‘Anticipatory batch insertion’ to mitigate perceived processing risk

Abstract

Literature on lot-sizing models with random yields has been traditionally limited to random occurrences that cannot be anticipated in advance; for instance, day-to-day production errors and minor machine repairs. However, in reality, manufacturing processes are subject to other risks that are anticipatory, or non-random, in nature. One example would be yield loss resulting from non-random events such as process, product or material changes. Yield uncertainties of these types are temporary in nature with an impact that decays over time until the manufacturing system fully re-stabilises. One way of reducing the impact of such events is to split the lot and to process a small sub-batch in advance to stabilise the process, thus absorbing the risk associated with the change event. We refer to this approach as ‘anticipatory batch insertion’. This paper presents an exploratory study to analyse the performance of batch insertion under various scenarios related to product sensitivity, risk magnitude and schedule hardness. Results indicate that batch insertion is most advantageous whenever the production schedule is loose, multiple products are sensitive to the risk and the risk magnitude is high.