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The General Motors Approach (1920s to Present)

A New Direction in Management

Early Ford put emphasis on and effort toward a vision that described a condition—the production flow ideal—but ultimately focused too little on product development and on organizing and managing the company in systematic ways. In contrast, General Motors (GM) put a lot of attention on developing systematic management and structuring the organization. Three concepts from GM’s then new management approach pertain in particular to our discussion here. They should look familiar to anyone who has worked in a mediumor large-sized company.

Rate-of-Return for Decision Making

The GM financial committee relied on a rate-of-return analysis (costbenefit analysis or return-on-investment calculation) for decision making on investments. The predicted return determined the choices that were made, as opposed to early Ford’s idea to do what is necessary to pursue an ideal.

In other words, make money became the guiding vision or overall direction for further development of the business or the factory. We were now not moving in a particular direction (aiming at successive target conditions on the way to a vision) but rather judging and selecting options independently based on their rate of return.

No other financial principle with which I am acquainted serves better than rate of return as an objective aid to business judgment. . . .

We are not in the business of making cars, we are in the business >of making money.

Alfred P. Sloan, Jr., President of General Motors, 1923–37; Chief Executive, 1937–46; Chairman of the Board, 1937–56

Maximizing the Output of Individual Processes

Early GM seems to have concluded that low costs are achieved when large quantities are produced with high machine utilization. Management began to think of the production value stream in terms of separate segments or departments, viewing each as an island, and created incentives that led those departments to produce as much as possible as fast as possible in order to reduce cost according to managerial accounting calculations (pieces per man hour per department or segment of the value stream).

Centralized Planning and Control Based on Managerial Accounting Data

GM introduced a decentralized divisional operating organization, but, increasingly, with centralized operational decision making and control. That control was based on setting quantitative targets for the divisions and reporting back performance metrics from the divisions. Decision making was based heavily on analysis of reported managerial accounting data.

Of course, GM also introduced well-known practices to influence the consumption side of the equation. These included segmenting the consumer market and providing each segment with a product line, an annual model change, segment-specific marketing, and providing credit to consumers. Since this book is about organization management, I will concentrate on changes GM introduced inside the company, on the management side of the equation.

Intended and Unintended Effects

The results of General Motors’ new approach and practices were dramatic and positive. GM achieved phenomenal success, grew to be the world’s largest corporation, and greatly influenced the nature of business management. Over the following decades GM’s management approach was widely publicized and was adopted by countless other

companies. By the 1950s it had become general practice at U.S. corporations and at companies around the world. Today it is so pervasive that it is essentially invisible. It is simply how things are done.

I should add one qualification to the above paragraph however: GM’s managerial approach achieved great success in the market conditions that prevailed through the 1960s. In later years, under different conditions, the same management approach no longer worked as successfully.

Let’s take a look at some of the effects that those three GM concepts had on how companies are managed. Again, the following should look familiar to anyone who has worked in a manufacturing company.

Effect of Rate-of-Return for Decision Making

GM’s formula-based rate-of-return decision-making approach is effective enough in a growing market when there are business opportunities from which to choose, but it becomes less so in the crowded or low-growth marketplaces we have today.

GM’s approach involved, to a degree, selecting between options in the early days of the U.S. automobile industry, when there were multiple options from which to choose. But in a lower-growth market with many competitors, the immediately profitable opportunities— the low hanging fruit—will have been picked. In this situation, management’s task becomes more one of nurturing promising processes, products, and situations into profitability than selecting ones that would be directly profitable.

The ROI approach of General Motors is more about making choices than about improving and adapting. For example, in the second half of the twentieth century, Detroit automakers opted repeatedly to not significantly enter the market for small cars, even as that market grew noteworthy, because from an ROI-selection perspective it was not profitable. The media has often criticized these decisions, but that denunciation is at least partially misplaced. Executives were making those decisions rationally and correctly, in accordance with the management system within which they worked.

In contrast, Toyota’s approach is about getting people to work systematically and creatively at the detail level to do what is necessary to achieve ambitious target conditions, which at first pass may not make it through a rate-of-return calculation. As shown in the previous chapter, Toyota utilizes cost benefit analysis less as a means for determining direction or what to do, and more as a means for figuring out how to cost-effectively achieve a desired condition.

If we go even further with our ROI thinking and use it to evaluate individual decisions or steps, then the result is likely to be suboptimization. According to systems theory, trying to maximize the individual parts of something reduces the effectiveness of the whole.

As we make these comparisons between GM and Toyota, we should keep in mind that it is not a judgment. The two approaches represent reactions to different conditions at different points in time in the history of the automobile industry. What’s most important is that we understand their long-term effects on an organization.

Effect of Maximizing the Output of Individual Processes

Seeking to maximize individual process output—for example, by measuring each process separately with a pieces per man hour calculation—generates the following effects on a value stream:

  • A process or department becomes even more decoupled from the next process as it strives to produce as much as possible as fast as
  • Since changeovers interrupt production, there is a natural tendency to avoid them and produce large
  • The next process in the value stream does not yet need all those parts that were produced too soon, so the parts must be stored as in-process (Inventory which is, by the way, counted as an asset by the managerial accounting system.)
  • When the next process finally does use the parts, it will discover defects among However, it is impossible to trace the root

causes of those defects because the parts were produced some time ago, and the conditions in the preceding process that caused the defects have long since changed.

This situation repeats over and over all the way through the value stream and results in a total lead time through the factory that is measured in days or weeks, whereas the total value added time is actually only minutes. Interestingly, when we speed up a process to improve its pieces-per-man-hour numbers, we only reduce the minutes of valueadding time and do nothing to reduce the days and weeks of lead time. You can observe these effects in factories around the world.

To keep inventory from swelling too much in this situation, we started placing limits on inventory buffers and set targets for inventory levels, without necessarily understanding the actual situation in the factory processes. The goal then became trying to schedule each individual segment of the value stream so accurately that items would be made not long before the next segment actually needs them. But this holy grail is not consistently attainable in the real world, even with sophisticated software, because process conditions up and down the value stream are constantly changing.

It takes a certain amount of inventory to hold a value stream together, and the quantity of inventory required depends on the current performance characteristics of the processes in that value stream. If we reduce inventory targets to below this level, then shortages, expediting, and emergency freight will increase. Every day’s work in the factory then involves adjusting schedules and expediting. Such daily adjustments in turn cause even more volatility in the value streams, and soon everyone in the factory becomes almost completely occupied with trying to make the production quantities and shipments.

People in an organization act rationally in a way that maximizes their success. Putting the emphasis on departmental output maximization, rather than on optimizing the overall flow for the customer, means that the natural interests of the departmental manager may come into conflict with the long-term survival interests of the company. In the long run, overall cost will be higher and the organization will become so

involved in firefighting that it is standing still, even though the departmental manager is meeting and even exceeding his or her objectives.

To put it briefly, systems theory tells us that we cannot optimize a system by trying to maximize its individual parts.

Effect of Centralized Planning and Control Based on Managerial Accounting Data

As the above description of everyday life in a factory illustrates, with centralized decision making from a distance based on accounting data, management tends to lose connection with, and understanding of, the actual situation on the work floor. Trying to manage from a distance through data abstractions often results in managers making incorrect assumptions and inappropriate decisions, and trying to make adjustments and adaptations too long after the fact. In addition, on-site managers naturally try to make the numbers upon which they are evaluated look good, which means that even less accurate information is reaching the decision makers in the levels above.

Not only are the centrally controlled divisions unable to adapt autonomously and quickly, but the decision makers in the central office are basing their decisions on inaccurate, after-the-fact quantitative abstractions.