Dynamic Process Improvement

This paper explores the economics of investing in gradual process improvement, a key component, with empirically supported importance, of the well known Just-in-Time and Total Quality Control philosophies. We formulate a Markov decision process, analyze it, and apply it to the problem of setup reduction and process quality improvement. Instead of a one-time investment opportunity for a large predictable technological advance, we allow many smaller investments over time, with potential process improvements of random magnitude. We use a somewhat nonstandard formulation of the immediate return, which facilitates the derivation of results. The policy that simply maximizes the immediate return, called the last chance policy, provides an upper bound on the optimal investment amount. Furthermore, if the last chance policy invests in process improvement, then so does the optimal policy. Each continues investing until a shared target state is attained. We derive fairly restrictive conditions that must be met for the policy of investing forever in process improvements to be optimal. Decreasing the uncertainty of the process (making the potential improvements more predictable) has a desirable effect: the total return is increased and the target state increases, so the ultimate system is more productive. Numerical examples are presented and analyzed.