What motivates customers to participate in social commerce? The impact of technological environments and virtual customer experiences

It is imperative for contemporary businesses to proactively search for ways of continuously improving the performance of their supply chains. Supply chain coordination and integrated decision making across the supply chain among various supply chain partners are frequently employed towards this end. Such supply chain coordination strategies include the use of common cycle time, quantity discounts, optimal lot sizing, quality improvements and inspections, etc. An important issue lacking in the supply chain literature relates to the incorporation of such quintessential and omnipresent human factors as errors in quality inspections and production improvements due to learning. This paper provides a simple but integrated mathematical model for determining an optimal vendor–buyer inventory policy by accounting for quality inspection errors at the buyer’s end and learning in production at the vendor’s end. The objective is to minimize the joint annual cost incurred in the supply chain. A numerical example is presented to illustrate the application and the substance of the proposed model. We discuss how such integrated models can be used for justifying investments in such strategic and operational areas as relationship management, product design, process design, and personnel training. We also provide some very interesting and challenging future research directions.

An integrated supply chain model with errors in quality inspection and learning in production

There is strong evidence that data items stored in organizational databases have a significant rate of errors. If undetected in use, those errors in stored data may significantly affect business outcomes. Published research suggests that users of information systems ~Robert Zmud was the accepting senior editor for this paper. ISRL Categories: AD05, BG03, HC0201

Can humans detect errors in data? Impact of base rates, incentives, and goals

Appropriate inspection is a significant component of production systems. In this paper a model is developed to determine the optimal placement of inspection stations within n-stage linear production systems. This model accommodates two types of inspector fallibility: "predictable," which implies that the error rates are known and constant, and "erratic," which requires a random variable to describe inspector performance. Cost per good unit accepted by the customer is used as the optimizing criterion. The cost-quality response surface is explored through a sequential sensitivity analysis. Our results indicate that under certain conditions the level of predictable inspector fallibility significantly impacts the number and placement of inspection stations as well as cost per good unit produced. The modeled systems, however, were quite insensitive to the variability of inspector performance.

This production-inspection model provides management with information on the optimal number and placement of inspection stations for specific planned or existing serial production systems. It can also be used by management to explore various policy options, such as the cost implications of increasing the quality vs. the quantity of inspection stations. The data required by the model can be obtained at reasonable cost provided management is willing to estimate or determine judgementally certain of the variables.

The Impact of Inspector Fallibility on the Inspection Policy in Serial Production Systems

Ever since the pioneering work of Dodge and Romig (1929), acceptance sampling has rapidly gained wide application in industry, particularly in the following stages of manufacturing: incoming materials inspection, on-line production control, and finished product quality auditing. The purpose for which sampling inspection is being applied varies from case to case. For a producer, some possible aims are:

https://www.jstor.org/stable/info/1402898

A Review of Acceptance Sampling Schemes with Emphasis on the Economic Aspect

SUMMARY Acceptance sampling plans are designed under the assumption of perfect inspection. However, inspection tasks are not, even under ideal inspection conditions, free of error. In this paper we consider the effects of inspection error on probability of acceptance, average outgoing quality, and average total inspection. These measures are examined under both replacement and non -replacement assumptions. Also, a method is presented whereby an acceptance sampling plan may be designed which explicitly considers inspection error.

The effects of inspection error on single sampling inspection plans

In this paper the effects of inspector error on a cost-based quality control system are investigated. The system examined is of a single sampling plan design involving several cost components. Both type I and type II inspector errors are considered. The model employs a process distribution, thus assuming that a stochastic process of some kind governs the quality of incoming lots. Optimal plan design is investigated under both error-free and error-prone inspection procedures and some comparisons are made.

The economic effects of inspector error on attribute sampling plans

This paper develops and provides formulas for calculating the Average Outgoing Quality (AOQ) when attributes examination is subject to Type I and Type II inspection errors. Nine different rectification inspection policies are considered. Each AOQ expres..

The Effect of Inspection Error on Average Outgoing Quality

This paper examines the results of Dodge's CSP-1 continuous sampling plan under inspection error. Both type I and II errors are explicitly included. Their effect on the average number of items inspected in a 100% screening sequence, average number of items passed during sampling inspection, average fraction of items inspected, proportion of items passed under sampling, and average outgoing quality are examined. Further, the relationships necessary to determine compensating sampling plans, considering inspection error, which yield the desired actual AOQL, are developed. Use of the compensating plan method is illustrated through an example.

The Dodge CSP-1 Continuous Sampling Plan Under Inspection Error1

Maritz (1966) and Lemon & Krutchkoff (1969) each describe discrete empirical Bayes smoothing techniques. These techniques essentially attempt to approximate the prior distribution function. Here a continuous smoothing technique which is based on a smooth and continuous approximation to the prior density function is presented. Results from a Monte Carlo study of the Poisson distribution are reported which show that the continuous smoothing technique has desirable small-sample properties. Some comparisons with discrete smoothing techniques are also made.

G. Kemble Bennett

Papers

The effects of inspection error on single sampling inspection plans

The economic effects of inspector error on attribute sampling plans

The Effect of Inspection Error on Average Outgoing Quality

The Dodge CSP-1 Continuous Sampling Plan Under Inspection Error1

A continuous empirical Bayes smoothing technique