Quoted Lead Time Research Articles

Strategies for integrating lead time and customer-order decisions

Make-to-order manufacturers are facing unprecedented challenges to fill their customer orders quickly and to reduce their operating costs. In this paper, we first describe and discuss order taking and lead time quoting practice in a real world make-to-order manufacturing system. We then develop a model for this practical problem to study the conflicting objectives, of delivering customized orders quickly and maximizing expected profit. The primary goal of this paper is to develop a general model for studying the trade-offs and the interrelationships among the following manufacturing performance measures: manufacturing flow times, quoted lead time reliability (earliness and tardiness), throughput (or demand) rates, inventory levels, resource utilization, and expected profits. These and other related managerial issues are explored. Our structural results yield insights into basic choices between filling customer orders quickly and maximizing expected profit.

Makespan-related criteria for comparing schedules in stochastic environments

The ultimate goal of stochastic modelling in shop scheduling is to select the sequence with the best statistical distribution and use it to book capacity and quote delivery dates. For tractability reasons, stochastic models usually employ the expected value of the makespan as the criterion (instead of really looking at the whole distribution). In practice, this criterion is much harder to satisfy than solving for the (already strongly NP-hard) deterministic makespan. Therefore, other criteria have been proposed, and it is important to ask which one is best for long-term expected benefits. This paper analyses and compares several existing criteria for that purpose. We also suggest adding a variance minimisation objective, so that the quoted lead time required to satisfy a given service level will be minimised.

Managing Stochastic Multiproduct Systems: Model, Measures, and Analysis

We consider a model for managing a single stage that produces multiple items. The production rates are finite, and there are switchover times. The interarrival times and quantities of demands for the items are random, and demand may occur for a set of items. We consider order focussed measures: cost based on response times; service levels based on quoted lead times and Type-1 service. We operate the stage in the following manner: (1) there is a cyclic schedule that determines the sequence of items and the number of times a particular item is produced in a cycle; (2) given a cyclic schedule, production of each item follows a modified base-stock policy or a (s, S) policy. We present a simulation based procedure to obtain good values for the base stock levels or S (for any fixed S − s) for each of the above performance measures. Numerical results indicate that good solutions can be obtained with modest computational effort. We also report on a real world implementation of this model.

Recent developments in single product, discrete-time, capacitated production-inventory systems

We present here some recent results in single-product, capacitated production-inventory systems in discrete time. The key results are: (1) structure of optimal policy for single stage systems; (2) analysing via a shortfall process; (3) using simulation to optimize; (4) an approximation using tail probabilities. We consider periodic demand, and multiple stages — serial, distribution and assembly. Related topics of re-entrant flow shops, lead time quotation and value of information are also discussed briefly.

Trade-off between quoted lead time and price

In a make-to-order environment, lead time and price can play a crucial role in determining the financial success of a firm. Their importance increases when demand is sensitive to the quoted lead time and price. A model is presented which uses the quoted lead time and price as a mechanism to determine the optimal demand level. The relationships between the model parameters and their impacts on the firm's profit is also analysed. In addition, the effect of the number of job requests, and the mean processing time are examined. Based on the results presented in this paper, there is clear indication that the firm's profit is sensitive to the inventory holding rate, and that the inventory holding cost component is redundant in the presence of a tardiness cost component.

A general approximation for the single product lot sizing model with queueing delays

The objective of this paper is to derive a general approximation for the single product lot sizing model with queueing delays, explicitly including a non-zero setup time. Most research focuses on bulk (batch) arrival and departure processes. In this paper we assume an individual arrival and departure process allowing the modelling of more realistic demand patterns. A general approximation of the expected lead time and the variance of the lead time is derived. The lead time probability distribution is approximated by means of a lognormal distribution. This allows the manufacturer to quote lead times satisfying a specified customer service level as a function of the lot size. The main result is a convex relationship of the expected lead time and the quoted lead time as a function of the lot size. The results are illustrated by means of numerical examples.

A lot sizing model with queueing delays: The issue of safety time

The objective of this paper is to introduce the concept of safety time in a make-to-order production environment. The production facility is represented as a queueing model, explicitly including a non-zero setup time. A methodology is presented to quantify the safety time and to compute the associated service level based on the queueing delay. The main result is a convex relationship of the expected waiting time, the variance of the waiting time and the quoted lead time as a function of the lot size and a concave relationship of the service level as a function of the lot size. Most models in the literature assume batch arrivals. We relax that assumption so that an individual customer arrival process is allowed. We therefore have to derive a new closed form analytical expression for the expected waiting time. Both the deterministic and a stochastic case are studied.