Panorama of Greek Supermarkets, 2003.

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Exhibit 2 Organizational Chart

GENERAL MANAGER

(Nikos Veropoulos)

COMMERCIAL DIRECTOR (Haris Kirlis)

LOGISTICS DIRECTOR (Nikos Kolias)

FINANCIAL & ADMINISTRATION

DIRECTOR

Logistics Production Replenishment

Chief Purchasing Officer (Spiliotis)

Chief Sales Manager

Exploitation Manager

Financial Manager

IT Manager (Diamantaras)

Investments

Administration Area Stores Manager 1 (Karavidas)

Area Stores Manager 12

...

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Exhibit 3 Information Used by Employees and Suppliers for Deciding Orders

Product sales during the last replenishment cycle: the number of items that have been sold since the last time the product was ordered.

Replenishment cycle start date: the last time that the product was ordered. This gives the decision maker an indication of whether the product is a fast or slow mover. Fast-moving items should be treated more carefully, as they may result in stock-outs more easily than slow-movers.

Average weekly sales: the number of items sold on average within a week (from Monday to Sunday), usually looking six weeks back. For seasonal products, it is useful that the order decision maker has an indication of the product sales for the respective season last year.

Product stock in the store: This piece of information, in combination with the average weekly sales, help the decision maker judge the level of stock in the store and increase it or decrease it by accordingly adjusting the order quantity.

Date of last sale: when the product sold for the last time, during the last replenishment cycle. This information is useful in case the product is out-of-stock, so the decision-maker can understand the duration of the out-of-stock and estimate sales if there had not been an out-of-stock.

Minimum-order-quantity (MOQ): the minimum number of items that the decision maker should order. The order quantity should be expressed in integer multiples of MOQ. MOQ is usually the number of items in the product’s delivery unit, e.g., in a case-pack.

Product assortment: For the order decision maker it is necessary to know if a product is in the store’s assortment or not. For example, if the retailer stops a deal with a supplier, then its product should be withdrawn from the store’s assortment and, thus, not ordered. Similarly, the decision maker should be informed of all new items that are added to the assortment so as to order them.

Promotions: Promotional items, e.g., price promotions, volume promotions, etc., may generate more sales than their non-promotional counterparts. Thus the order decision maker should be able to distinguish promotional from non-promotional items.

Marketing plan activities: As with promotions, marketing activities such as television or radio commercials, outdoor posters etc., usually have an effect on consumer demand. Knowledge of the existence of these activities, especially those expected to have a significant impact on consumer demand, allow the decision maker to order increased quantities and protect the store against out-of-stocks.

Non-delivered item or back-order: If the last time there was an order to the central warehouse but the product was not delivered because the distribution centre was out-of-stock, the order decision maker needs to know. If the distribution centre keeps back-orders, then he/she needs to know so as not to re-order the product and vice-versa.

Shelf-space capacity: the number of items fitting in the shelf space allocated to the product. By knowing shelf-capacity, the decision maker can decide whether to keep the shelves full, with higher store inventories, lower operating costs and lower risk for an out-of-stock, or to follow a strategy that favours higher inventory turns.

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Exhibit 4 Buyer Screen of the System

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Exhibit 5 Providing an Order Proposal

Order Proposal

e.g. 6.000 items

Store Assortment

Order proposal criteria (“system

intelligence”)

Products not included in the order

proposal

e.g. 1.200 items

e.g. 2 items

Final Order

e.g. 500 items

INSEAD 5306 17


Exhibit 6 The Collaborative Pilot Setting

Information Sharing between Retailer and Supplier

Supplier Sales

Store Manager

Collaboration Platform

Order suggestion

Order confirmation

POS data Store assortment Store promotion

activities Stock

_____________

Product marketing activities

Market knowledge …

POS data Store assortment Store promotion

activities Stock

_____________ Store knowledge Competition

performance in the store …

INSEAD 5306 18


Exhibit 7 The Data Standardization Problem

Relation between Retailer-SKU, product EAN code and Supplier-SKU. The relationship between the codes is not one-to-one

Nutella 200gr

Retailer SKU

EAN Code

R-SKU-1

Supplier SKU

Nutella 200gr

Nutella 200gr 10% off

Nutella 200gr +10gr

EAN-1 EAN-2 EAN-3

Nutella 200gr Batch-1

Nutella 200gr Batch-2 …

S-SKU-1 S-SKU-2

Example

INSEAD 5306 19


Exhibit 8a OOS Reduction Results from the Pilot Test

Exhibit 8b Reduction in the OOS Rate between 2001 and 2002

Out-of-shelf rate - Centralized Products

5,0%

1,9%

0,6% 0,8%

9,2%

0,6% 0,9%0,2%

2,6%

0,7%

4,9%

0,9%

0,0%

1,0%

2,0%

3,0%

4,0%

5,0%

6,0%

7,0%

8,0%

9,0%

10,0%

Wrong orderquantity

No order Not delivered bythe CWH

Not replenishedfrom back-room

Other Total

2001 2002

-62.8%

-78.2%

-47.2%

-4.5%

-78.9%

-61.7%

48.4%

-64.9%

-27.3%

-92.7%

-43.5%

-232%

-1000%

-800%

-600%

-40.0%

-200%

00%

200%

400%

600%

Pilot

Control

Μη αναπλήρωσηαπ αποθήκηκαταστήµατος

Συνολική ∆ιαφορ

Αλλαγή Κωδικού

Έλλειψη Προµηθευτή / Κ.Α.

Καθόλου Παραγγελία

Λανθασµένη Ποσότητ

-62.8%

-78.2%

-47.2%

-4.5%

-78.9%

-61.7%

48.4%

-64.9%

-27.3%

-92.7%

-43.5%

-23.2%

-100.0%

-80.0%

-60.0%

-40.0%

-20.0%

0.0%

20.0%

40.0%

60.0%

Pilot Stores

Control Stores

wrong quantity

No order

OOS supplier/ CW

No replenishment from backroom

Change of code

Total difference

INSEAD 5306 20


Exhibit 9 User Feedback after Initial Rollout in October 2002

Number of people (store managers/associate managers) answering the questionnaire: 80

QUESTION YES NO System Is the system user-friendly? 45 33 Is the time you start the ordering process the desired one? 64 15 Is the time you send the order the desired one? 60 18 Is the time between start and send enough to prepare a proper order? 62 17

Order update How long does it take you to print the suggestive order? 30min How long does it take you to prepare the order in the store? 2h How long does it take you to type-in/ update the order on the system? 2-3h Total time to prepare and send the order (on average): 5h Order to the Central Warehouse Do you find the product category grouping useful? 65 14

Do you find the column “minimum-order-quantity” useful? 71 8 Do you find the column “suggestive-order–quantity” useful? 36 42 Do you find the column “salesman-suggestive-order-quantity” useful? 33 31 Do you find the column “sales” useful? 63 15 Do you find the column “start-end” useful? 59 19 Is it easy to update the order quantity in the respective field? 72 7 Are there delays during the update (of the web page)? 71 6 If yes, what hour of the day? 14-15:00 Flags Is it helpful to see special indications/ flags for some products? 53 22 Are the symbols meaningful? 50 24 Would you like the symbols to change by letters? 41 29

General Is the suggestive order quantity correct? 12 66

Are there products that have sales but are not suggested by the system? 68 9

Do the new products appear on-time? 14 63 If you reject an item, do you see it again in the order proposal? 42 31 If some of the ordered items are not delivered, do you see them again in the order proposal?

41 33

Are there products that are not delivered, yet they do not appear in the “undelivered” section?

53 22

If yes, do you look for them? 43 13

To order INSEAD case studies please contact one of the three distributors below:

ecch, UK and USA Centrale des Cas et de Médias Pédagogiques

ecch UK Registered Office: ecch USA Registered Office: www.ecch.com www.ecch.com www.ccip.fr/ccmp/ Tel: +44 (0)1234 750903 Tel: +1 781 239 5884 Tel: 33 (0) 1.55.65.65.97 Fax: +44 (0)1234 751125 Fax: +1 781 239 5885 Fax: 33 (0) 1.40.54.06.93 E-mail: [email protected] E-mail: [email protected] E-mail: [email protected]

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