Reducing Aircraft Manufacturing Cost
An aircraft manufacturer hired Ventana to discover why the firm appeared to lose production control whenever it had its greatest marketing successes. Ventana approached the problem by constructing a simulation model of the company based on discussions with managers and engineers as well as company operating plans and reports. When Ventana tested the model against historical data, it failed Ventana’s validity tests. Managers’ presumptions about how the company worked were inconsistent with existing data. The company rationalized this inconsistency by assuming that the data were inaccurate or irrelevant. However, additional data-gathering activities designed to limit uncertainties and resolve discrepancies continued to show that current perceptions were inconsistent with the data.
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.