Linking Product Planning and Process Design Decisions

Linking Product Planning and Process Design Decisions Jay S . Kim School of Management, Boston University, 621 Commonwealth Avenue, Boston, MA 02215

Larry P. Ritpnan nomas J. Galligan, Jr. Profmor of Operations and Strategic Management, Boston College, Chestnut Hill, MA

W. C. Benton and David L. Snyder Academic Faculty of Managernent Sciences, College of B u s i m , f i e Ohio State University, 1775 College Road, Columbus, OH 43210

ABSTRACT Manufacturing strategy reflects the goals of the business strategy and directs the manufacturing

function in achieving them. In this study, we focus on two decision areas with crucial implications for manufacturing strategy: product planning and process design. Using mathematical programming models as a research tool, we test several conjectures in the manufacturing strategy literature regarding linkages betwem the two decisions.

Our results show that a close integration betwem these decisions helps control the product offerings, stabilize process requirements, improve process technology choices. and inmase net cash flows over time. Using the concept of environmental clustus, we found that a close linkage is more critical when the environment is more complex, less uncertain, and tighter. Based on these fmdings, we present some managerial implications and suggestions for future research.

Subject Areas: Mathmatical Programming, hducHon/Opcrations Management, Simulation, and Strategy and Policy.

INTRODUCTION

Manufacturing strategy reflects the goals and objectives of the business strategy, and directs various decisions of the manufacturing function to achieve them. In this study, we focus on two decision areas with crucial implications for manufacturing strategy: product planning and process design. Product planning deter- mines the timing, product features, and sets of products to be offered in response to the opportunities and threats in the market. It helps determine the fm’s competitive position in the market. Product planning also has implications for the manufacturing process and capacity requirements. It plays a significant role in determining what tasks the manufacturing function should achieve. Theories on dominant orientation [16] and competitive priorities [18] [26] consider product plans as a key element of business strategy.

procesS design specifies how these manufacturing tasks should be achieved. It guides the organization’s technological choices, and dictates the timing and quantity of process technology acquisition and disposal. Thus, process design con- strains a firm’s capability to implement the product planning decisions. Strategic concepts such as efficiency versus flexibility [9] [ 1 11 [ 191, and economies of scale and scope [6] [7] are closely related to this process decision.

The clcse relationship between product plans and process choices was observed by Utterback and Abemathy [23] and Abemathy [l]. Hayes and Wheelwright [9] [ 101 fomdized this relationship with the conceptual h e w o r k of a product-process matrix. In addition, Wheelwright [26], Richardson, Taylor, and Gordon 1171, and

44

19921 Kim, Ritztnan, Benton and Snyder 45

Swamidass I201 contended that consistency in decisions directed towards the goals and strategies of the business unit is a critical element of manufacturing strategies. The linkage between product planning and process design is therefore an important part of manufacturing strategy implementation. Our study focuses on how this linkage should be formulated.

A two-way relationship exists between the product planning and process design decisions, although the importance placed on each directional flow can vary. By defining the range of products offered, product strategy influences what types of manufacturing capabilities should be developed. This relationship is established in a top-down direction. Conversely, the types of manufacturing capabilities determined by process decisions can influence the choice of products. This linkage has a bottom-up direction. Depending on the priority given to each direction, two different patterns of linkage are possible. These pattems highlight the different roles the manufacturing function can play in the strategy formulation process. Hayes [8] distinguished them as either ends-ways-means or means-ways-ends. Wheelwright [26] characterized the relationship as either reactive or proactive from manufacturing’s point of view. Swamidass and Newell [21] provided empirical evidence that manufacturing managers should seek a more proactive role, particularly when the environment is not too uncertain.

Our study analytically tests propositions on the linkage between the two decisions. We explore two basic research questions. First, is it always better to integrate product and process decisions with a strong two-way relationship in both directions? If so, how do integrated decisions differ from non-integrated decisions? Second, is the performance difference between the integrated and non-integrated decisions contingent upon the environment? If so, under what business environment is the integration of product and process decisions most critical?

The research methodology consists of three components. First, we represent product planning and process design decisions with mathematical programming models. Two different scenarios are formulated to highlight different linkage patterns. Second, we develop a simulation model to evaluate the consequences of the different scenarios under various environments. Third, we statistically test the research hypotheses. Selection of environmental factors is guided by previous research, and factor levels come from the data collected through a limited field study. Strategic implications of the different decision environments are addressed.

The next section describes the decision models used in this research. It is followed by a description of the simulation process. We then present the environmental factors and analyze the results.

DECISION MODELS

Consider a hypothetical situation involving product planning and process design. Given a set of potential products, product planners evaluate the introduction of each one on the basis of its potential demand and contribution. They decide when and how long to place the product on the market, determine whether it will compete on the basis of price or product differentiation, and assess the effect on the demands of other products currently on the market or yet to be offered. In contrast, manufacturing’s process design task involves providing both the type and amount of process technology necessary to produce the required quantities of products offered by the product planners. Clearly, these decisions are of paramount

46 Decision Sciences [Vol. 23

importance to an organization because of their consequences for revenue, cost, and long-term survival.

Two extreme alternatives for making product and process decisions are considered in this research. The fmt, the unlinked decision Scenario, represents the sequential consideration of product planning and process design decisions. Initially, product planners determine which products to offer based on estimates of demand, price, cost, and existing ptocess technology. Then, manufacturing evaluates its process technology requirements in an effort to minimize costs given the product plans. The second, the linked decision scenario, represents the simultaneous consideration of the two decisions. This scenario implies better coordination between the decisions, as often suggested by the umcept of simultaneous engineering. Thus, while the unlinked scenario corresponds to manufacturing playing a reactive role in the strategy formulation process, the linked decisions correspond to manufacturing playing a more proactive role.

We emulate these decision scenarios using mathematical programming. In the unlinked scenario, the product planning decisions are formulated as a profit maxi- mizing problem. The results of this problem are then used in a cost minimization problem that represents the process design decision. In contrast, the linked decision framework brings the two decisions together with a linking constraint.

The following formulations are viewed as a research tool to examine the need for coordination between these decisions. They include such realistic elements as product substitution, entrandexit strategies, and competitive priorities [22] [25]. Also captured are how product plans are related to the type, efficiency, and capacity of process technologies. Table 1 defines the notation used in the formulations.

First, the formulation for the linked scenario is presented.

subject to D i t = &Pimdimt - Ei.&nQii.m&imt;

i = 1,2, ..., I ; t = 1,2 ,..., T,

Qii'mt 2 & , , ~ P ~ m t k ~ m ~ r + Pimkimt - l]qiy;

i, i' = 1, 2, ..., I; i f i'; m = 1, 2, ..., Mi; t = 1, 2, ..., T,

Qji'mt Pimkijndii' ;

i, i' = 1, 2, ...,I; i * i'; m = 1, 2, ..., Mi; t = 1, 2, ..., T,

QiiImt 5 ICm'Pi.m'ki'm'tlqii';

i,i'= 1,2 ,... , I ; i # i ' ; m = l , 2 ,..., M i ; t = 1,2 ,..., T,

C,Pim= 1;

i = 1,2 ,..., I ,

2, = zm;

k = 1, 2, ..., K,

19921 Kim, Riaman, Benton and Snyakr

ZkJ-1 + x, - y , = 2,;

t = 1,2 ,..., T , k = 1,2 ,..., K,

Q & & t ; (9)

5 z,; (10)

t = l , 2 ,..., T, j=1 ,2 ,..., J,

t = 1,2 ,..., T , k = 1,2 ,..., K,

= (0,l); (1 1)

t = 1, 2, ...,I; m = 1, 2, ..., Mi,

2 0; (12) i, i' = 1,2, ...,I; i * i'; m = 1,2, . . . ,Mi; t = 1, 2, ..., T,

2 0; (13) i = 1,2 ,..., I ; t = 1,2 ,..., T,

2 0; (14)

k = 1,2 ,..., K , t = 1,2 ,..., T,

2 0; (15) k = 1,2 ,..., K ; j = 1,2 ,..., J;t= 1,2 ,..., T.

The primary decision variable for product planning is the zero-one variable Pi,,,. The expected demand for each product is determined by D, in (2). Constraints (3), (4), and (5) together force the demand substitution to take place when similar products are being offered in the same period. Constraint (6) requires that only one entrance/exit strategy be employed. Constraint (7) initializes the amount of process technology held, and constraint (8) maintains the balance of technology as it may be added or disposed of through time. Constraint (9) defines the total process requirements and forces sufficient capacity to be available. Constraint (10) sets the upper limit of capacity allocation. In effect, constraint (9) links the product and process decisions by requiring enough process technology to be available to produce the mix of products planned.

can be estimated based on various strategic choices such as entrance/exit strategies and emphasis on low price or high performance design. Additionally, we assume that process requirements (rU) and process efficiency (ski) can be estimated for given products and process technologies. Table 2 depicts an example set of parameters. Product 1 represents a standard product that goes though the full life cycle, while products 2 and 3 represent revised versions of product 1 with some extra features added or excluded, which are offered during a limited portion of the product life cycle. Table 2 also shows different entrance/exit strategies. The strategy 1 for product 1 represents an early-entry late-exit alternative, while strategy 2 represents

We assume that individual product demand (di,,J and unit price

48 Decision Sciences pol. 23

Table 1: Summary of notation.

Decision Variables for Product Planning

Pi,,, - 1, if product i is introduced/withdrawn following strategy m; 0, othenvise

D, - units of product i to be actually demanded in period t Qiitmr - proportion of demand for product i with strategy m lost in period t due to the

existence of similar product i' in the market

Decision Variables for Process Design

X,

Y,

2, Akjt

Parameters for Product Planning

- additional capacity of technology k acquired at the beginning of period t, in machine hours per period capacity of technology k disposed of at the beginning of period r, in machine hours per period - capacity of technology k held in period I , in machine hours per period

= capacity of technology k to be allocated to perform processj in period t, in machine hours per period.

=

number of units of product i to be demanded in period t if introduction strategy m is pursued 1, if dimt > 0; 0, if dimt = 0 discounted unit contribution of product i in period t discounted price per unit of product i in period t discounted materials cost per unit of product i in period t discounted labor and variable overhead cost per unit of product i in period t proportion of demand for product i lost due to the existence of similar product i' capital investment required for product i in period t if strategy m is pursued

Parameters for Process Design

rY skj oh bk fh u,

= number of machine hours required to perform processj per unit of product i = efficiency of technology k in performing process j (0 I sk. I 1) - discounted unit acquisition cost of technology k in p r iod t - discounted unit salvage value of technology k in period t = discounted fixed overhead cost to keep unit of technology k in period t = discounted variable overhead and labor cost to operate unit of technology k

in period t

late-entry pursuing high-volume demand in the maturity stage of the product life cycle. The process capability may be interpreted as follows: technology 1 represents a specialized equipment for process 1, technology 2 represents general-purpose equipment that is versatile but inefficient, while technologies 3 and 4 represent new technologies (such as flexible automation) that are capable of performing various processes relatively efficiently.

The formulations for the udinked scenario are now presented. First, the product planning model is

Maximize EJ,ci&lit - C J i C m h i m ~ i , (16)

19921 Kim, Ritzman, Benton and Snyder 49

Table 2: Examples of key parameters.

Demand level for each product (dimt)

i m 1 2 3 4 5 6 1 1 10 20 40 60 60 30

2 0 0 30 50 50 30 3 0 0 0 0 0 0

2 1 0 0 0 0 0 0 2 0 0 0 40 50 40 3 0 0 0 0 40 40

3 1 0 0 0 0 0 0 2 10 20 20 0 0 0

t

Unit price of each product (pit)

i 1 2 3 4 5 6

1 2.0 2.0 2.0 1.8 1 .o .6 2 .o .o 3.0 3.0 3.0 2.0 3 1.6 1.6 1 .o 0 0 0

Process requirement of each product (rV)

i 1 2 3 4 5 6 1 .4 .3 .5 1 .o 0 0 2 .4 .3 .5 I .o 1.2 0 3 .3 0 .5 1 .o 0 0

Process capability of each technology (skj)

k 1 2 3 4 5 6 1 1 .o 0 0 0 0 0 2 .2 .3 .3 .4 .3 .2 3 .9 .8 .7 0 0 0 4 .7 .7 .8 .9 .7 .6

i

In this model, the objective function (16) maximizes the expected contribution less the total investment for product development, based on the existing level of process technologies. An important distinction between (1) and (16) is related to the definition of the contribution coefficient. For the linked scenario, the contribution in (1) can be exactly determined as


In contrast, the contribution in the unlinked scenario (16) is estimated as

because the unit overhead and labor cost (vi,) must be approximated. This is given by

Equation (19) approximates the variable cost by summing all the process requirements rii multiplied by average cost for performing a unit of process j, assuming that the existing process technologies are maintained in the future. Therefore, future changes in the process design are not considered in the product planning decision, thus reflecting the lack of close integration.

Having determined the entrandexit strategies, and thus the individual product’s demand, the process design model of the unlinked scenario can be stated as follows:

subject to (7), (8), (lo), (14), (IS), and

t = l , 2 ,..., TYj=l ,2 , . . . , J ,

I , = (ilDil > 01.

Note that constraint (21) forces the model to consider only the products that are offered in the current period, excluding the products that may be introduced in the future. As a result, the unlinked model has two blind spots: the cost coefficients for the product planning model are based on the current technology mix, and the process design model does not consider products that are contemplated for the future. Thus, this model merely attempts to efficiently meet the given demand and reflects a reactive role of manufacturing in the product and process decisions.

THE SIMULATION PROCESS

The models of the previous section are developed as an experimental tool rather than as a decision support tool. The simulation process described here includes the decision models as one of the four modules that are used in the experimental study. The first module generates the matrices of all the coefficient parameters over the entire experimental period. This module provides the complete set of alternative

19921 Kim, Ritunan, Benton and Snyder 51

product demands, contributions, and technology costs. The second module then takes out a certain portion of the global parameter matrices that corresponds to the planning horizon for the decision period, and converts the actual parameter values into the forecast values depending on the uncertainty factor. The third module then activates the MILP (mixed integer linear programming) software and solves the product-process decision formulations. The fourth module accepts the decisions made by the linked and unlinked models, and calculates the results of the current decision period t based on the actual values of the parameters. In the next period t+l, the second module is updated and modules 3 and 4 are invoked. This process repeats until the end of the simulation is reached.

We highlight two significant aspects of this simulation process. First, the inherent uncertainty in the long-term decision making is reflected by allowing the forecasted coefficients to differ from the actual values. Initially, the decision environment generates the actual values of coefficients. At each simulation period, however, forecasted values are generated randomly around the actual values, and the product and process decisions are made based on the forecasted values. The results of decisions are then evaluated with the actual coefficient values. Second, the simulation process is designed to reflect a rolling decision environment. This rolling decision environment captures the essence of strategy formulation as a continuing process of assessing future opportunities, making decisions, and evaluating results. The pattern in the stream of these decisions may be captured as strategy [15]. This dynamic process of strategy formulation is similar to the rolling schedule scheme as found in the production planning literature [3]. These models generally have been applied at an intermediate planning level and have been used to examine issues such as planning horizon length and replanning frequency in sequential decision environments [4] [5 ] . In contrast, our research examines a long-range planning level and focuses on the value of integrated decision making. Although the unlinked model is inferior to the linked model in a deterministic and stable environment, the inherent uncertainty and rolling decision envitonment can affect this relationship.

ENVIRONMENTAL FACTORS

A changing environment provides different opportunities and threats to a manufacturing firm, and the f m reacts to these changes by making various strategic decisions [2] [ 141 [ 151. Therefore, manufacturing strategy should be understood within the specific context of the firm’s environment.

Environmental Clusters and Hypotheses

Corporate strategy researchers tend to adopt a multi-dimensional approach in understanding the environmental structure, such as the constructs developed by Lawrence and Dyer [ 131 for information complexity and resource scarcity. Com- pared to the single-dimensional approach (such as in [21]), this multi-dimensional approach allows a richer analysis of environmental factors.

To implement a multi-dimensional approach, we apply the concept of cluster that was utilized in various production control studies [12]. Several factors which are expected to have a similar impact are grouped into a single cluster. Thus, rather than studying numerous factors individually, only a handful of clusters need to be


experimentally tested. Based on the literature [2] [13] [U], we investigate three clusters of environmental characteristics: (1) environmental complexity, (2) envi- Kumlelltal- ’ ty, and (3) envimmend tightness. (See Table 3 for the descriptim of various factors in each cluster.)

Environmental complexity refers to the size and difficulty of product-process decisions. As wider arrays of products are to be considered and more diverse process technologies are available, the decision process of choosing a particular product or a particular technology becomes more complex. Van Dierdonck and Miller [24] suggested that more information processing system involvement (IPS0 might be needed with higher task complexity. Also Lawrence and Dyer [13] suggested a tighter control when task complexity is high. In our study, more PSI and tighter control translate into the use of the linked decision model. Therefore, our hypothesis on environmental complexity is

H1: Integrated decision making performs better than non-integrated decision making, particularly when the environment is more complex.

Environmental u n c e h t y represents the d e p of errors in forecasting demand and each technology’s process efficiency. High environmental uncertainty would seem to reduce the relative superiority of the linked model over the unlinked model. Swamidass and Newel1 [21] found that manufacturing managers participated less in the strategy formulation process under highly uncertain environments, implying a reduced strategic value of integrated decision making. Therefore, our hypothesis on environmental uncertainty is

H2: Integrated decision making jmforms better than non-integrated decision making, particularly when the environment is less uncertain.

Environmental tightness represents how fierce the competition is in the market. It is similar to the concept of tolerance for slack proposed by Van Dierdonck and Miller [24]. When competition is fierce and profit margin is low, it would seem to require more integrated decision making. Therefore, our hypothesis on environmental tightness is

H3: Integrated decision making perfom better than non-integrated decision making, particularly when the environment is more restrictive.

Experimental Design

Each environmental cluster has two treatment levels, HIGH and LOW, creating eight different environments in which both decision models are evaluated. When an environmental cluster is set at a HIGH or LOW level, all of the individual factors in that cluster are set at their HIGH or LOW levels, respectively. In preliminary tests, each individual factor was tested to synchronize the levels within each cluster. In other words, the HIGH and LOW levels of each factor were determined such that their impact on the relative performances of the two models is in unison with other factors in the same cluster.

We chose factor levels to bracket the extremes actually found in practice. Our choices were derived from a limited field study of three very different companies

19921 Kim, Rimtian, Benton and Snyder 53

(ranging from customized to standardized products) in the machine tool industry. The raw data from the study had to be interpreted and translated into the format necessary for the models. Table 3 shows the HIGH and LOW values for each experimental factor, and Table 4 shows the averages and ranges for the fixed factors. The variability around the average in Table 4 is created by differences in products, entrance-exit strategies, life cycle demand patterns, and technologies.

With each replication of a cell, there were twenty planning sessions. The first five sessions were used to achieve steady state and the last fifteen sessions for data collection. A 5-period MILP problem was solved in each planning session. A period was defined to be one year, giving it a 5-year planning horizon. A 2-year replanning frequency and 10-year horizon could just as easily have been captured by the 5-period model. We conducted four replications of each cell, using different random number seeds.

There are two reasons for our choice of horizon length and number of replications. First, the current pace of technological change is shortening the useful life of equipment purchases. A horizon of 5 or 10 years is often more than sufficient to assure the current decisions are unaffected by not extending the horizon length. Second, the research budget constrained the number of replications. The integrated model is quite large with more than 1500 constraints and 1100 decision variables, of which 360 were zero-one variables.

The two decision models are compared on the basis of average net cash flow over the fifteen experimental periods. After the decision models are solved at each decision period, the cash inflow is determined by multiplying the unit contribution margin by the realized demand volume. Then the cash outflow is determined as the sum of the product development investment, cost of technology acquisition (minus the salvage value of disposed technology), fixed cost of maintaining each process technology, variable cost of operating each process technology, and overtime penalty cost resulting from capacity shortage (which may happen due to the forecast error). The difference between cash inflow and outflow is the net cash flow of the particular decision period t. Finally, the performance measure, denoted as UN/LN, is computed by dividing the average net cash flow from the unlinked model 0 by the one from the linked model (LN). This ratio variable facilitates the comparison over various factor levels.

ANALYSIS OF RESULTS

We first examine whether, and if so why, the integrated decisions generated superior performance. Second, we investigate the environmental impact on the performance differentials.

Linkage and Performance

Table 5 shows the average ratios between the net cash flows generated by two models (UN/LN). Two points stand out. First, as expected, the linked model generally perfom better than the unlinked model. Overall, the average net cash flow of the unlinked model is 96.5 percent of that of the linked model. Since the linked model makes the two decisions simultaneously, the implication of product decisions for process requirements, and the effect of pa~icular process choice on the product profitability, are all considered. In contrast, the unlinked model makes the product

54 Decision Sciences [vol. 23

Table 3: Value of each experimental factor at the two levels.

Environmental Complexity Number of product lines High: 10 product lines Low: 5 product lines

High: Ranging from 2 to 5 (average 3.6 processes) Low: Ranging from 1 to 4 (average 2.4 processes)

High: Ranging from 2 to 4 periods (average 3 periods) Low: Ranging from 4 to 6 periods (average 5 periods)

High: Ranging from 20% to 180% of average demand Low: Ranging from 60% to 140% of average demand

High: Increasing number of technologies for more flexible types (12 technologies capable to perform 1 process, 16 technologies capable to perform 2 processes, and 20 technologies capable to perform 3 processes)

(12 technologies for each type)

Average number of different processts required for each product

Length of product life cycle

Variablity of demand over product life cycle

Total number of different process technologies

Low: Same number of technologies for all types

Environmental Uncertainty Average percentage error in forecasting demand for each product

High: f 20% Low: f 5%

High: +lo% - +40% Low: t 5% - f201

Environmental Tightness Level of average contribution margin

Average percentage error in estimating technology efficiency

High: Average margin equals two times the estimated fixed and variable costs Low: Average margin equals three times the estimated fixed and variable costs

decision without considering its impact on the process requirements. Similarly, when a process decision is made, its impact on the profitability of the future product opportunities is ignored. The unlinked model also has to approximate the processing cost of a product on the basis of the current technology mix, while the linked model evaluates the contribution margin and cost of a product more accurately. With all these conditions, the superior performance of the linked model is not unexpected.

What might be somewhat unexpected is that the unlinked model sometimes performed slightly better than the linked model. There are two explanations. First, the decision model solves problems with forecasted parameters. The actual values of parameters differ from the forecasted ones, and the solution based on forecasts is not necessarily optimal for the actual problem. Second, the decision model solves

19921 Kim, Ritzinan, Benton a i d Snyder 55

Table 4: Values of fixed factors. ~ ~~~

Average Range Parameters for Product Planning dimt 100 0 to 400 4ii' .3 0 to .5 hinu 1OOOOO loo00 to 700000

Parameters for Process Design 'ij Skj

bk! fh Ukt

ah

5.0 0 to 10.0 .7 0 to 1.0

150 70 to 250 50% of ah 20% of ah 20% of akr

20% to 70% 5% to 40% 5% to 40%

Discount Rate 10% -

the problem in a rolling schedule scheme with a limited planning horizon. The solution generated by the linked model is optimal only within the planning horizon, but it may not be the global optimum. Therefore, the unlinked model could generate a solution which is globally better, although it is not optimum for the particular planning horizon.

To gab insight on the consequences of different patterns of linkage, we examine the characteristics of the decisions made by the two models. Table 6 presents some key statistics. All differences between the two models are statistically significant, either at the .01 or .001 level.

Product Decisions

The unlinked model consistently offers a larger proportion of total products. Table 6 shows that the unlinked model introduced 60 percent of all possible products, while the linked model limited the proportion to 53 percent. The linked model considers the changes in the process requirement mix before it decides to introduce a new product, while the unlinked model does not. Thus the linked model tends to control the offerings of different products more tightly. This observation is consistent with Hayes and Wheelwright [ll] and Wheelwright [26]. They contended that a business strategy dominated by the marketing function introduces more products without considering the difficult tasks imposed on the manufacturing function.

Process Decisions

Table 6 shows that the unlinked model generates both a larger acquisition cost and a larger disposed salvage value, suggesting that technology mix changes more widely when the product and process decisions are not integrated. Since the unlinked model tends to introduce a larger portion of total available products, it appears to create a more unstable process requirement mix. This inefficiency in the process decision can be attributed to the myopic decision making of the unlinked model.


Consider now the overall process flexibility generated by each decision model. A technology's process flexibility measures the proportion of the entire processes it can perform, and the overall process flexibility is a weighted average over all technologies. As shown in Table 6, the linked model generates a higher level of process flexibility. It considers the future changes in product mix (thus future changes in the process requirements) before acquiring a particular technology. Anticipating the changes in the process requirements, it seems to acquire more flexible, general-purpose equipment. In contrast, the unlinked model tends to acquire more special-purpose equipment that can efficiently perform the process requirements at the given time. Turning now to the cost structure, Table 6 shows that the linked model generates a larger fixed cost and a smaller variable cost than does the unlinked model. Since a higher fixed cost and lower variable cost were assigned to more automated technologies, we conclude that the linked model ac- quires more automated technology.

The effect of this myopic decision pattern of the unlinked model can also be seen in a stability statistic of net cash flow. Table 6 shows that the coefficient of variation in net cash flow is much higher under the unlinked model. Unlinked decision making not only generates lower net cash flow overall, but also creates a larger period-by-period variation. This highly unstable cash flow could cause financial difficulties, particularly for firms operating under tight capital constraints.

Linkage and the Environment

Analysis of variance (ANOVA) was conducted on the data summarized in Table 5. It showed that the complexity factor has a significant impact on the performance differentials between the two models at the .01 level of significance. The other two factors, uncertainty and tightness, affect the decision models in the hypothesized direction, but are not statistically significant with only four replications per cell.

Linkage and Complexity

Table 5 shows that the UN/LN ratios are consistently smaller when complexity is higher. As hypothesized, integration is particularly important in more complex environments. The high complexity setting means that there are more products to choose from, the process requirements between products differ widely, the product life cycle is shorter, the demand for each product fluctuates more widely over its life cycle, and there are more process technologies to choose from. There are more opportunities to consider, and the penalty of not analyzing them carefully seems to be greater. This result is consistent with the empirical study by Van Dierdonck and Miller [24].

It is interesting that the unlinked model often performs slightly better than the linked model with low complexity. When complexity is low, the benefit of integrating product and process decisions appears to decrease. An examination of the detailed decisions showed that the linked model passed over many product opportunities which the unlinked model opted to take profitably. This reluctance in offering products might have been reinforced by the limited variety of flexible technologies (as set up in the low-complexity environment), which otherwise could help the linked model cope with unstable process requirements resulting from the introduction of a new product.

19921 Kim, Rimnan, Benton and Snyder 57

Table 5: Average W/LN ratios of net cash flow under different environments.*

Complexity Tightness Uncertainty High LOW

High High .912 1.030 LOW .879 1.009

LOW High LOW

.95 1

.930 1.004 1.009

Overall .965

* If the value of ratio is less than 1 .O, it represents the case where the linked model performed better than the unlinked model. In contrast, if it is greater than 1 .O, it means that the unlinked model performed better than the linked model.

Linkage and Uncertainty

We hypothesized that the difference between the two models would be larger when the environment is less uncertain. Although the original hypothesis was not statistically supported by the ANOVA test, the overall direction of differences was as hypothesized. From Table 5 , it can be seen that the average value of UN/LN is .974 with the high uncertainty, while it is .957 with the low uncertainty. When the environmental uncertainty is higher, the unlinked model performed closer to the linked model. This result is consistent with the empirical study by Swamidass and Newel1 [21], who found that more uncertainty makes manufacturing managers less proactive in strategy formulation.

Linkage and Tightness

We hypothesized that the difference between two decision models would increase as the environment becomes tighter. Even though the impact is not statistically significant at the .05 level given only four replications per cell, the overall results are in the expected directions. Table 5 shows that the UN/LN ratio is lower (meaning the difference is larger) when the environment is tighter. We therefore speculate that integration is more valuable when the profit margins are tight.

Managerial Implications and Future Research Areas

The experimental results provide several managerial implications. First, an improved performance generally can be expected from tighter linkages between the product and process decisions. With more integration, a firm simultaneously considers the process-effect of offering (or withdrawing) a particular product and the product-effect of acquiring a particular process technology. As a result, the firm tends to control the product mix more tightly and to acquire more versatile (or flexible) process equipment. When the two decisions are not integrated, the process requirements of the manufacturing function become less stable. Second, the linked model did not always perform better than the unlinked model. The value of integration diminishes due to the uncertainty in parameter forecasts and the limited planning horizon. A firm may have to put more effort in improving its forecasting


Table 6: Summary of performance by the two decision models. ~

Linked Model Unlinked Model Product and Process Decisions

Process flexibility** .203 .174 Proportion of products offered** .53 1 .597

Cost Factor as a Percentage of Total Cash Inflow (%)*

Acquisition cost of technology ** 17.5 21.7 Fixed cost** 21.1 19.9 Variable cost** 10.7 12.0 Overtime penalty*** .7 .5 Salvage value of disposed technology** (7.7) (10.0) Net cash flow** 36.9 33.7

Product development investment ** 20.8 22.1

Stability of Net Cash Flow Coefficient of variation of net cash flow** 1.24 1 .so

*Cost measures are expressed as percentage of the total cash inflow. Therefore, the sum of

**A 1-test with 32 matched samples shows that the difference is statistically significant with

***A t-test with 32 matched samples shows that the difference is statistically significant with

all cost factors and the net cash flow (italicized) becomes 100 percent.

p<.001.

p<.o1

accuracy and planning further into the future in order to materialize the benefits from tight linkage between decisions. Finally, the differences between the two decision models depend on the environment. When a firm operates under a highly complex and tight environment, the need for more integration appears to grow. Meanwhile, the benefits of integrating the product and process decisions decrease when a f m cannot accurately forecast the demand volume and process efficiency.

Certainly, more research can be done within the scope of the proposed model. First, planning horizon length and replanning frequency can have a considerable impact on performance when using rolling horizons [4] [5]. Second, the individual factors contributing to the environmental clusters deserve further testing, particularly those in the complexity cluster. Other factors, such as the discount rate, can also be tested with further experiments. In order to improve the generalizability of this analytical research, the parameter values beyond the machine tool industry should be explored. Case studies could also help operationalize the variables and parameters included in the model. Peceived: May 25, 1990. Accepted: April 16, 1991.1

REFERENCES

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Jay S. Kim is Assistant Professor of Operations Management at Boston University’s School of Management. He received his Ph.D. in operations management from The Ohio State University, his M.B.A. from Bowling Green State University, and his B.A. in management from Seoul National University, Korea. His research is focused on implementing manufacturing strategy, particularly on the strategic relationship between new product introduction and process selection decisions in a dynamic manufacturing environment. Dr. Kim has published i n the Journal of Purchasing and Materials Management and is a member of Decision Sciences Institute, TIMS, APICS, and POMS.

Larry P. Ritzman is the Thomas J. Galligan, Jr. Chair in Operations and Strategic Management at Boston College. He is Professor Emeritus at The Ohio State University where he served as Professor of Operations Management for 23 years. He received his doctorate from Michigan State University. His research and teaching interests are in operations strategy, production and inventory systems, layout, forecasting, and a diagnostic approach to operations management. Dr. Ritzman has published in a number of journals, including Decision Sciences, Behavioral Decision Making, Journal of Operations Management, Management Sciences, Operations Research, Interfnces, and others. He is a coauthor of the second edition of Operations Managenient: Strategy and Analysis and Disaggregation: Problems


in Manufacturing andService Organizations, as well as two volumes on applying decision methodologies to p t a l problems. Dr. Ritrman has served two terms as a vice president and board member of Decision Sciences Institute, and was elected as Fellow of the Institute in 1987. He is also a member of APICS, TIMS, and POMS.

W. C. Eknton is Pmfessor of Opuations Management at The Ohio State University. He received his doctorate from Indiana University, Bloomington. His research interests include materials management, inventory planning and control, aggregate planning and master production scheduling interface, and vehicle routing. Dr. Benton has published in Journal of Operations Management, International Journal of Production Research, Decision Sciences, IEE Transactions, Naval Research Logistics Quarterly, The Jountal of Purchasing and Materials Managerrrent, and others. He is a member of the Decision Sciences Institute. TIMS. and APICS.

David L. Snyder is Assistant Pmfessot of Management Sciences at The Ohio State University. Dr. Snyda holds a W.D. in operations management from the University of Michigan. an M.B.A. in statistics and management science from the University of Michigan, and a B.A. in mathematics from Oberlin College. His nseatch interests include modeling the linkage between infrastructural decisions and attainment of competitive priorities. and manufacturing strategy.

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Linking Product Planning and Process Design Decisions

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