The effects of management inertia on the supply chain performance of produce-to-stock firms

Industrial Marketing Management 34 (2005) 614 – 628

The effects of management inertia on the supply chain performance of produce-to-stock firms Michael F. SmithT, Richard A. Lancioni, Terence A. Oliva Marketing Department, Fox School of Business and Management, 1810 N 13th Street, Temple University, Philadelphia, PA 19122, United States Received 15 January 2004; received in revised form 12 October 2004; accepted 10 November 2004 Available online 10 May 2005

Abstract This article examines the impact of inertia on the management of the firm’s supply chain operations and the effects it can have on a produce-to-stock firm’s ability to respond to external market pressure and develop corrective strategies. The research methodology used is based on earlier Catastrophe Modeling that looked at inertia in organizational design, competitive pressure, and competitive response. The model demonstrates how latent variables, such as customer pressure and supply chain inertia can influence a finished goods supply chain management’s response under various conditions. The model was tested and validated using questionnaire data gathered from a sample of members of the Council of Logistics Management. The model was used to estimate individual finished goods firm inertia response estimates. We incorporate these estimates in a brief examination of three produce-to-stock firms from the sample to give readers an idea of the usefulness of the approach in examining supply chain inertia. D 2005 Elsevier Inc. All rights reserved. Keywords: Produce-to-stock management inertia; Supply chain inertia; Catastrophe modeling; Organizational inertia; Customer pressure; Supply chain response

1. Introduction The management of supply chains in today’s highly competitive environment requires that logistics managers respond quickly to competitive challenges, inventory shortages, customer complaints, inaccurate order processing, and unreliable transportation situations. New technologies and processes such as radio frequency identification (RFID) and collaborative planning, forecasting, and replenishment (CPFR) initiatives are being incorporated into supply chain management operations to provide managers with a competitive edge. In today’s intensely competitive environment, bunprepared corporations that have not whipped their supply chains into shape are beginning to feel the squeeze in the form of slashed profit margins, anemic market share and plummeting customer satisfactionQ (Roberts-Witt, 2002; p.1).

T Corresponding author. Tel.: +1 215 204 1682. E-mail address: [email protected] (M.F. Smith). 0019-8501/$ - see front matter D 2005 Elsevier Inc. All rights reserved. doi:10.1016/j.indmarman.2004.11.003

Managerial inertia, exhibited by logistics managers, may inhibit their understanding of the impact of operational changes on performance and preclude the timely development of strategies to correct them. bManagerial inertia is also an important issue inhibiting the optimization of the supply chain. Managers are often stuck to the old way of doing things, thus being incapable of adopting the new management philosophies required to succeed in the new business environmentQ (Patosalmi, 2003; p. 3). A lack of responsiveness to environmental change can reduce the efficiency of the firm’s supply chain operations, increase logistics costs and result in lost revenue for the firm. The authors of a study on successful supply chain management operations noted b. . . supply chain leaders also were shown to have flexible, adaptable business processes for customer relations, supplier management, new product design, and other supply chain operationsQ (Anderson & Mulani, 2003; p. 3). In the area of leadership, one supply chain executive indicated that inertia was the biggest problem with CEO leadership (Pellet, 2002; p. 3).

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product flows in their supply chains and remove any inefficiency that may have been caused by management inertia on the part of their suppliers (Todd, 2001). The ability of companies to respond to changes in their competitive environment is a central concern of seminal organizational design theories (Galbraith, 1977; Thompson, 1967). In the area of supply chain organization, researchers have largely ignored the issue of inertia. Very little attention is paid in the field to why some firms respond more aggressively to competitive challenges or to disruptions in their supply chain system and others do not. There are theoretical arguments that have tried to explain organizational inertia. They have linked inertia to the distribution of organizations within populations, overall organizational performance, and the rates of failure of organizations (Gresov, Haveman, & Oliva, 1993). Inertia in supply chain management has been associated with the failure of logistics managers to understand the dynamics that exist between the operational areas in supply systems, a total disregard for the trade-off effect in supply chain decision making, and lack of knowledge of how supply chain systems operate as dynamic systems (Lancioni, 1986). In general, all supply chains have seven separate and distinct decision areas that supply chain management is responsible for. These include the management of customer orders, customer account revenue manage-

The management inertia in an organization often affects supply chain performance in two ways. First it can lead to a slow down in the upstream processes in the supply chain (Todd, 2001). This involves the relationships of the manufacturers with their suppliers and includes inbound transportation coordination, inbound inspection, JIT planning, stocking demand level forecasting, and production scheduling and planning. Management inertia results in long lead times and bclumsy distribution and replenishmentQ (Todd, 2001). Another effect on upstream processes is the increase in supplier transaction costs (Todd, 2001). Transactions costs can increase dramatically, if the buyer–supplier relationships in the supply chain are not managed effectively and if management inertia impedes the firm’s purchase ordering processes (Todd, 2001). The second way in which management inertia can affect a produce-to-order supply chain is in its down stream operations that include order fulfillment, customer service, on-time delivery, and the other operational areas directed to providing customer valueadded (Todd, 2001). Produce-to-order customers are increasingly b. . .becoming more intolerant of (supply chain) inflexibility. . .and are having to deconstruct their supply chains. . .Q because of it (Todd, 2001; p. 3). This inertia has led many customers to engage in outsourcing and partnership relationships on their own to improve the finished

Pressure On A Supply Chain

Supply Chain Response to Inertia

Areas Affected in A Supply Chain

Possible Areas of Inertia

Revenue per Customer Account

Layers of Mgt. Decisions In A Supply Chain System

Ratio of Unplanned Shipments to Total Shipments

615

Customer Complaints

Orders per Customer Account

Stockout

Customer Control of Inbound Shipments

Costs

Supply Chain Mgt. Coordination in Promotion Programs

Ratio of backorders to total shipments

Supply Chain Mgt Coordination in New Product Introductions

Ratio of Incomplete Orders to Total Orders

Fig. 1. Logistics decision effects in a supply chain: inertia influences.

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ment, in bound transportation of customer orders, finished goods inventory and stock management, warehousing storage, materials handling and customer complaint management. Supply chain management is a process-oriented system of numerous management functions that includes purchasing, production, transportation and customer service systems that are adversely affected when management inertia exists. The scope of a supply chain in a firm includes b. . .both internal enterprise processes and external business contributions from suppliers, transporters, channels and end users. . .Q (Varian, 1979; p. 4), each of which must be integrated into a smooth operating supply system. Each of the functions in a supply chain is a separate operational area and cost center in a firm, but when managed as a supply chain system they all impact on each other. Any management inertia will result in reduced efficiency, reduced overall performance of the supply chain, and a reduction in the service output of the system. For firms to develop bquick response systemsQ that are demanded by their customers, they cannot operate their supply chains utilizing the old management practices of building large inventories and expediting all shipments to customers (management inertia) (Walters, 1971). Management has to change and employ channel process synchronization whereby enlightened managers begin to share plans, forecasts, inventory status and purchase order status information (Walters, 1971). As shown in Fig. 1, the operational areas directly affect each other. The areas of possible inertia, such as the layers in the firm’s management decision process, the planning and coordination of sales and marketing promotional programs, and the integration of supply management into the new product introduction process will cause a delay in the organizational responses to unplanned shipments, backorders, and complete shipments to customers. The overall effect leads to system suboptimization in the management of the supply chain. Examples of the specific effects of inertia are discussed in the following sections of the paper.

2. Impact of managerial inertia on produce-to-stock supply chains 2.1. Inventory and net worth A good example of how management inertia can financially affect a firm’s supply chain operation is demonstrated in the relationship between assets, reflected in current inventory, and a firm’s net worth reflected in the following formula: Return on Net Worth ¼ Profit Margin  Asset Turnover  Leverage Net profit Net Profit Net Sales Total Assets ¼   : Net worth Net Sales Total Assets Net Worth

Inventory is a part of the current assets of a firm. Competitive and market pressures necessitate that a company react to market changes and reduce inventory to achieve economies in the supply chain, but if the firm fails to manage its inventories correctly and inertia is high, the net profit of the firm will be seriously affected as potential increases in asset turnover are more than off-set by increases in operating expenses. Minimizing inertia is critical, since it will dramatically affect the overall profit performance of a firm due to the fact that inventory is the largest cost component in a supply chain, accounting for up to 45% of the costs in a logistics system (Stock & Lambert, 2001; p. 193). Inertia in the management of inventories in a supply chain can have deleterious affects across all aspects of a firm. For example, increased stock-outs, late deliveries, increased backorders and expedited deliveries can result. bPerformance measures such as the old standby of ROA (Return-on-assets), and the newer EVA (Economic Value Added) as well as other measures which measure capital use efficiency have become more and more common organizational drivers. In fact, many an executive’s bonus is often, at least in part, dependent on how efficient capital is used (Williams, 2003; p. 4).Q The drive for more efficient working capital use coupled with the need to more quickly respond to changes in customer demand and shorter orderto-delivery cycle time (Williams, 2003) makes management inertia in inventory a real danger for a supply chain. Management inertia often causes supply chain managers to improperly manage produce-to-stock inventory resulting in the b. . .stockpiling of large quantities of raw materials; loading-up the shop floor with work-in-process; and pack warehouses with finished goodsQ (Williams, 2003; p. 4). This behavior increases working capital needs, causes erratic and longer lead times and results in increasing overall supply chain costs (Williams, 2003). The ramifications of management inertia on produce-to-stock inventory management affect several departments in a company and affect several down-stream processes. The effect is generally negative resulting in ineffective sales and operations planning, poor forecasting, inadequate product specifications, over planning, ineffective production scheduling, low quality, bottlenecks, long cycle times, product and process problems, high costs, poor vendors, and wrong performance measures to name a few (Williams, 2003). 2.2. Unresolved customer service complaints Postponing the resolution of customer service complaints or spending little time on dealing with them is a sign of inertia in a supply chain. The result of this inertia is that firms experience a higher number of backorders, increased transportation costs, and a reduction in the overall revenue in their customer accounts. This form of supply chain inertia is one of the most serious issues in logistics management. The handling of customer complaints becomes a moment of truth in maintaining and developing long-term customer relation-

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ships (Windham, 2003). A firm can lock in customers for life by treating them fairly during a dispute. Frequently, customers point to getting b. . .a fair shake and a just resolution as reasons for continued purchasesQ (Windham, 2003; p. 3). L.L. Bean, in fact, built an empire on the simple axiom that no customer should have to buy a product that he or she is not completely satisfied with (Windham, 2003). Not responding quickly to resolve produce-to-stock customer complaints, because of management inertia, will result in serious consequences for firms including lost sales, reduced profits, poor sales-force morale, and lost customers. With so little b. . .price warranty, and quality differentiation between products, consumer loyalty turns on only one thing-service. . ..Q And that means resolving complaints quicklyQ (Windham, 2003; p. 2). Buyers do not expect companies to be lethargic when it comes to resolving their complaints. Inertia only exacerbates an already difficult situation. Buyers have become more demanding. They expect fair pricing and quality service to be a part of their relationship with a supplier. They are more self-reliant and independent, and if their current supplier’s product or service does not give them what they want, they will jump ship (Windham, 2003). Management inertia has had a dramatic affect on buyer satisfaction as found in a study by the American Society of Quality that showed that buyer satisfaction dropped by 5– 10% between 1994 and 1997 (Windham, 2003). Management inertia towards the resolution of buyer complaints is often bolstered by the belief that if buyers do not complain the company is doing well. But this is false, because more than 95% of customers do not complain (Windham, 2003). Many customers quietly leave and do not place another order. Silent attrition is deadly, because companies do not get a chance to rectify problems and keep the customer. The negative consequences of management inertia towards complaint resolution is dramatically demonstrated in the massive bcustomer churnQ trend that is occurring among US firms. Companies are now losing 50% of their customers in 5 years and 50% of their investors because of it (Windham, 2003). Organizational inertia can be overcome by developing a management structure that facilitates communication with customers to better understand their complaints and problems and is sufficiently flexible to be able to respond to unplanned events. The reduction in management inertia can have dramatic affects on a firm’s sales, profits and overall customer service costs. For example, a 5% increase in overall customer retention b. . .equates to a 25–55% increase in the profitability of a business (Windham, 2003; p. 2).Q At the same time b. . .it can be 30-to-40 times more expensive to acquire new customers than it is to manage existing customers (Windham, 2003; p. 3)Q. 2.3. Stock-outs The increasing level of stock-outs in a supply chain is another symptom of a rising level of inertia in the

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management of a supply chain. The results of inertia, here, can have a significant impact on customer revenue and results in lost sales, lost orders, and even lost customers. Increasing levels of stock-outs, caused by management inertia, may provide the impetus for the customer to consider other alternatives (Walters, 1971). For instance, customers may switch to different priced products, brands, buy substitute products, and/or switching stores. Clearly the last decision alternative is the least desirable for a firm in the retail trade, but high levels of inertia in responding to repeated stock outs could result in a high level of lost customers in any industry. One way to understand the impact of inertia is having on a firm’s stock-out situation is to objectively assess the orderto-delivery process. This is important for a firm often limp’s along with a well intentioned, but an ineffective approach to their stock-out problem (Emmelhainz, Stock, & Emmelhainz, 1989). The result is that management often is frustrated with the half-hearted, ineffectual actions it takes to resolve the issue without any improvement forthcoming. These short-sighted solutions are frequently not well thought out and take the form of simplistic band-aids, such as, calling for a 100% stocking policy, packing warehouses with finished goods near major markets and major customer locations and ramping-up manufacturing to increase the inventory of finished goods (Emmelhainz et al., 1989). Such policies only compound the problem and result in a greater stock-out problem. A firm that is responsive to customer stock-out issues and knows that consumers are willing to switch package sizes may use the information to convince retailers and wholesalers that they too should accept these substitutes (Miklas, 1979). If brand switching takes place, the firm loses the profit contribution from the sale. Also, the substituted brand may become the consumer’s first choice in the future (Emmelhainz et al., 1989). 2.4. Trade promotions and logistics management One of the most frequent examples of management inertia in firms is the lack of coordination between the sales and marketing departments and the logistics group in planning for trade promotions. It is important for the marketing department to integrate its advertising plans and expected demand from the promotion with the delivery schedule and inventory levels required to satisfy customer demand. In those firms where management inertia is high, little or no coordination is present. Trade promotions are critical components in a firm’s overall sales and customerrelationship strategy. Companies leveraging the right (supply chain) internal processes, tools, and information are in a great position to leverage trade promotion activity to drive incremental sales, build brand loyalty, and improve ROI— not just product-by-product, but across their entire product line (Bargman, 2003). If this is not done, then either frequent over-stocks or under-stocks in a logistics system may result in the failure of the trade promotion. Such inertia

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is often found in the retailing industry where the logistics department is not routinely consulted in the planning of a trade promotion. The typical scenario that takes place is that the trade promotion is launched, and when under-stocks occur, the logistics group has to quickly order additional stock to keep up with customer demand. Of course, the opposite situation often occurs when more stock is ordered to cover for any inaccuracies in the trade promotion forecast. The variation in stock levels leads to an increase in inventory carrying costs, expedited transportation charges, and customer communication expenses. Generally, trade promotions can provide numerous benefits for a firm if the supply is managed properly. They include improved trade relations, customer retention, and increased sales for a firm (Bargman, 2003). 2.5. Inertia and new product introduction In industries where new product introductions are necessary for the maintenance of market share and to gain competitive advantage, well-coordinated new product introductions are important. This is particularly true in competitive consumer product industries such as toiletries and personal care. For instance, in the men’s shaving market, Gillette has more than a 50% market share and maintaining its leadership is a continual challenge. In the shaving cream segment, Pfizer with its Barbasol and Old Spice brands aggressively challenged Gillette’s leadership position with the introduction of new products every 6–12 months (Lancioni & Foster, 1997). The company responded with its own new product introduction programs that were well timed and coordinated. Eliminating inertia took several years to complete. During that time the company experienced several new product introduction failures, and committed itself to resolving the problem. Unfortunately, how well the supply chain supports the introduction of new products is not well understood by firms encumbered by management inertia who fail to make the connection (Pellet, 2002). The most important key to successful new product promotions is having the right product, at the right place, at the right time, and at the right costs. This cannot be accomplished without a well-managed supply chain (Pellet, 2002). Several courses of action have been proposed to minimize management inertia in managing supply chains in the area of new product introductions. For instance, management must be convinced that collaboration with suppliers is far different from simply treating them as just vendors. Strategic supplier collaboration is essential and it means integrating systems and processes into the supply chain that include decision-making and product strategy levels. Firms should choose suppliers that the firm can learn from and leverage their expertise. The best suppliers may be those that have also worked with a firm’s competitors and have developed a breadth of experience. Firms should strive to develop collaborative supply chain relationships with its

suppliers and treat them like they are part of the company. Finally, firms need to develop the IT systems to make collaboration in the supply chain work effectively in the new product introduction process (Bargman, 2003). Management inertia cannot be tolerated when responding to competitive pressures. The logistics department is a part of the new product planning process from the initial design phase to the introduction of the product. Sales forecasts, inventory levels, delivery schedules, and trade promotions are jointly planned to insure that the new product introduction process is seamless.

3. Bull-whip effect To effectively manage a produce-to-stock inventory system efficiently is not easy given the impact the bull-whip effect can have on a supply chains. The bull-whip effect is generally regarded as the expediting of customer orders in a supply chain and placing demands on all logistics functions to provide fast response (Murphy, 2001). It can effect several operational areas including manufacturing scheduling, inventory levels, lead times, transport costs, shipping and receiving costs, customer service levels and shipment profitability. Many factors can lead to the bull-whip effect in a supply chain including high–low pricing that often leads to forward buying; a lack of synchronization between customer order placement and delivery times; and lot-sized based discounts. Management inertia in the management of supply chains is one of the leading causes of the bull-whip effect (Murphy, 2001; Pellet, 2002). Symptoms of this inertia manifest themselves when multiple demand forecasts are done because there is no clear idea as to what end demand sales may be. This results in long lead times and serious back-order situations. The bull-whip effect is often caused by management’s ignorance of raw material availability and the allowance of unrestricted orders by customers. Controlling the bull-whip is not easy and often takes years to reduce its high cost effects. The single most effective solution is to establish a channel wide EDI communication and information system that integrates the ordering and supply conditions of all of the channel members. The first step is to: (1) build trust among the participating firms; (2) reduce lead times; (3) encourage the sales force to stop competing amongst themselves; (4) base sales incentives on profitability and the not sales; (5) product allocation based on past customers sales; and (6) limit purchase quantities until the order can be controlled (Bargman, 2003; Murphy, 2001).

4. Analyzing the impact of inertia on supply chain management In the interest of exploring the impact of inertia on produce-to-stock supply chain systems, we conducted a

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survey of supply chain managers to assess their organization’s performance in the areas of customer pressure on the firm, supply chain inertia and operational responses to these variables. Organizational responses to pressure for change in the presence of inertia have been modeled using Catastrophe Theory (Gresov et al., 1993). The theory looks at how firms respond to changes in their competitive environments. The term catastrophe is unfortunate since it tends to connote disasters like tornados and earthquakes rather than sudden shifts or changes in behavior that may mark the overcoming of organizational inertia. In particular, a cusp model based on Thom (1975) ideas of structural stability under change as popularized by Zeeman (1974, 1977), and Zeeman, Hall, Harrison, Marriage, and Shapland (1976) will be used. Its main advantage is that it can handle organizational (Supply Chain Management) situations that exhibit both revolution and evolution. Since this is the most common catastrophe model found in the literature, we will only give a basic overview. Those interested in more reading should start with the works of Woodcock and Davis (1978) and Fararo (1978). Varian (1979, p. 15) characterizes catastrophe theory as the field, which looks at the interactions between short-run equilibria and long-run dynamic processes. In theoretical terms we assume that we have a dynamical system that can be described by the potential function g and the behavior function f (Thom, 1975): gðZ; X ; Y Þ ¼ 1=4 Z 4  X Z  1=2Y Z 2

ð1Þ

Z V ¼ f ðZ; X ; Y Þ:

ð2Þ

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For example, in mechanics it is the point at which a pendulum stops, while in our study it is the point where inertia and customer pressure reflect optimal versus suboptimal supply chain response reflected in ability to fill orders properly. Relative to Eqs. (1) and (2), such gradient dynamic system may be characterized by:  dZ / dt = 0. And given f(Z,X,Y), where Z is dependent, the preceding differential indicates that Z changes in the direction of decreasing potential at a rate proportional to the slope of the field. The equilibrium set comprises those values for Z for which dY / dt = 0, while the partial of f with respect to Z equals zero. For example, if a system is given by f(Z,Y) = 1 / 4 Z 4  2 Z 2Y, then the equilibrium positions are given by Z 3 ZY = 0. For any given Y there is an equilibrium at Z = 0, and for positive values there are equilibria at Z = + or  Y 1/2. This system is deterministic, i.e., just like a pendulum’s location at time t + 1 depends on its position at the immediately preceding time t, exact values of Y predict exact values of Z, if we know their trajectory (history). Anticipating future discussion, we further note that in such systems the behavior from one time period to the next is correlated. The term state-descriptive is often used to categorize these kinds of systems. If we subtract the linear variable X from the left side of the foregoing example, we get the functional equivalent of a cusp model shown in Eq. (3) below. This equation describes the curved surface shown in Fig. 2, where the straight lines which are orientation references for the three axes whose origin (0, 0, 0) is found at the back middle of the surface. Z3  X  X Y ¼ 0 ð3Þ

Based on the discussions in Fararo (1978), Cobb (1978) and Isnard and Zeeman (1976) we note that the term bcatastropheQ refers to those points where basic processes change toward states of minimum or maximum potential.

ð4Þ 27X 2 ¼ 4Y 3 : As will be discussed in the next section, due to the presence of the prominent fold (pleat) in Fig. 2, cusp models B

Origin (0,0) F

G

D

C Low

Low

ly pp

Su Mg rti ne

t. I

xis

Y-A

ain

Ch High Low

X*

X'

Customer Pressure

a

X-Axis Fig. 2. Model of supply chain response.

High

Z-Axis

E

Supply Chain Response

High

A

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can capture a wide variety of interesting system behaviors parsimoniously. Eq. (4) describes the projection of the boundaries of the overlapping areas in Fig. 2 onto the XYplane, thereby identifying the locus of the catastrophe points in the system. The cusp model derives its name from the peaked shape describing the area of overlap. Anticipating future discussion, the top sheet of the surface represents bhighQ or very responsive supply chain response, while the bottom sheet represents blowQ or very unresponsive supply chain response.

5. A cusp model of supply chain response driven by inertia and customer pressure The cusp formulation shown in Fig. 2 uses three variables modified from Gresov et al. (1993): 1) Customer Pressure, 2) Supply Chain Management Inertia, and 3) Supply Chain Response. Customer pressure and the degree of inertia drive the supply chain response of the organization. At the extremes, if inertia is high and customer pressure is low, response will be relatively slow and minimal. On the other hand if the inertia is low and customer pressure is high the response will be fast and maximal. As the relationship between customer pressure and inertia changes, so does supply chain response. We now look at the variables in more detail. 5.1. Dependent variable The dependent variable (Z) is the level of supply chain response. In the present formulation, Z is a function of the customer pressure generated on the firm (X) and the degree of inertia in supply chain management system ( Y). The Zfunction becomes bimodal for given x,y pairs within the cusp region (area of the overlap) shown in Fig. 2. That is, a given x,y pair (inertia and customer pressure) can give rise to two different z values which indicates the extent of responsiveness. In short, within this region, one organization with a given x,y pair will have a very responsive supply chain, while another with the same values will be unresponsive. The choice of the appropriate z-value is determined by examining the prior history of the firms (Thom, 1975; Zeeman, 1976). In terms of Fig. 2, the value of the dependent variable is measured by its position along the vertical axis. Outside the cusp area things are normal, a given x,y generates only one response type. 5.2. Independent variables The X-variable, which is shown as moving from left to right in Fig. 2, is customer pressure. In catastrophe models, the X-variable is characterized as the control or normal variable because changes in this variable cause similar changes in the dependent variable. In Fig. 2, the variable represents left/right movement.

The Y-variable represents the degree of inertia in the supply chain (its willingness to respond). In Fig. 2 the variable is depicted as back/front movement. Large values of Y indicate that a high level of inertia characterizes the supply chain, while small values of Y indicates the supply chain is characterized by a low level of inertia (the question was reverse coded to indicate that longer planning horizons indicate lower levels of inertia). In cusp catastrophe models this is known as the splitting or bifurcating variable. As values of Y decrease (the origin is at the back of the figure), there comes a point where the surface splits into two sheets (bifurcates) as shown in Fig. 2. This means when inertia is high, the supply chain will be in one of two states very unresponsive or very responsive. While it may seem counter intuitive in some ways, it is not. Firms can get blockedQ into different positions. A supply chain system that has been designed to provide the fastest response (as opposed to flexible response) can get locked into that position and only provide quick response. This could be detrimental in terms of costs due to over response. 5.3. Model dynamics System behavior is shown by movement on the curved part of the surface (i.e., organizational adoptions of the new product versus the old product). It is important to understand that the origin (0, 0, 0) is located at the back middle edge of the figure behind points F and G. Supply chain management inertia is represented by back to front movement (and vice versa), and customer pressure is shown by right to left movement (and vice versa). The supply chain response is measured by vertical movement. At the front-left side of the figure there is very high supply chain inertia (very little lead time for planning) and customer pressure is low (it is the lowest region of the bottom sheet), while at the back-right side of the figure supply chain inertia is low (more lead time for planning) and customer pressure is high (the highest region of the top sheet). The supply chain responses for these two points are low and high respectively. 5.3.1. Changes in Y and X When the supply chain is flexible and inertia is low (low y-value), the supply chain response function is relatively flat as shown at the back of Fig. 2. Supply chain response is expected to be in direct proportion to changes in customer pressure. Points A and B, in Fig. 2, shows where this movement occurs on the surface. As supply chain inertia ( Y) increases, the resistance to respond differently increases and both the shape and vertical spread of the benefit function changes as shown in Fig. 2. At moderate levels of Y, the S-shape structure emerges as the supply chain starts to get locked into a response mode. If inertia increases, Y will reach a point where the surface splits. Beyond this point, the supply chain will be locked into a response mode. Hence, there is no longer a middle ground where the supply chain response will vary. As

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supply chain inertia ( Y) gets very large, the supply chain response will be slow or very fast depending upon the nature of existing operations. Under these conditions, the supply chain response will not change unless the customer pressure moves beyond the thresholds identified by the cusp (defined by xV, x*) at the bottom of Fig. 2. That is, supply chain response will not switch from the poor until x N x*, or from rapid until x b xV. When these thresholds are exceeded, there will be a sudden shift in response behavior. It will not be an evolutionary change from slow to fast or vice versa, but rather a rapid change. The size of the change in response will depend on the inertia present ( y-value). Related to this is the observation that shifts for a given value of x in one direction will not occur at the same values of x in the other direction. For example, once supply chain inertia has been overcome, due to customer pressure, and it becomes responsive, a drop in customer pressure will not send it back to its old ways. Points C, D, and E in Fig. 2 show this movement on the surface. The shift upwards to D occurs at a value of x where x N x*, while the shift to E occurs at a value of x where x b xV. Anticipating the later discussion, we briefly note the evolution of inertia over time. While the model is indifferent to actual movement, certain conditions (movements) may be more common than others in a given application. If we start with a new firm that has low supply chain inertia (we could start with high and reverse this), we expect over time that supply chain inertia will tend to increase, as the firm gets successful and the organization expands in terms of the number of levels of management. If customer pressure is around average (in the middle for the firm) the trajectory of the response function will move from the back to the front of the surface. However, if customer pressure is either a bit lower than average or a bit higher than average during this evolution, the supply chain response will start to bifurcate as the inertia increases, locking the firm into a low or very high response mode. Points F and G give the typical trajectory of this situation starting with low inertia. As the supply chain inertia continues to evolve, F and G move in different directions ( F with providing faster response, and G with shrinking responses) as shown in Fig. 2. In time the supply chain gets locked in to different response positions. However, if the customer pressure for G becomes sufficiently high, it will result in a sudden shift when the supply chain suddenly becomes highly responsive as indicated by the trajectory from C to D in Fig. 2. 5.4. Model estimation While the model may provide an interesting view of supply chain response in the presence of inertia and customer pressure, does it capture what happens in actual markets? In order to address the question, we present an empirical estimation of the model that deals with a cross sectional sample of supply chain managers from different industries. Since this is an exploratory study, we chose a

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heterogeneous convenience sample of appropriate managers over a more ideal longitudinal study (panel data) that would require multiple years to conclude. However, a longitudinal study would provide more insight into the dynamics of the impact of inertia and complaints on supply chain response. 5.5. The data source A random sample of members of the Council of Logistics (CLM) managers was drawn from a current membership roster. The survey was electronically sent by e-mail through Zoomerang, an on-line marketing research service, to 750 managers. A total of 102 surveys were received with a response rate of 13.6%. The sample firms represented a random sample of membership industries and firms characterized by sales and number of employees. There are 15 industries represented in the sample including chemical, petrochemical, oil, steel fabrication, retail, house-wares, wholesale distribution, software development, consumer electronic, auto manufacturing, auto parts manufacturing, appliance manufacturing, aluminum fabrication, paint and coatings, glass distribution and agricultural fertilizer distribution. The distribution of firms characterized by sales and number of employees is very diverse. The median firm sales revenue was from 100 to 500 million with 19.1% under 50 million, 11.7% from 50 to 100 million, 20.2% from 100 to 500 million, 16% over 500–1 billion, 21.3% over 1–5 billion and 11.7% over 5 billion. The median for number of employees was from 100 to 500 21.5% under 100, 38.7% over 100–500, 20.4% over 500–1500, 10.8% over 1500–3000, 5.4% over 3000–10,000 and 3.2% over 10,000. Out of the 101 surveys 87 were useable for our study. The firms in the sample reflect the general range of companies as described in the 2003 Edition of Thomas’s Register and the Department of Commerce Census of US Business Firms and Industries, 2003. 5.6. Estimation method Estimation of catastrophe models has been difficult because of their nonlinear characteristics. So while Eq. (1) is parsimonious in its ability to describe a large variety of complex behaviors, its nonlinear and implicit characteristics present estimation difficulties for standard techniques. Early efforts to estimate such models were necessarily simplistic. The first published empirical social science application of a catastrophe model is generally credited to Zeeman et al. (1976). It focused on institutional disturbances (riots and takeovers) in a United Kingdom prison. Their approach is best characterized as being quasi-graphical in nature. The technique was refined by Sheridan and Abelson (1983) who updated the method in a study of employee turnover. Taking a different tact, (Oliva, Peters, and Murthy (1981) modeled a collective bargaining situation that used a set of rule-based predictions about the dependent variable’s behavior. Their method made predictions about bargaining system behavior

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then used a Chi-square type measure to assess the accuracy of their predictions. Although more empirically satisfying than the Zeeman et al. (1976) method, it was simple and ad hoc. While these were important first attempts, they tended to be of limited value. An important step was made by Loren Cobb (1978, 1981) who examined statistical distributions for catastrophe models in the biosciences. Drawing on Cobbs analytical work, Guastello (1982, 1995) developed a promising statistical specification for the cusp model by starting with the following deterministic equation: dz = (Z 3  ZY  X) = 0. By inserting beta weights and setting dt equal to 1, he then developed the statistical expression: delta Z = Z 2  Z 1 = b 0 + b 1(Z 1)3 + b 2Z 1Y + b 3X + e. A limitation of both Guastello’s and Cobb’s methods is that they do not allow for a priori specification of the variable types. Instead these techniques find a catastrophe if it exists, identify (in a probabilistic sense) which independent variables are associated with the control factor (X in Eq. (1)) and which independent variables are associated with the splitting factor ( Y in Eq. (1)). Clearly, this is a problem when the researcher is trying to develop a confirmatory estimate for a specific catastrophe model. Additionally, Guastello’s and Cobb’s approach requires that the dependent variable be univariate. This limits the usefulness of these approaches for organizational research where latent (i.e., composite) variables are more often appropriate. One approach to the estimation problem was developed by Oliva, DeSarbo, Day and Jedidi (1987). Their method, called the General Multivariate Methodology for Estimating Catastrophe Models (GEMCAT), used a scaling approach that allows for a priori variable specification that includes multivariate constructs for all cusp variables. Oliva et al. (1987) approached the estimation problem as follows, shown in Eqs. (5) (6) and (7): Yt* ¼

J X

Yjt bj ;

ð5Þ

Zit ai ;

ð6Þ

Xkt ck ;

ð7Þ

j¼1

Zt* ¼

I X i¼1

Xt* ¼

K X k¼1

where, X*, Y*, Z* are the values of the latent variables for observation, and a, b, and c are the weights (impact coefficients) of the measures of the constructs X, Y, Z in Fig. 2. The approach is essentially the same as found in a variety of commonly used multivariate techniques. This allows the catastrophe Eq. (8) to be rewritten in terms of the definitions above: 0 ¼ Zt*3  Xt*  Yt* ! Zt* :

ð8Þ

From Eq. (9) the estimation goal is to minimize Eq. (8): min U ¼ te2t t ¼

ai ;bj ;ck

T  X 2 Zt*3  Xt*  Yt* ! Zt* ;

ð9Þ

where the error is equal to e t . For a given set of measures on the constructs, the object is to estimate the impact coefficients that define their respective latent variables. This is accomplished by making U as close to zero as possible (Oliva et al., 1987). The GEMCAT technique has been successfully applied in a number of different organizational and behavioral situations (Isnard & Zeeman, 1976; Kauffman & Oliva, 1994; Oliva, Oliver, & MacMillan, 1992; Lange, Oliva, & McDade, 2000). The original GEMCAT system never achieved widespread use. Since the program was developed as a prototype, it was rather inflexible, user-unfriendly, needed programming experience in APL, and required purchase of a compiler. In addition, the program did not provide any statistical tests on the parameters. Consequently, only a few researchers actually took the trouble to create the program, from a listing that was provided by the authors, and use it for their research. Using the basic multivariate conceptualization of the original GEMCAT, Lange et al. (2000) developed a reformulation called GEMCAT II, which was Windows compatible and added a number of new features. Working with one of the original GEMCAT authors, the new program was further improved and subjected to rigorous testing (Lange, 2000). The version used in this research, GEMCAT II V1.3, is a compiled executable generated by Borland’s Delphi V3.0, which exploits the built-in pointand-click facilities that are native to this language. Relative to the original GEMCAT, it features a slight generalization of the latent variable definitions and weight constraints, and it finds in a fraction of the time due to an efficient combination of minimization algorithms. These improvements made it feasible to implement resampling tests to determine the statistical significance of the indicator weights by bootstrap and jackknife procedures. In addition to facilitating competitive model testing by writing SPSS syntax files, the program also provides a Pseudo-R 2 index of model fit together with an approximate F-test of statistical significance. Alexander, DeShon, and Hanges (1992) note in their comparison of Cobb (1981) versus the GEMCAT approach, that for exploratory situations in which theory construction is the focus, or when the existence of catastrophe data is the issue, and univariate dependent measures are sufficient, Cobb related approaches are an appropriate choice. However, the GEMCAT approach is the best choice for theory testing or confirmatory contexts and those requiring multivariate indicators in the dependent variable. Given the present work is confirmatory (a catastrophe model has been specified), and the improve-

M.F. Smith et al. / Industrial Marketing Management 34 (2005) 614–628

ments in estimation provided by GEMCAT II, the following analyses are based on this program. We also note that the operationalization of the variables is consistent with the approach in Lang, R., Oliva, T., McDade, S., (2000). 5.7. Operationalization of the dependent variable (supply chain response) Two variables were developed. The two dependent indicator measures of adoption of supply chain response are defined as follows: Z 1 = Back orders as a percent of total customer orders; Z 2 = Completed orders shipped as a percentage of total customer orders. A key element in any supply chain system is the system’s capacity to process orders correctly and ship them in a timely manner. One indication of the latent variable system responsiveness is the number of back orders as a percent of total customer orders. An increase in the number of back orders may be an indication of an inventory management problem or an issue with processing complete orders in a timely manner. Completed orders shipped, as a percent of total customer orders, is an indication that the supply chain system is responsive in terms of processing the appropriate assortment or number of items to meet customer requirements. 5.8. Operationalization of the independent X-variable (customer pressure) X 1 = Extent of increase or decrease in number of customer complaints, over the past 6 months; X 2 = Extent of increase or decrease in number of stock-outs, over the past 6 months. Customers may increase pressure on a firm’s supply chain systems for a number of reasons including a lack of system responsiveness to current customer needs or an inability to respond appropriately, as defined by the customer, to changing customer needs. In either case, in light of the importance of appropriate supply chain response to the customers’ ability to conduct business profitably, customer pressure will increase as reflected in the number of customer complaints. Apart from measuring complaints directly, another key element in supply chain performance is stock-outs. Increasing stock-outs may be a sign of a decline in customer service, which may result in more customer pressure given that stock-outs impede the customers’ ability to provide high levels of service to their downstream customers. 5.9. Operationalization of the independent Y-variable (supply chain inertia) Y 1 = Number of days to provide input for the launch of a new product or service; Y 2 = Number of days given to provide input into planning the sales forecast.

623

Earlier, it was suggested that the responsiveness of the firm’s supply chain operations to customer pressure was a function of the amount of inertia in the organization. Operational rigidity inhibits the firm from responding to changes in the supply chain environment; in this case, responsiveness to customer pressure. One dimension of rigidity is responsiveness of the supply chain to execute the firm’s strategy and meet customer requirements through the effective planning and organization of the activities necessary to meet customer demand such has having the appropriate mix and depth on inventory on hand. For the latent variable supply chain inertia, we are measuring the firm’s integration of supply chain management into operational issues such as the time horizon provided supply chain managers for planning the launch of a new product or service and input into the sales forecasting process. Organizations that provide opportunities for supply chain managers to better plan for executing marketing strategies are in a better position to be responsive to customer demands. Where supply chain managers are not included in the planning and execution of marketing plans, sub-optimal performance is likely to result as supply chain managers struggle to make sure that the right mix of inventory is available and can be provided in a cost effective manner. In this case, inability to anticipate and respond to changes in customer demand inhibits the firm’s ability to respond appropriately to customer pressure.

6. Results Out of the 102 returned surveys we had 87 completed questionnaires with responses to all included variables representing 87 firms. Standard preprocessing of the data was done. Specifically, all responses were standardized. Then basic descriptive statistics, correlation analysis, and a cluster analysis were run to check for any anomalous responses. We also ran several regression analyses to see if a simple linear model would be sufficient, using the average of Z 1 and Z 2 as the dependent variable. The adjusted R 2 for this variable set was 0.025. Next we ran GEMCAT on the 87 respondents using both the Bootstrapping and Jackknifing options for parameter estimates. For the bootstrap and jackknife procedures we used 500 samples and 87 iterations respectively. All impact coefficients are significant at the 0.10 level or better, with the exception of X 1, which was fixed to meet the GEMCAT (Oliva et al., 1987). Table 1 gives the impact coefficients and results for the fitted model. The relative size of the impact coefficient gives its relative importance on determining the magnitude and direction of its respective latent variable. For example, X 1 (customer complaints) accounts for approximately 75% (sum of impact of X 1 and X 2 divided by impact of X 2) of the impact on the Latent X variable (customer pressure) as compared to X 2 (stock-

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outs). For the latent variable supply chain inertia, Y 2 (input into sales forecasting) accounts for approximately 63% of the impact versus Y 1 (launch of a new product or service), while Z 1 (back orders) and Z 2 (completed orders) have roughly equal impact on the Latent Z variable (supply chain response). GEMCAT generated the resulting latent variables shown in Appendix A. A correlation analysis showed the three latent variables are orthogonal (highest correlation between the dimensions was 0.216). Also included in the table are the estimated inertia measures for each firm (respondent organization). The inertia estimates are calculated by looking at the directional derivatives on the estimated surface for a given firm’s location (x, y, z set). In this case we are looking for the tangent in terms of (X) customer pressure and (Z) supply chain response, since we are assuming that in the end, customer pressure will drive response for a given inertia level. The equation for firm-inertia in this direction is the inverse of the slope at the point, and is equal to (3Z 2  Y = firm-inertia) at P(x, y, z).

7. Discussion We will provide implications of this study both in terms of the relationship between the three latent variables and their representation in the cusp model presented in Fig. 2 and then discuss the implications for a specific firm’s response estimate inertia, based on the catastrophe model. The estimated model fits the proposed relationships between customer pressure, supply chain inertia and supply chain response identified in Fig. 2. Specifically, as inertia increases, the ability of the firm to respond to customer pressure is reflected in lower levels of supply chain response. Conversely, lower levels of inertia are associated with higher levels of supply chain response as the firm is more willing to respond to

Table 1 Results of estimation procedure Manifest variables

Impact coefficient values

Sig. level a

X1 X2 Y1 Y2 Z1 Z2 Model sum of squares 0.144132 Residual sum of squares 0.0007460

0.01000 0.00323 0.03210 0.05350 0.02960 0.02980 Pseudo-R 2

0.000 0.010 0.100 0.030 0.025 0.030 df1

Est. Est. Est. Est. Est. df2

Pseudo-F

0.9529

4

82

414.5737

Fixed value

customer pressure. The individual impact coefficients in Table 1 indicate that customer complaints are the principle driver of customer pressure, relative to the level of stock-outs. The impact coefficients indicate that the relationship between complaints and stock-outs is negative and the overall impact of pressure on supply chain response is positive. This relationship is consistent with our assertion that customer pressure increases as customer complaints rise, which is associated with higher supply chain responsiveness. As customer pressure increases one would expect that the frequency of stock-outs would be reduced, as the firm becomes more responsive to complaints. The model represents the impact of customer pressure on supply chain responsiveness as mediated by organizational inertia. The positive impact of the latent variable for inertia suggests that providing supply chain managers with longer time to plan for changes in the firm’s marketing programs increases the responsiveness of the supply chain to customer pressure. In this model, integrating supply chain management into the forecasting process is a critical dimension of inertia and supply chain responsiveness. The effective integration of supply chain management into the forecasting process provides logistics managers with the ability to better anticipate and prepare for the demands that may be placed on the supply chain system. The ability to anticipate potential operational problems before they arise increases the firm’s flexibility and ability to respond to customer pressure and increases supply chain responsiveness. The impact coefficients for the latent variable representing supply chain response are also positive, demonstrating that high levels of supply chain responsiveness indicate more optimal performance, as shaped by higher levels of customer pressure and lower levels of organizational inertia as depicted in Fig. 2. The impact coefficients are represented by back orders as a percent of total orders and completed orders as a percent of total orders. The relationship between the two impact coefficients for supply chain response is positive. A sign of the effectiveness of any successful supply chain management system is the processing and shipping a high proportion of completed orders. Order completeness, as compared to partially filled orders, both reduces customer complaints and decreases overall supply chain costs. The positive association with backorders, on the other hand, may reflect a lagged effect between the operations of the focal firm and the entire supply chain system linking the manufacturer to the reseller or industrial user. In this case, back orders may be increasing in response to customer pressure that increases supply chain responsiveness to problems in the presence of lower organization inertia, as indicated in Fig. 2. More importantly, the model also allows us to estimate a specific firm’s response-inertia. If we had longitudinal data for these firms, we could better understand each

M.F. Smith et al. / Industrial Marketing Management 34 (2005) 614–628

625

0.20 0.18 0.16

59

0.14

SCRESPONSE

0.12 0.10 84 50

78

69

0.08 0.06

74 42

0.04 0.02

52 33

23

47

31 1363 30 73 48 56 38 41 43 6 32 53 1561 79 46 58 7 65 14 51 2 3 77 11 85 75 80 72 861 40 17 62 29 129 8 16 67 24 57 20 22 35

0.00

87 71 34 54 68 10 83 39

49 18 4

25

55 45 37

-0.02

27

36

-0.04

64 66

5 44 21

-0.06 -0.08

28 70 82

-0.10 -0.1

26 19

0.0

60

81 76

0.1

0.2

SCINERTIA Fig. 3. Firms distributed on top and bottom sheet.

firm’s situation and track its response-inertia over time. In particular, we are unable to know the history of the firms and do not know if they switched from one surface to another. Also, this does not allow us to make statements about any sudden shifts and simply gives us an idea as to how this set of firms seems to fall. We note that the distribution of firms is not uni-modal (an assumption of catastrophe theory). However, based on the distribution of firms (see Fig. 3) we note the split in the surface’s two sheets is around  0.04 (Latent Z). This is one of the markers indicating we have a catastrophe type response surface. It also appears from the distribution that a majority of firms in the sample are on the top or responsive sheet of the surface, but only barely so (i.e., above  0.04 on the Latent Z). In fact only 13 firms are on the lower surface. While this implies that most firm supply chains’ (74 out of 87) are on the responsive surface, 59 out of 74 firms in the area are under 0.04 indicating as low responsiveness. Looking at the last column in Appendix A the individual firm response inertia’s are given. These range from a low of  0.071 to high of 6.843 with a mean of 4.97. The inertia values are scaled so that positive numbers mean higher inertia (lack of organizational responsiveness) and negative numbers that reflect low inertia (high organization response). Most firms have higher rather than lower response inertia. This would appear to be a characteristic of organizations in general. While doing a detailed analysis with both the inertia and surface location information would require longitudinal data, we still can get some idea of what is going on by examining the firm response inertia. For example, firm #65 has relatively low customer pressure ( 0.00244) and supply chain inertia (0.07243),

and is apparently responsive (0.01173). One might surmise that the firm is able to respond to customer service issues regardless of the amount of customer pressure. This is reflected in its own response inertia, which is low ( 0.031). Descriptively, firm #65 has all of the necessary criteria for a responsive supply chain operation: it is a small to medium sized firm (sales approximately 50 million) that processes and ships paper stock and finished fabric products which are typically susceptible to obsolescence such as spoilage or the vagaries of fashion. Conversely, firm #70 has high overall supply chain inertia (2.102), but feels relatively high customer pressure (0.00023), has high organizational inertia (0.05335) and a low supply chain response ( 0.08263). This firm has a positive inertia value (2.102) that is consistent with the proposed model. This is a medium size firm (sales less than 500 million with less than 500 employees) that has not made the necessary investment in information technology to be more responsive to high customer pressure in the context of an unresponsive organizational structure. This may represent a typical firm that has invested little in technology. Here is a case where we need to know the past trajectory of the firm to make accurate statements. An example of a firm that operates in an intensely competitive mature industry and is experiencing lower levels of customer satisfaction, company is #81 that is a large department store retailer. This company is experiencing relatively low customer pressure ( 0.007), but exhibits high supply chain inertia (0.133) and a low level of supply chain response ( 0.076) and, as predicted, high levels of firm inertia (1.869). The high levels of inertia and lack of supply chain responsiveness may be a

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reflection of the maturity of the firm and the industry it operates in.

8. Management implications and limitations Ours is an exploratory study, which attempts to apply Catastrophe Theory driven modeling approach to the study of supply chain responsiveness. To this end, the authors sought a cross-section of many different firms across several industries to apply the model developed here. In our attempt to address supply chain responsiveness, in the context of a heterogeneous sample, we were unable to address qualifying factors that may shape responsiveness in specific instances. For example, operational issues that may impact supply chain management such as the importance of optimizing buyer–seller logistics in the form of just-in-time programs are not addressed. Furthermore, the authors were unable to identify the relative proportion of orders that reflected produce-to-order versus produce-to-sock shipments. The assumption here is that a description of what was produced or shipped was sufficient to categorize the operations modeled here as produce-to-stock. Future research should address supply chain responsiveness across various bproduce-toQ scenarios. We believe, however, that this research has shown that inertia in the management of supply chains can seriously affect the operational efficiency and productivity of a company. Because supply chain costs account for as much as 30% of a firm’s overall operating overhead, (Lancioni, 1986) any delay in responding to customer related issues that could reduce the order flow and sales revenue must be eliminated as quickly as possible. As pointed out by M. Christopher, bTurbulent and volatile markets are becoming the norm as life cycles shorten and global economic and competitive forces create additional uncertainty. The risk attached to lengthy and slow-moving logistics (inertia) pipelines has become unsustainable, forcing organizations to look again at how their supply chains are structured and managed (Christopher, 2000; p. 38).Q The findings in the study show the direct impacts of management inertia on the important operating areas in supply chains. Also, the study provides managers with insight into the tell-tale signs of when and where management inertia begins. Flexibility, agility, responsiveness, and speed are the key success factors in the management of a supply chain. Inertia thwarts these elements and prevents them from taking hold. Managing a supply chain is a multidimensional process where logistics managers are responsible for a number of operating areas that necessitate quick response solutions in order for a company to prevent the loss of market share or losing its differential advantage in a distribution channel. Companies like Pillsbury, Campbell Soup, ADM, Proctor & Gamble, Quaker Oats, and Fisher

Scientific owe their success and growth to highly responsive supply chain management structures where customer issues of all types are resolved in the shortest time possible and where management inertia has been eliminated. Proctor & Gamble, for example, has a bquick shipQ policy in all of its divisions for out-of-stocks. Any inventory short falls will be resolved in less than 24 h for all of orders of $10,000 or more. Fisher Scientific assigns customer-service teams to accounts that do more than $5 million a year with the company. Quaker Oats developed a bFast ResponseQ program for its major accounts that covers all operational areas of their supply chains including order tracking, order-placement, transportation scheduling, carrier analysis, warehousing, shipment damage, and packaging. Why management inertia develops in firms is hard to determine. Is it management complacency? Are sales and profit success their own worst enemies? Is it metaphorically like the bostrich sticking its head in the sand? Is it senior management’s lack of understanding of how supply chains function? Is it caused by company culture? Whatever the cause or causes, it is dangerous and should be eliminated.

Appendix A. Latent variable constructs and firm response-inertia estimates Respondent

Latent X

Latent Y

Latent Z

Number

Cust. pressure

Supply chain inertia

Sply. chn. response

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29

0.00512 0.00290 0.00290 0.01267 0.00244 0.00778 0.00512 0.00290 0.00290 0.01534 0.01000 0.00511 0.01000 0.00023 0.00511 0.01312 0.02068 0.00245 0.01267 0.00512 0.00245 0.00512 0.00245 0.00244 0.00290 0.00511 0.00512 0.00511 0.00511

 0.07243  0.0361  0.0361 0.10086  0.07243 0.03937 0.0282 0.0282 0.0282 0.07152  0.02212 0.0282  0.07243  0.07243  0.02212  0.07243  0.07243 0.09387 0.06453  0.07243  0.07243  0.07243 0.05335  0.04727 0.08269 0.0757 0.1875 0.03656 0.00304

0.00229 0.00229 0.00229 0.00576 0.06206 0.01173 0.00229 0.00229 0.00229 0.01173 0.00229 0.00229 0.01173 0.00229 0.01173 0.00229 0.00229 0.00229 0.08263 0.00229 0.08263 0.00229 0.04675 0.01064 0.00229 0.06862 0.00229 0.06206 0.00229

Responseinertia est 0.07086 0.03453 0.03453 0.110813 1.083003 0.080648 0.029773 0.029773 0.029773 0.112798 0.02055 0.029773 0.03115 0.07086 0.019158 0.07086 0.07086 0.095443 2.112845 0.07086 1.975885 0.07086 0.709019 0.01331 0.084263 1.488311 0.189073 1.191993 0.004613

M.F. Smith et al. / Industrial Marketing Management 34 (2005) 614–628 Appendix A (continued) 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87

0.00512 0.00512  0.00511 0.02290 0.00778 0.01534  0.00511 0.00245 0.00512  0.00778 0.00245  0.00244  0.01267  0.00511 0.02557  0.01000  0.00511 0.00512  0.01000  0.00511  0.00022 0.01045 0.00778 0.00245 0.00512  0.01000 0.00245  0.01534  0.00778  0.01000  0.01534 0.00512  0.00244 0.00512  0.01534  0.00244  0.00511 0.00512 0.02824 0.00512 0.00023 0.00512 0.02557  0.00511 0.00023  0.00511  0.00778  0.01000 0.01045 0.00512  0.01267  0.00733 0.00778  0.01801 0.00245 0.00512  0.00244  0.00244

0.07243 0.02911 0.07243 0.04727 0.08269 0.07243 0.02212 0.15117 0.07243 0.05054 0.07243 0.07243 0.07243 0.07243 0.07243 0.13301 0.07243 0.02212 0.00304 0.1875 0.07243 0.0361 0.02121 0.07243 0.05335 0.11902 0.07243 0.0361 0.00304 0.07243 0.09667 0.01793 0.00304 0.07243 0.10785 0.07243 0.11902 0.07243 0.05754 0.07851 0.05335 0.04636 0.07243 0.00304 0.07243 0.04727 0.14418 0.02212 0.03937 0.07243 0.07243 0.13301 0.02212 0.08269 0.07243 0.0282 0.07243 0.06453

0.01173 0.01173 0.01173 0.04675 0.02439 0.00229 0.02927 0.01064 0.00229 0.01064 0.00229 0.01173 0.04675 0.00229 0.06862 0.00229 0.00229 0.04675 0.01173 0.01173 0.08178 0.01173 0.04675 0.00799 0.01173 0.00229 0.01173 0.00229 0.00229 0.15183 0.08263 0.01173 0.01048 0.00229 0.05968 0.01173 0.05968 0.00229 0.01173 0.08178 0.08263 0.04675 0.00229 0.01173 0.05718 0.01064 0.08263 0.00229 0.08178 0.00229 0.00229 0.07607 0.08263 0.01173 0.08178 0.00229 0.00229 0.04675

0.03115 0.012168 0.03115 0.608399 0.261152 0.07086 0.2349 0.185133 0.07086 0.084503 0.07086 0.03115 0.583239 0.07086 1.340181 0.134583 0.07086 0.633549 0.044318 0.228778 1.933961 0.005178 0.676879 0.05328 0.094628 0.120593 0.03115 0.03453 0.004613 6.843275 2.144985 0.023348 0.035989 0.07086 1.176361 0.03115 1.187531 0.07086 0.098818 2.084901 2.101665 0.702029 0.07086 0.044318 0.908436 0.01331 2.192495 0.02055 2.045761 0.07086 0.07086 1.869003 2.026195 0.123968 1.933961 0.029773 0.07086 0.720199

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Richard A. Lancioni is a Full Professor of Marketing and Logistics, and Chair, Marketing Department, Fox School of Business and Management, Temple University, Philadelphia, PA. He has authored more than 120 articles in the field of logistics and marketing and has conducted numerous seminars for many of the Fortune 500 companies including IBM, General Motors, Exxon-Mobil, Roche Pharmaceutical, DuPont, Coca Cola, and many others. He has lectured and given seminars around the world including Europe, Japan, Australia, and South America. He is recognized as one of the leading logisticians in the US. His research interests include customer service, pricing management, supply chain management, and marketing. He is a member of the American Marketing Association and the Council of Logistics Management. Terence A. Oliva is a Full Professor of Marketing at Temple University’s Fox School of Business and Management. He has eclectic research interest and has published in a wide variety of journal including Mgt. Sci, JM, JMR, JCR, AMJ, Org. Sci., AMR, Behv., Sci., and others. His recent research focus has been in Catastrophe Theory applied to high technology products. He helped develop the GEMCAT program for estimating catastrophe models. Michael F. Smith is currently an Associate Professor of Marketing at the Fox School of Business and Management, Temple University, Philadelphia, PA. He has authored numerous articles in the fields of retailing, marketing, pricing, and channels of distribution. He has conducted numerous seminars for many of the leading US business firms. His research interests include channels management, retailing, supply chain management, and marketing management. He is a member of the American Marketing Association and the Council of Logistics Management.