The Journal of Japanese Operations Management and Strategy, Vol. 4, No. 1, pp. 1-18, 2013

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ADAPTIVE COLLABORATION STRATEGY FOCUSING ON FORECASTING DEMAND OVER PRODUCT LIFE CYCLE

Masayasu Nagashima

Sorbonne Graduate Business School

Michiya Morita Gakushuin University

ABSATRCT Demand uncertainty is one of the most critical factors in supply chain management (SCM). In coping with the uncertainty, the firm should improve the quality of data used for demand forecasting. Such improvement is to a large extent possible through effective collaborative relationships and activities with supply chain partners, especially, downstream ones. Our research is case-based and intends to profile supply chain collaboration strategy in terms of collaborative efforts on the demand uncertainty. We introduce a concept of adaptive collaboration depending on the product life cycle, value concepts of involved partners, and their process complementarities. As a matter of course, we propose a possible research direction for aligning product strategy with supply chain strategy. Keywords: supply chain collaboration, supply chain strategy, demand forecasting uncertainty INTRODUCTION Managing supply chains in today's competitive and globalized markets is increasingly challenging. Product and technology life cycles are increasingly shortened. More strategically designed relationships among partners of supply chains become a key in competition. All these trends result in a complex cross-border supply chain network of which involved partners hold their own interests. On the other hand, such situation has led to higher exposure to the error of demand forecasting (demand forecasting uncertainty) in the supply chain (Hameri and Paatela, 2005; Christopher et al., 2002). Essentially, demand uncertainty has significant negative effects on supply chain operation. Then enhancing the controllability or predictability of demand brings about lots of benefits in SCM. One of the remedies to reduce the degree of demand forecasting uncertainty is information sharing among such involved partners in the supply chain. It is an aspect of supply chain collaboration that is considered to improve supply chain performances through the integration of key activities or factors (Lee et al., 1997; Narasimhan and Kim, 2002; Zailani and Rajagopal, 2005; Kim, 2006; Flynn et al., 2010).

Supply chains are series of activities to create values for customers. Those activities should be aligned and operated consistently with each other to achieve higher value creation. The designed values supposed to meet needs of specific customers should be supplied to them without any degradation of the values. In other words, supply chains must be engineered to match product characteristics with customer requirements. The difficulty of demand forecasting stems from many reasons, but reflects partly the misfit between the designed value

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and the perceived one by customers, ceteris paribus. Mark-down, for example, aims to induce demands of now price-mismatched customers. But it sometimes blurs the invisible basic demand pattern, otherwise existed, of the original product. Besides such built-in mismatch, the market changes as the product life cycle proceeds through the stages; that is, customers’ shift of value judgment works to develop the value mismatch. Criteria of qualifier and order-winner change over the product life cycle (Hill, 1999). Then demand forecasting is possibly suffered because demand determinants as well as customers’ sensitivity to them change. Consequently, supply chain strategies must be dynamically matched to value changes over time so as to sustain competitiveness (Stonebraker and Liao, 2003). The basic mismatch between the product value and the customer one should be reduced as much as possible before the selection of demand forecasting methodology.

We focus on the demand uncertainty issue in SCM. The problem is not only concerned with forecasting methodology, but also caused by the mismatch between the product value and the customer one. We base our arguments on the understanding that such mismatch is often given birth by inappropriate activities in supply chain processes. Stock-out, an unfavorable outcome of supply chain operation, for example, disguises the true demand volume. Failure of timely delivery makes it. In such stock-out situation, any forecasting based on the sales data that tell only part of the real demand volume does not work well. Also, specific product values are injected into a product to meet the demand of the targeted customer segment. If the product is sold to different segments’ customers, the demand pattern emerged from the sales data looks different from the theoretically estimated one. The demand uncertainty is deceptive for product development people and also the company. We introduce a concept of adaptive collaboration strategy to reduce the mismatch and improve the demand unpredictability to implement effective SCM in actual business conditions. Collaboration among supply chain partners underlies such effective SCM according to the past literature described above. In our discussion of collaboration strategy, we firstly argue such collaboration should be implemented adaptively to the product life cycle, managerial value concepts of partners, and their processes’ complementarities in actual business situations. These factors become contingencies for supply chain collaboration. Secondly we discuss what activities should be marked to reduce the mismatch. In this line of arguments, we will emphasize a focus to activate effective collaboration in SCM. In our case, the focus is to reduce the mismatch between the proposed value by the company and the perceived one by consumers in the market. Under this focus, we draw an insight that the supply chain collaboration should go adaptively. But the generalization that any supply chain collaboration should be done adaptively will remain an agendum for future research.

Our methodology is case-based. Based on the previous paper by Itoh and Nagashima (2009), we argue the collaboration under a new framework in this paper. We expect these efforts could lead to a new research perspective on the collaboration in SCM as well as the development of practical knowledge related to it. LITERATURE REVIEW The supply chain collaboration assumes two or more companies working together to create competitive advantages and higher profits than can be achieved by acting alone (Flynn et al., 2010; Van der Vaart and Van Donk, 2008; Simatupang and Sridharan, 2002).

The supply chain collaboration has attracted research initiatives from various management fields such as marketing and strategic management, and it is therefore conceptualized and defined in different forms, such as integration, coordination, cooperation and information

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sharing (Das et al., 2007). It is increasingly accepted that the supply chain collaboration is a multi-dimensional concept (Das et al., 2006; Fabbe-Costes and Jahre, 2008). Especially, since products proceed through their life cycles, the stage of product life cycle is contingently associated with those multiple dimensions of supply chain collaboration (Stonebraker and Liao, 2003).

However, most of the research analyses on the supply chain collaboration, consider the collaboration as a one-dimensional construct that places more or less emphasis on only some aspects such as information sharing or co-managed inventory (Van der Vaart and Van Donk, 2008). Moreover, few study has considered how different factors of the collaboration work to improve performance (Van der Vaart et al., 2012).

Although the supply chain collaboration has been considered as one of the primary business strategies to improve performance (Flynn et al., 2010; Van der Vaart and Van Donk, 2008; Frohlich, 2002), it is still a very difficult practice and remains as an elusive goal in actual business (Beth et al., 2003). Various factors are associated with the nature and performance of collaboration. It is difficult for practitioners to implement the collaboration successfully without consideration of those factors.

We here review the research literature related to different key factors associated with the uncertainty of demand in the supply chain collaboration. They are technical demand forecasting problem, product life cycle, and retailer strategy.

Technical demand forecasting problem One of the remedies for reducing the demand forecasting uncertainty is collaborative information sharing among supply chain partners. But sharing even up-to-dated demand data is often not effective to reduce it, because the demand is itself changeable depending on retailer actions such as pricing, promotion, advertising and assortment planning (Cachon and Lariviere, 2001). Forecasting jointly considering such activities has been rarely done.

Although academic research has conducted a plenty of modeling analyses to do precise demand forecasting supported by retailers (Cohen et al., 2005), such modeling analyses mostly fail to meet the level of precision the retailers need in actual business situations. Unidentified determinant factors of demand work to spoil forecasting accuracy. When it comes to innovative new products, the forecasting becomes more difficult. Lee (2002) pointed out that, along with the demand forecasting uncertainty, it is important to consider the uncertainty resulting from inadequate capabilities of the supply chain. For example, manufacturer-retailer collaboration assumes various capabilities of involved organizations such as structure, internal and external coordination, and the ability to receive by EDI and follow KPIs (ECR Europe, 2001). The demand uncertainty also differs depending on environmental factors such as market changes brought about by changes of consumers’ tastes as well as competitive conditions. Chopra et al. (2007) described that the demand uncertainty differs depending on the stage of product life cycle that embraces those factors above. The introduction stage’s demand is most uncertain because of no availability of data, and the demand becomes more certain and amenable to irregularity handling when the market is saturated (Chopra and Meindl, 2007).

Product life cycle The product life cycle is generally represented by the unit sales curve for a product, extending from the time it is first placed on the market until it is removed (Buzzell and Robert, 1966). Schematically, the life cycle of a product having some newness alien to part of consumers

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may be approximated by a bell-shaped curve that is divided into several stages (Scheuing and Eberhard, 1969). Although the number of stages suggested in the product life cycle literature varies between four and six, a four-stage cycle - introduction, growth, maturity, and decline - is generally accepted (David and John, 1979).

The theoretical rationale behind the product life cycle concept emanates from the theory of diffusion and adoption of innovations (Rogers, 1962). That is, unit sales of a new product are slow just after introduction, because few consumers are aware of it. As consumer recognition and acceptance prevail, unit sales begin to increase at an increasing rate by the demonstration effect. This signals the start of the growth stage. However, as more competitors come in the industry, the rate of growth in unit sales will be accelerated by the effect of their competition, but such growth will be shared among the competitors. Eventually, the growth of unit sales starts to lose its momentum to shape a plateau, and the product enters into the maturity stage. Most of consumers experience the product. If the competitors bring in new and more competitive products, they may shift to those new ones with a drastic decline of the product’s unit sales (David et al., 1979).

The concept of product life cycle has contributed to marketing strategy by indicating that the demand determinants change over the cycle. In other words, marketing strategy should be adapted to the product life cycle (David et al., 1979).

The concept of linking operational processes of the supply chain with a product’s transition in the product life cycle is also glimpsed in the field of manufacturing (Hayes and Wheelwright, 1979). They suggested that there is an optimal manufacturing process for each stage in the product life cycle, and proposed a two-dimensional matrix framework each of the axes has a scale of the stages of product and process life cycles respectively. The framework helps the company navigate the matching between the product and the manufacturing process. Differentiating supply chains linked to product life cycle was also further refined and extended (Pagh and Cooper, 1998; Lamming et al., 2000; Childerhouse et al., 2002).

Furthermore, considering that products with short life cycles of one or two years are becoming increasingly common in several industries, Kurawarwala and Matsuo (1996) proposed an integrated approach to forecasting and inventory management for such short life cycle products.

Those supply chain discussions combined with the product life cycle were mainly in the field of upstream manufacturing, and not from the perspective of downstream retailers (Christopher et al., 2005). Retail strategy and product life cycles are closely linked. The life cycle stage of the product plays a vital role in the selection of retailer types. For example, the introduction of a new product featured by cutting edge technology is typically best supported by means of face-to-face selling to convince customers to accept the new technology, while during the mature period, selective distribution is often the best option (Combs, 2004). Furthermore, effective retail strategy development is possible based on real understanding of shopping behaviors and profiles of customers (Mason et al., 1993). Retail strategy needs to be supplemented by an in-depth understanding of organizational processes of shopping behaviors that could evolve over product life cycle (Christopher et al., 2005). Retail strategy A retail strategy defines a target market, a format that the retailing process uses to satisfy the target market’s needs, and competitive focuses based on which the process builds sustainable competitive advantages. The target market is the market segment toward which the retailing process commits resources and retail mix; that is, types of merchandise and services offered,

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pricing policy, advertising and promotion program, approach to store design and visual merchandising, typical locations, and customer services. The retailing format defines the nature of operations of the retail mix designed to satisfy the needs of its target market. A sustainable competitive advantage is an advantage that is not easily copied and thus can be maintained over a long period of time (Levy and Weitz, 2009).

A retailer, the warhead of the retailing process, is significantly influential to build customer loyalty by developing a clear, distinctive image of its retail offering and consistently reinforcing that image through its merchandise and service. The design and implementation of a retail mix to create an image of the retailer in the customer’s mind relative to its competitors is called positioning (Levy and Weitz, 2009). The positioning is important in the sense that the image in the customer’s mind is critical. Thus, the retailer needs to know what its own image is, and secure the consistency between what the target customers want and the image. A perceptual map is frequently used to know the relationship between the retailer preference of the customers and the image that the customers hold (Levy and Weitz, 2009). Performance outcomes The major reason why firms want to collaborate with other firms is to improve performance and to gain a source of long-term competitive advantages. Strengthening of competitive advantages is dependent on the relationships and networks developed by organizations (Day, 1994).

Generally agreed performance outcomes of supply chain collaboration found in the literature are, firstly, (1) increased responsiveness (Bowersox and Daugherty, 1995; Leenders et al., 1985; Davis and Manrodt, 1991; Nix, 2001), secondly, (2) product availability assurance (Bitner, 1995; Smeltzer and Siferd, 1998; Heinritz et al., 1991; Leenders et al., 1985), thirdly, (3) optimized inventory (Cooper and Ellram, 1993; La Londe and Masters, 1994), fourthly, (4) increased revenues (Andraski, 1999; Mentzer et al., 2000).

Although there are numerous studies on performance, there are few coherent studies to measure the performance of supply chain operations based on the degree of collaboration. CASES We have chosen an exploratory case study of the supply chain collaboration in the real world. Yin (1994) pointed that case studies are most appropriate for exploratory research. A manufacturing Company X of digital still camera transacts with three retailers F, B and A, each of which differentiates each other in positioning in the digital electronics products market in France. In this study, we restrict to the collaboration between Company X and Retailer F due to the page limitation.

The cases will show a series of collaboration through which a manufacturing Company X achieved the top market share in France from the lowest rank through the collaboration with Retailer F in the supply chain. They reduced the product shortage rate by 80%, and reduced the final product inventory level by half in digital still camera market over the 3 years period from 2005 to 2007, covering the introduction stage of the product life cycle characterized by high uncertainty of demand.

We divide the case period into three sub-periods, the first period is the time prior to these collaborative efforts (before May 2005), the second period is the first phase of supply chain collaboration (from May 2005 to September 2006), and the third one is the second phase of the collaboration involving implementation of collaborative planning forecasting and replenishment (CPFR) (from October 2006 to September 2007), in order to illustrate

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noticeable characteristics of the supply chain collaboration strategy. Data used We used three data sources for this case study. Firstly, qualitative data were derived from interviews. We conducted 60 interviews in total from 2005 through 2009. Specifically, the author interviewed twelve managers of Retailer F, B and A; eight managers from Company X; and 40 customers at the shop floor of the retailers with respect to buyer-supplier collaboration and related outcomes of such collaborations.

Secondly, demand forecast accuracy, the gap between forecast and actual, is used as a key performance indicator to manage the reduction of demand forecasting uncertainty. The comparative figures of the demand forecast accuracy during 2005-2009 were collected. Other performance data such as delivery lead time, product shortage (stock-out), inventory and sales revenues during the period of 2005-2009 were used to evaluate the performance outcomes.

Thirdly, to measure the market performance we used the market share data during the period from 2005 to 2009 of GFK, a market demand research company. Also we used Retailer F’s dossiers issued during 2005-2009 to know the evolution of new product technology. Digital still camera market Several companies compete head to head in digital still camera market. While customers want desired features at the lowest possible price in general, experts and professionals supposed to lead the market were keen more likely to reputation and credibility which are more related to product performance. For consumers, the specific order-winning criteria were resolution, design and price. As the competition went on, the barriers of entry rose quite high due to several factors such as scale economy, infiltration of brand awareness and recognition, and technological progress. In addition, establishing reliable relationships with distributors anew was one of the hardest challenges for new companies.

The French digital electronics market was the 3rd largest in EU. The demand grew rapidly in 2003 and sustained a momentum after 2003. For example, the saturation ratio of digital still camera was only 21.5 % in 2004. In 2007, 5 million units of digital cameras were sold and the ratio jumped to 56.9 % (see Table 1). These periods were at the growth stage.

Table 1 - Evolution of digital still camera market in France

Demand

Volume1000units

ValueMillionEuros

Volume1000units

ValueMillionEuros

Volume1000units

ValueMillionEuros

Volume1000units

ValueMillionEuros

4,000 1,199.9 4,600 1,219.0 4,630 1,169.9 5,000 1,160.1 Saturation ratio (%)

2004 2005 2006 2007

21.5 38.4 49.9 56.9 (Source: GFK data)

Digital still camera manufacturer X and its supply chain partner Retailer F Company X that has a strong business principle of “customer first,” entered the French digital still camera market in 2003. While it could not capture a large portion of the market so quickly, the overall market was expanding rapidly. Major retailers wanted the manufacturers to be involved in solving then chronic problem of delivery or availability. The key for raising the market share depended on building up a reliable supply chain that could meet the requirement of delivery as well as technologically up-dated product feature requirements.

On the other hand, Retailer F, the collaboration partner of Company X in this case, carries a broad and deep assortment of innovative new products, and provides customers with intimate

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display so that customers can touch and feel products adequately. The norm of the retailer is to maximize the customer satisfaction. The retailer has 81 shops and its own laboratory checks products to be sold at their shops for technical quality, ease of use, and price/quality ratio. All tests’ results are published in the “Retailer F dossier.” Floor sales staffs in their stores are trained to give customers meaningful personalized information and consultation using this “Retailer F dossier.” The Retailer F’s target customer is profiled as early adopter or the innovator who is ready to pay premium if the product satisfies them. Retailer F attracts customers by offering a pleasing ambience, attractive service, and a wide assortment of innovative merchandises. Supply chain challenges Company X wanted a stable demand for extended periods of time to maximize its manufacturing capacity utilization, while retailers wished to meet fluctuating demand flexibly. This conflict was perpetual. Hence, SCM, coordinating both of these preferences, needed to be pursued. In digital still camera market, high proliferation of product variety was the case as a result of the effort of meeting increasingly demanding customers. It multiplied the uncertainty of demand. Both Company X and Retailer F were not exceptions to be suffered. The life cycle of digital still camera was shortened accordingly. Moreover, six months lead time to procure key components gave birth to another problem of high possibility of stock-out or excessive stock for Company X as well as Retailer F. Moreover, the order-delivery relationship between Company X and Retailer F was another trouble. At the end of 2004, the order lead time of Company X’s digital still camera business from order reception to delivery to each Retailer F’s shop was 23 day. In addition, there was no any established standardized process and rule for ordering and delivery. The shortage ratio for digital still camera was 10% with the high inventory level equivalent to the quantity of demand for 54 days at Retailer F. Preparing for CPFR (May in 2005-September in 2006) To resolve those supply problems, a project including collaborative efforts with the retailer was initiated. As an initial step of the project, a mechanism with which Company X and Retailer F could share actual demand and inventory data was introduced together with the continuous replenishment policy (CRP) in 2005. Retailer F’s main objectives were to secure the desired service level of availability and minimize the level of inventory. The new replenishment process adopted was designed to work as follows: (1) At 10:00 am on Monday, Retailer F sends last week's sales performance by model, finalized orders for upcoming two months, and sales forecasts for upcoming four months to Company X; (2) At 10:00 am on Thursday, Company X sends a replenishment proposal to Retailer F as follows; Company X calculates how many pieces are wanted to fulfil the level of stock equivalent to 21 days’ demand (21 stock-days) based on the sales forecast of this coming week. The 21 stock-days is the yearly target mutually agreed between Company X and Retailer F. Company X sends the quantity of this gap as a replenishment proposal to Retailer F. (3) At 2:00 pm on the same day, Retailer F notifies their final orders to Company X as follows; Retailer F confirms this proposal as finalized order with only one exception. This exception is to make the adjustment of stock-days in May and December since Retailer F stocks the products at shop level only for this sales peak season of May and December to avoid any shortage. (4) On Tuesday, Company X ships the finalized quantity of the products to Retailer F's central

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warehouse; and (5) On Wednesday, Retailer F's central warehouse delivers an appropriate quantity of the products to each shop, which arrive on Thursday.

Under this new system, the order lead time for Retailer F is one week. The former lead time of 23 days was shortened remarkably, nearly to one third.

Then the immediate results were promising. The stock levels decreased by 1/3, while the sales revenues doubled at the end of March 2006. However, for a new model, no availability of its past sales data spoils the quality of sales forecast data. In fact, the shortage rate of a new product was two times more than the average of existing products at the introductory stage. Retailer F claimed, "It is not permitted to fail to deliver products to the customers who visit our store willing to purchase them, even if reducing inventory levels is desirable. Our corporate principle is to enhance customer satisfaction." Hence the next improvement target was in order. Company X and Retailer F started to commit themselves to collaborate with each other aiming to make more accurate sales forecasting to reduce stock-out. Implementation of CPFR By September 2006, digital still camera market hit the household penetration ratio of 50%. The size of the market was supposed to reach 5 million units in 2007.

While the newly adopted continuous replenishment system had begun functioning well, another challenge to respond to diversification of customer needs accompanying the growth of the product market was impending. Company X and Retailer F needed to tackle with the problem of high uncertainty about demands of new products launched to respond to the market trend. One idea was to improve the planning capability of new products. In other words, it was to reduce the misfit between such new products and market expectations as well as the error of forecasting. The misfit is a significant cause of poor forecasting. First of all, both companies designed a one-year aggregate planning framework for CPFR as a basic element of the planning capability. They set annual sales targets taking into account key information such as past sales data and seasonality. Based on the targets, they formulated the annual sales targets into eight weeks forecasts. As real sales came in, they identified the gap between the forecast and actual sales every week. They adjusted production based on the gap. This planning system was expected to increase the planning capability systematically. Collaborative planning Collaborative planning was a process by which the involved parties partake in determining the ideal level of supply for a given year. The goal of collaborative planning was to meet demands of Company X’s digital still cameras while maximizing profits.

Company X invited staffs of Retailer F to its research and development center in Japan when they developed new products for the market of Retailer F. The design collaboration took place at least once every quarter. Design collaboration ensured that any design changes were subject to communication between them.

The corporate research and development division of Company X had never disclosed information on new products to retailers before communicating it to sales companies like Company X France. However, thanks to the mutual trust established through collaborative planning, they started to communicate to the sales company and Retailer F at the same time.

Obtaining the information on best-selling models and their specifications such as resolution and LCD screen size from Retailer F, Company X shared annual sales targets, weekly seasonality, and promotional plans with Retailer F. Specifically, Retailer F was able to

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deploy demand forecasting by obtaining the information on features of wide-angle lenses, high performance zooming capabilities and image-stabilization functions of Company X’s spring 2007 models in advance (see Table 2 for the evolution for the product features of digital still camera). The main concern of Company X as to the demand of new products in this period was if new models with such features could be accepted with the price twice as expensive as those of existing competing products. As a result of directly linking its continuous replenishment system to Company X’s production function accompanied by these structural changes of collaboration, Retailer F was able to keep its shortage ratio to roughly 2% constantly throughout a year while then prevailing average shortage ratio was approximately 10%. Switching to new models did not only disturb forecasting, but also improve the forecasting accuracy.

Table 2 - Evolution of product features of digital still camera 2003-2004 2005-2006 2007-2008

Pixel Resolution (Megapixels) 3 6‐8 10Wide Angle (mm) 35 28 25Photo Shooting Function Image Stabilizer Face Recognition Individual RecognitionLCD Screen Size 2.5" 3.0" 3.5"Movie Function 320×240 dot 640×480　dot 1280×720 dotMemory Capacity 2GB 32GB 32GBOptical Zoom 6× 12× 18×

(Source: Hearing from Retailer F and Company X) Collaborative forecasting Forecasting is an intricate art of making projections about future conditions. To reduce the risk of error, the partners needed to collaborate on store level forecasts. These forecasts were converted to a series of store level orders, which were committed over a specific time horizon, eight weeks in this case.

After Company X France checked the data from 81 stores of Retailer F in France through the headquarters, Company X France and the headquarters of Retailer F set up eight weeks forecasts. Then Company X France made a five weeks commitment plan to be notified to the factory and indicated the quantity for the rest of further three weeks as forecast.

At the same time, the marketing department of Company X France made intensive investment on mass media advertising on their key products to boost the demands. Retailer F synchronized their promotional actions at the shop floor with the promotion by Company X. In the past, such promotional actions by retailers were often done without considering their relationships with those of supply chain partners. Performances of the collaboration When Company X entered the digital camera market in France in 2003, the production volume was decided based on its own forecasting. Since digital still camera market entered the stage of growth from 2006, an important issue to the company was how to boost sales volumes as high as possible during the first three months after introducing new products. They thought a requisite initial condition was to secure the fit between the product and consumers values. Then Company X considered that, the synchronization of product development with the product evaluation, defined by the four stars evaluation system of Retailer F, was very important in developing products that met the condition. Then Company X sent several factory engineers from Japan to the Retailer F’s laboratory to let them work

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together based on the evaluation system every single month. On top of the evaluation of new products through “Retailer F dossier,” sharing of market

information and trends, and synchronizing Company X’s mass media promotion with Retailer F’s trade actions in the shop front, brought about a large increase in sales and a significant reduction of inventory as well as shortages. Because the quality of feedback information as to the market and trends above mentioned as well as sales data from Retailer F was improved to a great extent, Company X could better the demand forecasting accuracy. Furthermore, the synchronization of Company X’s mass media investment with Retailer F’s trade actions in the shop front boosted the demand rates of new models in the first three months after launching them.

Table 3 shows the evolution of relationship between the number of stars by “Retailer F dossier” evaluation and performance outcomes of Company X (number of stock days, product shortages, replenishment lead times, and market share) over the three years period from 2005 to 2007 averaged for all digital still camera products of Company X. The number of Company X products with the four stars evaluation increased from three in the first half of 2005 to seven in the second half of 2007. The market share increased from 1.8% to 14.7%. In addition, there were significant improvements in replenishment lead times. These improvements together strengthened the supply chain capabilities, which in turn led to the market share expansion.

Table 3 - Relationship between numbers of stars of “Retailer F dossier” and performance outcomes

1st half 2nd half 1st half 2nd half 1st half 2nd half4 stars 3 3 6 3 5 73 stars 1 - - - - -

48.0 37.2 19.5 22.6 16.0 22.38.0 5.4 11.0 5.4 2.1 1.416.0 13.0 10.3 6.7 4.0 4.01.8 1.8 5.5 9.9 14.6 14.7

2005 2006 2007

Market share (%)

Stock daysShortages (%)Lead times (days)

No of stars

(Source: Company X data)

Table 4 shows the evolution of demand forecast accuracy for the product categories

together with that of the total during the period of 2005-2008. We apply the method of Mean Absolute Percent Error (MAPE) commonly used to calculate a performance measure for demand forecast accuracy. MAPE is the sum of absolute errors divided by the sum of the actual which is defined as Σ｜Forecast-Actual｜/ Σ Actual.

We can see the significant improvements of demand forecast accuracy but the entry category. As a total, the accuracy was remarkably improved from 30% deviation in 2005 to 2.6% deviation in 2008. Especially we found high accuracy improvement in specialized categories of high value items such as high zoom and stylish for which Retailer F sustains high competitiveness. These results suggest that as the company goes to differentiation, the collaboration from product development to selling works effectively. As shown by the result of the entry model, however, other factors could be influential on the performance of demand forecasting. Though this remains only as our speculative thought, C (cost) and D (delivery or availability) as well as competitors’ actions could be more influential determinants of the demand of the basic product category where product differentiation can be few. Furthermore, as the result of DSLR category shows, more differentiation including high price still goes with high degree of forecasting error regardless of the declining trend. It suggests us that there

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could be optimal differentiation as long as the demand forecasting error is concerned. The attributes such as high zooming and more sophisticated style are easily perceived by consumers. Promotional messages and actions could work effectively on them. When it comes to more differentiated or innovative products, the collaboration could be suggested to go more closely and considerately based on deep insights of customers. These product characteristics -related issues drawn from the results put on us another set of research agenda related to the supply chain collaboration.

Table 4 - Demand forecasting accuracy by product category during 2005-2008

2005 2006 2007 2008

DSLR※ 400－ － － 62.1% 48.9%High Zoom 249－399 25.6% 8.5% 10.2% 3.1%Stylish 149－299 33.7% 31.7% 8.6% 5.6%

Entry -149 49.2% 11.4% 54.8% －Total 30.0% 19.1% 2.0% 2.6%

Pricesegment(€）

Productcategory

Demand forecast accuracy

(Source: Company X data) Note) DSLR stands for Digital Single Lens Reflex camera

ADAPTIVE COLLABORATION: FROM FORECASTING TO CONTROLLABILITY OF DEMAND The firm should make efforts to construct and operate the most suitable supply process to the designed values and their customers. The efforts include reducing the demand uncertainty as shown by the cases described above. Stages to reduce the uncertainty of demand Firstly, given the degree of demand uncertainty for a product, there are two solutions. The first solution is to improve replenishment rules for it. For example, they can shorten the order interval to adjust to the demand pattern including its fluctuation. As the order interval is extended, the lead time for replenishment turns out long. The chance to adapt to the change of demand pattern is lowered. Also it raises the inventory level to lose supply efficiency and increases the risk of inventory spoilage or stock-out. The second solution is to enhance the demand forecasting capability by adopting more sophisticated forecasting tools as well as improving the quality of data. The latter is more important because the syndrome of GIGO (garbage-in, garbage-out) holds for any case. To improve the quality of data, we need to grasp the real demand pattern free from any noise such as stock-out. To improve the quality of demand data, the improvement of replenishment rules is effective.

The first phase of supply chain collaboration from May 2005 to September 2006 described above indicates how the involved partners collaborated to improve their existing replenishment rules. The improvements include changing the order rules and shortening the order interval. The improvements secured more timely and effective flows in the supply chain and then increased the data quality in terms of reduction of noise including stock-out. They did not rely on the technical sophistication of forecasting tools, but the effect was remarkable. The result triggered the next stage of improvement.

Secondly, the degree of demand uncertainty itself or unpredictable fluctuation becomes the target for improvement. It implies the demand pattern itself is a target to work on, not given. The gap between expected and real demands measures the degree of demand uncertainty. In other words, the degree of unexpectedness is the degree of demand uncertainty. Furthermore, this demand uncertainty differs depending on the stage of product life cycle (Chopra and

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Meindl, 2007). Following two gaps are involved in the degree of such unexpectedness. The first gap is between the designed and the realized values. This gap is plausibly generated by the supply chain processes including purchasing and manufacturing. The second gap is between the realized and the perceived values by customers. This one is mostly emanated from marketing, selling and after-market processes. Then we should put the focus of improvement on the whole processes related to these gaps. Such improvement will be extended over the whole processes of supply chain.

The second phase of the collaboration from October 2006 to September 2007 is related to this comprehensive gap reduction. The involved partners tried to decrease these gaps by their collaboration on designing products, planning production, marketing and selling. The collaboration was far extended to involve broader processes than the first phase. This collaboration resulted in higher demand rate as well as lower fluctuation than estimates extrapolated based on past experiences of new products. This extended collaboration synchronized with the product development made it possible for Company X and Retailer F to develop and introduce competitive products that could meet the anticipated customer needs and to appeal to the right customers for the product values. They led to the creation of real high demand rate with low unpredictable fluctuation. In general, a new product is characterized by high uncertainty of demand because these two gaps are relatively greater than those of existing products.

These two phases suggest us how to cope with the demand uncertainty. They shifted our handling of it from reactive forecasting to proactive planning, namely, increasing the controllability of demand. It means to decrease the vulnerability to the demand uncertainty as well as the noise of demand data in the first phase and then to reduce the coefficient of variation of demand, (i.e., to increase the demand rate and reduce the fluctuation,) in the second phase. To do so, we need to manage relevant value creation processes from design to sales throughout a product life cycle. The first phase covers the replenishment process and the second phase the more comprehensive process from the development of product to sales promotion at the sales floor at the different stage of product life cycle. Both phases differ in the degree of collaboration achieved even if the involved partners are the same. In other words, they adopted their collaboration effectively throughout a product life cycle. Then we discuss what determines such adaptive collaboration. A concept of adaptive collaboration Collaboration is used to be one of the important keys in SCM. The cases introduced are focused only on the reduction of demand uncertainty, but one important insight into the supply chain collaboration emerges. This is a concept of adaptive collaboration.

Generally speaking, a collaboration opportunity comes up when the involved partners can complement each other to perform supply chain activities more effectively. Such involved partners are essentially independent in terms of management or ownership. Then collaboration should be done over the intersection of their common interests. This is important because easiness or being implementable rather than theoretical possibility is meaningful for the collaboration in actual business situations. Then there are three important points to mobilize the collaboration. They are the identification of such intersection on which the collaboration brings about win-win merits, the mutual agreement on the intersection and collaboration merits, and the understanding of how to collaborate.

Any firm has processes for its value creation. The collaboration is a concerted effort of the involved partners to reengineer such processes from a more integrated viewpoint to attain

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higher value creation than otherwise. The degree of such integration characterizes the type of collaboration. Identifying the appropriate degree of collaboration shapes a collaboration strategy, a basic way of collaboration.

In our cases, three important factors work as contingent factors. These factors make the collaboration adaptive. The first factor is the product life cycle. It determines the potential size of merit of collaboration as well as how to compete for each product in the market. At early stage of the cycle the realizable profit may be not so large, but the success at that stage leads to the possibility of enormous profitability at later stages. The cycle also suggests what competence such as quality, cost and delivery is important to gain competitive advantages. The stage of the cycle determines the amount of merit to be attained in the collaboration as well as key performance indicators (KPI) of the collaboration. Then depending on the stage of the product life cycle the involved partners should choose how to collaborate.

The second factor is the commonality of the involved partners’ strategic focuses. What are their strategic aims or values? If they differ in this factor significantly, the intersection of interests becomes small to lead to lower possibility of the collaboration. In our cases, Company X and Retailer F both aim to be innovative in the products they offer and to gain high customer satisfaction through the products. Such commonality of their strategic aims tends to nurture collaborative efforts. They seek for the possibility of collaboration as wide as possible.

The third factor is concerned with the possibility of functional synergy. If the involved partners’ processes are not complementary or mutually adjustable, the technical or managerial possibility of collaboration is lowered. The fact that the involved partners have their own product laboratories that contributed effectively to the collaborative decision on the values of the product. They have their own similar judgment criteria based on which they could collaborate to decide the final configuration of product values. This is not a forced result from either side. Their wisdom has synergistic effects on the quality of decision. And their own processes, that is, upstream and downstream ones, work in a coordinated way to realize such values in the final market. Each of the partners had the processes that complemented each other. Thus, these two firms realized the right degree of supply chain collaboration covering appropriately the combined processes of these two companies. They had a deep insight into how each other should operate, which was also integral to the successful implementation of supply chain collaboration. RESEARCH IMPLICATIONS This exploratory study aims to contribute to actual implementation of the supply chain collaboration by introducing a concept of adaptive collaboration. The key is the adaptation of collaboration to certain important contingent factors. The proposed concept is expected to bring disparate research streams together to further the understanding of supply chain collaboration and give important implications to both researchers and practitioners. Theoretical Implications The first implication is that it provides a framework of the supply chain collaboration to understand what activities should be focused to reduce the demand uncertainty. Our case-based study confirmed the collaborative efforts between the supply chain partners were keys to improve the supply chain performances as many researchers advocated in the past literature. The demand uncertainty we focused disturbs supply chain operations significantly. One important reason we drew from our cases why collaborative efforts are possible to

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improve supply chain performances is that they are effective to reduce the demand forecasting uncertainty. We claim the demand uncertainty is caused by the mismatch between the product and the perceived values. The mismatch is given birth by not-well aligned activities in the supply chain. The supply chain collaboration is possible to align such activities to reduce the demand uncertainty and make it possible for the company to achieve maximal value creation.

The second implication is that the supply chain collaboration is effective if focused on the particular important elements to improve supply chain performances, that is, lead-time including replenishment period, flow control (replenishment rule), demand (rate and fluctuation), and quality conformance, proposed by Morita et al. (2012). We call a set of improvement efforts focused on each element of them a supply chain management initiative. Because the supply chain collaboration aims to align involved activities, there should be such clear initiatives reflecting specific goals or criteria to navigate the supply chain collaboration. The reduction of lead times and the enhancement of demand controllability our supply chain collaboration study focused on are part of such initiatives. We advocate that clear, systematic, strategic and effective initiatives underlie successful supply chain collaboration.

The last implication is that a concept of adaptive collaboration is a key to make collaboration successful. This enables us to address an important research gap by utilizing the different factors of collaboration. We identify three important contingency factors. They are product life cycle, managerial commonality of the involved partners such as value creation aimed, and functional synergy based on functional complementarities. These factors required the implementation of the supply chain collaboration to be done adaptively. In the sense, important decisions such as retailer choice and operational decisions should be made from such adaptive collaboration perspectives. The concept of adaptive collaboration, extracted from our case studies, is expected to be meaningful for actual companies to implement collaborative activities in the supply chain. The contingencies of three factors shape types of collaboration. It generates a concept of adaptive collaboration strategy. At this moment, we are forced only to propose the degree of collaboration in terms of the range of coordinated processes of the involved partners as a dimension to shape a strategy type such as high or low collaboration strategy. Also in our cases, high collaboration strategy means the coordination covers more extended processes. We want to emphasize that this concept could provide some valuable knowledge to implement the supply chain collaboration effectively with involved partners with different objectives that could be evolved over product life cycle. Implications for future research In business practice, supply chain collaboration has proved difficult to implement and supply chain collaboration remains an elusive goal. However, supply chain managers can benefit from this study by identifying the appropriate degree of collaboration to make the successful implementation of supply chain collaboration over time.

A proposition that all supply chain collaboration should be done adaptively, however, is not beyond our hypothesis. We picked up only the demand uncertainty reduction issue, though its relevancy is broad. But considering actual business situations, we expect this proposition will likely hold. This is our future research agendum.

Finally we remark on the issue of aligning product strategy with supply chain strategy, which was raised by Fisher (1997) and successively Lee (2002). Product strategy is concerned with designing product values and targeting customer segments. Supply chain strategy determines a basic way of providing such designed values to the segments to realize maximal value added. The alignment between these strategies is indispensable judging from those

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concerns. One significant problem here is how to align those two strategies. In the sense the concept of adaptive collaboration we proposed is considered to be important. Especially the initiatives that drive the collaboration are important. The demand uncertainty we focused is one of such key initiatives. One important result from the misalignment of product strategy and supply chain strategy, we think, is expressed as the mismatch we described above. It is very difficult to secure the effective alignment between those strategies from start. Even it is set up initially, business environments change and some causes of misalignment emerge. Then the company should adjust the alignment over time. With the initiatives mentioned above in mind, the company should align both adaptively through the collaborative efforts as described in our cases. A further research on this alignment issue of those strategies, however, remains to be done in future. CONCLUDING REMARKS This study proposes a concept of adaptive supply chain collaboration based on the real cases, featuring the demand uncertainty, which gives significant influence on the chain’s performance. The company has to dynamically adapt to competitive situations by aligning supply chain processes with required attributes of products at different stages of the life cycle. The meaning of “adaptive” connotes the existence of contingencies by which ways of collaboration change. The contingencies extracted from our cases are, firstly, the stage of product life cycle of the target product, secondly, sharing of common business philosophy or value propositions by the involved partners, and thirdly, complementarity of supply chain processes between the involved partners.

The collaboration focus in our cases changed adaptively from the reduction of order interval through the improvement of order rules to the control of the product demand over the life cycle depending on those contingencies. In this case study, Company X’s supply chain improvement focus was initially unclear and remained untouched. This resulted in poor market performances due to the mismatch between the product and the customer values. As the adaptation started, firstly Company X tried to reduce inventory and stock-out. The aims were understood also by Retailer F due to their common management principle, customer satisfaction or avoidance of stock-out. Improvement of the existing replenishment system was in order as the first step of their collaboration. The improved performances encouraged their further collaboration. At the next stage of collaboration, they focused on their main concern of demand uncertainty that new models with new features could be accepted with the price twice as expensive as those of existing competing products. This concern was shared by the two companies. Especially Retailer F emphasizes innovativeness of their merchandises. This next focus required high degree of collaboration that consists of the links from product development to replenishment. Then the Company X tried to drive supply chain initiatives by appropriate collaboration activities adaptively depending on the contingencies that could lead to reduce the mismatch between the product value and customer value.

The collaboration in the supply chain has effectiveness in terms of improvement of performance when appropriateness of the focus of collaboration and feasibility of the collaboration are secured. Meeting these conditions together explains the essence of the concept of adaptive collaboration well.

We conclude this paper by relating our findings to one important research and also practical issue of aligning product strategy with supply chain strategy (Fisher, 1997; Lee, 2002). The alignment of product strategy with supply chain strategy could be approached from the viewpoint of this adaptive supply chain collaboration concept. The alignment

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conveys the message that a consistent alignment of supply chain processes from developing a product to delivering it to customers is a key for winning a success in severe competition. Product values designed should be consistent with a way of delivering them to customers. Then a key to secure such consistency is the capability to design and implement appropriate collaboration between activity units, not only internal but also external. The concept of adaptive collaboration suggests us how to make such successful collaboration.

Research about desirable states is abundant, but the one suggesting how to realize such desirable states is few. Some research works argue the focus on the agility of supply chains is a key for the success of innovative products like digital still cameras in the case. But if considering the product life cycle applicable to any product, it is not enough. Since the product values change over the product life cycle, the strategy for production and marketing as well as for supply chain have to be changed adaptively. The concept of adaptive collaboration introduced in this study is expected to provide meaningful insights in this dispute. Further generalization along with this type of issue is our next agenda. ACKNOWLEDGEMENTS We are very grateful to Coeditor-in-Chief and the review team whose constructive comments have significantly improved the paper. REFERENCES Andraski, J. C. (1999), “Supply chain collaboration,” Food Logistics Website,

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