Jaydeep Balakrishnan

ERP implementation at SMEs: analysis of five Canadian cases 1,2

Purpose – The purpose of this paper is to explore the critical success factors (CSFs) of enterprise resource planning (ERP) system implementation in small and medium‐sized enterprises (SMEs).Design/methodology/approach – Five case studies of Canadian SMEs were conducted. They included interviewing individuals from five roles at each organization and gathering project documents. Following an evaluation of each projects success (within‐case analysis), cross‐case analysis was conducted to elicit influential and distinctive factors.Findings – Factors were identified that appeared to explain variation between successful and unsuccessful implementations at SMEs, besides factors that appeared to be innovative or counter‐intuitive in light of the established literature.Research limitations/implications – The study reinforces the need for more research that is focused on SMEs. All cases were of Canadian SMEs with either a manufacturing or distribution focus, potentially limiting the generalizability of findings t...

MULTI-PERIOD PLANNING AND UNCERTAINTY ISSUES IN CELLULAR MANUFACTURING: A REVIEW AND FUTURE DIRECTIONS

Abstract In this paper we review research that has been done to address cellular manufacturing under conditions of multi-period planning horizons, with demand and resource uncertainties. Most traditional cell formation procedures ignore any changes in demand over time caused by product redesign and uncertainties due to volume variation, part mix variation, and resource unreliability. However in today’s business environment, product life cycles are short, and demand volumes and product mix can vary frequently. Thus cell design needs to address these issues. It is only recently that researchers have been modelling uncertainty and multi-period issues. In this paper we conduct a comprehensive review of the work that addresses these issues. We present mathematical programming formulations as well as a taxonomy of existing models. Finally we suggest some directions for future research.

Dynamic layout algorithms : a state-of-the-art survey

It has been over a decade since Rosenblatt published his seminal paper on modelling the dynamic facility layout problem (DFLP). Since then, there have been improvements to Rosenblatts original dynamic programming model. Alternate solution methods have also been proposed. However, no comprehensive review of the research in the DFLP has been undertaken. In this paper we categorize the different works of research that have followed and discuss them. They include improved and more flexible solution methods, fathoming procedures, bound determinations and method comparisons.

A Hybrid Genetic Algorithm for the Dynamic Plant Layout Problem

Abstract The dynamic plant layout problem (DPLP) deals with the design of multi-period layout plans. Although an optimal solution method based on dynamic programming is available, it is not practical for large DPLPs. It has recently been shown that heuristics based on genetic algorithms can solve large DPLPs. In this research, we extend and improve the use of genetic algorithms by creating a hybrid genetic algorithm. A computational study is carried out to compare the proposed algorithm with the existing genetic algorithms and a recent simulated annealing algorithm. The study shows that the proposed algorithm is effective. Thus it may be useful in solving the larger problems.

Genetic search and the dynamic layout problem

Abstract An improved genetic algorithm for the dynamic layout problem is developed and tested in this research. Our genetic algorithm differs from the existing implementation in three ways: first, we adopt a different crossover operator, second, we use mutation, and third, we use a new generational replacement strategy to help increase population diversity. A computational study shows that the proposed GA is quite effective. Scope and purpose Most organizations today are operating in a dynamic and market-driven environment. To stay competitive, their facilities must be adaptive to market fluctuations. The dynamic layout problem devises a multi-period layout plan based on these demand fluctuations. In this paper, we propose an improved implementation of an existing genetic algorithm for solving the dynamic layout problem.

Dynamic cellular manufacturing under multiperiod planning horizons

Purpose – The purpose of this research paper is to discuss cellular manufacturing is discussed under conditions of changing product demand. Traditional cell formation procedures ignore any changes in demand over time from product redesign and other factors. However given that in todays business environment, product life cycles are short, a framework is proposed that creates a multi‐period cellular layout plan including cell redesign where appropriate.Design/methodology/approach – The framework is illustrated using a two‐stage procedure based on the generalized machine assignment problem and dynamic programming. This framework is conceptually compared to virtual cell manufacturing, which is useful when there is uncertainty in demand rather than anticipated changes in demand. A case study is used to explain how the concept would work in practice.Findings – One major characteristic of the proposed method is that it is flexible enough to incorporate existing cell formation procedures. It is shown through an ...

Solutions for the constrained dynamic facility layout problem

Abstract Much of the research in facility layout has focused on static layouts where the material handling flow is assumed to be constant over the planning horizon. But in todays market-based, dynamic environment, this assumption may no longer be true. Layout rearrangement may be required, for which available funds may be limited. This research investigates the facility layout problem under the two assumptions of changing demand and a constraint on the layout rearrangement funds. The problem is formulated, a new algorithm is proposed to solve the problem and it is compared to an extension of the old algorithm that has been used to solve the problem thus far. In addition, different factors that affect dynamic facility layout design and operation are statistically examined. The results indicate tha the proposed algorithm has advantages over the old one and that some of the factors and their combinations can have significant effects on layout design and operation.

FACOPT: a user friendly FACility layout OPTimization system

Abstract The facility layout problem is a well-researched one. However, few effective and user friendly approaches have been proposed. Since it is an NP hard problem, various optimization approaches for small problems and heuristic approaches for the larger problems have been proposed. For the most part the more effective algorithms are not user friendly. On the other hand, user-friendly methods have not been effective in handling the intricacies such as unequal department sizes. In this research, we present FACOPT, a heuristic approach that is effective and user friendly. The software uses two methods, simulated annealing and genetic algorithm to solve the facility layout problem. Computational tests are also done to identify good parameter values and to compare the performance of the two algorithms. Scope and purpose Various methods have been proposed for facility layout where departments are laid out within a facility. However, many of them are not flexible enough to handle intricacies such as unequal department sizes. Others do not provide user-friendly interfaces. Thus, there is a need for user-friendly software incorporating effective and flexible procedures. In this research we present FACOPT, a heuristic approach that is effective and user friendly. The software uses two different approaches to solve the facility layout problem effectively. FACOPT has a Visual BASIC interface and runs under a Windows environment for ease of use.

Software development cost estimation integrating neural network with cluster analysis

For software project planning control and management, an accurate estimate of software development cost is important. Past research has focused on using parametric models to predict development cost based on attributes such as lines of code or function points. This requires researchers to identify the set of factors that influence cost estimation before the system is constructed. We propose a non-parametric approach that integrates a neural network method with cluster analysis to estimate development cost. The integration of the two techniques not only allows for a more accurate cost estimate but also leads to an increase in the training efficacy of the network.

Manufacturing cell formation using similarity coefficients and a parallel genetic TSP algorithm: Formulation and comparison

Many algorithms have been proposed to form manufacturing cells from component routings. However, many of these do not have the capability of solving large problems. We propose a procedure using similarity coefficients and a parallel genetic implementation of a TSP algorithm that is capable of solving large problems of up to 1000 parts and 1000 machines. In addition, we also compare our procedure with many existing procedures using nine well-known problems from the literature. The results show that the proposed procedure compares well with the existing procedures and should be useful to practitioners and researchers.