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Since a firm’s management performance can be evaluated in terms of financial ratios, efficient management using financial factors is proposed as the key element for upgrading a firm’s productivity. Investigates productivity in terms of certain financial factors of large‐scale manufacturing firms in Taiwan. First determines several influential financial factors using factor analysis. Based on these factors, employs fuzzy clustering approaches to categorize the manufacturing firms into several patterns with distinct characteristics of financial factors. Using the characteristics of productivity and financial factors for each pattern, makes two kinds of analysis, and proposes some suggestions to improve the firms’ productivity.

Purpose – To propose a pattern analysis method to help firms rectify weaknesses of production management (PM) and thus promote their business performance. Design/methodology/approach – Total factor productivity and the associated partial productivity indices are defined, and four kinds of production planning ranges, i.e. long‐range planning, medium‐range planning, short‐range planning, and execution, are defined based on 14 PM issues. A fuzzy clustering approach is applied to group the sampled firms into several patterns based on the achievement degrees of production planning in order to investigate the particular characteristics of each pattern. Findings – After analyzing the productivity characteristics of each pattern, the correlation between productivity and production management can be determined. In this study, the business performance seems to be not completely correlated with the achievements of production management, since moderate production planning can provide optimal business performance. Research limitations/implications – The patterns produced from the proposed approaches depend on the sampled data set. A solid sampling method is important to this study. Practical implications – The sampled data are collected from the top 50 large‐scale manufacturing firms in Taiwan. The results obtained from this paper may not be consistent with the situations in the other countries. Originality/value – Referring to the findings from each pattern, a firm can further investigate its position in the industry to find ways of increasing its competitiveness.

Quality cost is usually considered as a means to measure the quality level in a quality system. Since the interrelationship among quality cost components is complex, a general quantitative model for describing their relationship is not easy to construct for improving the quality. In the assessments of quality cost, some hidden quality costs, such as the goodwill loss due to lost customers’ reliability, are often neglected in the existing analysis methods. This may lead to reaching a sub‐optimal decision. In addition, the assessments of quantitative quality cost items are usually approximated, and therefore are imprecise in nature. Based on these considerations, we propose fuzzy approaches to evaluate quality improvement alternatives because of its fuzzy nature. An evidence fusion technique, namely Choquet fuzzy integral, is employed to aggregate the quality cost information. A composite index is determined to find the best quality improvement alternative. Finally, a numerical example is used to demonstrate the applicability of the approach.

Discusses the external supply for industries of particular skill groups from academic institutions and illustrates this with a case of industrial management. Essentially, students graduating from academic institutions constitute the major portion of the labour market, and the required courses taught in schools represent the training of students. Takes into consideration three education levels ‐ junior college, college, and graduate school ‐ and classifies the training into six categories: basic knowledge, production, finance, marketing, human resources, and information. Suggests that the discrepancies between the quantities demanded by industries and supplied from schools, and the training expected by industries and received from schools provide useful information for both government and industries when making appropriate decisions.

Manufacturing firms are always faced with the problem of promoting operational performance and labor‐force management. The utilization of human resources is closely correlated with operations and production performance. This study investigates the correlation between human resource management (HRM) and business performance of large‐scale manufacturing firms in Taiwan. First, 16 subjects of HRM are designed to survey the importance level and achievement level of HRM by the sample firms. Productivity indices are also defined to measure business performance. Based on the survey, four critical HRM factors including 12 subjects are extracted by factor analysis. The difference between importance level and achievement level of subjects contained in each factor is examined. Furthermore, considering importance and achievement levels of HRM as features, fuzzy clustering analysis is employed to categorize the firms into four patterns. With various HRM characteristics, each pattern has different business performance in terms of productivity. Using a pattern approach, these findings can aid the firms in each pattern to improve their productivity by improving their HRM strategies.

Proposes an approach to improving production management (PM) of a firm through examining its resource utilization and product competence. To understand the status of PM in Taiwan, evaluates the achievement levels of 14 production management subjects from 50 large‐scale manufacturing firms. The subjects are further classified into four planning ranges of PM. Then productivity measures of these firms for resource utilization and product competence are determined. Two groups of patterns with distinct PM characteristics are found through a fuzzy clustering analysis based on resource utilization and product competence. Significant correlation has been found between resource utilization and product competence and PM. The PM patterns with good performance in terms of resource utilization and product competence are considered targets for firms of other patterns. The ways of adjusting PM for these firms of other patterns are then described in terms of the characteristics of the target patterns.

The purpose of this paper is to develop a new integrated approach for the strategic logistics outsourcing process through identifying the logistics independent success factors (ISFs) and linking them with the firm’s strategic objectives and logistics requirements. Then, the new integrated approach will be used to compare the outsourcing processes for the upstream and downstream supply chain members. Studies of logistics outsourcing reveal the strategic importance of this process and the increasing need for new strategic approach.

The design is based on mixed methodology and integrated approach. The fuzzy decision-making trial and evaluation laboratory technique has contributed to the construction of interdependent relationships, development of impact-relationship maps (IRMs) and identifying ISFs. The fuzzy quality function deployment technique was used to link the strategic objectives, logistics requirements and the ISFs to evaluate and select logistics service providers (LSPs) strategically. Finally, two case studies (upstream and downstream supply chains) are used to demonstrate the new approach effectiveness and to highlight the differences/similarities between the two streams.

In addition to the new strategic logistics outsourcing approach, this study analysed the impact relationships of the LSPs’ framework factors and constructed their maps. In all, 21 ISFs have been identified: 8 logistics key performance indicators, 7 logistics services and activities and 6 logistics resources and capabilities. The two streams’ comparison relived several differences in terms of strategic objectives, logistics requirements and ISFs.

The new approach for strategic logistics outsourcing can help firms to perform a better multi-stakeholder multi-criteria strategic outsourcing process. In addition, the upstream–downstream supply chain comparison increases our understanding how different supply chain members perform different outsourcing processes.

This is one of the pioneering studies that compares the supply chain upstream–downstream perspectives to highlight logistics outsourcing similarities and differences. To the best of author’s knowledge, this is one of the first logistics outsourcing studies that identifies ISFs for strategic logistics outsourcing, provides the first IRMs for the strategic logistics factors and develops a new integrated approach for strategic logistics outsourcing