Product Process Matrix

The product-process matrix, developed by Hayes and Wheelwright in 1979 was designed to show the trade-offs in operations and marketing by linking product plans and process choices. The model is based on traditional trade-offs evident in a single manufacturing facility environment. The product-process matrix has been empirically tested, but improvements in operations flexibility by applying advanced technologies have caused many to question the model’s continued validity. In recent years, the environment has changed significantly, with manufacturing companies offering more product customization as they gain process flexibility.

In addition, the model as originally developed, does not incorporate the supply chain perspective. New models are required that include the entire supply chain as well as the impact of developments in manufacturing. The operations strategy literature discusses the importance of defining the appropriate production process to support the competitive priorities specified in the business strategy. Building on the works of Skinner (1969) and Abemathy and Townsend (1975), this hierarchical structure was further analyzed by Hayes and Wheelwright as they looked at the relationships between marketing and operations.

They suggested that there should be a link between product plans and process choice that supports the overall business strategy (Hayes and Wheelwright, 1979). Furthermore, they contended that firms operating on or close to the diagonal of the product-process matrix will outperform those that hold significantly off-diagonal positions (see Figure 1). Much of the operations strategy literature since then has supported their assertions, and many operations management texts use the model to describe process choice in manufacturing.

More recently, research has been conducted to validate empirically whether firms actually link their process choice to product volume and customization and whether those decisions result in better performance.

Get quality help now

WriterBelle

Verified

Proficient in: Economics

4.7 (657)

“ Really polite, and a great writer! Task done as described and better, responded to all my questions promptly too! ”

+84 relevant experts are online

Hire writer

Safizadeh et al. (1996) in a study of 144 U. S. manufacturing plants, found that process choice was linked to product plans and competitive priorities and that there was some evidence of improved performance when they were well aligned. However, the authors recognized that some batch shops and continuous flow shops were able to increase their ability to customize products through lexible manufacturing systems and by using common parts and subassemblies.

Their work suggests that as flexibility increases it may be possible to move away from the diagonal on the matrix and still be successful. The authors go on to suggest that flexibility is a “complex phenomenon and the literature has gradually uncovered its multiple dimensions and their strategic implications. ” They also note that companies may appear to be off the line because product and process choices don’t happen simultaneously.

Companies may have partially implemented processes that would move them closer to the diagonal, but not have the processes completely in place. More work is required to determine the true relationship between process choice and product customization. About the same time, other researchers felt it was necessary to continue to validate the Hayes and Wheelwright model, given changes in manufacturing technology and practice. McDermott, Greis, and Fischer (1997) conducted an in-depth study of nine firms that made up 95% of the total U. S. market for portable electric tools.

Through surveys, semi-structured interviews, and plant tours, they determined that new production technologies and practices enable firms to provide flexibility, responsiveness, and low-cost production at the same time. Their results suggested that the process-product trade-offs may have changed and that the Hayes and Wheelwright model may no longer be suitable for describing the environment in that particular industry. They proposed that models based on mass customization (Pine, Victor, and Boynton, 1993) and flexibility (Collins and Schmenner, 1992) may better capture current manufacturing practices.

However, they suggested a need for additional research to provide a more prescriptive model for operations strategy. Significant advances in manufacturing and information technologies and changes in competitive priorities suggest that the Hayes and Wheelwright model should be revisited. One attempt to describe a new type of model, given the changes in business environments, is illustrated in Heim and Sinha (2001). They described electronic business-to-customer (B2C) operations and developed a framework in the form of a product-process matrix to explain elationships between electronic service products and service processes.

While their model was not empirically tested, it did suggest that the product-process matrix had the potential to be useful as a planning tool in other business environments. A study by Ariss and Zhang (2002) provided some evidence that companies might achieve multiple competitive performances rather than settle for fewer priorities because of trade-offs. Their survey of 31 firms in the Detroit area supported the hypothesis that because of flexible process capabilities firms fall within a wide scope of the diagonal on the product-process matrix.

While the sample was small, there was evidence that firms with a highly flexible process capability could perform well despite being off the diagonal. They suggested that flexible process capability is necessary to overcome the technological or economic constraints inherent in the product-process matrix. Matching Supply Chain Processes to Products While these more recent studies examine the appropriateness of the model regarding a firm’s ability to make process choices predicted by the matrix, they continue to look only at one firm and its process choices.

Recent literature suggests that rather than firms competing with other firms, the new competition is supply chain to supply chain (Bhattacharya et al. , 1995; Whipple and Frankel, 2000; Rice and Hoppe, 2001). There is little disagreement among operations strategy researchers that the operations strategy should be aligned with the marketing strategy and support the company’s overall strategy; hence, the relationship between product choice and process choice. Others might argue that the overall company strategy must support the supply chain strategy.

This leads to the question: is there a relationship between a supply chain’s product characteristics and competitive priorities with the supply chain firms’ process choices? As Lee notes (2004, p. 108) “Smart companies tailor supply chains to the nature of markets for products. ” Some researchers provide support for the suggestion that supply chain design should be based on the nature of demand for the product, i. e. , there must be a match between the type of product and the type of supply chain. Fischer (1997) divides products into two types, functional and innovative.

Functional products are likely to be staples with stable predictable demand versus innovative products with short life cycles, many varieties, and volatile demand. For functional products, supply chains should be physically efficient and manufacturing’s focus should be on minimizing inventory and maintaining high average utilization rates. For innovative products, supply chains should have responsive processes, and manufacturing should focus on where to strategically place inventory and deploy excess buffer capacity to meet changes in demand. Fisher suggests that making these alignments will provide a competitive advantage.

Lee further defined the characteristics of functional versus innovative products (2002). Functional products have low demand uncertainties, stable demand, long product life, low product variety, higher volume per stock keeping unit (SKU), along with other characteristics. Innovative products have high demand uncertainties, variable demand, short selling seasons, high product variety, low volume per SKU, along with other characteristics. He then defines four supply chain strategies to meet product demand. Efficient supply chains use scale economies and optimize capacity and distribution utilization.

Risk-hedging supply chains use strategies to pool inventory and other resources to avoid supply disruption. Responsive supply chains have strategies that are responsive and flexible and use build-to-order and mass customization processes. Agile supply chains utilize strategies to be responsive and flexible but also pool inventory or capacity resources to meet unpredictable demand with minimal disruptions. As Lee states, “Only those companies that build agile, adaptable and aligned supply chains get ahead of the competition” (2004, p. 105).

Determining how a supply chain can, or should be, improved to gain flexibility can be difficult. One proposition suggests that supply chain flexibility is affected by decisions or process choices inherent in the operations systems, logistics processes, the supply network, organizational design, and information systems of every firm within the supply chain for a given product (Lummus et al. , 2005). Evidence from this study reveals that practitioners recognize many different facets associated with supply chain flexibility but do not necessarily perceive differences between those in terms of greatest-to-least impact on flexibility.

Childerhouse, Aitken, and Towill (2002) detail how an organization achieved focused processes in their demand (supply) chain through a case study of a major U. K. lighting company. Figure 2 describes their proposed model for the development of focused demand chain strategies. They suggest that the marketplace today has diverse requirements for alternative products, and no single demand chain can best service all these requirements. Through a review of literature, they identified five key product characteristics that influence the design of supply chain strategies.

The characteristics included: duration of product life cycle, time window for product delivery, high versus low volume, variety in required product offerings, and variability in demand. From there, they documented the lighting company’s efforts to identify customer requirements and realign their supply chain strategy to focus on specific demand requirements. The company focused its efforts on the supply chain activities of planning (choosing between material requirements planning [MRP] and pull system execution [kanban]), simplifying the ordering and communication between “players” in the chain and improving the new product design process.

The company identified four focused demand chains, and over a four-year period transformed its supply chain in to a competitive, market-focused demand chain. Supply Chain Product-Process Matrix Building on the work of Hayes and Wheelwright, the product classification work of Fisher and Lee, and the focused supply chain discussion just outlined, it may be beneficial for companies to define a model that aligns product characteristics and processes across a supply chain. As supply chains compete with supply chains, companies within a supply chain must select processes consistent with end-customer value and competitive priorities.

The purpose of this research is to propose a model that may be used by companies when making product and process decisions across a supply chain. There is clearly support for the product-process matrix, i. e. , firms that operate on or near the diagonal will outperform those that are significantly off-diagonal. However, as noted, there is also some evidence that as companies adopt more flexible systems and identify inventory pooling strategies they may succeed despite operating further from the diagonal.

If demand characteristics are important for defining the competitive priorities and process choice within a company, why wouldn’t end-customer demand characteristics also be important for defining the process choices across a supply chain? The association between process choice and end-customer requirements across a supply chain has not been empirically tested. Therefore, the following proposition is suggested: Proposition 1: The competitive priorities and end-customer value with regard to a supply chain’s primary product line must be consistent with the supply chain firms’ process choices.

While Proposition 1 suggests the importance of alignment, it does not imply that each company in the supply chain must have similar processes, or even that the processes would have similar capabilities (i. e. , speed, volume, changeover, etc. ). What is suggested is that regardless of where the company is positioned in the supply chain, the focus must be on end customer requirements. If end-customers require a variety of products with short lead times and variable volumes, each partner in the chain must be able to react quickly to provide different varieties and ramp production up or down.

An end customer focused on low cost and consistent product in high volumes requires each partner to focus on streamlined production and efficient operations. How each partner meets the capability required by the end customer may differ, but the focus must be on what the supply chain’s end customer requires. As noted by both Lee and Fisher, there are key differences in the characteristics of products that affect supply chain design. Uncertainty surrounding the specific product design volume and delivery requirements increases the need for flexibility across the supply chain.

Each firm within the supply-chain plan processes must understand end-customer value to maintain the flexibility required by all upstream partners. Types of uncertainty include: the need for differently defined product (make-to-order [MTO]), the desire to choose from an existing group of designs (options), the need for different volume requirements, and the need to design new products. End-product uncertainty suggests that entire supply chains must be adaptable. Therefore, the following is suggested: Proposition 2: End-customer product uncertainty characteristics increase the need for supply chain flexibility and influence process choice.

As an example, consider the level of uncertainty associated with customer demand for fashion items, such as women’s handbags. There is great uncertainty associated with how well any particular design might be accepted and eventually purchased by the end customer. Systems must be established to move product to the location with the greatest demand. Demand characteristics, including general market response and seasonality, may affect the sales or shelf-life of the product and, therefore, require a supply chain that can make adjustments in production, design, and raw materials.

At the other end of the uncertainty spectrum resides a product like sugar. Stable demand and specific product characteristics allow for the development of a supply chain where minimal flexibility is required. These examples and the propositions offered suggest the need for a new model to define the relationship between processes and products across a supply chain. Successful supply chains will focus on end-customer demand and select the appropriate processes to match the demand characteristics. Figure 3 describes a proposed supply chain product-process matrix.

The proposed supply chain matrix takes into account Lee and Fisher’s perspectives on end-product customer characteristics. The horizontal axis represents product uncertainty that can vary from a repetitive standard product to a highly variable product, where the customer wants to participate in the design or at a minimum wants a customized product. The horizontal axis represents the needed flexibility for the entire supply chain and ranges from the highly efficient supply chain to one that uses processes for specific customer requirements.

Efficient supply chains can produce products in large quantities at a low cost and with short lead times. Responsive supply chains can respond to changes in customer requirements and produce a customized product in quantities as small as one. In Hayes and Wheelwright’s original model (see Figure 1), they suggest that companies will be most successful when operating closely on the diagonal. While a particular location on the proposed matrix (see Figure 3) does not suggest particular processes for the firms in the supply chain, it does suggest the types of processes required across the supply chain.

For example, the make-to-order clothing retailer must have processes that can gather size information, translate that information to drawings, and transfer the information quickly to the clothing manufacturer. The clothing manufacturer must have equipment that can interpret design requirements, quickly change from one size to another, and incorporate color and other choices. The textile manufacturer must stock or quickly produce the right color and thickness of material to match the particular customer’s needs.

The entire supply chain must be able to react to customized design. At the opposite extreme are supply chains that produce a standard product in one or a very small number of options and are focused on efficient production throughout the supply chain. Many supply chains today are finding more and more customers requiring customized products. Companies currently mass producing products, such as shoes, are considering how to build a product specific to the customer. In this example, the customer’s foot measurements are translated into a custom-fit product.

This movement from repetitive products to more mass-customized items suggests that supply chains in the future must be more responsive. Therefore, the following is suggested: Proposition 3: The goal of the supply chain’s firms, depending on the product and competitive environment is either to move up and to the left of the matrix without sacrificing efficiency and cost effectiveness or to move down and to the right without sacrificing flexibility and customization. Expectations for mass customization are certainly changing the competitive environment.

This push requires that companies redesign processes to gain flexibility without increasing costs. As an example, a local cabinet manufacturing firm requested help redesigning a manufacturing process that would allow it to customize cabinets for product sold though a large home improvement chain. The push from the store was to provide reasonably priced custom cabinets to its customers. In addition, lead-time from order receipt to ship was very short. The manufacturing company set a rip-to-ship goal of 24 hours.

The retail chain promised high sales volume. The company then needed to change its high-volume manufacturing processes in such a way that customization could occur without adding to costs-a move up and to the left on the supply chain product-process matrix (Figure 3). Results from this manufacturing change were expected not only to increase the company’s ability to customize its high-volume products, but to also increase margins for its low-volume, highly-customized products as manufacturing process improvements were moved to its low-volume lines.

For this product line, the improvement would be down and to the right of the matrix, improving costs without sacrificing flexibility and customization. The cabinet company example illustrates another issue in selecting processes within a specific company to match supply chain objectives. While the company aligned its processes to provide a customized, short lead-time product, it also provided a high-volume standard product. Many companies have multiple product lines with differing end-customer requirements.

It may be necessary to provide diverging paths through a manufacturing process to accommodate multiple customer objectives, or to create secondary processes. Either way, the key issue is to focus on end-customer requirements and vary process choice to accommodate product variation. Companies often find themselves members of multiple supply chains and must plan processes accordingly. At the same time, companies make process choices that are unique from other companies’ choices and provide them distinctive capabilities. They make trade-offs in process choice, which allow them to satisfy a given range of customer needs.

These decisions allow a broad range of customers with a variety of needs to be served by different companies with distinct capabilities. While this may limit one company’s capability to serve some customers, it opens opportunities for others to serve that market. Conclusions Determining supply chain processes is not an isolated task and should be considered in relation to end-customer requirements and the competitive priorities of the entire supply chain. Hayes and Wheelwright concluded that, with their well-known product-process matrix, companies focusing on aligning processes with product requirements will be most successful.

Today, as supply chains compete with supply chains, the entire supply chain must be focused on the end customer. Childerhouse et al. (2002, p. 687) summarized the perspective of a focused demand chain: The theory of focused demand chains is based on the premise that modern day marketplaces have diverse requirements for alternative products and services. No one demand chain strategy can best service all these requirements. Hence, focus is required to ensure demand chains are engineered to match customer requirements. This paper proposed revising the Hayes and Wheelwright model to incorporate a supply chain perspective.

Future work is needed to apply these concepts across multiple companies and industries to validate the proposed model. Rather than looking only at the company’s products and customer requirements, it is important to examine the role of end-customer product characteristics in the processes of all the firms that are partners in the supply chain. Supply chains have one role, which is to serve the end customer. Understanding that end customer should help firms set competitive priorities and establish processes to match end-customer demand.

REFERENCES

• Abernathy, W. J. , and Townsend, P. L. (1975). Technology productivity and process change. Technical Forecasting and Social Change, 7, 379-396.
• Ariss, S. S. , and Zhang, Q. (2002). The impact of flexible process capability on the product-process matrix: An empirical examination. International Journal of Production Economics, 76, 135-45.
• Bhattacharya, A. K. , Coleman, J. L. , and Brace, G. (1995). Re-positioning the supplier: An SME perspective. Production Planning & Control, 6, 218-226.
• Childerhouse, P. , Aitken J. and Towill, D. R. (2002). Analysis and design of focused demand chains. Journal of Operations Management, 20, 675-89.
• Fisher, M. (1997). What is the right supply chain for your product? Harvard Business Review, 75(2), 105-116.
• Hayes, R. H. , and Wheelwright, S. C. (1979). Link manufacturing process and product life cycles. Harvard Business Review, 57, 133-140.
• Heim, G. R. , and Sinha, K. K. (2001). A product-process matrix for electronic B2C operations: Implications for the delivery of customer value. Journal of Service Research, 3(4), 286-299.
• Lee, H. (2002). Aligning supply chain strategies with product uncertainties. California Management Review, 44(3), 105-118.
• Lee, H. L. 2004, October). The triple-a supply chain. Harvard Business Review, 102-112.
• Lummus, R. R. , Vokurka, R. J. , and Duclos, L. K. (2005). A delphi study on supply chain flexibility. International Journal of Production Research, 43(13), 2687-2708.
• McDermott, C. M. , Greis, N. P. , and Fischer, W. A. (1997). The diminishing utility of the product/process matrix: A study of the U. S. power tool industry. International Journal of Operations and Production Management, 17(1), 65-84.
• Rice, J. B. , Jr. , and Hoppe, R. M. (2001). Supply chain vs. supply chain: The hype and the reality. Supply Chain Management Review, 5(5), 46-54.
• Safizadeh, M. H. , Ritzman, L. P. , Sharma, D. , and Wood, C. (1996). An empirical analysis of the product-process matrix. Management Science, 42 (11), 1576-1591.
• Skinner, W. (1969). Manufacturing–missing link in corporate strategy. Harvard Business Review, 47(3), 136-145.
• Whipple, J. M. , and Frankel, R. (2000). Strategic alliance success factors. Journal of Supply Chain Management, 36(3), 21-28.