The implementation of technical practices and human factors of the toyota production system in different industries

The Toyota Production System (TPS) can result in huge benefits for companies and has been widely implemented around the world. Nevertheless, most adopters emphasize only the technical practices of the TPS, but neglect the critical role of human factors in the implementation of the TPS. Therefore, this study aims at developing an integrated TPS model that fully integrates the technical elements and human elements of the TPS with Total Quality Management (TQM) practices. An empirical study was conducted to confirm the causal relationships among these constructs. In the empirical study, questionnaires were mailed and e‐mailed to 620 companies that had implemented the TPS, and 151 of the completed questionnaires were valid. These 151 respondents were divided into eight categories. The results revealed that the implementation level of each construct was significantly different in each industry. The automobile and motorcycle industries have put a great deal of effort into the application of Humanity, Human Resource Management (HRM), and TQM practices and obtained good results. Other industries, however, ignored the implementation of HRM practices. The results also revealed that the early adopters enjoyed significant benefits from the implementation of the TPS, which led high‐tech companies to start adopting TPS practices and become lean enterprises. © 2011 Wiley Periodicals, Inc.     

TQM: A facilitator to enhance knowledge management? A structural analysis

This study aims to examine the multi-dimensional relationship of total quality management (TQM) and knowledge management (KM) in both service and manufacturing firms in Malaysia. Six key practices of TQM originated from the Malcolm Baldrige National Quality Award (MBNQA) were chosen; whereas the KM elements were made up of knowledge acquisition, distribution and application. Data were gathered from small, medium and large firms from both manufacturing and service sectors. Structural analysis was carried out for the hypotheses and the theoretical model testing. Findings show that strategic planning and human resource management have a positive and significant relationship with the dimensions of KM; whereas process management has significant effects on knowledge acquisition and knowledge distribution. Practically, the findings provide a useful direction for the management team in the manufacturing and service sectors by adopting the relevant TQM practices to further improve the firms’ knowledge management processes. Through a deeper comprehension on the association between TQM practices and the KM dimensions, the management team of these firms can focus their attention, efforts and resources on the specific TQM practices that can ensure successful KM process.     

Relationship between quality‐management systems and organizational innovations

The purpose of this study is to ascertain whether there is any correlation between the implementation of quality‐management systems (QMSs) in manufacturing firms and the use of certain organizational innovations in those firms. A conceptual model of the relationships among the constructs of “organizational innovations” and “level of quality management” is proposed and tested. The study finds a correlation between the level of quality management in an organization and the implementation of procedural organizational innovations. Because the study is pioneering and exploratory in nature, the analysis is confined to the use of a limited number of organizational innovations in Spanish manufacturing firms. The findings will assist managers to make appropriate strategic decisions when implementing QMSs. © 2010 Wiley Periodicals, Inc.     

Cost performance dynamics in lean production leveling

Balancing of production systems is one of the main lean manufacturing principles as it reduces in-process storage and related forms of waste. A dynamic systems approach is proposed to investigate challenges of implementing production leveling and associated costs. A lean cell producing at takt time is modeled using system dynamics. The model captures various lean tools influencing production leveling and their implications. Comparative cost analysis between various leveling implementation policies for stochastic demand with multiple products is conducted. Results showed that determining the most feasible leveling policy is highly dictated by both the cost and limitations of capacity scalability. In addition, delivery sequence plans of different products/parts needed to achieve mix leveling and lot sizes affect the feasible production leveling policy while implementing lean principles. The developed model and insights gained from the results can help lean manufacturing practitioners to better decide when and how to implement production leveling as well as determine both production lots sizes and sequence. They also emphasize the importance of cost analysis as assisting decision support tool in the trade-off required between the benefits of different levels of lean policies and their associated cost.     

An integrated multidimensional process improvement methodology for manufacturing systems

In this paper, an integrated multidimensional process improvement methodology (IMPIM) is formulated to address the yield management, process control and cost management problems of a manufacturing system. Simulation is used as a platform to implement the integrated multidimensional process methodology by incorporating the productivity, quality and cost dimension in a unified, systematic and holistic manner. Total Quality Management (TQM) addresses the quality parameters and Activity-Based Costing is used to manage the cost dimension of the system. Discrete event simulation is then used as a platform to perform process reengineering (Business Process Reengineering) and process improvement (TQM). The general implementation framework of the IMPIM is given with a step-by-step explanation. A conceptual discussion is also provided for the integrated methodology. The generic IMPIM is then formulated and the detailed implementation procedures for two case studies are compared with the generic methodology.     

Evaluation of mixed‐model U‐line operational designs

Often in lean manufacturing, multiple products are produced in U‐shaped manufacturing cells to simultaneously achieve product variety and production efficiency. We examine two design issues for mixed‐model U‐lines: work rules (a first‐come‐first‐serve rule and a crossover‐and‐return rule) and inventory flow choices (direct flow and buffered flow). Simulation results indicated that throughput and labor utilization can be improved by implementing a “buffer” with the first‐come‐first‐serve rule. Interestingly, the effectiveness of an inventory flow choice was dependent on the work rule (interaction effect), and relationships among performance dimensions differed across the designs.     

The moderating role of just‐in‐time on sociotechnical practices' effect over quality and workers' health

Sociotechnical (ST) theory emphasizes the notion that the design and performance of new systems can be improved, and can only work satisfactorily within an organization if the social and the technical aspects are brought together and treated as interdependent aspects of a work system. The implementation of lean manufacturing (LM) principles and practices has become popular, despite increasing demand on the current and emerging workforce to achieve higher levels of quality and flexibility with lower costs. Exploratory studies that investigate how LM practices influence the effect of ST practices on performance are still scarce, and a holistic view of such a relationship is needed. In this context, this study aims at verifying the moderating effect of just‐in‐time (JIT) practices on the relationship between ST practices and the performance of quality and workers' health. We carried out a survey with 144 different companies from Southern Brazil that are undergoing a lean implementation. Results indicate that ST practices have a positive significant relationship with such performance, and the concurrent adoption of JIT does not undermine quality and workers' health.     

Equipment replacement decisions and lean manufacturing

Traditional manufacturing systems are built on the principle of economies of scale. Here, the large fixed costs of production are depreciation-intensive because of huge capital investments made in high-volume operations. These fixed costs are spread over large production batch sizes in an effort to minimize the total unit costs of owning and operating the manufacturing system. As an alternative to “batch-and-queue,” high-volume, and inflexible operations, the principles of the Toyota Production System (TPS) and lean manufacturing have been widely adopted in recent years in the US [1, 2, 3 and 4]. In this paper, we illustrate an equipment replacement decision problem within the context of lean manufacturing implementation. In particular, we demonstrate how the value stream mapping (VSM) suite of tools can be used to map the current state of a production line and design a desired future state. Further, we provide a roadmap for how VSM can provide necessary information for analysis of equipment replacement decision problems encountered in lean manufacturing implementation.     

Evaluating the interaction between the manufacturing environment and planning and control systems

The choice of an appropriate infrastructure (systems and organizations) that is used to support the manufacturing process is often treated lightly in the literature. Historically, the selection of manufacturing planning and control systems has been influenced more by current “new wave” rather than by the environment faced by the firm. This study explores the interaction between the manufacturing environment and the planning and control system as it relates to performance. A mail survey of sample companies is used to classify manufacturing firms along the dimensions of production process, production volumes and the type of planning and control system used, and to estimate the processes-environment fit. Manufacturing performance is assessed through measures of inventory turnover, customer service, and delivery lead time. A random coefficient regression model is used to investigate if the fit between the manufacturing environment and planning and control systems is related to manufacturing performance.     

Examining business value of customer relationship management systems: IT usage and two-stage model perspectives

Empirical evidence for the business value of customer relationship management (CRM) systems remains unsolid in IS studies. This study proposes a new model for CRM value according to IT/IS usage theory and “two-stage model.” Empirical tests show that operational benefits of CRM are reflected in firms’ high revenue per employee, which leads to high profitability; strategic benefits of CRM are reflected in firms’ high customer satisfaction, which leads to high profitability and market valuation. Firm size positively moderates the operational and strategic benefits of CRM, while the industry’s product differentiation level negatively moderates operational and strategic benefits of CRM.     

Integrating Refined Kano Model,Quality Function Deployment,and Grey Relational Analysis to Improve Service Quality of Nursing Homes

Because of the aging trend in Taiwan, long‐term care plays an important role in the medical system. Finding ways to ensure medical and service quality in long‐term care facilities requires immediate attention. This study discusses service quality in nursing homes and the factors that affect service quality in Taiwan in a way that benefits the management of these organizations. Using a refined Kano model analysis, 12 high value‐added qualities, 4 low value‐added qualities, 1 critical quality, 6 required qualities, 1 potential quality, and 2 care‐free qualities were found. Furthermore, using a combination of quality function deployment and Grey relational analysis (GRA), it was found that medical service standards of the nursing homes require improvement. Items such as “educational training,'' “professional capability,” “degree of understanding patients,” “communication skills,” and “crisis management ability” were provided by an equal‐weight GRA. The improvement sequence was “educational training,” “professional capability,” “communication skills,” “understanding of patients,” and “crisis management ability” in an equal‐weight quantified GRA. The research results can provide nursing homes with an improvement sequence for service design and operational management. © 2012 Wiley Periodicals, Inc.     

Data-driven manufacturing: An assessment model for data science maturity

Today, data science presents immense opportunities by turning raw data into manufacturing intelligence in data-driven manufacturing that aims to improve operational efficiency and product quality together with reducing costs and risks. However, manufacturing firms face difficulties in managing their data science endeavors for reaping these potential benefits. Maturity models are developed to guide organizations by providing an extensive roadmap for improvement in certain areas. Therefore, this paper seeks to address this problem by proposing a theoretically grounded Data Science Maturity Model (DSMM) for manufacturing organizations to assess their existing strengths and weaknesses, perform a gap analysis, and draw a roadmap for continuous improvements in their progress towards data-driven manufacturing. DSMM comprises six maturity levels from “Not Performed” to” Innovating” and twenty-eight data science processes categorized under six headings: Organization, Strategy Management, Data Analytics, Data Governance, Technology Management, and Supporting. The applicability and usefulness of DSMM are validated through multiple case studies conducted in manufacturing organizations of various sizes, industries, and countries. The case study results indicate that DSMM is applicable in different settings and is able to reflect the organizations’ current data science maturity levels and provide significant insights to improve their data science capabilities.     

A pseudo‐stochastic approach for optimal decision making under limited information: a case of an aggregate production system

In this study, which is both analytical and numerical, we compute the effective information horizon (EIH), i.e., the minimal time interval over which future information is relevant for optimal control and for measuring the performance of a single part‐type production system. Optimal control modeling and process solving, which consider aspects of decision making with limited forecast, are exemplified by a single part‐type production system. Specifically, the analysis reveals practical situations in which there is both a performance loss as well as feasibility violation when only information expected within the planning horizon is considered. The analysis is carried out by developing a pseudo‐stochastic model. We follow previous “pseudo‐stochastic” approaches that solve stochastic control problems by using deterministic, optimal control methods. However, we model the expected influences of all future events, including those that are beyond the planning horizon, as encapsulated by their density functions and not only by their mean values.     

An empirical analysis of effective TQM implementation in the Hong Kong electronics manufacturing industry

The objective of this research is to develop a conceptual model of total quality management (TQM) implementation and test it empirically. The main research problem of this study is to investigate the effect of adopting TQM principles on quality achievement in manufacturing. The hypotheses specifying relationships between the core parts of the model including quality management infrastructure, core quality management practices, quality performance, and customer satisfaction are tested using survey research method. Data were collected from 50 managers of 25 manufacturing companies in Hong Kong. Correlation analysis on the ratings given by two different respondents from the same company shows that employee relations strongly facilitates the use of quality data and reporting, while training supports the function of product design, supplier quality management, and quality data and reporting. The results also indicate that employee relations and training have direct effects on quality performance which, in turn, relates to customer satisfaction. Practitioners are advised to build a supportive environment with a strong emphasis on human and organizational aspects to promote effective TQM implementation. © 1999 John Wiley & Sons, Inc.     

Performance optimization of integrated resilience engineering and lean production principles

This paper conducts performance assessment from integrated resilience engineering (IRE) and lean production points of view. This is the first study that evaluates the impact of integrated resilience engineering (IRE) on lean production principles. Second, this study considers integrated impact of lean production by a unique intelligent algorithm. The proposed algorithm is composed of radial basis function (RBF), multi-layer perceptron (MLP) and adaptive neuro-fuzzy inference system (ANFIS). Moreover, the algorithm is capable of handling both crisp and fuzzy data due to the existence of intelligent approach. The proposed algorithm is equipped with verification and validation mechanism through conventional regression, statistical methods and data envelopment analysis. To demonstrate the applicability of the study, a real-world pipe manufacturer is considered as our case study. The results showed that “pull system” and “fault tolerant” among lean and IRE factors, respectively have been implemented inappropriately, while other factors are either suitably executed or ineffective.     

Cloud-based design and manufacturing: A new paradigm in digital manufacturing and design innovation

Cloud-based design manufacturing (CBDM) refers to a service-oriented networked product development model in which service consumers are enabled to configure, select, and utilize customized product realization resources and services ranging from computer-aided engineering software to reconfigurable manufacturing systems. An ongoing debate on CBDM in the research community revolves around several aspects such as definitions, key characteristics, computing architectures, communication and collaboration processes, crowdsourcing processes, information and communication infrastructure, programming models, data storage, and new business models pertaining to CBDM. One question, in particular, has often been raised: is cloud-based design and manufacturing actually a new paradigm, or is it just “old wine in new bottles”? To answer this question, we discuss and compare the existing definitions for CBDM, identify the essential characteristics of CBDM, define a systematic requirements checklist that an idealized CBDM system should satisfy, and compare CBDM to other relevant but more traditional collaborative design and distributed manufacturing systems such as web- and agent-based design and manufacturing systems. To justify the conclusion that CBDM can be considered as a new paradigm that is anticipated to drive digital manufacturing and design innovation, we present the development of a smart delivery drone as an idealized CBDM example scenario and propose a corresponding CBDM system architecture that incorporates CBDM-based design processes, integrated manufacturing services, information and supply chain management in a holistic sense.     

Multiaspect‐based opinion classification model for tourist reviews

Tourist reviews on social media websites reflect the tourist's opinions concerning various aspects of a tourist place or service (e.g., “comfortable room” and “terrible service” in hotel reviews). Extracting these aspects from reviews is a challenging task in opinion mining. Therefore, aspect‐based opinion mining has emerged as a new area of social review mining. Existing approaches in this area focus on extracting explicit aspects and classification of opinions around these aspects. However, the implicit and coreferential aspects during aspect extraction are often neglected, and the classification of multiaspect opinions is relatively less emphasized in prior art. In this paper, we propose a model, namely, “enhanced multiaspect‐based opinion classification” that addresses existing challenges by automatically extracting both explicit and implicit aspects and classifying the multiaspect opinions. In this model, first, a probabilistic co‐occurrence‐based method is proposed that utilizes the co‐occurrence between aspects and sentiment words to identify the coreferential aspects and merge them into groups. Second, an implicit aspect extraction method is proposed that associates the sentiment words with suitable aspects to build an aspect‐sentiment hierarchy. Third, a multiaspect opinion classification approach is proposed that employs multilabel classification algorithms to classify opinions into different polarity classes. The effectiveness of the proposed model is evaluated by conducting experiments on benchmark and real‐world datasets. The experimental results revealed the supremacy of multilabel classifiers by achieving 90% accuracy per label on classification when extracting 87% domain‐relevant aspects. A state‐of‐the‐art performance comparison is conducted that also verifies the advantages of the proposed model.     

A decision support tool based on QFD and FMEA for the selection of manufacturing automation technologies

With the advent of the new challenge to design a more lean and responsive computer-integrated manufacturing system, firms have been striving to achieve a coherent interaction between technology, organisation, and people to meet this challenge. This paper describes an integrated approach developed for supporting management in addressing technology, organisation, and people at the earliest stages of manufacturing automation decision-making. The approach uses both the quality function deployment (QFD) technique and the failure mode and effects analysis (FMEA) technique. The principal concepts of both applications are merged together to form a decision tool; QFD in its ability to identify the most suitable manufacturing automation alternative and FMEA in its ability to identify the associated risk with that option to be addressed in the manufacturing system design and implementation phases. In addition, this paper presents the results of a practical evaluation conducted in industry.     

A TQM-Based Approach to QoS Management

Total Quality Management (TQM) is one of the most interesting and effective concepts of management. Although, as of today, it has been extensively applied to manufacturing and industrial sectors, TQM principles have not received wide acceptance in the area of Telecommunications Network and Service Management. Application of those principles in Telecommunications can be facilitated by employing pertinent techniques and tools. This paper focuses on aspects of the performance management of Telecommunications Networks and Services. As basic quality control mechanisms, two versions of a Dynamic Scaling Service (DSS) are used to regulate the end-user perceived quality: a Crisp DSS and a Fuzzy Logic DSS. A quality-oriented user behavior model is built and a comparative study between the different quality control schemes is presented. Concluding, the paper delineates the scope of TQM with respect to QoS management in telecommunications.