William C. Jordan

Chained cross-training of workers for robust performance

Training workers to perform multiple tasks can improve workforce agility for dealing with variations in workload. However, cross-training can be costly, time consuming to implement, is limited by worker learning capacity, and can lead to ambiguity about work responsibilities. Therefore, it is important to implement cross-training in the most efficient way and especially, due to the training time required, in a way that is robust to system changes. We use queueing and simulation analysis to investigate cross-training in the context of maintenance in a manufacturing plant. The tasks are independent and can be represented as a set of parallel queues that are served by dedicated and cross-trained workers. We propose a cross-training strategy called chaining, in which a few workers are strategically cross-trained, and show that it yields most of the benefits of cross-training all workers, with much less effort. Most importantly, we demonstrate that cross-training workers to form a “complete chain” is extremely robust in the following ways: (i) it is insensitive to the variety of ways a complete chain can be formed; (ii) it performs well even if there are major changes to or uncertainty in system parameters (such as mean task arrival rates); and (iii) performance is insensitive to control decisions that, without complete chaining, can significantly harm performance.

Fleet Sizing under Production Cycles and Uncertain Travel Times

This paper develops a model for sizing a fleet of containers used to ship parts from a single manufacturing plant to a group of assembly plants. Parts are produced in a deterministic production cycle, but container travel times are stochastic. Based on results for an infinite-channel service process, equations are developed relating fleet size to the probability of a container shortage. An example illustrates this fleet size-shortage probability trade-off and shows the impact of uncertainty level and the number of plants on fleet size.

Chained cross-training of assembly line workers

To function properly, assembly lines require the presence of every worker. When a worker is absent, management must scramble quickly to find a replacement. Cross-training workers to perform multiple tasks mitigates this difficulty. However, since cross-training is costly and limited by learning capacity and can confound the search for quality problems, it should be used judiciously. The present paper proposes a training strategy called chaining in which workers are trained to perform a second task, and the assignments of task types to workers are linked in a chain. It is shown that chaining is a practical and effective strategy for prioritizing cross-training to compensate for absenteeism on assembly lines.

Truck backhauling on two terminal networks

Truck backhauling reduces empty truck-miles by having drivers haul loads on trips back to their home terminal. This paper 1) examines the impact on backhauling opportunities of terminal locations and directional imbalances in the flow of freight from the terminals, and 2) develops a method for determining which truckloads should be backhauled. Backhauling is studied for two terminals sending full truckloads to many customers under steady-state conditions. This research develops two backhauling models. The first is a continuous model that makes simplifying assumptions about customer locations and travel distances. It results in formulae showing that 1) savings from backhauling increase at a decreasing rate as the directional flow of freight between two terminals becomes more balanced and 2) backhauling is an important, but often ignored, factor in terminal (e.g. trucking terminal, warehouse, or plant) location and supplier selection decisions. The second model is a more general discrete model that determines which loads should be backhauled to minimize empty truck-miles.

Integrated Assembly Line Loading, Design, and Labor Planning

Abstract Often multiple production lines (or machines, cells, or departments) produce a group of related products. In these environments, the following interrelated planning decisions must be made: 1. 1. Choosing the speed at which each line will run, 2. 2. Determining which products to produce on which lines, and 3. 3. Determining the number of workers assigned to each line. These decisions directly affect a plants labor and capacity utilization and are quite complex if the number of products and lines is large. For the purposes of cost estimating to bid on new work, labor negotiations, and training, these planning decisions must be made before precise demand information is available. Yet in many industries, such as the US automotive industry, restrictive labor agreements force the plant to live with these planning decisions for long periods of time. This paper enunciates this production planning problem actually facing industry, formulates it mathematically, and provides a practical solution approach.

Coordinating Product Distribution and Empty Equipment Allocation Decisions In Large Networks

AbstractGeneral Motors uses large equipment fleets (railcars and trucks) for shipping vehicles from assembly plants to dealers. Mode and routing decisions for product distribution (loaded movement decisions) affect where equipment is needed and where it will become available. This, in turn, affects decisions on allocating empty equipment to reloading points (empty movement decisions). Hence, loaded and empty movement decisions are interrelated. The benefits of coordinating empty equipment allocation decisions over a network are well known. Typically, however, loaded and empty movement decisions are made separately. This research studies the savings potential from coordinating loaded and empty movements in large networks.A tactical planning model is developed to minimize total transportation cost by making loaded and empty movement decisions simultaneously. Tests using actual GM vehicle delivery data compare total costs achieved through coordinating loaded and empty movements to total costs when these deci...