Coping with the Build-to-Forecast Environment

JOURNAL OFOPERATIONS MANAGEMENT Vol.9. No.2. April 1990

Coping with the Build-to-Forecast Environment AMITABH

S. RATURI*

JACK R. MEREDITH* DAVID M. MCCUTCHEON* JEFFREY D. CAMM*

EXECUTIVE

SUMMARY

High-value-added manufacturing companies today confront a competitive trend toward greater product customization in the face of reduced response times. This scenario is encountered most often in industries like machine tools, heavy construction equipment, heavy manufacturing in general, and computer software and hardware. The product is highly customized, yet competition requires manufacturers to deliver it with lead times significantly shorter than the manufacturing lead time. Generally, the scheduling practice here is to release the manufacturing order before the customer order is released, and subsequently match incoming customer orders to units in progress. This is referred to as the “build-toforecast” (BTF) approach. This study investigated the coping mechanisms used by manufacturing firms to alleviate this dilemma. The tactics vary with the firm’s business strategy, its operating environment, and its capabilities. We report on three case studies from firms building heavy machinery. The firms are similar in terms of the range of final product values, build times, customer delivery times, and the very large number of components. Also, their operations require the use of a variety of flexible and dedicated resources. Flexibility in manufacturing processes, modular bills of materials, subcontracting and expediting are some of the approaches that these firms use to help resolve the double bind of short lead times and high levels of customization. We review some of the operational problems peculiar to the build-to-forecast environment and suggest alternative approaches for dealing with them. The coping mechanisms are grouped according to the manner by which they help relieve the BTF problem’s severity. One set of mechanisms makes the problem less complex by simplifying products or the production process. Another set reduces the risks due to uncertainty in demand or supply. The third set provides engineering and manufacturing slack. While some or all of the mechanisms are used by the manufacturing firms studied, the predominance of particular mechanisms in each firm is explained by a contingency model developed in this paper. The case studies provide useful insights into the nature of the problem, and how the firm’s organizational environment often dictates the choice of mechanisms used to alleviate it. For example, these firms minimized their scheduling dilemmas with modular product designs, flexible processes, informal organization structures, or formal control mechanisms for limiting customization. We conclude by framing a number of research questions whose solutions would help such firms better manage their operations.

Manuscript received October 20, 1989; accepted October 30, 1990, after two revisions *University of Cincinnati, Cincinnati, Ohio 45221-0130

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INTRODUCTION For some manufacturing firms, recent trends in competitive conditions have created unique problems for their operations. New requirements have led to situations that do not necessarily fit with traditional operations management models. One important example of this is the creation of a different environment for manufacturers of high-cost, semi-customized products. Researchers have typically treated production scheduling problems of firms manufacturing customized, high value-added products as resource-constrained project scheduling problems (see Womer and Gulledge (1983) and Camm and Womer (1987)). Hendry and Kingsman (1989) and Robinson (1985) provide a summary of production planning problems in make-to-order (MTO) firms and lament the lack of attention they are given. The inherent assumption in MT0 production is that firms begin production of a customer order after the production order is received. These firms can usually afford the luxury of long delivery lead times or operate on some sort of a contractual basis with the customer. However, for a number of firms in the heavy manufacturing industry, the competitive environment has changed dramatically in the last decade. A predominant critical success factor, or “order winner” (Hill (1989, p. 38)), in these markets has become a short delivery lead time (Meredith (1988), Bower and Hout (1988), Schmenner (1988), Stalk (1988), Meredith and McCutcheon (1989), Nicholson (1989)). Shorter delivery times can have significant other benefits too, such as improved cash flow for the firm and lower inventory costs. To accommodate the delivery time constraints, many heavy manufacturing firms have implemented what they refer to as a “build-to-forecast” (BTF) schedule. This approach consists of forecasting end-product combinations of model variants, creating a master schedule of these end products, and then releasing production work orders before specific customer orders are received. The planned end products use component items that are largely unique to only a few variants, expensive to produce and stock, and have long lead times. Indeed, some of the component items, and usually the most expensive (such as castings), are ordered months before customer orders are received. When the customer orders arrive, the delivery lead time will typically be much less than the full “build” time (about half, in practice) and is set by the remaining work on the end products. The process may be visualized as follows. The products, perhaps 10 or 20 in various stages of being built, pass sequentially through the production process in 1.5 to 30 or so weeks, with one being shipped every one to three weeks. The production process is carefully balanced, with critical production skills being applied as they are planned in the master schedule. However, although the product is relatively customized, the manufacturer must still deliver it within a short lead time. Orders come in every one to three weeks for specific end-item combinations with delivery lead times of half the production time, say 7 to 15 weeks. Unfortunately, the customer orders rarely match the end products being built or, if still early in the production process, the combination of variants that were planned to be added. If it is still early in the process, the variant changes can still be made (if the basic model is an appropriate one) and the production process altered to accommodate the actual order. Other times, major changes may have to be made in the end product, with certain variants taken off the end product of an appropriate model and other variants put in their place; on occasion, this process will be so extensive that a loss is incurred. But sometimes there is not an appropriate unsold model to alter, even at a loss. And also, there are frequent occasions when products reach the end of the production process and do not have a

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customer. These are the major risks in the process and the greatest problem for management. This scenario is encountered most often in heavy manufacturing industries such as machine tools, construction equipment, and industrial machines. Miller (1979) addresses a similar situation through “hedging” in the master schedule. In Miller’s situation, orders are initiated with large safety stocks at the lowest level of the bill of materials until more information is available. As more accurate estimates of the actual orders are obtained, smaller safety stocks are used for midlevel parts. Then, when the actual orders are received, all safety stocks are dispensed with for the upper level items in the bill of materials. Miller offers this as a more cost-effective approach than holding safety stocks of finished goods. This situation differs from Miller’s in that the lot size here is one-there are no opportunities to cut back on the batch size or safety stocks. Moreover this is not a standardized set of enditems; every end-item may be largely customized. Also, in the typical material requirements planning (MRP) situation, the lowest level in the bill of materials consists of the cheapest parts that are combined to form components that in turn are combined to form subassemblies, and so on, increasing in value with level. In the BTF situation, the most expensive parts are at the lowest level of the bill of materials and consist of such items as castings and bed frames. Thus, ordering large safety stocks at this point is not feasible. It is also important to note that this is not the typical “assemble-to-order” (ATO) situation either; rather, the AT0 situation is more a subset of our BTF problem. In both situations, a forecast of end-items is made and in both situations the actual customer orders come in before the end products are completed. However, in the AT0 situation the build process stops at a predetermined point and WIP inventories are held until customer orders arrive. There is no chance of building the “wrong” end product and there is no chance, if inventories are properly maintained, that there might not be the right combination of items to produce any customer order. In the BTF situation, there is no stopping point in the production process and buffer inventories are avoided. Customer orders arrive throughout the production process and are matched to items in any state of production that will meet the due date. Also, the volumes are much lower in the BTF situation than is typical of the AT0 situation. As an example of the manager’s dilemma, we spoke on one occasion with a manager who had two very large machine tools (about a million dollars each) nearing completion without customer orders attached to them and did not know how he was going to dispose of them. Holding finished goods inventory is not only impractical but financially (and even physically) virtually impossible. Also, it is not practical to try to pull the machines out of the production process until they get a customer order to attach to them, i.e., hold WIP because of the disruption to the master schedule (as well as the financial and physical infeasibility). How firms cope with this situation is the problem we are addressing here. There are two basic approaches that might be taken to solve this problem. One approach is to reduce the problem’s severity-in effect, to reduce the gamble by somehow limiting the occurrence of a mismatch between customer orders and production units in progress. The second approach is to find better scheduling practices that would optimize the process of matching partially completed units to customer orders. Our discussion here is directed toward the former approach. Specifically, we identify operational strategies and coping mechanisms that firms use to lessen the problem’s impact. Follow-on research that concentrates on the scheduling approach is currently being conducted. The next section describes our research questions and methodology. We then report on three case studies we conducted to investigate current practices in the BTF environment of one

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industry. We use the case studies to develop a framework of the organizational mechanisms used to cope with the BTF problem. In doing so, we empirically substantiate and augment the contingency model of Van Dierdonck and Miller (198 1) for the situation where the critical competitive factor is speed of delivery. We then summarize our research findings with a brief discussion of BTF production scheduling problems and the need for future research in this area. RESEARCH

QUESTIONS

AND METHODOLOGY

There were two key motivations for this study. Anecdotal evidence (Meredith (1987)) suggested that the BTF problem previously discussed existed, but did not indicate the mechanisms being used to cope with it. Second, while research has emphasized the strategic concerns of firms working to reduce lead times, the organizational context has almost always been repetitive, make-to-stock production (see, for example, Hendry and Kingsman (1989)). We anticipated that a particular firm’s coping response would be contingent upon its environment, capabilities, and organizational structure, as noted in Van Dierdonck and Miller (1981). We also wished to determine the impact of such responses on the firm’s production and marketing tasks. The specific research questions that we posed for this investigation were: Ql . What are the specific operational strategies used by firms to deal with the complexities peculiar to the BTF environment? Q2. Do the operational strategies appear to be inlluenced by the firm’s business strategy, organization structure or capabilities? 43. Are the organizational responses and coping mechanisms the same across all organizations? If not, how do they differ and what are the impacts? To gather detailed, firm-specific information, the natural choice was to use a case study approach. The case study methodology suited the exploratory nature of this investigation, providing the needed background information and the means to develop initial frameworks. (See Eisenhardt (1989); Yin (1984); and Benbasat, Goldstein and Mead (1987) for rationales and explanations of the case study method.) Three sites were selected for in-depth study. All sites were in the machine tool industry and were known to be facing short-lead-time competitive situations, The selected sites were intentionally diverse-one privately held and two publicly held, one foreign-based and two domestic, and one small and two large-to capture a wide range of the BTF situation. From the experimental design perspective, this selection allowed comparisons similar to a fractional factorial research design. A series of interviews was conducted at each of these firms. A simple questionnaire was used to guide the discussions with manufacturing executives but the interviews were generally freeranging. At each site, interviews were conducted with the unit’s highest level manufacturing manager as well as with the manager(s) directly involved with scheduling and sales liaison. Other data (company background, performance, product brochures, etc.) were typically collected and reviewed before the interviews. Two or more investigators were involved with each interview and the sessions were usually tape recorded. Transcriptions and other interview notes were exchanged among the researchers soon after a visit to ensure that the information collected was complete, unbiased, and consistent. As noted, the machine tool industry was selected as the basis of our investigation because it was known to be facing reduced delivery lead times. We introduce the discussion of the case studies by providing some background information about the machine tool industry’s current practices. Much of the general information is drawn from various studies in the Cincinnati

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Machine Tool Studies Project (Meredith (1989a)). approach to dealing with the BTF problem.

Then,

a synopsis

is given of each firm’s

SCHEDULING PRODUCTION IN THE MACHINE TOOL INDUSTRY The pressure for reduced lead time in the machine tool industry has recently increased (see, for example, Meredith (1988)). Customers frequently cite delivery lead time as the primary factor in vendor selection. From the customers’ perspective, a delay in acquiring a particular machine may cause unacceptably high lost sales and/or unrecoverable losses in market share. As one manager noted, being able to quickly obtain the right machine for the market can mean a payback of only a few months or less. The relative importance of lead time as a significant purchase criterion in this industry is hence understandable (also see p. 106, U.S. Congress ( 1984)). The other critical aspect of the industry’s competitive environment is the necessity of offering a wide product range to suit diverse customer requirements. Volumes are relatively small and the significant differences among models make substitutions difficult. Since the individual items are typically very expensive and the demand for a specific, highly customized stock unit may never materialize, selling from stock is usually ruled out as an alternative for reducing the delivery lead time. Instead, the firm develops a forecast of demand for typical units, based on historical averages and salespeoples’ opinions. These forecasts are fairly distant (on the order of one year), since the average manufacturing build time is several months long. Procedures used for scheduling work in this environment appear straightforward (Meredith (1987) gives an example of one such scheduling methodology). However, they can result in severe problems at the manufacturing-marketing interface. Figure 1 depicts the typical manufacturing process and lead times in this industry. Lead time for some purchased items may be two to three times as long as the processing time. As with other heavy manufacturing industries, the longest lead times are for procuring large castings from international sources. There is usually limited flexibility in using the castings for other products. Other parts with long lead times include major components that determine specific product variants; an example of such components for machine tools is the pallet changer (while the pallet changer is an option, the machine that can be fitted with one is a “fixed variant”-see the Appendix for the definitions of some basic concepts used in this industry). Because of this product structure, scheduling in the BTF environment is usually initiated by a forecast of items by variant types. By about the halfway point in the manufacturing cycle, which may take three to five months, most parts have been authorized but the production order may still not have been pegged to a customer order (see the top chart in Figure 1). This would not be a problem for a firm that could sell whatever products it chooses to make, but few companies in these industries have that luxury. It is expensive to build units for which no demand materializes or conversely, to have the demanded variants unavailable. As orders trickle in over time, the disparity between projected and actual demand for specific variants must be dealt with. If orders can be matched with partially completed units, the delivery lead time for them is effectively reduced by the amount of preprocessing afforded by the forecasting, less the time required to rework the units to match the designs ordered by customers. If a manufacturing order is “firm” (i.e., matched to a particular customer order), the only remaining scheduling issue is whether any expediting must be done to avoid a delay to the customer. Indeed, if the order is firmed late in the manufacturing process, expediting may be completely avoided.

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However, as a unit progresses through later production stages without a firm customer order, it faces a growing risk of being completed and held as finished goods. In these industries, holding finished goods presents two significant problems. First, units are bulky and expensive to hold in either partially or completely finished form. The value added chart in Figure 1 illustrates that most of the value has been added by the time that customer orders are received. Second, a unit risks being rendered obsolete within a relatively short time because of the industries’ rapid technological advances. Despite these dangers, work on the unit may continue in the hope that a matching order will materialize. The production manager faces a dilemma in choosing between maintaining the schedule and adding value to a unit that may never have a customer, or unfreezing the schedule at some point to accommodate the mismatch, disrupting the production flow and/or under utilizing expensive production capacity. A final complication of the BTF environment is the impact of varied demand. Typical of capital equipment industries, the machine tool industry faces very cyclical demand, leading to severe problems in managing aggregate resources (Raturi and Amoako-Gyampah (1990)). In this environment, a decision to make a late match between a nearly completed unit and a customer order, with its likely large rework, may make financial sense if the demand is weak (since the opportunity cost of resources is small). However, the same policy may lead to unacceptably high rework and expediting costs when the demand for the firm’s products is high.

FIGURE 1 PROCESS IN THE MACHINE

THE MANUFACTURING

TOOL INDUSTRY

Percentage of Total Build Time

1

I

I

I

50

G40 Long-lead-tlme Items purchased

o PRODUCTION ACTIVITIES

I

I

60

70

a 60

1 100

Wlth &week dettvery lead times, machlnes are within these stages of the build cycle when promised to customers (20-40 week build time)

I-I

Machlnlng of major components

I 90

0

I

ek subassemblles

VALUE ADDED

ii 5 t 0 8

Flnlshlng and Runoff Testing

Assembly

YPICAL VALUE ADDED BREAKDOWN 5% Labor cost 20% Overhead 75% Materials of which:

6040-

30% Is raw materials 20% Is added wlth subassemblles 50% Is added In Flnal Assembly

0

10

Journal of Operations

I

I 20

30 40 50 60 70 Percentage of Total Build Time

Management

so

90

100

235

THE CASE STUDIES We conducted case studies in three firms making large industrial machines. The product is a high-value purchase for most customers who naturally expect some, if not a large, amount of customization. All three firms have been exposed to intense competition, centered primarily around responsiveness. At the same time, the build cycle for their products is typically very long. The manufacturing process in all the firms followed the pattern depicted in Figure 1: purchase of long-lead-time items, machining, unit assembly, final assembly and runoff. The overall build cycle varied from 20 to 40 weeks, depending on the product. Typically, competitive lead times for customer delivery are in the S- to 16-week range for standard items with catalog options, and the 20- to 25-week range for customized items. In all three firms, the long-lead-time items are usually castings that have procurement lead times of IO- 12 weeks, and total machining times of about 10 weeks. COMPANY

A

Company A is a manufacturer of large machine tools and competes primarily on quality and fast, on-time product delivery. It maintains a limited product line, specializing in one type of machine tool. Company A has developed considerable expertise in designing these machines and some of the control systems for them. The firm is privately owned and operates with relatively few levels of management. Lead time is a very important criterion in manufacturing planning. For example, the recent acquisition of a multi-million dollar manufacturing system was justified primarily on the basis of lead time reduction. The firm’s predominant near-term goal is to reduce their manufacturing cycle time using cellular manufacturing and just-in-time (JIT) concepts. One product line accounts for about 70% of the units shipped and close to 50% of the firm’s total sales. In this line, there are ten standard machines with five different power capacities and two different sizes. The ten standard models range in price from $30,000 to $100,000. Overall, the price of the line ranges from $30,000 (for a basic model) to over $500,000 for a large, highly customized machine. Most of the price differential for the expensive units is due to customization on the sophisticated electronic controls. Build time (excluding purchasing) is about five months and the machines are built to a forecast. Generally, most of the value is added in the last two weeks of parts machining; machining operations employ about 60% of direct labor. The other 40% of the labor is used in assembly, primarily the final assembly operations, which accounts for about 20-25% of the typical machine’s value added. The final stage following assembly is finishing, which employs only a few people in such jobs as painting and testing. Actual delivery times range from one week for common, low-end products which may be in stock to four months for more customized machines. In contrast to general industry practice, about ten standard machines are kept in stock. Lower end product variants constitute most of the models in stock; there may be none of a higher-end, less popular product maintained in this inventory. When a customer order for a standard machine materializes, the possibilities (considered sequentially) are: (1) Sell from stock, if a match exists. (2) Lock in an unmatched unit in progress. (3) If a machine with extra features is in stock, try to sell it. The customer may indeed be

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willing disabled (4) Supply This is COMPANY

to pay for these features. Otherwise, the options may be removed or simply for maintaining fairness among customers, if easily done. a temporary “loaner” of higher capability from the training/demo showroom. done primarily for the high-end machines.

B

Company B is a large international firm manufacturing a variety of machine tools and machining systems. Its product line is broken down by the size of the machines as follows: l

l l

small (cost < $100,000): medium ($lOOK-650K): large, including integrated

systems:

50% of sales 30-35% of sales 15-20% of sales

Company B sells its broad product line on the basis of the firm’s technological edge: product designs use recent advances in motors and controls but the technologies are well proven before being used for company B’s relatively high-volume production units. Most of the product design work is carried out by company B’s parent; the firm maintains comparatively little design expertise except in its field service/repair unit. Delivery lead times vary from ten days (small machines, some built to stock) to nine months (custom machines with long procurement, machining, and assembly lead times). The build cycle is about five to six months, longer (eight months) for large units and shorter (about three to four months) for the smaller units. Manufacturing procures castings, assembly kits and other longlead-time items based on a firm master production schedule. The schedule has a time horizon of six months. The lead time for procurement is about three to four months. The machining of parts takes about six to eight weeks, and is currently a bottleneck, due to the company’s recent increases in production volumes. The master production schedule has not taken the lags in capacity acquisition into account and company B is currently forced to operate with a large amount of overtime. Subassembly takes about two to four weeks, depending on the unit, and assembly and runoff vary from one and a half to four weeks. While this plant has a much longer assembly time than its sister units, the problems are due to inexperience, training, and turnover of the workers, as well as poor quality and erratic delivery of parts. Expediting would only reduce about 10% of the build time. The highest value-added components are the computer controls, added late in the build cycle, followed by castings (first in the build cycle), ball screws and head screws. In cases of mismatches between customer orders and available production units in process, company B will modify machines. If the modification work is within allowable time fences (which are delineated for each of the common modifications), the work may be done by the production department. Otherwise, modifications are done by the field service/repair unit that reports to marketing. To decide between retrofitting a machine or matching a customer order to a later unit, company B looks at the lead time to wait versus the customer’s importance (i.e., is it a small “Mom and Pop” outfit or a major customer?), plus the repair department’s backlog. COMPANY

C

This firm manufactures machinery with four product lines and competes primarily on quality, customization and on-time delivery. The two plants that manufacture their machines have separate charters. Plant CA manufactures small and medium parts while plant CB machines large parts and does the final assembly.

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Company C has considerable product design capability available to it. Maintaining a large design department has been essential for the firm’s emphasis on product customization. Through the company’s related operations, it also has process design experience available. Three changes planned in the near future are: (1) doubling production in the next five years, (2) moving all parts production to plant CA, and (3) providing a clean environment for final assembly in plant CB. In preparing for these changes, plant CA has engineered a master plan for modular production of all parts (about 12,000 for a typical machine). A typical unit costs about $150,000 and has a build cycle of about 30 weeks, consisting of 10 to 12 weeks lead time for purchases, ten weeks for machining, and the rest for unit assembly, main assembly, runoff, painting and packing. About 60-65% of the cost is locked in after machining (20 to 22 weeks into the build cycle), with electronic controls added in assembly contributing another $lO-15,000. The typical delivery time, in contrast, is about 8-12 weeks. The customer order usually arrives when the machine is 18 to 22 weeks into the build cycle. The company recently implemented a policy of not quoting a delivery lead time of less than eight weeks. About 10% of sales are accounted for by customized machines. The rest are based on modular catalog selections. The master schedule is created from a yearly forecast, which is reviewed at the end of every quarter. Changes to the production schedule are screened by a committee consisting of a representative from every group in the manufacturing function. A high percentage (about 90%) of the machines in the pipeline have already been sold. Salespeople do not have specific data on WIP, but are selling machines based on the same forecast from which the master schedule is made. While this procedure reduces the chances of selling machines that are unlikely to be in production, the implementation of the eight-week threshold for delivery time indicates that the problem still exists. Modularity of the options is the key mechanism to avoid matching problems. Major “teardown” or reconfiguration is infrequent for the same reason-modules can be quickly assembled and disassembled. Designing the machine with easily adaptable modules has also led to a significant reduction in product costs. A FRAMEWORK

FOR BTF COPING

MECHANISMS

There are several approaches used by these three firms to address the problem created by the simultaneous demands of increased responsiveness and customization. Both of these demands require flexibility in the organization to allow it to compete effectively (see, for example, Wheelwright (1989) or Slack (1989)). Swamidass (1988) refers to this as “Type 2” flexibility. As with his findings, our three case firms use multiple means of achieving this Aexibility. The overall framework for coping with the responsiveness-customization dilemma that emerges from our case studies is shown in Figure 2, a contingency model similar to that of Van Dierdonck and Miller (198 1). The BTF environment derives from a competitive situation that requires variety and customization in complex engineered equipment in the face of reduced delivery lead times. In combination with elements of the firm’s business strategy and organization structure, this environment (detailed in Figure 2) dictates an operations strategy that uses various coping mechanisms to reduce complexity, reduce uncertainty and provide slack (our interpretation of these terms is similar to that of Van Dierdonck and Miller (1981) and Galbraith (1973)). The firm’s choice of specific mechanisms is influenced by the elements of the business strategy and organization environment identified in the figure. It should again be noted that these coping mechanisms only reduce the severity of the BTF

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problem. The dilemma of matching partially completed units to incoming customer orders must still be performed. We are not addressing this matching problem or its attendant scheduling and expediting issues here. Even with the use of various coping mechanisms, the firm may still face a serious problem meeting the customization and lead time constraints. The coping mechanisms are grouped according to the manner by which they help relieve the BTF problem’s severity. One set of mechanisms makes the problem less complex by simplifying products or the production process. Another set reduces the risks due to uncertainty in demand or supply. The third set provides engineering and manufacturing slack. We discuss each group in turn.

COPING

MECHANISM

CHOICES

FIGURE 2 IN THE BUILD-TO-FORECAST

ENVIRONMENT

COMPETl77VE ENVlRONMENT

BUILD-TO-FORECAST ENVIRONMENT

I

FIRM SlTlJATlON

Expensive components, unique to specific products, must be committed to production well before customer orders received

BUSINESS STRATEGY a Importance of customer service vs.

I

r l

Decision-making formalization

Ownership: public or private Depth of management hierarchy a Performance evaluation criteria l Process & product design expertise l

l

TACTICS

COMPLEXITY REDUCTION D Product design l Process design

j-~--~ l

1. Complexity

Production capacity cushion

Reduction

The coping mechanisms in this group fall into two categories, according to whether they are designed to reduce the product’s complexity or the complexity of the production process. We start by looking at product design mechanisms. Product Design Company C used standardization to not only reduce the build cycle but also to simultaneously reduce the unit’s overall cost. Modular product design also helps reduce the time and cost of retrofitting machines, making the task of order matching less difficult. Given its abundant

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engineering capabilities, design for manufacturability is a natural mechanism for company C. In contrast, company B does not have this option afforded by slack engineering resources since the plant has little engineering capability. The impact of product design on process efficiency is well documented (see, for example, Bessant and Lamming (1989), and Sciberras and Payne (1984)); here we observed yet another example of product design enhancing a firm’s competitiveness. Company C’s substantial investment in product redesign over the last two years has paid off handsomely. The new line offers products with similar features but that are much cheaper to make. The modular design of the product also allows for easy reconfiguration. The substantial reduction in delivery times resulting from this has led to increased market share. Process Design

Process design is a common response among these firms for narrowing the gap between the delivery and build times. We found that each firm was swayed toward modular production to reduce the cycle time. In addition, cellular manufacturing has been adopted with the specific intent of facilitating faster movement of materials through the facility. Other mechanisms used here included adoption of flexible technology (usually automating major subsystems to reduce flow time or increase flexibility), “ship set” (full parts complement) scheduling for some components with the ultimate intention of reducing the lot sizes to one, and reductions in setup times. Company C, for example, is now in the process of reconfiguring to a full-fledged cellular manufacturing layout and is making substantial investments in flexible resources. Additionally, at company A, the theme of process flexibility was apparent not just from its maintenance of flexible engineering capabilities, but also from some of its recent process technology choices. The firm had made a large investment in customized flexible technology that will be used to simultaneously manufacture the four major components of its most common products.’ This allows for ship set production of these components, with lot sizes of one. Company C’s overall philosophy for scheduling is to fabricate ship sets. A flexible manufacturing system (FMS) processes a variety of part sets; the rest of the facility then coordinates its schedule to match that of the FMS-produced parts. This approach would be hampered if the supply of castings were erratic. However, the problem of procuring castings was not significant here, compared to A or B, since company C had spent considerable time developing a local foundry and had established a long-term procurement contract with it. In general, better control of the production schedule is an ongoing obsession with all the firms. All three companies use some form of master scheduling for overall planning. At the same time, they provide buffers to hedge against the highly uncertain events. 2. Uncertainty

Reduction

The second group of coping mechanisms includes two categories: supply management. We start by discussing the first of these.

demand management

and

Demand Management

Company B makes extensive use of demand management to limit problems in the production process; this was largely accomplished by altering the control systems and the reward structure. Here, management had established explicit time fences for each of the major design changes that might be carried out to match a unit in process with a customer order. This tactic effectively buffers the manufacturing function from the vagaries of customization. The company then evaluates the manufacturing function primarily on the number of units shipped to the marketing department. This has two effects: first, it forces marketing to make responsible forecasts (since

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they are responsible for any resulting unsold inventory) and second, it segregates the responsibility for efficiency and resource utilization from the responsibility for responsiveness. The former allows the firm to be cost competitive and the latter allows it to compete on the basis of customization (albeit limited) and delivery time. This segregation of responsibility is unique to company B among the firms studied. This division of responsibilities between the marketing and the manufacturing group requires that company B provide a special liaison between the groups. Changes to units in process must be negotiated through this liaison section. While about 50% of the potential specification changes can be carried out with little consequence, the remainder are subject to specific time fences, some of which are surprisingly restrictive. Several scheduling issues (which we do not address here) remain for accomplishing the liaison task effectively. These tasks include deciding what the master schedule should be, what time fences will be used for each of the variants, and specific details as to how the customer order should be matched with the production units in process. Only unassigned work-in-process (WIP), that is, production units not matched to specific customer orders, are available to the sales force. The sales force’s first priority is to sell what is in process. Matching is due date driven, so that the match for a new customer order is the appropriate unassigned machine nearest to completion. Sometimes, if in a bind, the liaison manager may assign a machine that was previously matched to another order. There has been no instance where a major teardown (that is, replacing already assembled components) was necessitated. The sales department does not have authorization to sell variants with long lead times; due dates are quoted only after manufacturing can give an assessment of how long it will take. Indeed, at this point in time, the sales department may well advise a customer to buy from one of its offshore sister units. The lack of full-fledged engineering capabilities also forces company B to turn away business to the parent unit if significant customization is desired. Thus, although this arrangement provides ample buffering for the manufacturing department, it limits the amount of customization that can be carried out. To compensate, the sales department conducts customization on completed, scheduled units. This task is performed by its repair shop. Managers pointed out that a significant problem with customization was providing the manuals, safety guidelines and training. Providing these necessary customized service elements often extended delivery times significantly more than did the equipment modifications. The final means by which company B manages demand is by having sales representatives negotiate due dates with customers, sometimes substituting units promised to other customers who are willing to wait. Supply Management

Companies B and C in particular sought to reduce uncertainty by stabilizing their supply of essential components. In company B, procurement of materials is the major source of uncertainty; as a result, the firm maintains some inventory of commonly demanded options to reduce stockout risk. In company C, the most critical uncertainty has been reduced by long term contracting of castings. 3. Slack Provision The third group of coping mechanisms is the reliance on slack resources in various forms within the organization. There were three areas in particular where these companies maintained considerable slack: finished goods inventory, engineering resources and production capacity.

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Finished Goods Inventory As noted earlier, company A holds a small stock of commonly ordered machines as finished goods inventory. Similarly, company B has a policy of level employment. As a result, the plant works near capacity even during low-demand periods. During these slow periods, B holds standard units in finished goods inventory as demonstrators in showrooms throughout the country. If sales are slow, the sales department augments the showroom space by leasing warehouses. This mechanism absorbs the mismatches in forecasted and actual demand as well as the differences between the company’s level production rate and sales. Engineering

Resources

Another source of slack is that of engineering resources that some firms use to facilitate reductions in delivery lead times. Company A maintains substantial in-house capability in design engineering that can be used, if need be, to engineer and develop most of their products’ components in case suppliers fail to deliver them. Orders for custom machines are less likely to delay the build cycle excessively at company A since it has substantial in-house capability to fabricate mechanical and electrical components. This coping mechanism has been hampered by the turnover of engineers. As the engineers change, so does the design philosophy, reducing the interchangeability of some long-lead-time components. Production Capacity Company A also makes use of slack in the form of its production process’s capacity cushion. Managers in company A view expediting as contributing to organizational effectiveness (for example, “it takes the fluff time out”). In one instance, the build time for a very large machine was effectively reduced to two weeks by extensive expediting. Since this was done without any apparent delay to other units or increasing the labor or other costs of expediting, the remark about “taking out the fluff time” seems correct. The company saw the improved cash flow from doing this as an advantage. RESEARCH

FINDINGS

All three firms face a competitive situation where the customers ask for, and expect, shorter delivery time. However, the problem was rarely seen by the managers as a scheduling or a forecasting issue. Instead, the schedule and the forecasts were usually viewed as sacrosanct and a wide variety of coping mechanisms were developed to absorb the mismatch risk without altering the firm’s methods of scheduling. The firms investigated in this study used operational strategies of complexity reduction, uncertainty reduction and slack provision, shown in Figure 2, to cope with the dilemma (Ql). While each of the firms used more than one of these strategies, their choices depended on the firm’s business strategy, organization structure and specific capabilities (Q2). There was significant evidence to suggest that the responses of the three firms had a wide variance (43). Business strategy had an impact by determining the coping mechanisms that a firm would use. For example, company B’s methods for increasing responsiveness while offering customization reflected its concern for maintaining certain functional goals. That is, because of its desire to be cost competitive and maintain stable employment, the company emphasized efficiency and high capacity utilization for manufacturing. Thus, manufacturing had only limited responsibility for customization, with time fences to guard against the disruption of manufacturing schedules. The company was willing to reduce its customization and absorb finished goods inventory to

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maintain production volumes and economies. The coping mechanisms at B are driven largely by an orientation toward cost containment (and high volumes) through limited customization. On the other hand, company C, which had often emphasized the role of engineering in its business strategy formulation, spent two years conducting an extensive redesign of its product line to allow it to offer some degree of customization despite its reduced delivery lead times. In contrast to company B, C’s manufacturing group (specifically, product and process engineering) was expected to be the primary source of the changes needed to meet the shifting market demands. The coping mechanisms at C are driven largely by an orientation toward finding engineering solutions. Organization structure affected how firms reacted in a number of ways. First, the number of layers of management had some important effects on the choice of mechanisms used, as noted by Van Dierdonck and Miller (1981). Managers at company A, where the structure is very shallow, did not appear to be as much concerned (compared to B and C) about the problem since they felt that they could adjust their systems on short notice without much bureaucratic resistance, and still maintain control. However, at company C, manufacturing control was much more formal, with schedules generated by a headquarters staff. The plant responsibilities were separated for part fabrication and final assembly. Modular product designs were viewed as an important way to reduce the risk of forecast-demand mismatch in the face of a greater number of management levels and more diffuse control over its manufacturing operations. Second, the formality of decision making within these firms seemed to have an impact. For example, company A’s loosely coupled management structure allowed them more flexibility in decision making. Burns and Stalker (1961) contend that organic organizations with open communication channels are better adapted to deal with environmental uncertainty, compared to mechanistic organizations (for example, company C). Here, in company A, management was comfortable with impromptu decisions on matching customer orders with production units and feels little need to formalize the matching process. In-house design and production capabilities of the firm provided avenues that were not necessarily available to competitors. Company C had extensive experience in product design, which was used to develop a more modular product design. It also had experience with cellular manufacturing techniques that it used to improve its process. In contrast, company B had little product design capability and had to rely on other mechanisms to maintain its competitiveness. While company A also maintained considerable product design capabilities, it chose to use these capabilities as a slack resource, employing its design staff to handle the contingencies that arose with special customer demands. Question 3 concerned the differences in the firms’ organizational responses to the challenge of providing a customized product in a short time. The approach that a firm might take appeared to be influenced by the previously mentioned conditions-the firm’s strategy, structure and skills. Like company A, firms may undertake complexity reduction projects to shorten process cycle times-if skills to do so are available. Or, like company B, firms may adopt strategies to build skeleton units to stock and segregate production from marketing, especially if the firm wishes to emphasize production efficiency more than customization. This approach relies on finished goods to reduce demand uncertainty. In a firm with multiple layers of management and fairly inflexible management systems, as in company C, responding to the customization and delivery time reduction may have to rely on product engineering skills to create modular product designs. (Company C coupled that approach with modular layout and flexible machining resources to further shorten its lead time.)

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Table 1 presents the specific coping mechanisms used in each of the three case situations. These mechanisms and their implications are discussed in the remainder of this section.

TABLE 1 COPING MECHANISMS USED IN CASE FIRMS UNCERTAINTY REDUCTION

COMPLEXITY REDUCTION

COPING MECHANISMS

PRODUCT DESIGN

PROCESS DESIGN

SLACK RESOURCES

SUPPLY MGT.

DEMAND MGT

FIRM COMPANY A Private

xxxx

xxx

COMPANY B Public

yg”’

c x

X

xxx

x

x

x

x

x

x

X

Four interesting reactions to the problem can be seen in company A’s operations. First, product and process redesign has reduced the scheduling dilemma. Product redesign allows for quicker assembly and disassembly to help alleviate scheduling problems; process redesign is aimed at reducing throughput time. For example, the firm had invested in llexible machines to allow “ship set” (a complete set of parts for assembly) production. This, compounded with the batch size reductions, has led not only to much shorter build times but also to significant WIP reductions. Second, the build time reductions can be exploited effectively because of company A’s relatively few layers in decision making. Its informal organization structure with open communication channels allows the firm’s managers to respond to customization requests fairly easily. Third, the firm often uses its manufacturing and engineering expertise to reduce the uncertainty in supply of materials: thus, while good vendor relationships are emphasized, procurement is not the firm’s most critical problem. Fourth, the slack present in some areas of its production process can be mobilized by occasionally expediting an important order, apparently without disrupting other work in progress. In these ways, the company takes a very broad view for resolving the scheduling problems inherent in the BTF environment. Company B’s operations lacked engineering skills, and were highly dependent on international sourcing. The firm implemented at least two formal organizational mechanisms to mitigate the problem. First, it separated the responsibility of manufacturing and marketing. This allowed manufacturing to pursue cost efficiency goals, while ensuring that marketing provides for

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customization and responsiveness within the limits of its own sales requirements. Second, company B established a formal liaison task to achieve coordination between the two departments, a task that involved the specification of formal time fences for each of the firm’s product variant categories. Company C largely relied on its the engineering expertise by creating a modular product design and flexible processes. Significant corporate level support was provided to accomplish this task. Formal mechanisms (for example, minimum delivery lead time of eight weeks) also alleviated the problem. Some of the risk-reducing mechanisms were straightforward and clearly provided dividends in other areas. For example, using standardization and modular products (with the modules specifically designed to reduce variant reconfiguration costs) not only simplified the scheduling task but also reduced costs, sometimes substantially. Similarly, process improvements that reduced build times generally provided cost savings as well. However, while these steps help allay the build-to-forecast dilemma, they were perceived insufficient to reduce build times to match competitive delivery times. All three firms view reduction of build time and delivery time and creation of more flexible processes as high priorities for the future. Currently, all three firms use at least two other mechanisms besides build time reductions to deal with this problem. First, they rely to some extent on expensive finished goods inventory to meet unexpected customer orders within competitive delivery times. Typically, common-variant models held in the showrooms act as buffer stocks. Company B also uses its showrooms to absorb unmatched units. Company A uses showroom models to bridge the gap between build time and delivery time by offering “loaners” for several weeks. The cost of using these mechanisms reflect the reluctance the firms have for altering the configuration of units in process. Second, each company also protects its manufacturing schedule by pressuring its sales force, to varying degrees, to sell units according to the existing production plans. Company A is willing to sell over-equipped showroom models and disable some of the options, rather than reconfigure a partially completed unit. Company B’s sales force sells largely from the master schedule, as does company A’s to some extent. In B’s case, as orders are matched to units in process, the salespeople target their efforts at selling the unmatched units, seeking customers that are likely to want the upcoming available models at the appropriate times. In the healthy economic climate that existed while these studies were conducted, the firms had the luxury of being able to select customers to suit the schedule, to some degree. While improving product and process design likely reduce both build times and cost, the use of finished goods buffer inventories and targeted sales efforts have other impacts. The first practice costs money; the second practice may cost the firm customers, An unresolved question is whether it may be more effective to tamper with the production schedule than to absorb the uncertainty through these buffering mechanisms. Are there ways of identifying the trade-off between the cost of maintaining a frozen schedule against the cost of altering the specifications of the units under construction? Can we calculate, or specify, the optimal decision points in the manufacturing process for altering product configurations that can minimize the cost risk and/or maximize customer service? We conclude our exploratory study by expanding upon these issues, pointing out areas that warrant future research. CONCLUSIONS

AND FUTURE

RESEARCH

DIRECTIONS

The three case studies presented here have identified a unique build-to-forecast problem in managing operations, as well as the range of coping mechanisms used by the three diverse

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manufacturing firms to address the problem. Managing operations in a BTF environment is complicated by several factors: market competition based on delivery lead times, complex production processes, a multiplicity of variants and options, quick technological obsolescence of variants and a high degree of customization in the final product. The use of the case study method was required here for the depth of analysis needed to identify the unique aspects of the BTF problem. Downey and Ireland (1979) argue that attempts to assess the environment in organizational studies should be examined along two dimensions: what is being measured (conceptualization), and how is it being measured (operationalization). The conceptualization of the environment here is in terms of the environmental attributes rather than in terms of the participant’s interpretation of the environment. The conceptualizations were operationalized qualitatively through unstructured interviews. Downey and Ireland note that “assessing environments in terms of their attributes may be a much more fruitful area for qualitative approaches” (p.635). As they point out, the use of qualitative data to study organizations is controversial: the controversies are however fueled by false perceptions of the notion of objectivity. We have evaluated the qualitative data of the subjective behavior of managers in these firms to arrive at the conclusions; and we have done it objectively. The case studies provided a “thick” description of the operations manager’s dilemma. The contingency relationships between operational tactics and strategy, environment, and capability have been explored in substantial detail. Specifically, the large firm (C) with a reputation for engineering excellence, and abundant engineering personnel, reduced the impact of the BTF dilemma by redesigning the product and using flexible technology. The project undertaken to accomplish this was so successful (it also reduced product cost dramatically) that it has since become the model for other divisions of the company. The large firm (B) with a unique management culture (for example, their orientation toward long-term employment) and systems (for example, the special separation of responsibilities of marketing and manufacturing groups) chose to position itself in the less-customized segment of the market, and used several buffers like inventory and time fences to alleviate the problem. The small firm (A) addressed the problem mainly by providing slack resources, a seemingly counter-intuitive path from the financial perspective since small firms cannot usually afford this. The explanation lies in the fact that this environment is amenable to closer monitoring of the resources; further, other coping mechanisms were being pursued by this firm also. Triangulation of our findings by the use of other research methods may be a rich area for future research. Indeed, a more quantitative orientation would suggest the use of the survey methodology. A good example of the kind of difficulties that might surface here is provided by Karmarkar, Lederer and Zimmerman’s (1990) study of production control and cost accounting systems. They note that the difficulty in measuring variables related to market conditions and the production process cause “weak associations between the dependent and independent variable in statistical analysis” (p.397). A longitudinal assessment of this situation may uncover a temporal relationship between the coping mechanisms and the state of the economy. Based on the field studies, two different sets of scheduling questions are raised in the BTF environment, depending on whether the customer order perspective or the item-oriented perspective is taken: The customer order perspective. Viewed from the customer order, or marketing, perspective, the relevant problem appears to be finding the “right” in-process unit for each new customer order. This unit should possess the characteristics that it has fixed variants similar to those requested, and is far enough along the production cycle that the delivery lead time requested by

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the customer can be satisfied. Thus, the key concerns are: How should a production item be matched with a new customer order? Should the policy allow for “unmatching” a “firm” order to reassign it to another customer? What is the basis for choosing the match? The item-oriented perspective. Perhaps of greater concern to manufacturing personnel is dealing with unmatched items. One potential, though unlikely, alternative is to halt production on a unit if it reaches a critical juncture without having a matching order. Another is to alter the item’s specifications to match an existing order or to reconfigure it to increase its probability of being matched. The latter alternative may be possible, if rework capacity or extra components are available. Several avenues for future research stem from this conceptualization of the problem. These avenues may be pursued prescriptively (e.g., modelling the alternate problems) or descriptively (e.g., more rigorously describing the order matching problem), quantitatively (e.g., through appropriate survey instruments) or quantitatively (e.g., case studies of other firms), and with a wide variety of research methods. Some managerial problems that may be of interest to the researcher include: The value-added problem. At what point in the build cycle should manufacturing unfreeze a production work order if no matching order is received? Popp (1965) and, more recently, Robinson (1985) provide conceptual treatments of this dilemma. Continuing to add value to an unmatched order can provide a shorter delivery lead time if a very similar order materializes. However, the longer that the production order remains frozen as it moves toward completion, the greater is the risk that no suitable order will arrive, particularly since the chances of it being economically reconfigured to another, similar specification diminish with each successive production step. The rework problem. When and according to what criteria should a unit be reconfigured for a different variant? The order-matching problem. How should customer orders be matched with production orders? How does commonality, or modular product design, affect the order-matching problem? (See, for example, Baker, Magazine and Nuttle (1986) for an excellent conceptual treatment of the problem in the assemble-to-order environment.) The unmatched unit problem. What are the mechanisms for handling an unmatched unit? Who is responsible for the inventory thus created? How long should this inventory be retained? (For example, see Jesse (1986).) Other problems. What are the benefits of accurate forecasting? (For example, Bodily and Freeland (1988).) What are the market implications of reduced delivery lead time? These potential new research directions relate to three levels of managerial decision: strategic (specification of delivery lead times), tactical (the value-added problem) and operational (the order-matching problem). Realistic data are provided in this paper for each of these problems and are available from the authors. Given the expectation that customization and short delivery times are going to be two parameters of increasing importance in the competitive marketplace of the future, these problems will be of significant importance for a large number of manufacturing companies. ACKNOWLEDGMENT This paper presents a portion of the results of Study 89-03 of the Cincinnati Machine Tool Studies Project conducted by the Operations Management Group in the College of Business Administration, University of Cincinnati. We would like to thank the reviewers for their helpful comments, and the sponsor firms for their participation.

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REFERENCES Baker, K.R., M.J. Magazine, and H.L.W. Nuttle. “The Effect of Commonality on Safety Stocks in a Simple Inventory Model.” Managemenr Science. vol. 32, 1986, 982-988. Benbasat, I., D.K. Goldstein, and M. Mead. “The Case Research Strategy in Studies of Information Systems,” MIS Quarterly, vol. 11, no. 3, September 1987, 369-386. Bessant, J., and R. Lamming. “Design for Efficient Manufacture.” In International Handbook of Production and Operations Management, Ray Wild (ed.) London: Cassell, 1989, 291-306. Bodily, Samuel E., and James R. Freeland. “A Simulation of Techniques for Forecasting Shipments Using Firm Ordersto-Date.” Journal of Operational Research Society, vol. 39, no. 9, 1988, 833-846. Bower,J.L., and T.M. Hout. “Fast-Cycle Capability for Competitive Power.” Harvard Business Review, NovemberDecember 1988, 110-I 18. Burns, T., and G.M. Stalker. The Management of Innovation. London: Tavistock Publications, 1961. Camm. J.D., and N.K. Womer. “Resource Allocation in the Crew Assembly Process.” International Journal of Production Research, vol. 25, no. 1, 1987, 17-30. Downey, H.K., and R.D. Ireland. “Quantitative versus Qualitative: Environmental Assessment in Organizational Studies.” Administrative Science Quarterly, vol. 24, December 1979, 630-637. Eisenhardt, K.M. “Building Theories from Case Study Research.” Academy of Management Review, vol. 14, no. 4, 1989, 532-550. Galbraith, Jay. Designing Complex Organizations. Reading, MA: Addison-Wesley, 1973. Hendry, L.C., and B.G. Kingsman. “Production Planning Systems and Their Applicability to Make-to-Order Companies.” European Journal of Operations Research, vol. 40, January 1989, 1-15. Hill, Terry. Manufacturing Strategy; Text and Cases. Homewood, IL: Richard D. Irwin, 1989. Jesse, Richard R. “On the Retention of Finished Goods Inventory When Reorder-Occurrence Is Uncertain.” Journal of Operations Management, vol. 6 no. 2, February 1986, 149-157. Karmarkar, U.S., PJ. Lederer, and J.L. Zimmerman. “Choosing Manufacturing Production Control and Cost Accounting Systems.” In Measures for Manufacturing Excellence, Robert S. Kaplan (ed.) Boston, MA: Harvard Business School Press, 1990, 353-396. Meredith, J.R. “Managerial Lessons in Planning and Implementing Automated Manufacturing.” CM Pub. no. A-412, Cincinnati, 1987. Meredith, J.R. “Installation of Flexible Manufacturing System Teaches Management Lessons in Integration, Labor, Costs, Benefits.” Industrial Engineering, vol. 4, April 1988, 18-27. Meredith, J.R. “Cincinnati Machine Tool Studies: Suppliers, Manufacturers, Users.” Working paper, University of Cincinnati, Cincinnati, OH, 1989a. Meredith, J.R. Managerial Lessons in Factory Automation: Three Case Studies in Flexible Manufacturing Systems. Operations Management Association Monograph no. 4. Waco, TX: Naman and Schneider, 1989b. Meredith, J.R., and D. McCutcheon. “Responsiveness: The Emerging Strategic Imperative.” Working paper, University of Cincinnati, Cincinnati, OH, 1989. Miller, Jeffrey G. “Hedging the Master Schedule.” In Disaggregation: Problems in Manufacturing and Service Organizations, Larry Ritzman, Lee Krajewski, et al. (eds.) Boston: Martineau Nijhoff Publishing, 1979, 237-256. Nicholson, T.A.J. “Measuring Customer Service and Managing Delivery.” In International Handbook of Production and Operations Management, Ray Wild (ed.) London: Cassell, 489-503, 1989. Popp, W. “Simple and Combined Inventory Policies, Production to Stock or to Order.” Management Science, vol. 1 I, no. 9, July 1965, 868.873. Raturi, A.S., and K. Amoako-Gyampah. “Some Operations Strategy Issues When Demand Has High Cyclicality: A Study of the Machine Tool Industry.” Working paper, University of Cincinnati, Cincinnati, OH, 1990. Robinson, E.P, Jr. “Product Variety, Production Strategy, and Distribution Strategy.” Unpublished Ph.D. dissertation, The University of Texas, Austin, Texas, August 1985. Schmenner, R.W. “The Merit of Making Things Fast.” Sloan Management Review, Fall 1988, 1 I-17. Sciberras, E., and B. Payne. International Competitiveness and the UK Machine Tool Industry. London: Technical Change Center, 1984. U.S. Congress. Senate. Committee on Foreign Relations. Joint Hearing on U.S. Machine Tool Industry: Its Relation to National Security. 98th Gong., 1st sess., November 28, 1983. Washington, D.C.: U.S. Government Printing Office, 1984. Slack, N.D.C. “Focus on Flexibility.” In International Handbook of Production and Operations Management, Ray Wild (ed.) London: Cassell, 1989, 50-73.

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Harvard Business Review, July-August 1988, Stalk, G., Jr. “Time-The Next Source of Competitive Advantage.” 41-51. Swamidass, Paul M. Manufacturing Flexibility. Operations Management Association Monograph no. 2. Waco, TX: Naman and Schneider, 1988. Van Dierdonck, R., and J.G. Miller. “Designing Production Planning and Control Systems.” Journal of Operations Management, vol. 1, no. 3, 1981, 37-46. In International Handbook of Production and Operations Wheelwright, S.C. “Competing through Manufacturing.” Management, Ray Wild (ed.) London: Cassell, 1989, 15-32. Womer, N.K., and T.R. Gulledge, Jr. ‘A Dynamic Cost Function for an Airframe Production Program.” Engineering Costs and Production Economics, vol. 7, 1983, 213. Yin, R. Case Study Research. Beverly Hills, CA: Sage Publications, 1984.

APPENDIX SOME DEFINITIONS:

VARIANTS,

I

OPTIONS AND SKELETONS

Preprocessing is facilitated by product design approaches that allow a range of products to use the same long-leadtime components. However, the impact that a particular component has on production planning depends on its use in the end-products. To explain where these components have the most significant impact with modular production systems, we briefly review three concepts. They are: 1. variants, fixed or alterable, which are generally decided upon early in the build cycle. 2. skeleton units, or the combination of variants that would result without any changes midstream in the build cycle, and the 3. options, which are added toward the end of the build cycle. Variants refer to the basic module sets that every item must have. Fixed variants are relatively unchangeable once specified (or changeable at a very high rework cost). Examples of fixed variants include 1 IOV versus 220V machinery, an operating system in a systems installation, or the two-door versus four-door choice in an automobile. Alterable variants can be changed more easily. Alterable variants might include the memory size in a computer, or the engine size in an automobile. The skeleton unit represents a particular variant combination that would result if no changes are made during the rest of the production cycle. In a number of industries, skeletons are delivered to third parties based on a forecast. In such cases, the problem of scheduling options is usually external to the build-to-forecast problem. We therefore confine our discussion to the more difficult situation of managing the production of skeletons and variants. Options refer to the “bells and whistles” available for the product. These components are relatively inexpensive to alter, and are typically added during the product’s final assembly stage (see Figure 1). Examples include panelling and instrumentation on heavy machinery, software in systems installations, and the add-ons, such as air-conditioning and radio, in an automobile. Options may be manufactured and/or added on by third parties such as distributors or dealers, as in the automobile industry.

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