We describe a set of models that are used to manage the product and process design in a mass placement printed circuit board (PCB) assembly cell. Our models can be divided into two categories. First, we characterize the cell design problem and develop models to design an efficient assembly cell. Second, we present models for optimizing the different operational aspects of the assembly cell. These models were developed to assist the managers of a large electronic manufacturing firm in establishing mass placement PCB assembly cells.
We propose an agglomerative heuristic cluster analysis framework for application to the part‐family and machine‐cell formation problems associated with group technology. This framework addresses the notion of concurrently forming clusters of parts (families) and machines (cells) based on natural between‐part and between‐machine relationships and the strength of association relating pairs of parts with pairs of machines. An illustrative model is presented and operational aspects demonstrated using a small problem.
Underlying standard control‐chart methodology is the assumption of independent and identically distributed (IID) random variables. However, autocorrelation and other time‐series effects frequently occur in manufacturing and service quality applications. As a result, it has been suggested that standard methods be extended by using time‐series modeling. A little statistical analysis can go a long way, especially when time‐series effects are strong enough to frustrate efforts directed toward discovery of special causes. But a practical limitation on the use of time‐series modeling is that its implementation requires some sophisticated statistical skills, whereas standard control charts require only elementary statistical knowledge. The choice raises the questions of management philosophy, statistical techniques, and computation.
Redesigning and improving business processes to better serve customer needs has become a priority in service industries as they scramble to become more competitive. We describe an approach to process improvement that is being developed collaboratively by applied researchers at US WEST, a major telecommunications company, and the University of Colorado. Motivated by the need to streamline and to add more quantitative power to traditional quality improvement processes, the new approach uses an artificial intelligence (AI) statistical tree growing method using customer survey data to identify operations areas where improvements are expected to affect customers most. This AI/statistical method also identifies realistic quantitative targets for improvement and suggests specific strategies predicted to have high impart. This research, funded in part by the Colorado Advanced Software Institute (CASI) to stimulate profitable innovations, has resulted in a practical methodology used successfully at US WEST to help set process improvement priorities and guide resource allocation decisions throughout the company.
Computer‐integrated manufacturing implementation is often hindered by human resource issues like stress. By focusing on one type of potential human obstacle to the integration of islands of automation in our exploratory case study, we examine the sources of stress and the lack of job control experienced in three functional departments: computer‐aided design and manufacturing, manufacturing planning and control, and computer numerical control/robot manufacturing; we also suggest management interventions to alleviate stress in each group of workers. We used three methods to collect data: self‐reporting, observation at the job level, and physiological screening. Our results indicated little difference in stress levels among the three groups. However, we found that each group faced a different set of pressures and exercised different levels of control over their jobs. We suggest specific managerial action to overcome the most critical pressures.
We give a tutorial on bottleneck dynamics. Bottleneck dynamics is a scheduling framework that uses approximate dual resource prices to make decentralized decisions. The basic idea is to establish a price for a resource as a function of the set of jobs that need to be processed by the resource. Tasks are then sequenced according to a cost/benefit ratio. Starting with one resource sequencing problems, we describe how priorities for jobs can be developed and how they translate into resource prices. We then describe how resource prices can be approximated in a multiresource situation and how lead times which are critical for these approximations can be accurately computed. We also describe a number of studies that have shown bottleneck dynamics to be an effective approach in several different problem areas.
Cyclic scheduling has been primarily studied under deterministic assumptions. In practice, stochastic variability exists and must be taken into account. In this paper, the descriptive Markov chain model of Bowman and Muckstadt is extended to cover demand variability. A production control algorithm is developed using cyclic time and task criticality estimates from the model. Application of the algorithm to a case study shows that material release and anticipatory inventory buildup decisions can be effective in reducing inventory holding and overtime costs when significant demand variability is present.
This paper is an extension of Billington, who used the framework of the economic production quantity (EPQ) to model setup cost reduction. In the present paper, we use the EPQ model as a starting point to investigate the nature of setup costs and the effect of setup time reduction on the increase in available capacity. Reducing setup is vital to a company's success because a lengthy changeover of machinery is expensive: it demands long production runs to justify its cost, and these, in turn, lead to excessive inventory and to a slow response to customer needs. As in Billington, setup reduction is modeled as a function of an annual amortized investment. The paper examines the behavior of the setup time, the inventory cost, the lot size, and the freeing up of machine time in the face of a capacity constraint. A solution algorithm is provided to find setup times that minimize the sum of setup and holding cost, subject to a constraint on machine availability. The analysis sheds light on the true nature of setup cost and on the opportunity cost of not reducing setups. In the constrained optimization, the Lagrangian multiplier gives an estimate of the marginal value of adding one time unit of machine capacity, or, alternatively, of reducing one unit of setup time.
A serious gap is emerging between what is espoused as total quality management and what is actually being implemented. Examples of actual implementation failures are given. In addition to affecting the offending firms, these gaps threaten the viability of the quality management movement in the United States. Their causes and appropriate counter measures are worthy topics for research.
The paper examines the problem of finding a sequence of a finite set of expansion projects to meet a deterministic demand projection at minimum discounted cost. As the general problem is NP‐complete, we focus on the case when the demand growth is in the exponential form, which is quite often found in applications of electric power generation, transportation, water resources and communication. We find that projects' annual costs are an effective ranking criteria for determining an optimal expansion sequence. As the annual cost for a project is a function of time or capacity level, an optimal expansion sequence can be obtained easily if dominance orders of the annual costs exist for all pairwise projects, which is likely to be the case for most practical problems. When the annual costs for the projects are in conditional order, a branch‐and‐bound algorithm has been developed to effectively reduce the searching range. Our extensive computational results show that the approach is very efficient compared with the other approaches and always solves the problem to optimum although the optimality of the approach still remains to be proven.