Methodologies, modeling approaches, and the interactions between various system elements involved in inspection-allocation and sensor-distribution problems, that influence operational quality decisions, are discussed. The surveyed papers fall into two broad categories: inspection-oriented quality-assurance strategies and diagnosis-oriented sensor-distribution strategies. Within each subarea, individual papers are further classified according to the system characteristics of the physical processes being investigated and the modeling characteristics of the approaches being used. As evident from nearly 100 journal articles published in the past four decades, these two problems have received considerable attention from researchers in quality engineering, management science, operations research as well as robotic vision arenas. We find that the inspection-allocation problem has been studied rather comprehensively whereas the relatively new sensor-distribution problem has plenty of opportunities for researchers...

A survey of inspection strategy and sensor distribution studies in discrete-part manufacturing processes

This paper describes the elements of the inspection allocation problem and surveys some of the models proposed in the literature. The limitations of the existing work are noted and possible approaches to the solution of more realistic problems are menti..

A Survey of Models for Allocating Inspection Effort in Multistage Production Systems

This paper presents a methodology for optimal allocation of sensors in a multistation assembly process for the purpose of diagnosing in a timely manner variation sources that are responsible for product quality defects. A sensor system distributed in such a way can help manufacturers improve product quality while, at the same time, reducing process downtime. Traditional approaches in sensor optimization fall into two categories: multistation sensor allocation for the purpose of product inspection (rather than diagnosis); and allocation of sensors for the purpose of variation diagnosis but at a single measurement station. In our approach, sensing information from different measurement stations is integrated into a state-space model and the effectiveness of a distributed sensor system is quantified by a diagnosability index. This index is further studied in terms of variation transmissibility between stations as well as variation detectability at individual stations. Based on an understanding of the mechanism of variation propagation, we develop a backward-propagation strategy to determine the locations of measurement stations and the minimum number of sensors needed to achieve full diagnosability. An assembly example illustrates the methodology.

Optimal sensor distribution for variation diagnosis in multistation assembly processes

Manufacturing systems typically contain processing and assembly stages whose output quality is significantly affected by the output quality of preceding stages in the system. This study offers and empirically validates a procedure for 1 measuring the effect of each stage's performance on the output quality of subsequent stages including the quality of the signal product, and 2 identifying stages in a manufacturing system where management should concentrate investments in process quality improvement. Our proposed procedure builds on the precedence ordering of the stages in the system and uses the information provided by correlations between the product quality measurements across stages.

The starting point of our procedure is a computer executable network representation of the statistical relationships between the product quality measurements; execution automatically converts the network to a simultaneous-equations model and estimates the model parameters by the method of least squares. The parameter estimates are used to measure and rank the impact of each stage's performance on variability in intermediate stage and final product quality. We extend our work by presenting an economic model, which uses these results, to guide management in deciding on the amount of investment in process quality improvement for each stage.

We report some of the findings from an extensive empirical validation of our procedure using circuit board production line data from a major electronics manufacturer. The empirical evidence presented here highlights the importance of accounting for quality linkages across stages in a identifying the sources of variation in product quality and b allocating investments in process quality improvement.

Process and Product Improvement in Manufacturing Systems with Correlated Stages

Process and product improvement in manufacturing systems with correlated stages

A method for determining where to locate the inspection stations in a multistage production process with imperfect inspection is presented. Dynamic programming is used to establish that the optimal expected total cost function at every stage is piecewise linear and concave. While the optimal policy at every stage usually consists of one “inspect” region and one “do not inspect” region, this policy structure is found not to hold in general. A computation scheme based directly on the dynamic programming formulation is proposed, a time-sharing computer program is discussed, and the results of an example problem are presented.

Optimal Location of Inspection Stations in a Multistage Production Process

As a method of solving multiple-criteria decision making problems with a single quantitative objective and multiple qualitative objectives, the post-model analysis (PMA) approach is proposed. The essence of PMA is to support the trade-offs between a quantitative objective and multiple qualitative objectives so that the decision maker can find a perceived most preferred nondominated solution. To this end, the optimal solution of a quantitative model is found first, without regard for qualitative factors. The solution is then evaluated in terms of qualitative objectives. When the initial quantitatively optimal solution is adjusted to allow improvement of qualitative goals, opportunity costs of achieving qualitative goals are incurred. In this process, an expert system and/or graphical display can be used. PMA therefore provides a way to incorporate quantitative models into knowledge-based expert systems.

Multiple‐criteria decision making including qualitative factors: the post‐model analysis approach

A simulation model that mirrors the actual practices of lenders in setting qualification requirements for mortgage loans is used to assess the impact of permanent buydowns and buyups on the maximum sale price that buyers can afford. Buydowns are reductions in the coupon rate traded off against higher discount points; buyups are the reverse. Although buydowns have little effect on afford ability, buyups can have significant effects. Their potential is largely unrealized, however, because of the call risk to lenders of loans carrying premiums. The authors propose a “discount recovery” provision, which would be similar to a prepayment penalty except that the payment upon prepayment would be tied to the amount of the discount foregone by the lender. This would protect lenders against call risk, giving them every reason to offer buyups as a way of expanding demand.

E. Gerald Hurst

Papers

Optimal Location of Inspection Stations in a Multistage Production Process

Multiple‐criteria decision making including qualitative factors: the post‐model analysis approach

Affordability and mortgage design: Permanent buydowns and buyups