Wai Peng Wong

Supply chain performance measurement system using DEA modeling

– This paper aims to illustrate the use of data envelopment analysis (DEA) in measuring internal supply chain performance., – Two DEA models were developed – the technical efficiency model and the cost efficiency model. The models are further enhanced with scenario analysis to derive more meaningful business insights for managers in making resources planning decisions., – The information obtained from the DEA models helps managers to identify the inefficient operations and take the right remedial actions for continuous improvement. More importantly, the opportunity cost (forgone profit) calculated serves as a good reference to managers to make efficient decisions on resource allocations., – Results are based on the deterministic data set. Future enhancement of the study would be to look into the possibility of modeling DEA in a stochastic supply chain environment (non‐deterministic) due to the fact that supply chain operates in a dynamic environment., – The proposed DEA‐based approach provides useful managerial implications in the measurement of supply chain efficiency. The study proves the usefulness of DEA as a decision‐making tool in supply chain., – This paper provides useful insights into the use of DEA as a modeling tool to aid managerial decision making in measuring supply chain efficiency.

A review on benchmarking of supply chain performance measures

Purpose – The purpose of this paper is to redress the imbalances in the past literature of supply chain benchmarking and enhance data envelopment analysis (DEA) modeling approach in supply chain benchmarking.Design/methodology/approach – The paper is sorted into sections: benchmarking, definitions, concepts and literature survey, supply chain benchmarking, problems in supply chain benchmarking, existing tools used in benchmarking, problems in existing tools and motivation of using DEA as a supply chain benchmarking tool. A range of recently published (1995‐2004) works is referred and aims to provide insights into possible new research focus in supply chain benchmarking.Findings – Provides information about new trends in supply chain benchmarking and what are the existing problems. Recognizes the lack of mathematical modeling tool in addressing this issue.Research limitations/implications – Supply chain benchmarking is gearing towards a holistic approach. DEA is deemed to be an appropriate benchmarking too...

Supply chain performance measurement system: a Monte Carlo DEA-based approach

A supply chain operates in a dynamic platform and its performance efficiency measurement requires intensive data collection. The task of collecting data in a supply chain is not trivial and it often faces with uncertainties. This paper develops a simple tool to measure supply chain performance in the real environment, which is stochastic. Firstly, it introduced the Data Envelopment Analysis (DEA) supply chain model to measure the supply chain performance. Next, it enhanced the model with Monte Carlo (random sampling) methodology to cater for efficiency measurement in stochastic environment. Monte Carlo approximations to stochastic DEA have not been practically used in empirical analysis, despite being an important tool to make statistical inferences on the efficiency point estimator. This method proves to be a cost saving and efficient way to handle uncertainties and could be used in other relevant field other than supply chain, to measure efficiency.

Knowledge Management performance measurement: measures, approaches, trends and future directions

This paper presents a review on Knowledge Management (KM) performance measurement in the past two decades. Various tools and techniques that have been developed are discussed and presented chronologically to show how KM performance measurement has changed during this period. Each tool and technique is evaluated and classified based on the types of measures and approaches used. This paper also proposes six new categories: traditional, advanced, deterministic, stochastic, general result oriented, and specific result oriented, to complement the previous classification schemes. Future research directions for KM performance measurement are identified and presented in a holistic framework to act as a guideline for new researchers who wish to embark on this field.

Performance evaluation of supply chain in stochastic environment: using a simulation based DEA framework

Supply chain operates in a dynamic platform and its performance measurement requires intensive data collection from the entire value chain. The task of collecting data in supply chain is not trivial and it often faces with uncertainties. This paper develops a simple tool to measure supply chain performance in the real environment, which is stochastic. Firstly, it introduces the data envelopment analysis (DEA) supply chain model in combination with Monte Carlo simulation to measure the supply chain performance in the stochastic environment. Secondly, a GA-based heuristic technique will be presented to improve the prediction of the performance measurement. This methodology offers an alternative to handle uncertainties in supply chain efficiency measurement and could also be used in other relevant fields, to measure efficiency.

Business-process management: a proposed framework for future research

Firms have embarked on the business-process management (BPM) initiative to gain competitive advantages. However, many are still struggling to understand the concept. Moreover, most firms have not approached this initiative deliberately and formally due to a lack of understanding of its nature. This article first aims to provide a clear description of BPM, which includes its definition, evolution and two alternative perspectives. Second, to help enhance the understanding of BPM conduct, it discusses various BPM strategies and the conditions conducive to BPM. Lastly, this article culminates by proposing a framework for future research which is able to guide practitioners in implementing BPM successfully.

How efficient are real estate and construction companies in Iran's close economy?

This paper empirically explores various efficiency aspects of real estate and construction companies in Iran in light of their remarkable growth in recent years. The technique used to measure the efficiency is DEA and the sample is taken from the construction and real estate companies in Iran. Results from our analysis reveal that most of the real estate and construction companies in Iran are generally technical, scale and mix efficient. However, they are less cost efficient due to increasing higher cost of production. The sensitivity analysis results also suggest that the real estate and construction companies are experiencing diseconomies of scale. As Iran is an underdeveloped market and is currently one of the most interesting frontier markets in the world, this research contributes substantially to investors and multinationals (who have growing desire to gain exposure in this market) by providing current information about the efficiencies of real estate and construction companies in Iran. In addition, this research also provides some institutional voids on how to penetrate market environment in Iran through real estate development.

Budget Allocation for Effective Data Collection in Predicting an Accurate DEA Efficiency Score

We analyze how to allocate the budget for data collection effectively when data envelopment analysis (DEA) is used for predicting the efficiency. We formulate this problem under a Bayesian framework and propose two heuristics algorithms, i.e., a gradient-based algorithm and a hybrid GA algorithm to solve this optimization problem. Our results indicate that effective allocation of budget for data collection can greatly reduce the overall data collection effort in comparison with a uniform budget allocation.

Organizational context, supplier management practices and supplier performance

Purpose – This paper aims to examine the relationships among supplier management practices, organizational context and supplier performance. The contexts selected for supplier management practices are economics transactional practices and high involvement work practices (HIWP); while power asymmetry and competition intensity are considered within the organizational context.Design/methodology/approach – A questionnaire survey was conducted on a multinational semiconductor company. A two‐phase statistical analysis, which comprised phase one (reliability and factor analysis), and phase two (hierarchical multiple regression analysis), was used to analyze the data.Findings – The study provides empirical evidence to support the conceptual and prescriptive statements in the literature regarding the impact of supplier management practices and the dynamics between organizational context and supplier management towards supplier performance. The results show that high involvement work practices (HIWP) mediate the im...

A preliminary study on using Data Envelopment Analysis (DEA) in measuring supply chain efficiency

This paper illustrates the use of Data Envelopment Analysis (DEA) in measuring supply chain efficiency. Two models were developed – Technical Efficiency (TE) model and Cost Efficiency (CE) model. The information obtained enable managers to identify inefficient operations and take appropriate actions for improvement. The opportunity cost serves as a good reference on resource allocations. The models are further enhanced with scenario analysis to derive more meaningful insights for resources planning. The study proves the usefulness of DEA as a decision-making tool in supply chain. Future research could look into the possibility of modelling DEA in a stochastic supply chain environment.