Nallan C. Suresh

Machine-component cell formation in group technology: a neural network approach

SUMMARY This paper presents a neural network clustering method for the part-machine grouping problem in group technology. Among the several neural networks, a Carpenter-Grossberg network is selected due to the fact that this clustering method utilizes binary-valued inputs and it can be trained without supervision. It is shown that this adaptive leader algorithm offers the capability of handling large, industry-size data sets due to the computational efficiency. The algorithm was tested on three data sets from prior literature, and solutions obtained were found to result in block diagonal forms. Some solutions were also found to be identical to solutions presented by others. Experiments on larger data sets, involving 10000 parts by 100 machine types, revealed that the method results in the identification of clusters with fast execution times. If a block diagonal structure existed in the input data, it was identified to a good degree of perfection. It was also found to be efficient with some imperfections i...

An empirical investigation of the impact of strategic sourcing and flexibility on firm's supply chain agility

Purpose – The purpose of this paper is to investigate two potentially key drivers of a firms supply chain agility, namely strategic sourcing and firms strategic flexibility. Despite some theoretical and conceptual works suggesting that some elements of these two constructs may relate to agility, this has not yet been assessed together empirically. This study aims to address this gap in the literature.Design/methodology/approach – This study involves an empirical investigation of a theory‐based model based on the competence‐capability framework, and a dynamic capabilities theoretical perspective, where the internal competencies of strategic sourcing and firms strategic flexibility relate to the dynamic capability of the firms supply chain agility. This investigation also includes the testing of a possible mediation effect of firms strategic flexibility on the relationship between strategic sourcing and the firms supply chain agility. The model is tested utilizing data from 144 US manufacturing firms ...

A neural network system for shape-based classification and coding of rotational parts

The classification and coding of parts for group technology applications continue to be labour intensive and time-consuming processes. In this paper a pattern recognition approach utilizing neural networks is presented for the automation of some elements of this critical activity. As an illustrative example, a neural network system is used to generate part geometry-related digits of the Opitz code from bitmaps of part drawings. It is found to generate codes accurately and promises to be a useful tool for the automatic generation of shape-based classes and codes.

Would a risk-averse newsvendor order less at a higher selling price?☆

We model a risk-averse newsvendors decision-making behavior with some commonly used classes of utility functions within the expected utility theory (EUT) framework. Under fairly general conditions of EUT, we show that a risk-averse newsvendor will order less than an arbitrarily small quantity as selling price gets larger if price is higher than a threshold value, i.e., the optimal order quantity decreases as the selling price increases.

Performance of hybrid cellular manufacturing systems: A computer simulation investigation

Abstract In this paper, the performance of hybrid cellular manufacturing (CM) systems is compared with that of functional layouts that use traditional job shop procedures as well as part family scheduling rules, under a variety of shop operating conditions. Unlike much of past research in group technology (GT), this work examines the entire shop floor, in which the CM systems comprise cells and a remainder shop organized as a functional layout. The experimental factors investigated include: the system (two functional layouts and five hybrid systems); setup factor, a surrogate for the degree of part family similarities; scheduling rule, which includes both job shop and part family-oriented rules; and lot size. The performance measures used were flow time, work-in-process inventory, machine utilization and flow ratio. Several findings emerged that form a useful addition to the literature. While part family-oriented scheduling rules significantly outperformed job shop rules, little difference was detected among the former. The results also indicated that the performance of the remainder shop deteriorated with increasing conversion, due to erosion of pooling synergy.

Performance of Fuzzy ART neural network for group technology cell formation

This study investigates the performance of Fuzzy ART neural network for grouping parts and machines, as part of the design of cellular manufacturing systems. Fuzzy ART is compared with ART1 neural network and a modification to ART1, along with direct clustering analysis (DCA) and rank order clustering (ROC2) algorithms. A series of replicated clustering experiments were performed, and the efficiency and consistency with which clusters were identified were examined, using large data sets of differing sizes and degrees of imperfection. The performance measures included the recovery ratio of bond energy and execution times, It is shown that Fuzzy ART neural network results in better and more consistent identification of block diagonal structures than ART1, a recent modification to ART1, as well as DCA and ROC2. The execution times were found to be more than those of ART1 and modified ART1, but they were still superior to traditional algorithms for large data sets.

Towards an integrated evaluation of flexible automation investments

SUMMARY This paper presents a decision-support-system (DSS) structure for flexible automation investments. It takes into account: (1) a comprehensive investment context for multimachine systems; (2) developments in modelling flexibility in economic evaluation; and (3) an integrated physical/financial design and evaluation, for capitalizing on recent developments in DSS technologies and automated decision/risk-analysis tools.

Sequence-based clustering for Web usage mining: A new experimental framework and ANN-enhanced K-means algorithm

We develop a general sequence-based clustering method by proposing new sequence representation schemes in association with Markov models. The resulting sequence representations allow for calculation of vector-based distances (dissimilarities) between Web user sessions and thus can be used as inputs of various clustering algorithms. We develop an evaluation framework in which the performances of the algorithms are compared in terms of whether the clusters (groups of Web users who follow the same Markov process) are correctly identified using a replicated clustering approach. A series of experiments is conducted to investigate whether clustering performance is affected by different sequence representations and different distance measures as well as by other factors such as number of actual Web user clusters, number of Web pages, similarity between clusters, minimum session length, number of user sessions, and number of clusters to form. A new, fuzzy ART-enhanced K-means algorithm is also developed and its superior performance is demonstrated.

An institutional theory perspective on Six Sigma adoption

Purpose – The purpose of this paper is, first, to utilize institutional theory to assess motivation for the adoption of Six Sigma. Second, to examine the role of an organizations innovation implementation climate and the fit between the innovation considered and the values of the organizations members on the implementation of Six Sigma. Third, to study the impact that the adoption and implementation of Six Sigma has on organizational performance.Design/methodology/approach – Methods advocated in case study research were employed in the conduct of seven case studies. The research protocol consisted of identifying organizations in a variety of manufacturing industries, and conducting focused interviews with a minimum of three respondents in each company in order to improve validity.Findings – This paper suggests that institutional theory proves to be an effective means by which to examine the adoption of Six Sigma. In addition, support for innovation implementation model suggested by Klein and Sorra is fo...

A goal-programming approach for design of hybrid cellular manufacturing systems in dual resource constrained environments

In this study, a goal-programming model is proposed for the design of hybrid cellular manufacturing (HCM) systems, in a dual resource constrained environment, considering many real-world application issues. The procedure consists of three phases. Following an initial phase involving a Pareto analysis of demand volumes and volatility, a machine-grouping phase is conducted to form manufacturing cells, and a residual functional layout. In this phase, over-assignment of parts to the cells, machine purchasing cost, and loss of functional synergies are attempted to be minimized. Following the formation of cells and the functional layout, a labor allocation phase is carried out by considering worker capabilities and capacities. The total costs of cross-training, hiring, firing and over-assignment of workers to more than one cell are sought to be minimized. An application of the model on real factory data is also provided in order to demonstrate the utility and possible limitations. The industrial problem was solved using professional mathematical programming software.