The C-17 airlifter (supplied by Boeing) was chosen for the initial pilot program because it was the new airplane. The responsibility for performing ongoing logistics support had not yet been transferred from Boeing and its suppliers to the military. The civilian infrastructure for supporting the plane was in existence and the Government infrastructure was not. The key basis for believing that leaving the C-17 logistics support in the hands of Boeing would yield better aircraft availability and lower cost was a preliminary high-level analysis by Boeing. In this analysis, there were several ways that Boeing would achieve the initiative goals, but they were more conceptual than concrete and quantitative. Moreover, the different ideas were not put in a common framework that would ensure that the ideas were synergistic rather than cannibalistic.

Boeing asked Ventana to build a simulation model that would better establish the basis for believing that Flexible Sustainment would yield the promised benefits to the Air Force. This was accomplished by building a System Dynamics model that covered all of the ideas that Boeing had for improving the Air Force logistics system. The model confirmed the likelihood that the initiative would yield better aircraft availability and lower cost, but the relative importance of the ideas for achieving the goal was fundamentally different than anticipated. Some ideas were found to yield illusory advantages while one idea was found to be much more important than expected.

Because the SD model was so completely encompassing and concrete, a Ventana consultant became a key spokesperson for the Flexible Sustainment initiative. He explained the initiative at both the operational (Air Mobility Command) and strategic (Pentagon) levels. The Flexible Sustainment initiative on the C-17 program was deemed a success, and similar programs were started on a number of other aircraft and other military programs. Taken together, these initiatives constituted the largest privatization of a Government activity in the history of American government.

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The pricing decision was so critical to the company’s future that it initially asked three different organizations to make cost forecasts (two internal cost estimating groups and a consultant). Executives hired Ventana as a fourth estimator almost as an afterthought. Our assignment was to analyze the current situation and to predict changes in assembly labor hours. To do this, we built a manufacturing model that simulated the company’s operations and policies. Then, we calibrated it using historical production data drawn from the early years of the defense program. We discovered that the company’s traditional learning curve logic explained little (if any) of the company’s cost history. The key sources of assembly ‘learning’ involved: (a) hiring and layoff policies, (b) the amount of work impacted by engineering errors or lateness, (c) the conformance of work to the assembly plan, and (d) changes in the size of the assembly task resulting from subcontracting, product redesign and process improvements.
With the causes of historical labor costs roughly understood, Ventana used the simulation model to project future costs based on existing plans. Because the model parameters we had estimated from the historical data were fundamental drivers of cost, quality and schedule, our forecast did not merely extrapolate from the historical data. We predicted a steady cost decline lasting about two years, with a significant 23% reduction coming in the first year. After that, however, we predicted that improvement would falter, unless the company funded more engineering work to force cost down further. Under one planning scenario, we predicted a cost increase starting three years in the future. Overall, the contractor regarded the forecast as good news, and it signed a long-term contract with the lower price that the Government demanded.

A year later Ventana contacted the company to find out how accurate the first year of our forecast was. The data showed that the labor hour decline was 24% instead of the 23% we had predicted. This discrepancy was well within the accuracy of the data. We also learned that two of the other three estimators had faired less well, despite spending more time and effort. One had forecasted a change in labor cost of 34% during the first year, while another had projected significantly less improvement than we did. The performance of the third estimator was not revealed to us.

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Starting in 1990, the company had began downsizing and outsourcing. Traditional in-house fabrication and subassembly operations were subcontracted or farmed out, and even the detailed design of components was sent out for bid by vendors. The idea was to improve the financial leverage of the company’s capital by retaining only the key aspects of the business: sales, system engineering and final assembly. In doing this, the company was on the leading edge of a management prescription that subsequently became very popular in the 1990s. Superficially, the transformation appeared to work. Internal costs declined, and the pre-subcontract evaluations for outsourced work clearly showed that vendors would produce parts (or subassemblies) for less cost than the company could. The problem was that no one within the company had taken a critical look at the results of the intended transformation.

Ventana began by building a highly aggregated simulation model of company cash flow using four decades of historical financial and operating data. The top-down viewpoint was particularly important because the business was complex. Traditional accounting practices made unambiguous ‘apple-to-apple’ comparisons difficult. Changing accounting rules and cost allocation schemes camouflaged the trends. Ventana wanted to get above the allocations and focus on the bottom line. Perhaps surprisingly, the company possessed copious data, but it had never put together a coherent high level picture. It took four weeks to collect data covering annual operating expense, procurement expense, investment capital spending dollar volume of sales, and total production rate.

When the cost and sales data were put on a constant dollar basis, the picture that emerged was different than the company expected. Instead of seeing unit production cost declining since the transformation, unit costs were going up. In broad terms, operating expenses had fallen about 34% over the five years since the transformation started, but the procurement expense had risen by 55%. Overall, the unit production cost was up about 25%. Moreover, the overall cost trend after the transformation looked roughly the same as before it started. Detailed investigation of new product introduction and extraordinary circumstances failed to account for the higher cost. A careful look at outsourcing evaluations made prior to contract awards, however, revealed some serious flaws. The complexity of the outsourcing issues made the intuitive judgments and conventional wisdom unreliable. Even experienced professionals had trouble separating outsourcing impacts from other changing business phenomena.

Several issues surrounding the business transformation remained unclear, but a persuasive hypothesis is that the company reasoned too superficially in downsizing and outsourcing work. The transformed company could afford to be less skilled in some traditional manufacturing disciplines (where it was quite strong), but it needed more skills in others (where it was weak). In particular, the transformation reduced skill requirements for cash management, manufacturing expertise and detailed design engineering; but it increased them for contract negotiation, vendor coordination, system engineering and cost estimating. The company had not worked to augment its skills in the areas of greater importance.

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ESD verified the model’s effectiveness by successfully applying it to additional projects. In one case, Ventana’s forecast of a critical program’s schedule was markedly different from the program plan and from all other expert forecasts. The Ventana forecasts proved highly accurate. This success prompted a visit from the contractor’s headquarters to find out how Ventana, using only data released to the Government, had managed to beat the in-house forecasts that used full information. Most importantly, the Ventana model also revealed, for each project, how adjustments to deadlines and budgets could save both time and money.

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After more than 2 years of trying to implement a working system, however, Boeing fired the company, and Ventana agreed to build the system. Ventana did not accept the Boeing system specification, however, and built the system it thought would work. This system lacked several features in the Boeing specification, but it also had features not listed by Boeing. After 6 months, Ventana had a working prototype using 2 people (instead of 50) and applying a rapid prototyping approach to development. Over the following year, the system was improved and tested satisfactorily. The features not in the original Boeing specification turned out to be some of the most important. One was a portable implementation of the software that could be used in conjunction with the main software system. The other was a method of “cleaning” low quality data and keeping track of the cleaning.

The Air Force recognized that the ongoing task of finding and fixing data errors was the most challenging aspect of their logistics management activities. To meet the challenge, Ventana developed a data cleaning and logistics optimization system. The system combines a central database that facilitates changing, documenting, resolving differences and maintaining accountability for changes in the data, with a model of how parts status affects logistics system performance. By comparing model predictions to measured performance and automatically tracing the parts most responsible for the difference, the system identifies the data errors with highest impact on performance.

Evidence for the effectiveness of the system has emerged from real world tests. In a key exercise in December, 2000, twenty-five asset managers were relieved of normal duties for a week to clean the fleet data using conventional techniques. After this exercise, it took an analyst only five minutes with the Ventana system to sort through data on tens of thousands of parts and find twenty new errors, each of which was more significant to logistics performance than the errors identified conventionally. Most of the errors located the conventional way would have made little difference in calculating optimal numbers and locations of parts. On the other hand, without first correcting the twenty additional errors, any asset optimization would have been ludicrously inaccurate.

Perhaps surprising given the success, Ventana was not selected as the vendor to develop the spares management systems for other Boeing programs. Ventana was unwilling to make the schedule and cost promises that alternative vendors made. These promises were unrealistic, and they were not kept. The C-17 program refused to give up the Ventana system even after a corporate Boeing system became available. The Ventana system, built only as a prototype, has been preferred and used for the past 25 years.

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The massive volume of data and the complexity of the business had confused managers to the point that misanalysis of problems was commonplace. Moreover, the company’s in-house econometric model for understanding the sources of operating problems was invalid. Ventana discovered that the company was vastly overestimating the impact of part shortages on their assembly operations while underestimating the importance of declining work force experience. This discovery led to reformulation of the model, more data collection, and a thoroughly validated model that clearly established the root cause of production control problems.

Ventana’s initial success led to a series of assignments with the company that lasted more than four years. Ventana uncovered the causes of several important strategic problems and debunked many entrenched company myths. Firmly held beliefs were altered about a broad range of issues, including: (a) the correct interpretation of engineering and manufacturing operating data, (b) the required level of indirect labor, (c) the significance of classical learning curves, (d) the strategic drivers of productivity and schedule, (e) the effect of overtime policies, and (f) a host of critical planning assumptions used for aggregate capacity planning.

Ventana used models to accurately benchmark company performance and troubleshoot specific operational problems. The Ventana models predicted future performance in a number of different areas, and these forecasts proved considerably more accurate than other company forecasts based on management consensus. Unfortunately, the company didn’t realize benefits from most of these forecasts, because senior management found it hard to accept them. Ventana learned the extent to which corporate cultural factors could make hard realities difficult to accept. In particular, executives often wanted to postpone ‘bad news’, and this made it difficult to take timely action.

Nonetheless, some company managers continued to use the understandings gained from the Ventana project to redirect their organizations toward solving the root causes of their operational problems. Examples included: (a) training of engineers in design techniques for reducing part counts, (b) simplifying assembly operations, and (c) efforts to retain an experienced work force. These initiatives started very slowly, about three and a half years after Ventana first recommended them. It took almost eight years to create a significant body of evidence that the original recommendations were sound.

More than twenty years have elapsed since Ventana first made its recommendations. The company has demonstrated the effectiveness of reducing part counts and simplifying assembly operations via hundreds of engineering/manufacturing projects. The benefits are now generally accepted and convincingly proven, and the learning has proliferated across the corporation.

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