Improving Throughput Analysis at General Motors

The Problem

At a time when General Motors was facing increasing competitive pressures, higher quality demands and a sluggish economy, the giant automaker was unable to effectively analyze the productivity and throughput of its manufacturing operations. Many of its factories during the late 1980s and early 1990s were missing production goals, working unscheduled overtime and experiencing high scrap costs. Although GM overall had excess production capacity at the time, production bottlenecks and other problems were causing the company to lose money even on high-demand products. In addition to creating problems for existing products, the lack of an effective throughput analysis capability was impeding the launch of new products, resulting in lost sales. In 1991, GM reported a $4.5 billion loss – a business record at that time.

The Analytics Solution

Recognizing the broad scope of the problem, GM responded with a three-pronged analytics-based effort focusing on production line data collection, throughput modeling and algorithms, and throughput improvement processes. In tackling the production data collection component, GM recognized the importance of limiting data inputs from the massive universe of production data available, to avoid bogging down the analytical processes. The goal was to develop models and algorithms with modest data requirements that still produce meaningful results. Repeated trial and validation efforts established the appropriate data inputs in the areas of workstation speed changes, scrap counts and classification of workstation stoppages (such as equipment failure and safety stops).

In the interest of making analysis be fast, accurate and easy to use, GM built analytic simulation models for simpler situations, as well as detailed discrete-event simulation models for more complex situations. They were built into an analysis tool that revealed, for each production line evaluated, the hidden bottlenecks that were impeding throughput.

GM’s “Throughput Improvement Process” (TIP) is a procedure whereby plant-floor personnel and management can use the analytics-based techniques to study and remove bottlenecks. TIP steps include identifying the problem, analyzing it, generating action plans, implementing the solution, and subsequent evaluation.

The Value

For GM, the quantitative benefits of the Throughput Improvement Process and its underlying analytics include a 26% increase in manufacturing productivity between 1997 and 2004 and ongoing annual combined savings and incremental revenue improvements of over $2 billion. Qualitative benefits include creating standard data definitions and manufacturing performance measures, facilitating communication and transferring lessons learned throughout GM’s global operations.