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Assessing responsiveness of a volatile and seasonal supply chain: A case study
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Int. J. Production Economics 104 (2006) 709–721 www.elsevier.com/locate/ijpe
Assessing responsiveness of a volatile and seasonal supply chain: A case study Chee Yew Wonga,, Jan Stentoft Arlbjørnb, Hans-Henrik Hvolbyc, John Johansena a
Center for Industrial Production, Aalborg University, Fibigerstræde 16, DK-9220 Aalborg, Denmark b LEGO System A/S, Global Supply Chain, DK-7190 Billund, Denmark c Department of Production, Aalborg University, Fibigerstræde 16, DK-9220 Aalborg, Denmark Received 16 April 2004; accepted 3 December 2004 Available online 16 February 2005
Abstract This paper describes a structural approach to assess the responsiveness of a volatile and seasonal supply chain. It is based on a case study in an international toy company. Fisher’s (Harvard Bus. Rev. 75(2) (1997) 105–117) Model of ‘‘innovative’’ and ‘‘functional’’ products and the corresponding ‘‘market responsive’’ and ‘‘physically efficient’’ supply chains constitutes the backbone of this assessment. Four risk-influencing determinants—forecast uncertainty, demand variability, contribution margin, and time window of delivery are found suitable to assess the responsiveness of the toy supply chain. Assessment of the company’s product differentiation model shows that toy products are mostly innovative or ‘‘intermediate’’, but not functional. A proposed new product differentiation model using risk-influencing determinants has enabled the toy company to differentiate its new products, to deal with volatility, and to design for a responsive supply chain. These findings have also enabled the extension of Fisher’s Model to volatile supply chains. This new product differentiation model adds a physically responsive supply chain for ‘‘intermediate’’ products into the Fisher’s Model. r 2005 Elsevier B.V. All rights reserved. Keywords: Responsiveness; Supply chain strategy; Product differentiation; Supply chain planning; Toy industry
1. Introduction The need for differentiating products and managing them in different ways has been well recognized Corresponding author. Tel.: +45 9635 7100;
fax: +45 9815 3040. E-mail address: [email protected] (C.Y. Wong).
(Fuller et al., 1993; Fisher, 1997; Mason-Jones et al., 2000; Li and O’Brien, 2001; Childerhouse et al., 2002; Towill and Christopher, 2003). Subsequently, available literature has established simple rules to differentiate supply chains based on strategic determinants. Generally, functional products with stable demands are supplied by a lean production or a physically efficient supply chain; while innovative
0925-5273/$ - see front matter r 2005 Elsevier B.V. All rights reserved. doi:10.1016/j.ijpe.2004.12.021
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products with volatile demands are supplied by a market responsive or an agile supply chain (Fisher, 1997; Christopher et al., 2004). This approach to differentiation is commonly known as Fisher’s Model (Fisher, 1997). Li and O’Brien (2001) further extended Fisher’s Model (Fisher, 1997) by adding a physically responsive strategy (make-to-stock (MTS)) and considered make-to-order (MTO) as a physically efficient supply chain and make-fromstock (MFS) as a market responsive supply chain. There are several empirical applications of the knowledge on differentiated supply chains. An excellent example is the case study by Childerhouse et al. (2002) on a lighting manufacturer. This case study presents a structured framework for practitioners to differentiate lighting products by using the concept of ‘‘focused demand chain’’. In this case study, light products are differentiated by five determinants: duration of product life cycle, time window for delivery, volume, product variety and variability. Childerhouse et al. (2002) have also demonstrated an improvement in supply chain performance due to this approach. The lighting case is in fact suitable for products with nonseasonal, long life cycles (4–6 years) and relatively predictable demands. However, volatile products such as fashion clothing and toys have relatively shorter product life cycles (from 6 months to 2 years), and they are subjected to higher seasonality and demand uncertainty. Empirical research on differentiating volatile and seasonal products has not been widely explored. As a result, three key questions on how to differentiate volatile and seasonal supply chains arise: (1) what determinants are significant for volatile, seasonal and short-lived products? (2) What level of responsiveness is required for a volatile and seasonal supply chain? (3) Are all volatile products ‘‘innovative’’ and can they be effectively supplied by a market-responsive supply chain? These questions are partly answered by the current literature. For instance, supply chain design for volatile and seasonal products is determined greatly by forecast uncertainty and contribution margin (Fisher, 1997). Fisher (1997) further classifies volatile and seasonal products as ‘‘innovative’’ and motivates the need for a market responsive supply chain. In addition, a case study
on highly seasonal skiwear has demonstrated the need for an accurate response strategy by means of reading early sales-data and shortening lead-times (Fisher and Raman, 1996). However, as industrial examples are rare, there is no unvarying agreement yet on which set of determinants are suitable for which industries at which levels of volatility. As a response to the above research questions, this paper studies how volatile and seasonal products can be differentiated. A toy company is chosen as a case of a volatile and seasonal supply chain. This company mainly produces construction toys based on a MTS (forecast) strategy. In the recent years, toy demand has become highly unpredictable and fashion driven. This situation leads to higher forecast errors and shorter product life cycles, and generates excessive inventory and markdowns. Therefore, the case company faces increasing competition and declining profit margin, and they are searching for an improved differentiation strategy, particularly the opportunity to be more responsive. In this case study, a structured framework of responsiveness assessment is used to determine the required responsiveness levels in order to improve the company’s product differentiation model. This structured framework is further explained in the following section.
2. Structured framework of assessment A structured framework of assessment is used to guide the process in defining the required levels of responsiveness and then to assess the responsiveness provided by the company’s product differentiation model. It consists of the following five major steps: 1. 2. 3. 4.
choose significant determinants; develop a framework of responsive assessment; determine the required level of responsiveness; assess responsiveness provided by the company’s product differentiation model; 5. propose an improved product differentiation model. Step one refers to the choice of a significant and relevant set of determinants that allows the
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company to define the required levels of responsiveness. Suitable determinants are chosen via a careful study on literature of differentiating (or clustering) supply chains, especially the DWV3 (demand variability, time window of delivery, volume, variety, variability) used by Towill and Christopher (2003) and Childerhouse et al. (2002). The choice of suitable determinants is further guided by three agreed principles which consider customer needs, investment risk, seasonality and product life cycles. Then the chosen determinants and the theories for differentiated responsive supply chains (Fisher, 1997; Li and O’Brien, 2001) are used to develop a framework of responsiveness assessment (step two). This theoretical framework allows the required levels of responsiveness to be defined by the respective sets of thresholds for the chosen determinants. This framework defines three levels of responsiveness: the physically efficient, the physically responsive and the market responsive supply chains. It also delineates how these supply chains can be managed at strategic and operational levels. In step three, this framework is used to define the required responsiveness levels by analyzing the demand characteristics of the toy supply chain. Data on demand characteristics are collected based on the chosen determinants from the case company, and then products with certain characteristics are positioned into the framework of responsiveness assessment. In step four, the responsiveness provided by the company’s product differentiation model is then compared with the required levels of responsiveness. Overall, these four structured steps have allowed understanding of the degree of matching or mismatching of the company’s product differentiation model with the required responsiveness and have enabled development of a new product differentiation model. Thus, the structured framework of responsiveness assessment is considered pragmatic and valuable by the toy company.
3. Choose significant determinants for assessing responsiveness Literature on product differentiation frameworks has suggested several suitable determinants
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for differentiating products (Pagh and Cooper, 1998; Lamming et al., 2000; Mason-Jones et al., 2000; Christopher and Towill, 2001; Childerhouse et al., 2002). From these frameworks, it is learned that the choices of supply chain strategies are basically dependent on determinants related to market and product characteristics. In this paper, a suitable set of determinants was chosen by the toy company, based on three principles. First, the set of determinants shall meet customer requirements. Second, the set of determinants shall enable the company to make investment trade-off decisions (capacity, production, inventory, etc.) based on risk considerations. Finally, the set of determinants shall reflect the dynamics of seasonality and product life cycles. The second and third principles above are essential for a highly volatile and seasonal supply chain. In a volatile supply chain, determinants must enable trade-off assessments between the level of investment on responsiveness and the risks of obsolete inventory, lost sales and markdowns (Fisher, 1997). Furthermore, seasonality and lifecycle factors are significant because demands for volatile and seasonal products vary greatly among different seasons and life cycles (Hayes and Wheelwright, 1979). Subsequently, based on the above principles, four suitable determinants are chosen and agreed by the toy company. They are forecast uncertainty, demand variability, contribution margin and time window of delivery. Forecast uncertainty is the level of uncertainty that a demand forecast can be realized. Forecast uncertainty influences investment risk directly because it affects the levels of obsolete inventory, lost sales and markdowns (Fisher, 1997). Generally, forecast errors for toy products are high due to unexpected consumer trends, supply uncertainty and competition (Johnson, 2001). The risks of lost sales and inventory obsolescence increase whenever forecast uncertainty increases. Therefore, forecast uncertainty is chosen to be the main risk factor for making investment decisions on production and inventory. Demand variability refers to the day-to-day or week-to-week variability of demand faced by the manufacturers and the retailers. Levels of demand variability influence the choices of inventory
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policies and production strategies. For example, Just-In-Time (JIT) line production is more sensitive to demand variability than is batch production with material requirement planning (MRP) (Newman and Sridharan, 1995). If demands vary greatly from one period to another, the need for volume flexibility is definite. It is necessary to include demand variability as a determinant for the toy supply chain, because the demands for different toy products vary greatly from one period to another. Spiky toy demands are usually generated during product introduction or high season, and dramatic troughs may follow anytime thereafter. Because demand variability is one of the chosen determinants, volume consideration is actually included. High-volume demand allows for a lean production strategy (Childerhouse et al., 2002) and a MTS strategy (Vollmann et al., 1997). Understanding of volume and demand variability enables the separation of base and surge demands (Gattorna and Walters, 1996). Base demands are more predictable and can be fulfilled by a physically efficient or a physically responsive strategy or even outsourced to low-cost countries, while surge demands can be fulfilled by a market responsive strategy or a focused factory (Skinner, 1974; Gattorna and Walters, 1996). These principles are applicable especially during preparation for product introduction and high season, as predictable and unpredictable portions of the volume may be separated and hybrid supply chain solutions may be utilized. A high contribution margin is regarded as the incentive for investing in innovative products (Fisher, 1997). Generally, innovative products have higher contribution margins but higher forecast uncertainty. Such products require a higher level of responsiveness so that lead-time is short enough to reap the high contribution margins despite high forecast uncertainty. As indicated earlier, the nature of high forecast uncertainty and demand variability shows that it is necessary to consider the contribution margin to support the investment decisions in lead-time reduction and excess buffers. Time window of delivery refers to the lead-time given by the retailers from order to delivery. Time window of delivery is an important determinant as
it reflects one of the customer requirements (Fisher, 1997). It also reflects the probability that customers will cancel orders or purchase other products instead of waiting, as retailers’ shelves are not allowed to be empty. Competitors with faster response times might take away shelf space if a toy manufacturer is slower in response. Consequently, the extent to which production can be postponed (or speculated otherwise) depends greatly on time window of delivery, and visibility and lead-times of demand information. The above four chosen determinants were validated by the toy company in order to ensure their significance. However, the company decided to exclude determinants such as duration of product life cycle, stage of product life cycle, volume, and product variety, though they are considered as important determinants for assessing responsiveness by the literature (Hayes and Wheelwright, 1979; Fisher, 1997; Mason-Jones et al., 2000; Childerhouse et al., 2002; Aitken et al., 2003). Even so, these determinants have not been included due to the following reasons. For toy products, the duration of product life cycle is not necessarily correlated with forecast uncertainty and demand variability. Even 5–6-year-old products may still have high forecast errors during the peak season. On the other hand, short-lived products may have lower forecast errors. Obviously, the stage of the product life cycle influences the volume and requirements on leadtime and service levels. However, the use of the product life cycle as a determinant might contradict forecast uncertainty; therefore, product life cycle is excluded for the purpose of assessing responsiveness. In addition, product variety might be another important determinant for the toy supply chain because it influences the extent to which postponement can be achieved. However, it is currently difficult to define what variety means for construction toys. Bricks inside a box of toy product can be installed in many different ways and thus have high variety. Product variety is normally built within the finished goods (normally in a box with many different bricks). Even though product variety is left out in this paper, it is still believed to be an important factor to be included in future research.
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4. Develop a framework of responsiveness assessment Using the above chosen determinants and the theories on differentiated responsive supply chains (Fisher, 1997; Li and O’Brien, 2001), a framework of responsiveness assessment is developed. Table 1 illustrates the established framework with three different clusters of supply chains. Each cluster is defined by a range of determinants and supplied with a specific supply chain. Each supply chain has its strategic purpose and operations approach. Cluster 1 involves functional (Fisher, 1997) or commodity-like products. These products have low forecast uncertainty and demand variability. However, their contribution margins are usually low due to ruthless price competition. As their demand is more predictable, the main focus is to maintain high utilization of assets, to maintain high inventory turnover, and to reduce lead-time as long as it does not increase cost. This strategy
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may be called a physically efficient supply chain (Fisher, 1997). However, the level of efficiency for functional products depends greatly on the acceptable lead-times. A longer time window of delivery allows a MTO environment in which production is scheduled according to customer orders without investment in raw material and finished goods inventory. As the acceptable lead-time decreases, manufacturers may seek to improve lead-times provided there is no increase in total costs. The acceptable lead-time for retailers is usually short because many consumer goods are kept on the shelves for sales. Therefore, a MTS strategy becomes necessary. Because the demand for these products is predictable, MTS manufacturers can usually commit to a long production frozenhorizon (usually one month or longer). Cluster 2 represents products with medium forecast uncertainty, contribution margins and demand variability, but short time window of delivery. These products have characteristics
Table 1 Framework of responsiveness assessment Strategic and operational levels of responsiveness/ efficient
Cluster 1: Physically efficient supply chain for ‘‘functional’’ products
Cluster 2: Physically responsive supply chain for ‘‘intermediate’’ products
Cluster 3: Market responsive supply chain for ‘‘innovative’’ products
Determinants Forecast uncertainty Demand variability, (c.o.v.)a Contribution margin Time window for delivery
Low (o20%) Low (c.o.v.o0.5) Low Long (44 weeks)
Medium (20–40%) Medium (0.5oc.o.v. o1.5) Medium Short (o5 days)
High (440%) High (c.o.v. 41.5) High Short (o5 days)
Supply predictable demand efficiently at the lowest possible physical cost
Supply with adequate inventory to meet high service level and lead-time
Response quickly to unpredictable demand to minimize stock-out, forced markdown and obsolete inventory
Operational level Manufacturing focus
High asset utilization
Deploy excess buffer capacity
Inventory strategy
High inventory turns
Balance asset utilization and capacity buffer Intermediate inventory buffers
Lead-time
Shorten lead-time as long as not increasing costs MTO or MTS Packaging/molding/ distribution centers
Strategic purpose of the supply chain
Manufacturing process Order penetration point
Source: Adapted from Fisher (1997) and Li and O’Brien (2001). a c.o.v. ¼ coefficient of variant of demand.
Invest moderately to reduce lead-time MTS Distribution centers
Deploy significant buffer of parts and finished goods Invest significant to reduce lead-time MTS and ATO Packaging/distribution centers
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between the functional and innovative products; therefore, they are called ‘‘intermediate’’ products. These products demand for a very short lead-time, thus a MTS strategy becomes necessary. This strategy allows investment in finished good inventory based on a forecast at bearable risks, which can be called a physically responsive supply chain (Li and O’Brien, 2001). This strategy requires a medium level of contribution margin as the risk premium for the changes in demand mix. Furthermore, risk from forecast uncertainty and demand variability can be reduced by using real time demand information and frequent replenishment strategy. Cluster 3 is suitable for innovative products. Whenever the forecast uncertainty and demand variability are high, and the time window of delivery is short, manufacturers must respond quickly to the unpredictable demand in order to minimize stock-out, forced markdown and obsolete inventory. This strategy may be called a market responsive supply chain (Fisher, 1997). A market responsive supply chain requires capacity and inventory buffers as well as an investment to reduce lead-times. A high contribution margin is the incentive for using such strategy, because investments in buffer and lead-time reduction are normally substantial. To mitigate the risk of excess investment, manufacturers may seek to secure visibility of real-time demand and supply information so that postponement by an assembly-toorder (ATO) strategy can be employed (Pagh and Cooper, 1998; Van Hoek, 1998). This is because visibility of the real-time demand information may enable the use of a rapid or quick response strategy (Fisher, 1997; Lawson et al., 1999), in which production of finished goods can be postponed by speculating the mix of components. The three clusters of supply chains above are distinguished by thresholds of the four determinants. Applicability of these thresholds varies. The thresholds for forecast uncertainty and time window of delivery may be applied to any product, because they are similar to the Fisher’s Model (Fisher, 1997). The thresholds for contribution margin cannot be revealed because they are sensitive figures. The thresholds for demand variability for the toy industry are measured by
the coefficient of variant (c.o.v.) of demand. Demand variability is a measure of the bullwhip effect and it can be very industry specific, depending on the batch-sizing and reordering practices. As such, every industry has its own level of volatility and seasonality; therefore, different industries may have different sets of thresholds. However, the proposed thresholds are suitable for fashionable and seasonal products such as clothing, skiwear and toys because they have similar demand patterns. Using this framework and the four chosen determinants, the required levels of responsiveness for the toy products is defined in the next section. However, not all products are expected to fall into all the thresholds for one of the three clusters of responsiveness. Thus, hybrid solutions may be necessary, especially when one product has one demand characteristic in one season and other demand characteristics in other seasons.
5. Determine the required levels of responsiveness The required levels of responsiveness are determined based on 18 months of data (2002/3) from 667 toy products. This sample size is considered adequate because it represents 80% of the total sales. Fig. 1 illustrates how these responsiveness levels are established based on the determinants and predetermined thresholds. Determinants are shown on the left side and the corresponding products are positioned on the right side of Fig. 1 to show how a near perfect match between the responsiveness framework and the toy products is achieved. Time window of delivery is left out because most products had similar lead-times, but varied greatly in different seasons. Overall, the toy company needed 37% physically responsive and 58% market responsive supply chains. Cluster 1 is located at the regions with low forecast uncertainty in Fig. 1. There are only nine functional products (1%) with low forecast uncertainty, contribution margin and demand variability in this cluster. This is because most toys have short product life cycles and their demands are influenced by fashions. These nine functional products were suitable for a physically efficient
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Demand Variability
Contribution Margin
Forecast Uncertainty
Products matching responsiveness level
0%
Low
Low
Medium High
Low < 20%
715
Products not matching responsiveness level Percentage of Analyzed Products 10%
20%
30%
40%
Cluster 1: Physically Efficient (1%) for functional products
Low
Medium
Medium High Low
High
Medium High
“Dream” products (0%)
Low
Low
Medium High
Medium
Low
20-40% Medium
Cluster 2: Physically Responsive (37%) for “intermediate” products
Medium High Low
High
Medium
Cluster 3: Market Responsive (51%) for innovative products
High Low
Low
Medium High
High > 40%
To avoid or only Make-to-order for “suicide” products (3%)
Low
Medium
Medium High Low
High
Medium
Cluster 3: Market Responsive (7%) for innovative products
High
Fig. 1. Responsiveness requirements of toy products.
supply chain. Another important observation is that, only these nine products had low demand variability and the remaining products had medium to high demand variability. This means that demands for most toy products were highly variable. Cluster 2 consists of toy products with medium forecast uncertainty, medium contribution margin, and medium demand variability. Approximately 37% of the analyzed products were ‘‘intermediate’’ products. As mentioned earlier, ‘‘intermediate’’
products are neither functional nor innovative. Therefore, the forecast uncertainty and demand variability of ‘‘intermediate’’ products are not as high as the innovative products. This situation allows for investment in finished good inventory based on a forecast at bearable risks via a physically responsive supply chain (Li and O’Brien, 2001). The company’s product differentiation model was actually a physically responsive supply chain; thus, these products were supplied with an appropriate level of responsiveness.
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Cluster 3, which is suitable for a market responsive supply chain, is represented by three groups of innovative products in Fig. 1. These innovative products had medium forecast uncertainty and medium contribution margin but high demand variability, medium forecast uncertainty but high contribution margin, and medium to high contribution margins and demand variability. Cluster 3 represents 58% of the analyzed products, but unfortunately they were not supplied with the right level of responsiveness. Besides the above three clusters, the analysis also discovered two other types of products— ‘‘dream’’ and ‘‘suicide’’ products. ‘‘Dream’’ products are products with a low forecast uncertainty and yet high contribution margins. However, the analysis found no such product. This situation is explained by keen competition and innovativeness in the toy market. These products, if they exist, are called ‘‘dream products’’ because the toy industry has difficult to create toy products with potentially high profit margin but low forecast uncertainty. ‘‘Suicide’’ products are products with high forecast uncertainty but low contribution margin. There was 3% of such product. In fact, these products were not expected to face high forecast uncertainty, but the forecast uncertainty and demand variability were increased by the unexpected competition. It is neither viable to produce these products in advance as physical stock nor effective to invest in a market responsive supply chain. Thus, these products are best avoided or made to order (MTO).
6. Assess responsiveness of the company’s product differentiation model In this section, the required and provided responsiveness levels are compared. Generally, a toy supply chain consists of the supplies of raw material and components, the molding and decoration of components, the packing of components to become finished goods, and the distribution of finished goods to the retailers. These different activities were managed by the toy company with three types of supply chains, as illustrated in Fig. 2. The first supply chain was
mainly for toy products with special orders, for example promotional toys for the food chains. These products were MTO or supplied by a typical physically efficient supply chain. Few products, especially those for Christmas campaigns, were assembled to order (ATO). Unfortunately not many products could be managed in this way. Indeed, most products (95%) were supplied by the MTS supply chain, and retailer orders were delivered from the distribution centers (DC). The company’s product differentiation model under the responsive assessment dealt with the MTS supply chain. For the MTS supply chain, toy products were produced as stock and pushed to the distribution centers according to forecasted demand plus appropriate buffers. Therefore, from theoretical perspective, the company’s product differentiation model considered toy products as functional and supplied these products by a physically responsive supply chain. The company’s product differentiation model did not have a consistent set of determinants. It was mainly based on the perceptions of sales people; for example, important products from a marketing perspective should be served with higher service levels. The main advantages of the company’s product differentiation model were the ease of coordination and communication of priority. Furthermore, the product differentiation model was a simple one based on service levels. Toy products were supplied with 95%, 85%, and 70% service levels, respectively, for A, B, and C products. A and B products were supplied with higher service levels because they were normally new products of the year. A product might be downgraded to a B product when it began to be phased out, and consequently the same for the B to C products. Because A products required the highest service level, they were produced most frequently (usually twice per month) and given the highest priority during tight capacity. A products were buffered to meet the highest service level requirement; therefore this represents the extreme level of physically responsiveness. Conversely, C products were not buffered against demand uncertainty and variability, and they might be produced once every one or two months. Therefore, C products were considered physically responsive, but closer on the continuum to the physically efficient supply chain.
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Supply chain (1): MTO supply chain for special order/ Event products Design
Supply
Mould
Supply chain (2): ATO supply chain for some campaign products Supply chain (3): MTS supply chain for most products CDC
Pack
Legend: Product flow
DC
Retailers
Service Level for MTS Supply Chain
Buffer of Finished Goods at Distribution Centers (DC)
A
95%
Yes
CDC Center Distribution Center
B
85%
No
DC
C
70%
No
Order flow
Distribution Center
Fig. 2. The company’s product differentiation model and supply chain strategy.
The responsiveness provided by the company’s product differentiation model is assessed based on understanding of the company’s product differentiation model and supply chain strategy. Table 2 compares the required levels of responsiveness with the provided levels of responsiveness. Two sets of data are presented in order to visualize the differences between the percentage of products and the percentage of sales. This is important because many products can be at a particular responsiveness level, but they might contribute to only a small amount of sales. Overall, the toy products in clusters 1 and 3 were not supplied with the appropriate level of responsiveness. Referring to cluster 1, only 1% of the analyzed products was suitable for a physically efficient supply chain, but they were supplied by a physically responsive supply chain instead. Cluster 2 is comprised of products supplied with the appropriate responsiveness. These were ‘‘intermediate’’ products, which were matched with a physically responsive supply chain. They represented 37% of the analyzed products and contributed to 51% of the sales. These products were mostly new products of the year, supplied with high service levels. Subsequently, approximately 58% of the analyzed products were innovative products, which contributed to 45% of the sales. These products were suitable for a market responsive supply chain, but they were supplied by a physically responsive
supply chain instead. Market responsive strategy was not applied in the toy supply chain mainly due to the long production lead-time (4–6 weeks frozen horizon) and low demand visibility. The company was unable to postpone packing by means of buffering packing capacity and unable to provide the right strategic components. This assessment result was presented and discussed with the managers in the toy company. Subsequently, the use of the risk-influencing determinants in addition to the product differentiation approach has assisted the company in verifying the need for a market responsive supply chain, which triggers the needs for lead-time reduction, sharing and using demand information and flexibility. This assessment result has allowed them to verify the opportunity to reap higher profits from their innovative products. Unfortunately, this opportunity had been ignored previously by their product differentiation method. This explains the nature of the low stock turnover and high level of obsolete inventory in the toy supply chain. On the contrary, with a more market responsive supply chain, the company is expected to gain an increase in sales and profitability instead of suffering from excessive lost sales and obsolete inventory. This assessment also leads the company to a new product differentiation model for, which calls for a market responsive supply chain for 58% of the toy
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Table 2 Assessment of company’s product differentiation model Required levels of responsiveness
Types of data
Provided responsiveness Aa
Ba
Ca
Totalb
Cluster 1: Physically efficient supply chain for functional products
% of Product % of Sales
0 0
0 0
1 1
1 1
Cluster 2: Physically responsive supply chain for intermediate products
% of Product % of Sales
2 14
18 27
17 10
37 51
Cluster 3: Market responsive supply chain for innovative products
% of Product % of Sales
3 5
12 22
43 18
58 45
a Products with service level A, B and C above are supplied with physically responsive supply chain with different service levels, provided by the company’s product differentiation model. b Another 3% of the analyzed products (the ‘‘suicide’’ products) are excluded in this table.
MTO supply chain for special order/ Event products
Design
ATO supply chain for some campaign and innovative products MTS supply chain for “intermediate” and innovative products
Supply
Mould
Pack
DC Physically Responsive
Buffer packing capacity for ATO innovative products
Legend: Product flow Order flow CDC Center Distribution Center DC
CDC
Retailers
Market Responsive (Initial delivery) Market Responsive (Subsequent delivery)
Distribution Center
Fig. 3. Proposed new product differentiation model.
products and a physically responsive supply chain for 37% of the products. The new product differentiation model with the addition of a market responsive supply chain is shown in Fig. 3. For practical reasons, a simplified model with manageable numbers of supply chains is proposed. The purpose of the physically responsive supply chain (MTS) is to supply products with adequate inventory to meet high service levels and short lead-times. Market responsive supply chain is supplied with a hybrid of MTS and ATO strategies in which initial delivery is made-to-stock
(MTS), and subsequent delivery is ATO. The market responsive strategy is only feasible if the packing lead-time is substantially reduced, and the buffers of components are adequate and reliable. This assessment result has assisted the toy company in investing in time reduction, sharing and using demand information, and being flexible. As a result, a lead-time reduction program is initiated. Especially for the peak season, the time window of delivery should be reduced to one week. With shorter lead-time, ‘‘intermediate’’ products and innovative products (initial delivery) are
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expected to produce less obsolete inventory and to achieve a better service level. Similarly, shorter lead-time allows subsequent delivery for the market responsive supply chain. Moreover, new products can be added to the product portfolio by using the four risk-influencing determinants and the framework of responsiveness as shown earlier in Table 1. The company has also identified operational constraints which resist the transformation from a physically responsive to a market responsive supply chain. For example, lead-times of components from outsourced suppliers (especially Far-east) have become the main concern. Furthermore, estimating forecast uncertainty for new products and seasonal products is another difficulty. Hence, the toy company is in the process of removing these obstacles and implementing lead-time reduction programs. Furthermore, assessing responsiveness and improving the product differentiation model has become a continuous effort in the toy company.
7. The proposed extension of Fisher’s Model The attempt to assess the responsiveness of the toy supply chain has led to additional knowledge concerning Fisher’s Model (1997). The results show that volatile products such as toys did not fit exactly into Fisher’s model. This leads to an extension to Fisher’s model as shown in Fig. 4. Fisher’s theoretical model needs to be adapted to the actual situation in the volatile supply chain. To illustrate the proposed extension, forecast uncertainty and contribution margin are chosen as the main determinants. This is because investments in lead-time reduction are best decided by the knowledge of the levels of forecast uncertainty and contribution margin. This new differentiation model distinguishes five types of products. The upper left and lower right quadrants are suggested by Fisher (1997) as the physically efficient and market responsive supply chains, respectively. However, a physically efficient supply chain is usually not applicable for a highly volatile and seasonal supply chain. Thus, the main addition to Fisher’s model is a physically responsive supply chain for ‘‘intermediate’’ pro-
719
Forecast Uncertainty Low
Low Contribution Margins
High
High
Physically Efficient Supply Chain for Functional Products
MTO for “Suicide” Products
Physically Responsive for ”Intermediate” Products Physically Responsive Supply Chain for “Dream” Products
Market Responsive Supply Chain for Innovative Products
Fig. 4. Extension of Fisher’s Model for volatile supply chain.
ducts, located in the middle of the new model. ‘‘Intermediate products’’ usually have medium forecast uncertainty and contribution margins, such that the risk of investment in a finished goods inventory can be offset by shorter lead-time. The term ‘‘physically responsive supply chain’’ is originally proposed by Li and O’Brien (2001) as a MTS strategy. This new differentiation model further allocates ‘‘intermediate’’ products to this strategy and defines their characteristics. The products located in the upper right quadrant are called ‘‘suicide’’ products. These products emerge due to high demand uncertainty and demand variability, but low contribution margins. These products should be avoided, or they should be made-to-order (MTO). This is because products with low contribution margins but high forecast uncertainty are relatively riskier to invest in finished goods inventory. Therefore, careful monitoring of forecast uncertainty may prevent manufacturers from investing in these ‘‘suicide’’ products. The lower left quadrant is for the ‘‘dream’’ products, because volatile supply chains rarely manage to design innovative products with high contribution margins but low forecast uncertainty. Whenever any manufacturer introduces such a ‘‘dream’’ product, many other manufacturers may imitate the idea and introduce similar products with fierce price competition, which may quickly
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drive the contribution margins down but increase the forecast uncertainty. Overall, the product differentiation model above can be used for both functional (commodity) products and fashionable (volatile) products. However, as forecast uncertainty and contribution margins are used as the critical determinants, this model is more suitable for the volatile and seasonal products. These are products for ‘‘fashion markets’’ that have short life cycles, low demand predictability, high volatility and high impulse purchase, as defined by Christopher et al. (2004). Therefore, this model is applicable to fashion clothing and skiwear (Ghemawat and Beuno, 2003), Christmas goods including toys (Chen, 2001; Johnson, 2001), and even fashionable power tools (Fisher et al., 1994).
8. Conclusion Ever since Fuller et al. (1993) discovered the need for tailoring products with appropriate logistics services and the risk of an averaging mindset, the research for differentiating products has been extended to the supply chain by Fisher (1997). This paper further extends this knowledge, especially for a volatile and seasonal supply chain. In addition to the empirical example from the lighting industry (Childerhouse et al., 2002), this is another attempt to differentiate supply chains with multiple determinants; perhaps, it is the first of its kind for volatile toy supply chains. The responsiveness assessment has revealed the need for a physically responsive supply chain for ‘‘intermediate’’ products, and a market responsive supply chains for innovative products, while the toy company chose to supply almost all toy products with a physically responsive supply chain. This variance led to the proposal of a new product differentiation model for the company, and transformation towards a more responsive supply chain for the volatile toy supply chain. In addition, this paper provides three new theoretical contributions applicable to the volatile and seasonal products or fashion markets. First, the case has verified the effectiveness of the framework of assessment, especially the ap-
proaches for selecting suitable determinants and for assessing the responsiveness of a product differentiation model. Forecast uncertainty, demand variability, time window of delivery, and contribution margin are found to be the most relevant determinants for the volatile toy supply chain. Second, responsiveness frameworks from Fisher (1997) and Li and O’Brien (2001) are combined (Table 1) to enable a responsiveness assessment of the volatile toy supply chain. In this new responsiveness assessment framework, suitable determinants and their thresholds are established. Finally, the assessment has provided insights for an extension of Fisher’s framework (Fig. 4). New classifications such as the ‘‘intermediate products’’ for physically responsive supply chain, the ‘‘suicide’’ products and the ‘‘dream’’ products are added. To further enhance the knowledge of product differentiation, it is suggested to research on how to improve the management of different types of products and how to trade-off between capacity and inventory (Stratton and Warburton, 2003) for a volatile and seasonal supply chain.
References Aitken, J., Childerhouse, P., Towill, D., 2003. The impact of product life cycle on supply chain strategy. International Journal of Production Economics 85, 127–140. Chen, J., 2001. Coordination of the supply chain of seasonal products. IEEE Transaction on System, Man and Cybernetics—Part A: System and Humans 31 (6), 524–532. Childerhouse, P., Aitken, J., Towill, D.R., 2002. Analysis and design of focused demand chains. Journal of Operations Management 20 (6), 675–689. Christopher, M., Lawson, R., Peck, H., 2004. Creating agile supply chains in the fashion industry. International Journal of Retail & Distribution Management 32 (8), 367–376. Christopher, M., Towill, D., 2001. An integrated model for the design of agile supply chains. International Journal of Physical Distribution and Logistics Management 31 (4), 235–246. Fisher, M., 1997. What is the right supply chain for your product? Harvard Business Review 75 (2), 105–117. Fisher, M., Hammond, J.H., Obermeyer, W.R., Raman, A., 1994. Making supply meet demand in an uncertain world. Harvard Business Review 72 (3), 83–92. Fisher, M., Raman, A., 1996. Reducing the cost of demand uncertainty through accurate response to early sales. Operations Research 44 (1), 87–99.
ARTICLE IN PRESS C.Y. Wong et al. / Int. J. Production Economics 104 (2006) 709–721 Fuller, J.B., O’Conor, J., Rawlinson, R., 1993. Tailored logistics: The next advantage. Harvard Business Review 71 (3), 87–96. Gattorna, J.L., Walters, D.W., 1996. Managing the Supply Chain—A Strategic Perspective. Macmillan, London. Ghemawat, P., Beuno, J.L., 2003. Zara: Fast fashion. Harvard Business Case, April 2003, pp. 1–35. Hayes, R.H., Wheelwright, S.C., 1979. Linking manufacturing process and product life cycle. Harvard Business Review 57 (1), 133–140. Johnson, M.E., 2001. Learning from toys: Lessons in managing supply chain risk from the toy industry. California Management Review 43 (3), 106–124. Lamming, R., Johnsen, T., Zheng, J., Harland, C., 2000. An initial classification of supply networks. International Journal of Operations and Production Management 20 (6), 675–691. Lawson, B., King, R., Hunter, A., 1999. Quick Response: Managing Supply Chain to Meet Customer Demand. Wiley, West Sussex, England. Li, D., O’Brien, C., 2001. A quantitative analysis of relationships between product types and supply chain strategies. International Journal of Production Economics 73 (1), 29–39.
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Mason-Jones, R., Nylor, J.B., Towill, D., 2000. Engineering the leagile supply chain. International Journal of Agile Management Systems 2 (1), 54–61. Newman, W.R., Sridharan, V., 1995. Linking manufacturing planning and control to the manufacturing environment. Integrated Manufacturing System 6 (4), 36–42. Pagh, J.D., Cooper, M.C., 1998. Supply chain postponement and speculation strategies: How to choose the right strategy. Journal of Business Logistics 19 (2), 13–34. Skinner, C.W., 1974. The focused factory. Harvard Business Review 52 (3), 113–121. Stratton, R., Warburton, R.D.H., 2003. The strategy of agile and lean supply. International Journal of Production Economics 85, 183–198. Towill, D., Christopher, M., 2003. A taxonomy for selecting global supply chain strategies, Proceedings of the 10th International Conference of European Operations Management (EUROMA), Italy, vol. I, pp. 817–826. Van Hoek, R., 1998. Reconfiguring the supply chain to implement postponed manufacturing. International Journal of Logistics Management 9 (1), 95–111. Vollmann, T.E., Berry, W.L., Whyback, D.C., 1997. Manufacturing Planning and Control Systems. Irwin/McGraw Hill, Boston.
Int. J. Production Economics 104 (2006) 709–721 www.elsevier.com/locate/ijpe
Assessing responsiveness of a volatile and seasonal supply chain: A case study Chee Yew Wonga,, Jan Stentoft Arlbjørnb, Hans-Henrik Hvolbyc, John Johansena a
Center for Industrial Production, Aalborg University, Fibigerstræde 16, DK-9220 Aalborg, Denmark b LEGO System A/S, Global Supply Chain, DK-7190 Billund, Denmark c Department of Production, Aalborg University, Fibigerstræde 16, DK-9220 Aalborg, Denmark Received 16 April 2004; accepted 3 December 2004 Available online 16 February 2005
Abstract This paper describes a structural approach to assess the responsiveness of a volatile and seasonal supply chain. It is based on a case study in an international toy company. Fisher’s (Harvard Bus. Rev. 75(2) (1997) 105–117) Model of ‘‘innovative’’ and ‘‘functional’’ products and the corresponding ‘‘market responsive’’ and ‘‘physically efficient’’ supply chains constitutes the backbone of this assessment. Four risk-influencing determinants—forecast uncertainty, demand variability, contribution margin, and time window of delivery are found suitable to assess the responsiveness of the toy supply chain. Assessment of the company’s product differentiation model shows that toy products are mostly innovative or ‘‘intermediate’’, but not functional. A proposed new product differentiation model using risk-influencing determinants has enabled the toy company to differentiate its new products, to deal with volatility, and to design for a responsive supply chain. These findings have also enabled the extension of Fisher’s Model to volatile supply chains. This new product differentiation model adds a physically responsive supply chain for ‘‘intermediate’’ products into the Fisher’s Model. r 2005 Elsevier B.V. All rights reserved. Keywords: Responsiveness; Supply chain strategy; Product differentiation; Supply chain planning; Toy industry
1. Introduction The need for differentiating products and managing them in different ways has been well recognized Corresponding author. Tel.: +45 9635 7100;
fax: +45 9815 3040. E-mail address: [email protected] (C.Y. Wong).
(Fuller et al., 1993; Fisher, 1997; Mason-Jones et al., 2000; Li and O’Brien, 2001; Childerhouse et al., 2002; Towill and Christopher, 2003). Subsequently, available literature has established simple rules to differentiate supply chains based on strategic determinants. Generally, functional products with stable demands are supplied by a lean production or a physically efficient supply chain; while innovative
0925-5273/$ - see front matter r 2005 Elsevier B.V. All rights reserved. doi:10.1016/j.ijpe.2004.12.021
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products with volatile demands are supplied by a market responsive or an agile supply chain (Fisher, 1997; Christopher et al., 2004). This approach to differentiation is commonly known as Fisher’s Model (Fisher, 1997). Li and O’Brien (2001) further extended Fisher’s Model (Fisher, 1997) by adding a physically responsive strategy (make-to-stock (MTS)) and considered make-to-order (MTO) as a physically efficient supply chain and make-fromstock (MFS) as a market responsive supply chain. There are several empirical applications of the knowledge on differentiated supply chains. An excellent example is the case study by Childerhouse et al. (2002) on a lighting manufacturer. This case study presents a structured framework for practitioners to differentiate lighting products by using the concept of ‘‘focused demand chain’’. In this case study, light products are differentiated by five determinants: duration of product life cycle, time window for delivery, volume, product variety and variability. Childerhouse et al. (2002) have also demonstrated an improvement in supply chain performance due to this approach. The lighting case is in fact suitable for products with nonseasonal, long life cycles (4–6 years) and relatively predictable demands. However, volatile products such as fashion clothing and toys have relatively shorter product life cycles (from 6 months to 2 years), and they are subjected to higher seasonality and demand uncertainty. Empirical research on differentiating volatile and seasonal products has not been widely explored. As a result, three key questions on how to differentiate volatile and seasonal supply chains arise: (1) what determinants are significant for volatile, seasonal and short-lived products? (2) What level of responsiveness is required for a volatile and seasonal supply chain? (3) Are all volatile products ‘‘innovative’’ and can they be effectively supplied by a market-responsive supply chain? These questions are partly answered by the current literature. For instance, supply chain design for volatile and seasonal products is determined greatly by forecast uncertainty and contribution margin (Fisher, 1997). Fisher (1997) further classifies volatile and seasonal products as ‘‘innovative’’ and motivates the need for a market responsive supply chain. In addition, a case study
on highly seasonal skiwear has demonstrated the need for an accurate response strategy by means of reading early sales-data and shortening lead-times (Fisher and Raman, 1996). However, as industrial examples are rare, there is no unvarying agreement yet on which set of determinants are suitable for which industries at which levels of volatility. As a response to the above research questions, this paper studies how volatile and seasonal products can be differentiated. A toy company is chosen as a case of a volatile and seasonal supply chain. This company mainly produces construction toys based on a MTS (forecast) strategy. In the recent years, toy demand has become highly unpredictable and fashion driven. This situation leads to higher forecast errors and shorter product life cycles, and generates excessive inventory and markdowns. Therefore, the case company faces increasing competition and declining profit margin, and they are searching for an improved differentiation strategy, particularly the opportunity to be more responsive. In this case study, a structured framework of responsiveness assessment is used to determine the required responsiveness levels in order to improve the company’s product differentiation model. This structured framework is further explained in the following section.
2. Structured framework of assessment A structured framework of assessment is used to guide the process in defining the required levels of responsiveness and then to assess the responsiveness provided by the company’s product differentiation model. It consists of the following five major steps: 1. 2. 3. 4.
choose significant determinants; develop a framework of responsive assessment; determine the required level of responsiveness; assess responsiveness provided by the company’s product differentiation model; 5. propose an improved product differentiation model. Step one refers to the choice of a significant and relevant set of determinants that allows the
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company to define the required levels of responsiveness. Suitable determinants are chosen via a careful study on literature of differentiating (or clustering) supply chains, especially the DWV3 (demand variability, time window of delivery, volume, variety, variability) used by Towill and Christopher (2003) and Childerhouse et al. (2002). The choice of suitable determinants is further guided by three agreed principles which consider customer needs, investment risk, seasonality and product life cycles. Then the chosen determinants and the theories for differentiated responsive supply chains (Fisher, 1997; Li and O’Brien, 2001) are used to develop a framework of responsiveness assessment (step two). This theoretical framework allows the required levels of responsiveness to be defined by the respective sets of thresholds for the chosen determinants. This framework defines three levels of responsiveness: the physically efficient, the physically responsive and the market responsive supply chains. It also delineates how these supply chains can be managed at strategic and operational levels. In step three, this framework is used to define the required responsiveness levels by analyzing the demand characteristics of the toy supply chain. Data on demand characteristics are collected based on the chosen determinants from the case company, and then products with certain characteristics are positioned into the framework of responsiveness assessment. In step four, the responsiveness provided by the company’s product differentiation model is then compared with the required levels of responsiveness. Overall, these four structured steps have allowed understanding of the degree of matching or mismatching of the company’s product differentiation model with the required responsiveness and have enabled development of a new product differentiation model. Thus, the structured framework of responsiveness assessment is considered pragmatic and valuable by the toy company.
3. Choose significant determinants for assessing responsiveness Literature on product differentiation frameworks has suggested several suitable determinants
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for differentiating products (Pagh and Cooper, 1998; Lamming et al., 2000; Mason-Jones et al., 2000; Christopher and Towill, 2001; Childerhouse et al., 2002). From these frameworks, it is learned that the choices of supply chain strategies are basically dependent on determinants related to market and product characteristics. In this paper, a suitable set of determinants was chosen by the toy company, based on three principles. First, the set of determinants shall meet customer requirements. Second, the set of determinants shall enable the company to make investment trade-off decisions (capacity, production, inventory, etc.) based on risk considerations. Finally, the set of determinants shall reflect the dynamics of seasonality and product life cycles. The second and third principles above are essential for a highly volatile and seasonal supply chain. In a volatile supply chain, determinants must enable trade-off assessments between the level of investment on responsiveness and the risks of obsolete inventory, lost sales and markdowns (Fisher, 1997). Furthermore, seasonality and lifecycle factors are significant because demands for volatile and seasonal products vary greatly among different seasons and life cycles (Hayes and Wheelwright, 1979). Subsequently, based on the above principles, four suitable determinants are chosen and agreed by the toy company. They are forecast uncertainty, demand variability, contribution margin and time window of delivery. Forecast uncertainty is the level of uncertainty that a demand forecast can be realized. Forecast uncertainty influences investment risk directly because it affects the levels of obsolete inventory, lost sales and markdowns (Fisher, 1997). Generally, forecast errors for toy products are high due to unexpected consumer trends, supply uncertainty and competition (Johnson, 2001). The risks of lost sales and inventory obsolescence increase whenever forecast uncertainty increases. Therefore, forecast uncertainty is chosen to be the main risk factor for making investment decisions on production and inventory. Demand variability refers to the day-to-day or week-to-week variability of demand faced by the manufacturers and the retailers. Levels of demand variability influence the choices of inventory
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policies and production strategies. For example, Just-In-Time (JIT) line production is more sensitive to demand variability than is batch production with material requirement planning (MRP) (Newman and Sridharan, 1995). If demands vary greatly from one period to another, the need for volume flexibility is definite. It is necessary to include demand variability as a determinant for the toy supply chain, because the demands for different toy products vary greatly from one period to another. Spiky toy demands are usually generated during product introduction or high season, and dramatic troughs may follow anytime thereafter. Because demand variability is one of the chosen determinants, volume consideration is actually included. High-volume demand allows for a lean production strategy (Childerhouse et al., 2002) and a MTS strategy (Vollmann et al., 1997). Understanding of volume and demand variability enables the separation of base and surge demands (Gattorna and Walters, 1996). Base demands are more predictable and can be fulfilled by a physically efficient or a physically responsive strategy or even outsourced to low-cost countries, while surge demands can be fulfilled by a market responsive strategy or a focused factory (Skinner, 1974; Gattorna and Walters, 1996). These principles are applicable especially during preparation for product introduction and high season, as predictable and unpredictable portions of the volume may be separated and hybrid supply chain solutions may be utilized. A high contribution margin is regarded as the incentive for investing in innovative products (Fisher, 1997). Generally, innovative products have higher contribution margins but higher forecast uncertainty. Such products require a higher level of responsiveness so that lead-time is short enough to reap the high contribution margins despite high forecast uncertainty. As indicated earlier, the nature of high forecast uncertainty and demand variability shows that it is necessary to consider the contribution margin to support the investment decisions in lead-time reduction and excess buffers. Time window of delivery refers to the lead-time given by the retailers from order to delivery. Time window of delivery is an important determinant as
it reflects one of the customer requirements (Fisher, 1997). It also reflects the probability that customers will cancel orders or purchase other products instead of waiting, as retailers’ shelves are not allowed to be empty. Competitors with faster response times might take away shelf space if a toy manufacturer is slower in response. Consequently, the extent to which production can be postponed (or speculated otherwise) depends greatly on time window of delivery, and visibility and lead-times of demand information. The above four chosen determinants were validated by the toy company in order to ensure their significance. However, the company decided to exclude determinants such as duration of product life cycle, stage of product life cycle, volume, and product variety, though they are considered as important determinants for assessing responsiveness by the literature (Hayes and Wheelwright, 1979; Fisher, 1997; Mason-Jones et al., 2000; Childerhouse et al., 2002; Aitken et al., 2003). Even so, these determinants have not been included due to the following reasons. For toy products, the duration of product life cycle is not necessarily correlated with forecast uncertainty and demand variability. Even 5–6-year-old products may still have high forecast errors during the peak season. On the other hand, short-lived products may have lower forecast errors. Obviously, the stage of the product life cycle influences the volume and requirements on leadtime and service levels. However, the use of the product life cycle as a determinant might contradict forecast uncertainty; therefore, product life cycle is excluded for the purpose of assessing responsiveness. In addition, product variety might be another important determinant for the toy supply chain because it influences the extent to which postponement can be achieved. However, it is currently difficult to define what variety means for construction toys. Bricks inside a box of toy product can be installed in many different ways and thus have high variety. Product variety is normally built within the finished goods (normally in a box with many different bricks). Even though product variety is left out in this paper, it is still believed to be an important factor to be included in future research.
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4. Develop a framework of responsiveness assessment Using the above chosen determinants and the theories on differentiated responsive supply chains (Fisher, 1997; Li and O’Brien, 2001), a framework of responsiveness assessment is developed. Table 1 illustrates the established framework with three different clusters of supply chains. Each cluster is defined by a range of determinants and supplied with a specific supply chain. Each supply chain has its strategic purpose and operations approach. Cluster 1 involves functional (Fisher, 1997) or commodity-like products. These products have low forecast uncertainty and demand variability. However, their contribution margins are usually low due to ruthless price competition. As their demand is more predictable, the main focus is to maintain high utilization of assets, to maintain high inventory turnover, and to reduce lead-time as long as it does not increase cost. This strategy
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may be called a physically efficient supply chain (Fisher, 1997). However, the level of efficiency for functional products depends greatly on the acceptable lead-times. A longer time window of delivery allows a MTO environment in which production is scheduled according to customer orders without investment in raw material and finished goods inventory. As the acceptable lead-time decreases, manufacturers may seek to improve lead-times provided there is no increase in total costs. The acceptable lead-time for retailers is usually short because many consumer goods are kept on the shelves for sales. Therefore, a MTS strategy becomes necessary. Because the demand for these products is predictable, MTS manufacturers can usually commit to a long production frozenhorizon (usually one month or longer). Cluster 2 represents products with medium forecast uncertainty, contribution margins and demand variability, but short time window of delivery. These products have characteristics
Table 1 Framework of responsiveness assessment Strategic and operational levels of responsiveness/ efficient
Cluster 1: Physically efficient supply chain for ‘‘functional’’ products
Cluster 2: Physically responsive supply chain for ‘‘intermediate’’ products
Cluster 3: Market responsive supply chain for ‘‘innovative’’ products
Determinants Forecast uncertainty Demand variability, (c.o.v.)a Contribution margin Time window for delivery
Low (o20%) Low (c.o.v.o0.5) Low Long (44 weeks)
Medium (20–40%) Medium (0.5oc.o.v. o1.5) Medium Short (o5 days)
High (440%) High (c.o.v. 41.5) High Short (o5 days)
Supply predictable demand efficiently at the lowest possible physical cost
Supply with adequate inventory to meet high service level and lead-time
Response quickly to unpredictable demand to minimize stock-out, forced markdown and obsolete inventory
Operational level Manufacturing focus
High asset utilization
Deploy excess buffer capacity
Inventory strategy
High inventory turns
Balance asset utilization and capacity buffer Intermediate inventory buffers
Lead-time
Shorten lead-time as long as not increasing costs MTO or MTS Packaging/molding/ distribution centers
Strategic purpose of the supply chain
Manufacturing process Order penetration point
Source: Adapted from Fisher (1997) and Li and O’Brien (2001). a c.o.v. ¼ coefficient of variant of demand.
Invest moderately to reduce lead-time MTS Distribution centers
Deploy significant buffer of parts and finished goods Invest significant to reduce lead-time MTS and ATO Packaging/distribution centers
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between the functional and innovative products; therefore, they are called ‘‘intermediate’’ products. These products demand for a very short lead-time, thus a MTS strategy becomes necessary. This strategy allows investment in finished good inventory based on a forecast at bearable risks, which can be called a physically responsive supply chain (Li and O’Brien, 2001). This strategy requires a medium level of contribution margin as the risk premium for the changes in demand mix. Furthermore, risk from forecast uncertainty and demand variability can be reduced by using real time demand information and frequent replenishment strategy. Cluster 3 is suitable for innovative products. Whenever the forecast uncertainty and demand variability are high, and the time window of delivery is short, manufacturers must respond quickly to the unpredictable demand in order to minimize stock-out, forced markdown and obsolete inventory. This strategy may be called a market responsive supply chain (Fisher, 1997). A market responsive supply chain requires capacity and inventory buffers as well as an investment to reduce lead-times. A high contribution margin is the incentive for using such strategy, because investments in buffer and lead-time reduction are normally substantial. To mitigate the risk of excess investment, manufacturers may seek to secure visibility of real-time demand and supply information so that postponement by an assembly-toorder (ATO) strategy can be employed (Pagh and Cooper, 1998; Van Hoek, 1998). This is because visibility of the real-time demand information may enable the use of a rapid or quick response strategy (Fisher, 1997; Lawson et al., 1999), in which production of finished goods can be postponed by speculating the mix of components. The three clusters of supply chains above are distinguished by thresholds of the four determinants. Applicability of these thresholds varies. The thresholds for forecast uncertainty and time window of delivery may be applied to any product, because they are similar to the Fisher’s Model (Fisher, 1997). The thresholds for contribution margin cannot be revealed because they are sensitive figures. The thresholds for demand variability for the toy industry are measured by
the coefficient of variant (c.o.v.) of demand. Demand variability is a measure of the bullwhip effect and it can be very industry specific, depending on the batch-sizing and reordering practices. As such, every industry has its own level of volatility and seasonality; therefore, different industries may have different sets of thresholds. However, the proposed thresholds are suitable for fashionable and seasonal products such as clothing, skiwear and toys because they have similar demand patterns. Using this framework and the four chosen determinants, the required levels of responsiveness for the toy products is defined in the next section. However, not all products are expected to fall into all the thresholds for one of the three clusters of responsiveness. Thus, hybrid solutions may be necessary, especially when one product has one demand characteristic in one season and other demand characteristics in other seasons.
5. Determine the required levels of responsiveness The required levels of responsiveness are determined based on 18 months of data (2002/3) from 667 toy products. This sample size is considered adequate because it represents 80% of the total sales. Fig. 1 illustrates how these responsiveness levels are established based on the determinants and predetermined thresholds. Determinants are shown on the left side and the corresponding products are positioned on the right side of Fig. 1 to show how a near perfect match between the responsiveness framework and the toy products is achieved. Time window of delivery is left out because most products had similar lead-times, but varied greatly in different seasons. Overall, the toy company needed 37% physically responsive and 58% market responsive supply chains. Cluster 1 is located at the regions with low forecast uncertainty in Fig. 1. There are only nine functional products (1%) with low forecast uncertainty, contribution margin and demand variability in this cluster. This is because most toys have short product life cycles and their demands are influenced by fashions. These nine functional products were suitable for a physically efficient
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Demand Variability
Contribution Margin
Forecast Uncertainty
Products matching responsiveness level
0%
Low
Low
Medium High
Low < 20%
715
Products not matching responsiveness level Percentage of Analyzed Products 10%
20%
30%
40%
Cluster 1: Physically Efficient (1%) for functional products
Low
Medium
Medium High Low
High
Medium High
“Dream” products (0%)
Low
Low
Medium High
Medium
Low
20-40% Medium
Cluster 2: Physically Responsive (37%) for “intermediate” products
Medium High Low
High
Medium
Cluster 3: Market Responsive (51%) for innovative products
High Low
Low
Medium High
High > 40%
To avoid or only Make-to-order for “suicide” products (3%)
Low
Medium
Medium High Low
High
Medium
Cluster 3: Market Responsive (7%) for innovative products
High
Fig. 1. Responsiveness requirements of toy products.
supply chain. Another important observation is that, only these nine products had low demand variability and the remaining products had medium to high demand variability. This means that demands for most toy products were highly variable. Cluster 2 consists of toy products with medium forecast uncertainty, medium contribution margin, and medium demand variability. Approximately 37% of the analyzed products were ‘‘intermediate’’ products. As mentioned earlier, ‘‘intermediate’’
products are neither functional nor innovative. Therefore, the forecast uncertainty and demand variability of ‘‘intermediate’’ products are not as high as the innovative products. This situation allows for investment in finished good inventory based on a forecast at bearable risks via a physically responsive supply chain (Li and O’Brien, 2001). The company’s product differentiation model was actually a physically responsive supply chain; thus, these products were supplied with an appropriate level of responsiveness.
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Cluster 3, which is suitable for a market responsive supply chain, is represented by three groups of innovative products in Fig. 1. These innovative products had medium forecast uncertainty and medium contribution margin but high demand variability, medium forecast uncertainty but high contribution margin, and medium to high contribution margins and demand variability. Cluster 3 represents 58% of the analyzed products, but unfortunately they were not supplied with the right level of responsiveness. Besides the above three clusters, the analysis also discovered two other types of products— ‘‘dream’’ and ‘‘suicide’’ products. ‘‘Dream’’ products are products with a low forecast uncertainty and yet high contribution margins. However, the analysis found no such product. This situation is explained by keen competition and innovativeness in the toy market. These products, if they exist, are called ‘‘dream products’’ because the toy industry has difficult to create toy products with potentially high profit margin but low forecast uncertainty. ‘‘Suicide’’ products are products with high forecast uncertainty but low contribution margin. There was 3% of such product. In fact, these products were not expected to face high forecast uncertainty, but the forecast uncertainty and demand variability were increased by the unexpected competition. It is neither viable to produce these products in advance as physical stock nor effective to invest in a market responsive supply chain. Thus, these products are best avoided or made to order (MTO).
6. Assess responsiveness of the company’s product differentiation model In this section, the required and provided responsiveness levels are compared. Generally, a toy supply chain consists of the supplies of raw material and components, the molding and decoration of components, the packing of components to become finished goods, and the distribution of finished goods to the retailers. These different activities were managed by the toy company with three types of supply chains, as illustrated in Fig. 2. The first supply chain was
mainly for toy products with special orders, for example promotional toys for the food chains. These products were MTO or supplied by a typical physically efficient supply chain. Few products, especially those for Christmas campaigns, were assembled to order (ATO). Unfortunately not many products could be managed in this way. Indeed, most products (95%) were supplied by the MTS supply chain, and retailer orders were delivered from the distribution centers (DC). The company’s product differentiation model under the responsive assessment dealt with the MTS supply chain. For the MTS supply chain, toy products were produced as stock and pushed to the distribution centers according to forecasted demand plus appropriate buffers. Therefore, from theoretical perspective, the company’s product differentiation model considered toy products as functional and supplied these products by a physically responsive supply chain. The company’s product differentiation model did not have a consistent set of determinants. It was mainly based on the perceptions of sales people; for example, important products from a marketing perspective should be served with higher service levels. The main advantages of the company’s product differentiation model were the ease of coordination and communication of priority. Furthermore, the product differentiation model was a simple one based on service levels. Toy products were supplied with 95%, 85%, and 70% service levels, respectively, for A, B, and C products. A and B products were supplied with higher service levels because they were normally new products of the year. A product might be downgraded to a B product when it began to be phased out, and consequently the same for the B to C products. Because A products required the highest service level, they were produced most frequently (usually twice per month) and given the highest priority during tight capacity. A products were buffered to meet the highest service level requirement; therefore this represents the extreme level of physically responsiveness. Conversely, C products were not buffered against demand uncertainty and variability, and they might be produced once every one or two months. Therefore, C products were considered physically responsive, but closer on the continuum to the physically efficient supply chain.
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Supply chain (1): MTO supply chain for special order/ Event products Design
Supply
Mould
Supply chain (2): ATO supply chain for some campaign products Supply chain (3): MTS supply chain for most products CDC
Pack
Legend: Product flow
DC
Retailers
Service Level for MTS Supply Chain
Buffer of Finished Goods at Distribution Centers (DC)
A
95%
Yes
CDC Center Distribution Center
B
85%
No
DC
C
70%
No
Order flow
Distribution Center
Fig. 2. The company’s product differentiation model and supply chain strategy.
The responsiveness provided by the company’s product differentiation model is assessed based on understanding of the company’s product differentiation model and supply chain strategy. Table 2 compares the required levels of responsiveness with the provided levels of responsiveness. Two sets of data are presented in order to visualize the differences between the percentage of products and the percentage of sales. This is important because many products can be at a particular responsiveness level, but they might contribute to only a small amount of sales. Overall, the toy products in clusters 1 and 3 were not supplied with the appropriate level of responsiveness. Referring to cluster 1, only 1% of the analyzed products was suitable for a physically efficient supply chain, but they were supplied by a physically responsive supply chain instead. Cluster 2 is comprised of products supplied with the appropriate responsiveness. These were ‘‘intermediate’’ products, which were matched with a physically responsive supply chain. They represented 37% of the analyzed products and contributed to 51% of the sales. These products were mostly new products of the year, supplied with high service levels. Subsequently, approximately 58% of the analyzed products were innovative products, which contributed to 45% of the sales. These products were suitable for a market responsive supply chain, but they were supplied by a physically responsive
supply chain instead. Market responsive strategy was not applied in the toy supply chain mainly due to the long production lead-time (4–6 weeks frozen horizon) and low demand visibility. The company was unable to postpone packing by means of buffering packing capacity and unable to provide the right strategic components. This assessment result was presented and discussed with the managers in the toy company. Subsequently, the use of the risk-influencing determinants in addition to the product differentiation approach has assisted the company in verifying the need for a market responsive supply chain, which triggers the needs for lead-time reduction, sharing and using demand information and flexibility. This assessment result has allowed them to verify the opportunity to reap higher profits from their innovative products. Unfortunately, this opportunity had been ignored previously by their product differentiation method. This explains the nature of the low stock turnover and high level of obsolete inventory in the toy supply chain. On the contrary, with a more market responsive supply chain, the company is expected to gain an increase in sales and profitability instead of suffering from excessive lost sales and obsolete inventory. This assessment also leads the company to a new product differentiation model for, which calls for a market responsive supply chain for 58% of the toy
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Table 2 Assessment of company’s product differentiation model Required levels of responsiveness
Types of data
Provided responsiveness Aa
Ba
Ca
Totalb
Cluster 1: Physically efficient supply chain for functional products
% of Product % of Sales
0 0
0 0
1 1
1 1
Cluster 2: Physically responsive supply chain for intermediate products
% of Product % of Sales
2 14
18 27
17 10
37 51
Cluster 3: Market responsive supply chain for innovative products
% of Product % of Sales
3 5
12 22
43 18
58 45
a Products with service level A, B and C above are supplied with physically responsive supply chain with different service levels, provided by the company’s product differentiation model. b Another 3% of the analyzed products (the ‘‘suicide’’ products) are excluded in this table.
MTO supply chain for special order/ Event products
Design
ATO supply chain for some campaign and innovative products MTS supply chain for “intermediate” and innovative products
Supply
Mould
Pack
DC Physically Responsive
Buffer packing capacity for ATO innovative products
Legend: Product flow Order flow CDC Center Distribution Center DC
CDC
Retailers
Market Responsive (Initial delivery) Market Responsive (Subsequent delivery)
Distribution Center
Fig. 3. Proposed new product differentiation model.
products and a physically responsive supply chain for 37% of the products. The new product differentiation model with the addition of a market responsive supply chain is shown in Fig. 3. For practical reasons, a simplified model with manageable numbers of supply chains is proposed. The purpose of the physically responsive supply chain (MTS) is to supply products with adequate inventory to meet high service levels and short lead-times. Market responsive supply chain is supplied with a hybrid of MTS and ATO strategies in which initial delivery is made-to-stock
(MTS), and subsequent delivery is ATO. The market responsive strategy is only feasible if the packing lead-time is substantially reduced, and the buffers of components are adequate and reliable. This assessment result has assisted the toy company in investing in time reduction, sharing and using demand information, and being flexible. As a result, a lead-time reduction program is initiated. Especially for the peak season, the time window of delivery should be reduced to one week. With shorter lead-time, ‘‘intermediate’’ products and innovative products (initial delivery) are
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expected to produce less obsolete inventory and to achieve a better service level. Similarly, shorter lead-time allows subsequent delivery for the market responsive supply chain. Moreover, new products can be added to the product portfolio by using the four risk-influencing determinants and the framework of responsiveness as shown earlier in Table 1. The company has also identified operational constraints which resist the transformation from a physically responsive to a market responsive supply chain. For example, lead-times of components from outsourced suppliers (especially Far-east) have become the main concern. Furthermore, estimating forecast uncertainty for new products and seasonal products is another difficulty. Hence, the toy company is in the process of removing these obstacles and implementing lead-time reduction programs. Furthermore, assessing responsiveness and improving the product differentiation model has become a continuous effort in the toy company.
7. The proposed extension of Fisher’s Model The attempt to assess the responsiveness of the toy supply chain has led to additional knowledge concerning Fisher’s Model (1997). The results show that volatile products such as toys did not fit exactly into Fisher’s model. This leads to an extension to Fisher’s model as shown in Fig. 4. Fisher’s theoretical model needs to be adapted to the actual situation in the volatile supply chain. To illustrate the proposed extension, forecast uncertainty and contribution margin are chosen as the main determinants. This is because investments in lead-time reduction are best decided by the knowledge of the levels of forecast uncertainty and contribution margin. This new differentiation model distinguishes five types of products. The upper left and lower right quadrants are suggested by Fisher (1997) as the physically efficient and market responsive supply chains, respectively. However, a physically efficient supply chain is usually not applicable for a highly volatile and seasonal supply chain. Thus, the main addition to Fisher’s model is a physically responsive supply chain for ‘‘intermediate’’ pro-
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Forecast Uncertainty Low
Low Contribution Margins
High
High
Physically Efficient Supply Chain for Functional Products
MTO for “Suicide” Products
Physically Responsive for ”Intermediate” Products Physically Responsive Supply Chain for “Dream” Products
Market Responsive Supply Chain for Innovative Products
Fig. 4. Extension of Fisher’s Model for volatile supply chain.
ducts, located in the middle of the new model. ‘‘Intermediate products’’ usually have medium forecast uncertainty and contribution margins, such that the risk of investment in a finished goods inventory can be offset by shorter lead-time. The term ‘‘physically responsive supply chain’’ is originally proposed by Li and O’Brien (2001) as a MTS strategy. This new differentiation model further allocates ‘‘intermediate’’ products to this strategy and defines their characteristics. The products located in the upper right quadrant are called ‘‘suicide’’ products. These products emerge due to high demand uncertainty and demand variability, but low contribution margins. These products should be avoided, or they should be made-to-order (MTO). This is because products with low contribution margins but high forecast uncertainty are relatively riskier to invest in finished goods inventory. Therefore, careful monitoring of forecast uncertainty may prevent manufacturers from investing in these ‘‘suicide’’ products. The lower left quadrant is for the ‘‘dream’’ products, because volatile supply chains rarely manage to design innovative products with high contribution margins but low forecast uncertainty. Whenever any manufacturer introduces such a ‘‘dream’’ product, many other manufacturers may imitate the idea and introduce similar products with fierce price competition, which may quickly
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drive the contribution margins down but increase the forecast uncertainty. Overall, the product differentiation model above can be used for both functional (commodity) products and fashionable (volatile) products. However, as forecast uncertainty and contribution margins are used as the critical determinants, this model is more suitable for the volatile and seasonal products. These are products for ‘‘fashion markets’’ that have short life cycles, low demand predictability, high volatility and high impulse purchase, as defined by Christopher et al. (2004). Therefore, this model is applicable to fashion clothing and skiwear (Ghemawat and Beuno, 2003), Christmas goods including toys (Chen, 2001; Johnson, 2001), and even fashionable power tools (Fisher et al., 1994).
8. Conclusion Ever since Fuller et al. (1993) discovered the need for tailoring products with appropriate logistics services and the risk of an averaging mindset, the research for differentiating products has been extended to the supply chain by Fisher (1997). This paper further extends this knowledge, especially for a volatile and seasonal supply chain. In addition to the empirical example from the lighting industry (Childerhouse et al., 2002), this is another attempt to differentiate supply chains with multiple determinants; perhaps, it is the first of its kind for volatile toy supply chains. The responsiveness assessment has revealed the need for a physically responsive supply chain for ‘‘intermediate’’ products, and a market responsive supply chains for innovative products, while the toy company chose to supply almost all toy products with a physically responsive supply chain. This variance led to the proposal of a new product differentiation model for the company, and transformation towards a more responsive supply chain for the volatile toy supply chain. In addition, this paper provides three new theoretical contributions applicable to the volatile and seasonal products or fashion markets. First, the case has verified the effectiveness of the framework of assessment, especially the ap-
proaches for selecting suitable determinants and for assessing the responsiveness of a product differentiation model. Forecast uncertainty, demand variability, time window of delivery, and contribution margin are found to be the most relevant determinants for the volatile toy supply chain. Second, responsiveness frameworks from Fisher (1997) and Li and O’Brien (2001) are combined (Table 1) to enable a responsiveness assessment of the volatile toy supply chain. In this new responsiveness assessment framework, suitable determinants and their thresholds are established. Finally, the assessment has provided insights for an extension of Fisher’s framework (Fig. 4). New classifications such as the ‘‘intermediate products’’ for physically responsive supply chain, the ‘‘suicide’’ products and the ‘‘dream’’ products are added. To further enhance the knowledge of product differentiation, it is suggested to research on how to improve the management of different types of products and how to trade-off between capacity and inventory (Stratton and Warburton, 2003) for a volatile and seasonal supply chain.
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