Vidyaranya B. Gargeya

Success and failure factors of adopting SAP in ERP system implementation

Purpose – Enterprise resource planning (ERP) systems are software packages that allow companies to have more real time visibility and control over their operations. This paper aims to investigate and analyze common circumstances that occur within most ERP projects, and determines the areas that are key to success versus those that contribute to failure.Design/methodology/approach – The study is based on a content analysis of published articles reporting SAP implementations in 44 companies.Findings – Identifies six common factors that are indicative of successful or non‐successful SAP implementations. It has been found that the lack of appropriate culture and organizational (internal) readiness as the most important factor contributing to failure of SAP implementations in 15 companies. The presence of project management approaches and appropriate culture and organizational (internal) readiness are the most important factors contributing to the success of SAP implementations in 29 organizations.Research lim...

Alignment between business environment characteristics, competitive priorities, supply chain structures, and firm business performance

Purpose – This paper aims to analyze the relationships between four constructs – business environment characteristics, competitive priorities, supply chain structures, and firm business performance.Design/methodology/approach – By establishing a conceptual model and conducting structural equation modeling analysis using collected industrial survey data, the study provides a systemic understanding of the relationships between the individual business environment characteristics, the individual competitive priorities and supply chain structures, and the impact of alignment between these elements on firm business performance.Findings – Results of the study reveal that the differences in both strategic and supply chain responses to business environment between high‐ and low‐performing firms in the US textile manufacturing industry are striking. The findings provide evidence to corroborate the impact of the alignment between business environment characteristics, competitive priorities, and supply chain structur...

Scheduling research in multiple resource constrained job shops: a review and critique

Over the past several years, a number of survey, classification, and review articles have focused on scheduling research in machine [only] constrained job shops. Barring the work of Treleven (1989), there is no reported research that presents a detailed review of the issues related to scheduling and sequencing in job shops with multiple resource constraints. In his article, Treleven reviewed the research in job shops constrained by machines and labour. Job shops are not only constrained by machines and labour, but by auxiliary resources (in the form of tooling, etc.) as well. This paper extends the work of Treleven by reviewing the literature on scheduling in job shops constrained by more than one resource and comparing the scheduling research in auxiliary resource-constrained job shops with that of labour-constrained job shops. In addition, this article raises some issues for future scheduling research in multiple resource-constrained job shops.

Just‐in‐time manufacturing in Ghana

Just‐in‐time (JIT) production has received a great deal of attention worldwide in the last couple of decades. Most research has examined the effects of the JIT philosophy and practice in developed countries (such as the USA, Canada, and Australia). Barring a couple of studies, not much attention has been paid to the study of the implementation of JIT in less developed countries. Based on a survey of 48 manufacturing firms in Ghana, this paper examines the implementation of JIT production systems in a developing country. The results suggest that Ghanaian manufacturing firms which have invested in JIT production systems are different from firms that have not invested in JIT production in terms of their efforts in employees’ training, setup time reduction, cellular manufacturing, continuous quality improvement, and supplier partnership. At the same time, JIT firms are not significantly different from non‐JIT firms with regard to the use of measurement systems.

Impact of JIT on inventory to sales ratios

Just‐In‐Time (JIT) production has received a great deal of attention, worldwide, since its introduction in Japan a few decades ago. It has been well documented that some of the main benefits of JIT implementation are reduction of inventories, lead‐time reduction, and cost savings. Most of the previous research on the impact of JIT on firm performance has either been anecdotal (one‐firm studies), or cross‐sectional (comparing JIT firms with non‐JIT firms at one point in time) in nature. This paper focuses on studying the impact of JIT on inventories to sales ratios prior‐ and post‐adoption based on actual performance of 74 firms as reported in COMPUSTAT data. Results show that the total inventory to sales ratio and raw material inventory to sales ratio decreased post‐implementation; however, there has not been any statistically significant change in work‐in‐process inventory to sales ratio and finished goods inventory to sales ratio post‐implementation.

SCHEDULING IN THE DYNAMIC JOB SHOP UNDER AUXILIARY RESOURCE CONSTRAINTS : A SIMULATION STUDY

Traditionally, job shop research has only considered constraints related to machine and labour availability. With the advent of flexible manufacturing systems and just-in-time manufacturing, practitioners have recognized the importance of auxiliary resources (e.g. tooling) in production activity control and shop scheduling. In recent years, it has been recognized that theory and practice based on labour-constrained job shops cannot be generalized to auxiliary resource-constrained job shops. This paper presents a study of scheduling in the dynamic job shop under auxiliary resource constraints. Local and lookahead dispatching and resource assignment rules, and a global Contingency Based Scheduling (CBS) approach are developed and evaluated in a dynamic job shop constrained by auxiliary resources. Several traditional measures of performance are employed, including root mean square of tardiness, average system time and percentage of auxiliary resource changes. As shop utilization increases, the study reveals that the CBS algorithm is the only scheduling mechanism that consistently provides high performance on all three measures.

Strategic Sourcing and Supplier Selection in the U.S. Textile–Apparel–Retail Supply Network

This research investigates the key causal linkages in supply chain management—the impact of strategic sourcing and supplier selection on firm performance in the U.S. textile—apparel—retail supply network. A conceptual framework was developed and the empirical survey-based research methodology was used to gather data from the U.S. textile—apparel—retail complex. The data collection resulted in 181 responses, representing a 38.2% response rate. Structural equation modeling was used to assess the research model and test the research hypotheses. The research findings support that strategic sourcing has a significant and positive effect on business performance, and supplier selection has a significant and positive effect on the firms ability to gain competitive advantages. The research concludes with implications, limitations, and directions for future research.

Global sourcing shifts in the U.S. textile and apparel industry: a cluster analysis

Abstract Globalization of the U.S. textile and apparel industry has been significantly spurred in the last decade by trade agreements such as the North American Free Trade Agreement (NAFTA), and the Caribbean Basin Initiative (CBI). In terms of the U.S. textile and apparel manufacturers and retailers, the competitive pressure from markets and consumers has forced many firms to rely increasingly on global sourcing to sustain competitiveness. In the last decade, U.S. textile and apparel manufacturers and retailers have been actively involved in global sourcing to find suppliers who could meet the criteria of low cost, high quality, reliable delivery, quick response time, and flexibility. The purpose of this study is to investigate the changes in U.S. global sourcing patterns for yarn, fabric, and apparel. Cluster analysis is used to analyze trade data in 1993 and 2002 in order to investigate the patterns of U.S. global sourcing of textile and apparel products. Results indicate that U.S. textiles and apparel sourcing patterns have shifted, with a substantially increased emphasis on apparel imports from Mexico and the CBI countries in 2002 as compared to imports from mainland China and Hong Kong in 1993. Sourcing patterns of yarn and fabric indicate that Mexico upgraded its place in U.S. imports of yarn and fabric in 2002. In addition, further longitudinal analysis of U.S. apparel imports from mainland China, Hong Kong, CBI countries, and Mexico, which were the major players in the U.S. global sourcing of apparel, shows that there was a dynamic change in the status of these four regions in terms of their respective percentage shares in U.S. apparel sourcing. The paper concludes that the U.S. textile and apparel global sourcing patterns in last decade reflect the dramatic changes in the competitive and dynamic global textile and apparel business environment. Global sourcing is indeed a fine-tuning business strategy that requires balanced and comprehensive consideration of economic, trade, environmental, and competitive factors.

Reinforcing QFD with group support systems: computer‐supported collaboration for quality in design

Over the last decade quality function deployment or QFD, thanks to the efforts of Akao and others, has gained widespread popularity in its applicability to business and industry. Many organizations have adopted it as a tool of continuous improvement in their quest for quality through total quality management (TQM). QFD in simple terms, has been looked on as a mechanism of translating the customers’ expectations of a particular product or service into product planning, parts development, process planning, and production planning. Explores the robustness of QFD for translating the available knowledge within a product design group into appropriate design choices, ones that consider the customer’s view of quality throughout the product’s entire life cycle. Conventional QFD analysis allows equity of participation through “consensus”, but often trades outcomes influenced by expertise for those attained with “fairness”. This process may lead to less than optimal results. Discusses the role of group support systems (GSS) to improve the qualitative discussion of the whats and the hows in the QFD process. Also introduces influence allocation processes, methods that allow differential weighting of participants and an incremental usage of knowledge within groups. Discusses their potential impact for QFD analysis.

Tool requirement planning in stochastic job shops: a simulated annealing approach

Abstract Tool management in shop floor control has been discussed widely in the recent literature. The need for an effective tool management system has been underscored by the impact of tool availability in flexible manufacturing systems and cellular manufacturing shops. With the understanding that most job shops face a budgetary constraint on the units of tools to be maintain, this paper presents an optimization model based on queueing theory for determining the number of units of tools to be maintained in stochastic job shop production systems. The optimization model seeks to minimize total tool related cost under some managerial and operational constraints. To plan tool requirement based on the optimization model, an improved simulated annealing algorithm is presented. Starting with an arbitrary initial solution, the algorithm searches for a solution among the combinatorial alternatives in a controlled random fashion. Unlike the iterative improvement algorithms, the simulated annealing algorithm is applicable to multimodal objective function due to its capability of probabilistic hill-climbing. To demonstrate the performance of the model and the algorithm, a medium-scale numerical example is also discussed in detail.