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Crossing functional lines: achieving successful strategy implementation through effective internal partnering.

Crossing Functional Lines: Achieving Successful Strategy Implementation Through Effective Internal Partnering

Successful strategy implementation requires the input and cooperation of all players in the company. The tendency to "box" management functions - so that manufacturing does not talk with R&D, R&D does not talk with marketing, marketing does not talk with manufacturing, and sales does not talk with anyone in the organization - creates functions empty of responsibility and void of interaction. Dating back to the late 1700s, this "division of labor" is the basis for much departmentalization due to its common-sense appeal of having departments that consist of experts in particular fields. Traditionally, each functional area performs specialized portions of the organization's tasks. Practitioners and academics tackle such "vertical activities" more easily since the activities require expertise in only one functional area. However, individual functional-level strategies resulting from this departmentalization are the center of much intra-organizational conflict and the source of many strategy implementation problems.

The nomenclature appearing in recent business and academic publications underlines the need: "factory of the future," "design for manufacturability and assembly," "early manufacturing involvement," "swarming," "design-factory fit," "design for competitiveness," and "concurrent engineering." Such collaboration across functional areas has resulted in part-number reductions, faster time to market, standardization of part features, and improved quality. In other words, effective internal partnering can lead to better products introduced into the marketplace much faster.

Improved cross-functional coordination tends to be following three major approaches: organizational design issues, improved communication, and decision support systems/models. (While some academics point out the importance of using appropriate evaluation systems to facilitate interaction, we do not see any managerial reports linking functional areas by means of modified reward systems.) The focus of the anecdotal evidence has been on the development of new products, with the coordination of R&D and manufacturing receiving a vast amount of attention. Additionally, the terminology mentioned previously falls largely into the organizational design and communications mechanisms.

Our work suggests, however, that the need for interfunctional coordination occurs in managing existing product lines as well. This is noticeably true in multiple-product companies (width and depth in product lines) that face conditions of constrained capacity or time limitations. In these companies, much conflict, and thus the need for strong internal partnering, centers around marketing- and manufacturing-related issues.

This recurring conflict between marketing and manufacturing in multiple-product companies is hardly surprising given the often opposing goals and the daily dynamics of the two functions. Marketing strategy involves the specifying price, communications, and distribution in order to generate demand for the company's products. Manufacturing strategy seeks to provide the supply that satisfies market demand by determining how, and at what rate, to manufacture products. Naturally, objectives, plans, and values of marketing and manufacturing groups parallel these respective demand and supply regulation goals.

While such decisions are often made independently, the two functional areas overlap around the issue of the firm's product, as shown in figure 1. Thus, the interdependence of the operational decisions appears indisputable, particularly given the simultaneous impact on the customer. Table 1 summarizes and categorizes the extremes of three major conflict areas between marketing and manufacturing.

Table : Table 1 Conflict Areas Between Marketing and Manufacturing
Area of Marketing Manufacturing
Conflict Objective* Objective*


Managing Diversity:
1. Product line many & complex few and simple
 length/breadth models models
2. Product customization customer "stock"
 specifications products
3. Product line changes product changes planned, only
 immediately; necessary
 high risk changes; low
 risk


Managing Conformity:
4. Production scheduling constant change inflexible
5. Capacity/Facility accept all critically
 planning orders evaluate "fit"
 of orders


Managing Dependability:
6. Delivery immediate; as soon as
 large inventory possible; no
 inventory
7. Quality control high standards reasonable control


*Each functional area pursues these objectives within reason. For example, marketing does not want so much variety that salespeople are overloaded with information to the point that they cannot effectively demonstrate/discuss the products. Likewise, manufacturing recognizes the costs associated with monotony in the production process.

Reasons for conflicting goals and the resulting tension between marketing and manufacturing vary and are not mutually exclusive within an organization. Sources of conflict include physical separation of the two functions, individual orientation, different data requirements, cultural differences, ambiguity of corporate strategy, pressure from rapid growth, environmental changes, proliferation of automated operations, capital constraints, and company size.

Decision-support systems appear to be a major mechanism for facilitating marketing-manufacturing interaction in multiple product companies. Microcomputer-based decision models aim to improve and expedite the planning and decision making processes in organizations. The models are not intended to make decisions for managers, but rather to provide informational and computational support to improve decision-making. Since all decision-making involves predicting the likely consequences of decision alternatives, the model allows the decision-maker to imitate the problem and view the possible outcomes of various decision scenarios. More subtly, such a support system brings marketing and manufacturing together during the development, operationalization, and discussion of the model. This type of interaction should lead to positive interfunctional inquiry rather than negative confrontation and resolve some of the conflicts that threaten or eliminate critical interactions between the two groups.

The key to an effective computer-based model is that it be small-scale and flexible to allow ease of use. Thus, the goal is to develop a model complex enough to incorporate major marketing and manufacturing issues, yet simple enough to encourage use. At the same time, the model must be such that both marketing and manufacturing feel their input is at least as valuable, if not more so, than the other party's.

Our research provides some very interesting results regarding the power of a decision-support system as a facilitating mechanism. Using decision-support models in three company situations, we find that models can help reduce the conflict resulting from physical separation, individual orientation, and data and cultural differences.

The first company, an agricultural enterprise, faced a tremendous geographic boundary problem. The marketing group was located in the United States, with the production group in a South American country. Not only did the company experience interfunctional coordination problems resulting from the physical separation, but data, cultural, and language differences existed also. As evidenced in joint annual planning meetings, bringing the two groups together in a physical sense was not enough to resolve the conflict existing between what marketing could sell and what production could produce.

The problem experienced by the two groups centered around the allocation of capacity. Marketing felt that it knew what the consumer wanted and was willing to pay during a particular time period, while manufacturing felt that it knew what grew best and when. Both sets of assumptions were entirely valid, but severely myopic. Marketing was relying on external information for its planning while manufacturing relied on internal information, and neither group understood the advantages of working with the other group.

Essentially, the decision support system needed to show the company's marketing and manufacturing managers how various capacity planning scenarios affect a product's total revenue. By using a Lotus 1-2-3 model, the company's management team (including marketing and production) can quickly explore the profit implications of different marketing and manufacturing decisions. For example, results of comparing alternatives for one product found only a slight difference in per-unit contribution with a smooth production schedule (manufacturing's preference) when compared to producing to hit peak price periods (marketing's preference). The company would need to decide whether or not savings from having a balanced production process is greater than the dollar difference. The approximate $25,000 revenue difference between allocating capacity to smooth production or hit peak price periods and allocating on an ad hoc basis (e.g., the production schedule that fits the whims of the scheduler) shows that some framework for understanding the financial impact of the company's capacity allocation decisions pays off.

In addition to allowing rapid revenue calculations under different capacity allocation scenarios, the model (with the aid of macros on Lotus 1-2-3) also allows the user to make changes to marketing and/or manufacturing data easily and to evaluate the revenue impact given such changes. This rapid changing of marketing and manufacturing input data allows for easy evaluation of different products, given the multi-product nature of the company.

A second, more generic model has been evaluated using data from two companies. The model, a product/account matrix (figure 2), allows us to simultaneously evaluate the profit impact of different accounts purchasing various amounts of a company's products. We capture marketing concerns regarding sales volume and price along the vertical axis (accounts/demand) and manufacturing concerns regarding capacity and costs along the horizontal axis (products/supply). The software we use to operationalize the model is Interactive Financial Planning System (IFPS) by EXECUCOM System Corporation. Product/ Account Matrix

Figure 2 Product 1 Product 2 Product 3 All Others TOTALS Account A Account A Account A All Others TOTALS

Using the model at a paper and plastic cups and dinnerware company, we evaluated three major decisions faced by the company:

* Customizing products for one of its major accounts.

* Accepting the order of a potentially large account (an account the company had been pursuing heavily).

* Decreasing price during the off season. The company experiences constrained capacity throughout its production processes, except for one process during the third and fourth quarters of the year.

Regarding the customization of products for one account, marketing felt that it could not turn down the request because of the potential for harming close working relationships. However, the manufacturing group did not see how the company could accept the order because of the costs and time involved. The uncertainty facing the company was whether or not it could handle the order, realistically, on very short notice. Marketing thought manufacturing should do whatever was necessary to fit the order into the production schedule. Manufacturing argued that the order would have to wait until the following year when they could fit it into the long-term forecast and production plan. Another option facing the company was decreasing price in the off season versus maintaining idle capacity on one of its manufacturing processes during this time of the year. The company needed to decide if it should decrease price during the last two quarters - getting customers to "load up" - which in turn will decrease demand during the first and second quarters when capacity is constrained.

We found these situation-specific outcomes:

* The customization of products for a major account yielded long-term profits, given the possibility that the company might lose the account otherwise.

* Sacrificing accounts to obtain one large account had a positive impact upon profit.

* Maintaining idle capacity yielded greater profits than decreasing prices.

We used the model to evaluate two alternatives that the second company, a manufacturer of electronic analog circuit modules, isolation amplifiers, and power converters, was curious about:

* Increasing sales rep commissions on sales of the best-selling product.

* Increasing rep commission and decreasing price on the best-selling product.

The company experiences excess production capacity throughout the year. As a small company, the owner wants to maintain steady employment for rank-and-file employees who have been with the company for many years. Maintaining steady employment means that the company produce and sell products continuously.

Model results showed managers that both alternatives would yield higher profits than with the company's current situation, assuming elastic demand and that increased selling effort would shift the product's demand curve. More importantly, however, the changes necessary in the demand module of the model force recognition that the company is basing its sales estimates on production capacity rather than realistic demand expectations. Unless the company really can sell more, it will lose money due to finished goods sitting in inventory.

While the usefulness of using interfunctional models to reduce conflict and facilitate interaction is made apparent by these examples, the true value of modeling as a facilitating mechanism is captured in the following comment by the marketing manager of one of the companies:

"We never believed each other before. Production would tell us something, and we would think that they could make the product if we really wanted it. The same was true for them. We made forecasts and talked about prices and they didn't pay that much attention to us. However, numbers are universally accepted. When both sides see the profit implications of our varying decisions, we begin to work together in a much better fashion."

A model using data from both marketing and manufacturing allows separate data-gathering processes, yet interaction at the alternative-evaluation level. The model allows functional managers to see the possible revenue consequences of both individual and combined decision-making processes. A microcomputer-based model allows greater recognition of trade-offs forced on other functions, as well as constraints faced by the other functions.

The study of interfunctional partnerships informs managers about the conflict areas likely encountered in implementing proposed strategies. It is not enough to set corporate-level strategies. Functional-level actions are what take place in the marketplace. The effective implementation of corporate-and business-level strategies depends upon functional groups working in partnership with one another. Our research suggests the use of decision-support systems as a facilitating mechanism between marketing and manufacturing in multiple product companies.

PHOTO : Figure 1 Marketing and Manufacturing Converge on Product Decisions

Victoria L. Crittenden is assistant professor of marketing at Boston College in Chestnut Hill, Massachusetts. She has a DBA from the Harvard Business School. Her research focuses on the need for coordination between marketing and manufacturing in order to facilitate successful strategy implementation, and she has been published in Sloan Management Review, Industrial Marketing Management, Information and Management, Manufacturing Strategies, and Issues in Pricing Research.
COPYRIGHT 1991 Institute of Industrial Engineers, Inc. (IIE)
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Author:Crittenden, Victoria L.
Publication:Industrial Management
Date:Jan 1, 1991
Words:2251
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