Calculate Value in Manufacturing Flexibility

Stephanie A. Weiss

Buying a new production tool seems like a straightforward decision, but new research suggests that conservative estimates may not give enough weight to the value of flexibility in manufacturing.

Many photonics companies manufacture (and sell) production tools such as laser markers and machine vision systems; they also buy these systems to fix their own production problems, especially as customers demand higher volumes. Both marketing and production managers could benefit from more accurate ways to assess the ultimate value of production tools.

The traditional measure of a tool's value weighs its cost against the potential improvements in capacity and yield. These figures produce a rough estimate of how long the tool will be in use before it pays for itself.

Researchers at Penn State University and the Center for Army Analysis say that conservative estimates may be scaring people away from extremely beneficial technologies, especially in these days of flexible, on-demand manufacturing.

Elena Katok and colleagues say that manufacturing tools can benefit a company in three ways: improving manufacturing efficiency, increasing manufacturing capacity or increasing "decision flexibility." Most conservative mathematical models for gauging a tool's potential benefits rely exclusively on the first two benefits, ignoring or significantly undervaluing the third.

Katok writes in a research paper that decision flexibility is a critical element in businesses that must begin production before they have a clear picture of actual demand. A tool that enables managers to delay production can save time and money in the end.

The team's research paper (lems.smeal.psu.edu/katok/Simpaper9_14.pdf) provides as an example a company that produces custom-printed material on an eight-week cycle in which demand varies greatly. Some of the material requires collating and folding, which the company was doing by hand. At peak demand times, the company increased its manufacturing capacity by using an outside vendor.

The company's finance department initially rejected a request to buy a collating machine, based on a conservative estimate that it would take six years for the machine to pay for itself. Katok's analysis showed that it would pay for itself in less than six months because of the increased decision flexibility. Based on that analysis, the company bought the machine, and its performance confirmed Katok's analysis.