Delivery guarantees and the interdependence of marketing and operations

Production and Operations Management, Fall 2002 by Chatterjee, Subimal, Slotnick, Susan A, Sobel, Matthew J

We define lead time as time from order to delivery, minus processing time. In other words, for the sake of brevity, we use lead time for what might be considered "preprocessing lead time" (from a customer's standpoint, lead time is from order to delivery). Our model provides insights about how to calculate lead times, and what the impact of a given policy will be on operations. We show that, under a range of assumptions, the optimal lead-time quotation (one that balances potential loss of revenue with possible tardiness penalties) is a log-linear function of job processing time.

The remainder of the paper is structured as follows. Section 2 discusses related research in due-date setting and lead-time quotation. In Section 3, we present results linking the lead-time rule with the resulting processing time in operations. In Section 4, we characterize an optimal lead-time policy, and in Section 5, we demonstrate its impact on arrivals, load, mean processing time, and mean cycle time in operations, sketch the computation of parameters for the optimal policy, and present a numerical example. Section 6 presents our conclusions and future research.

2. Related Work

The coordination of delivery commitments with shop-floor activities is related to the issue of due-date setting in the scheduling literature. In a manufacturing firm, shop managers may set due dates for incoming jobs and schedule them so that the due dates can be met. Failure to meet the due dates may result in different types of penalties, such as giving the customer a rebate for late delivery or losing market share to competitors who can deliver the goods sooner. Over the past two decades there has been considerable research on due-date assignment in a manufacturing setting. See Cheng and Gupta (1989) for a survey of the early papers; for summaries of more recent work see Philipoom, Rees, and Wiegmann (1994), Lawrence (1995), and Easton and Moodie (1999). We limit the discussion here to the papers that are most relevant to our current work

Table 1 presents the relevant papers and indicates the attributes of each with respect to six features, four of which are shared by the present paper (in the first row). "Stochastic" refers to a model that incorporates uncertainty (generally, but not always, in arrival and processing times). This attribute is shared by all but two papers (Ozdamar and Yazgac 1997; Keskinocak, Ravi, and Tayur 2001), and they are included because of other features (rejection and multiple stages, respectively). "Rejection" means that whether or not a potential job remains for processing can depend on a decision of the firm ("job acceptance") or the customer (balking). An entry in the next column indicates that the paper has a profit- or revenuemaximizing objective. Multiple decision-making stages refers to coordination between marketing and operations (Weng 1999; Erkoc and Wu 2000), except for one paper that develops a multi-stage planning system (Ozdamar and Yazgac 1997). The inclusion of pricing and sequencing considerations is listed in the last two columns. We organize our discussion around these six features.