A smart manufacturing adoption framework for SMEs

Smart Manufacturing (SM) a revolutionary paradigm that aims to improve production systems’ performance in terms of quality, time, cost, and flexibility, as well as human and machine decision-making capabilities. Most large enterprises have already taken first steps towards adopting SM. Small and Medium-sized Enterprises (SMEs) on the other hand, are struggling with developing a SM adoption roadmap. Our research builds on the real and perceived needs and challenges faced by manufacturing SMEs and advances the field by developing and evaluating an SME-specific ‘SM adoption framework’. We have employed a multiple case study approach to acknowledge the lessons learned by selected early-adopter SMEs that have recently implemented and deployed SM tools and practices. We propose an SM adoption framework with five vital steps that SMEs interested in SM should follow: (i) identify manufacturing data available within the SME, (ii) readiness assessment of the SME data-hierarchy steps, (iii) developing SM awareness of SME leadership and staff, (iv) develop a SM tailored vision for the SMEs, and (v) identify appropriate SM tools and practices necessary to realise the tailored SM vision. Moreover, the results of the case study analysis enabled us to formulate many generalisations.     

Cost management through product design: target costing approach

The purpose of this study is to explore the role of target costing in managing product costs while promoting quality specifications that will meet customer requirements. In addition, it aims to develop a target costing module that will simplify implementation of target costing especially in small and medium enterprises (SMEs). Tools of operations management such as quality function deployment (QFD) and value engineering (VE) have been considered and evaluated in terms of their integration into the target costing. In order to investigate the total effect, an implementation of the QFD deployment–target costing process (QFD–TC process) was conducted in a small manufacturing company. Previous studies on target costing were mostly conducted in lead companies. This study focuses on implementation of the QFD–TC process in SMEs. Overall, it was found that the QFD–TC process was an essential technique in managing the costs of both a product and the overall production process. Combining target costing with QFD and VE techniques provides companies with a competitive cost advantage.     

Improving business objectives by reducing testing in the transition from development to production

This work focuses on finding a balance between the requirements to maintain product initial failure rate and the need to improve business objectives. We build on quality function deployment (QFD) by developing an innovative methodology for reducing testing in the transition from development to production. The methodology allows the number of tests to be reduced, without increasing the risk of lowering product quality. Our methodology assumes that testing affects product initial failure rate and business objectives simultaneously. Hence, a QFD-based methodology comprising two matrices is developed. Matrix 1 translates the desired improvement in the business objectives into the relative importance of the various test types. Matrix 2 translates the product failure improvement needs into the relative importance of the various test types. We implement the methodology in a company developing and manufacturing medical devices. We find that the events “vital tests concerning the business objectives” and “vital tests concerning the failures in products” are mutually exclusive. Hence, we can conclude that it is possible to achieve a significant reduction in the number of tests conducted on the medical devices. The results show that the tests that can be reduced are those that will not affect product initial failure rate but that do have a significant impact on business objectives.     

Concurrent multifunction deployment (CMFD)

Quality Function Deployment (QFD) is a powerful tool for quality planning in product design. In the course of time, the QFD method has passed over several improvements and adaptations to meet specific requests of particular working environments. The current needs and challenges in developing radical innovative and life-cycle optimised products require a concurrent approach of product planning against a complex set of objective functions (e.g. quality, cost, assembly, manufacturing, environment, technology, service, disposal, etc.). An advanced form of QFD that integrates concepts of concurrent engineering for planning product development with respect to multi-objective functions is presented in this paper. This framework is called Concurrent Multifunction Deployment (CMFD). TRIZ method was initially exploited to determine the appropriate vectors of intervention in formulating the CMFD methodology. A systematic algorithm supports the CMFD deployment process. It takes into account results from the analysis, innovation and evaluation phases over the product design process, too, ensuring a superior integration of the planning activities within the product development process.     

QFD optimization using linear physical programming

A new approach to quality function deployment (QFD) optimization is presented. The approach uses the linear physical programming (LPP) technique to maximize overall customer satisfaction in product design. QFD is a customer-focused product design method which translates customer requirements into product engineering characteristics. Because market competition is multidimensional, companies must maximize overall customer satisfaction by optimizing the design of their products. At the same time, all constraints (e.g. product development time, development cost, manufacturing cost, human resource in design and production, etc.) must be taken into consideration. LPP avoids the need to specify an importance weight for each objective in advance. This is an effective way of obtaining optimal results. Following a brief introduction to LPP in QFD, the proposed approach is described. A numerical example is given to illustrate its application and a sensitivity analysis is carried out. Using LPP in QFD optimization provides a new direction for optimizing the product design process.     

A DSS approach to developing systems to support production planning and control in smaller companies

Smaller Manufacturing Enterprises (SMEs) would benefit from manufacturing decision support systems (DSSs) in which the links between customer orders and manufacturing operations were maintained throughout the production planning process. However, systems with this characteristic are not available at prices that most SMEs can afford. This paper describes how a prototype DSS with this feature was developed and validated using data from a manufacturer of fencing materials. A hybrid relational database/object-oriented approach to modelling the manufacturing process is outlined. Static and slowly changing data about the manufacturing system were stored on a relational database, while more dynamic production planning information was built into an object model. The system made use of a 'Bill of Production' for each manufactured item that contained both materials and operation information, and was constructed at the time it was required from information held on the database.     

An approach based on fuzzy sets for manufacturing system design

The aim of this work is to provide Small and Medium Enterprises (SMEs) with adapted design methods for manufacturing systems. In a total quality strategy, the design activity consists of searching for the means able to satisfy all users' needs from company customers to floor plant workers. That includes usual costs, delays and quality targets but also employees' involvement. Due to the variety of data sources and to the continuous changes of goals, the information is imperfect. The main concerns are uncertainty, inaccuracy and vagueness of both objectives and solutions. The fuzzy set theory provides a quite convenient framework to represent all those imperfections using one mathematical objet. Fuzzy logic is used to aggregate specifications and to deploy solutions. Possibility theory finally allows a graded evaluation of expected performances. The obtained result is graded, providing the engineer with some degrees of freedom for the realization of the manufacturing system. The valuation of the design quality is an undeniable progress towards the continuous improvement of the whole system performance. Finally, such a criterion may also be used for an empirical search for a convenient solution, as for the automatic computation of an optimal result.     

Performance measurements in the implementation of CIM in small and medium enterprises: An empirical analysis

If a company has a strategic goal of achieving better productivity and quality, it is not only necessary to have action programmes that support productivity and quality, but also score-keeping system in manufacturing must integrate both the strategy and the actions. Performance measurements must appraise, reinforce and reward improvements in terms of action programmes being used to pursue productivity and quality. Performance improvement in small- and medium-sized enterprises (SMEs) can be achieved through the implementation of computer-integrated manufacturing (CIM). Many SMEs do not consider the implications of long-term benefits of CIM, but only consider short-term financial benefits when they decide about the implementation of CIM. They simply ignore the strategic benefits and intangibles. However, performance measurements considering financial, non-financial, tangible and intangible costs, and benefits have not received significant attention from SMEs while deciding on CIM. Nevertheless, CIM plays an increasingly important role in all aspects of competitiveness both in terms of product development and production techniques in SMEs. Therefore, considering the importance of investment in CIM by SMEs, an attempt has been made in this paper to study the implications of performance measurements on the implementation decisions. A framework has been developed to study the investment justification on CIM in SMEs with the help of an empirical study conducted in British SMEs. Finally, the conclusions are presented.     

A fuzzy QFD program modelling approach using the method of imprecision

The use of quality function deployment (QFD) to aid decision making in product planning has gained extensive international attention, but current QFD approaches are unable to cope with complex product planning (CPP) characterized by involving multiple engineering characteristics (ECs) associated with significant uncertainty. To tackle this difficulty, in this paper, fuzzy set theory is embedded into a QFD framework and a novel fuzzy QFD program modelling approach to CPP is proposed to optimize the values of ECs by taking the design uncertainty and financial considerations into account. In the proposed methodology, fuzzy set theory is used to account for design uncertainty, and the method of imprecision (MoI) is employed to perform multiple-attribute synthesis to generate a family of synthesis strategies by varying the value of s, which indicates the different compensation levels among ECs. The proposed methodology will allow QFD practitioners to control the distribution of their development budget by presetting the value of s to determine the compensation levels among ECs. An illustrative example of the quality improvement of the design of a motor car is provided to demonstrate the application and performance of the modelling approach.     

A Hybrid GDM-SERVQUAL-QFD Approach for Service Quality Assessment in Hospitals

Quality is one of the most critical factors in health systems. To improve service quality in hospitals, we propose a hybrid approach incorporating Group decision-making (GDM), Service Quality measurement (SERVQUAL), and Quality function deployment (QFD). QFD is used to translate patient needs into quality characteristics (derived from SERVQUAL), while taking into account interrelationships between design requirements and quality characteristics. One of the most important steps in the QFD process is to prioritize quality characteristics from the patients’ viewpoint, which is inherently affected by two types of uncertainties: human mental perception and patient heterogeneity. In this research, a novel two-stage GDM approach is employed to cope with these uncertainties in an integrated manner. Furthermore, the proposed methodology is applied in a public hospital to effectively enhance quality characteristics, which will be considered in the QFD. Accordingly, the proposed methodology provides a framework for engineering professionals and managers in hospitals to assess and manage quality characteristics.     

Quantification and integration of Kano’s model into QFD for optimising product design

With increasing concerns on customer needs in today’s competitive market, the issue of incorporating customer requirements into product design arises the interest of both researchers and practitioners. Quality Function Deployment (QFD) is a well-known methodology for customer-driven product design. However, conventionally, QFD analysis has a major challenge in understanding customer needs accurately. Kano’s model, which studies the nature of customer needs, provides a way for a better classification of customer needs. However, seldom research contributions are found in terms of integrating Kano’s model with QFD quantitatively. In this research, a novel integration approach is proposed. At first, Kano’s model is quantified by identifying relationship between customer needs and customer satisfaction (CS). Next, both qualitative and quantitative results from Kano’s model are integrated into QFD. Finally, a mixed non-linear integer programming model is formulated to maximise CS under cost and technical constraints. In this research, an illustrative example associated with the design of notebook computers is also presented to demonstrate the availability of the proposed approach.     

A quality function deployment-based model for materials selection

Materials play a key role during the entire product design and manufacturing phase as a wrongly selected material may often lead to premature product failure causing loss of revenue and repute of the concerned manufacturing organization. While selecting the most suitable material for a specific application, the designers often need a sound and systematic methodology to deal with this problem having multiple candidate alternative choices and conflicting objectives. Most of the previously applied methodologies for material selection have either adopted criteria weights estimated using subjective judgments of the designers or failed to give due emphasis on the voice of the customers to meet their requirements. In this paper, a maiden venture is taken to solve the material selection problems using a quality function deployment (QFD)-based approach that can integrate the voice of the customers for a product with its technical requirements. The applicability and solution accuracy of this QFD-based material selection model is demonstrated with the help of four illustrative examples. To ease out the materials selection decision-making process, a user-friendly software prototype in Visual BASIC 6.0 is also developed.     

A portfolio model for component purchasing strategy and the case study of two South Korean elevator manufacturers

A new approach to purchasing portfolio modelling, stemming from Kraljic's matrix, for developing purchasing strategies that are aligned with competitive priorities, is developed to address the weaknesses of existing approaches that are preventing widespread application, especially in SMEs. The importance of strategic purchasing to achieving competitive priorities and the need to align it with business strategy is argued through a literature review, which is also used to establish that purchasing portfolio modelling is an effective tool in achieving this alignment. The new approach is applied to two South Korean elevator manufacturers.     

A fuzzy expected value-based goal programing model for product planning using quality function deployment

Product planning is one of four important processes in new product development using quality function deployment (QFD), which is a widely used customer-driven approach. In this article, a hierarchical framework for product planning using QFD is developed. To tackle the fuzziness in functional relationships between customer requirements and engineering characteristics (ECs) in product planning, the least squares method is incorporated into fuzzy regression to investigate those functional relationships, by which a more central tendency can be obtained. Furthermore, a fuzzy expected value-based goal programing model is proposed to specify target values of ECs. Different from some fuzzy product planning approaches for QFD, the proposed programing model has unambiguous interpretations. An illustrated example of a quality improvement problem of emulsification dynamite-packing machine design is given to demonstrate the application and performance of the proposed approach.     

Practice of manufacturing strategy: Evidence from select Indian automobile companies

This paper presents findings based on an extensive survey of Indian automobile companies. Five companies have been selected for detailed case studies. Their experiences in the manufacturing strategy process are analysed. Although the companies represented a diversity in terms of sales volume, product range and geographic location, they shared several commonalties including use of advanced manufacturing technologies and other improvement activities. Competitive strength was sustained through quality, innovation and delivery. The process of strategy formulation varied among the companies in terms of participants, complexity and degree of formalization, the practice of each company is different. The process of manufacturing strategy formulation seems to be in line with corporate strategy, though the companies followed a traditional top down approach in formulation of manufacturing strategy under the umbrella of corporate strategy. A taxonomy is proposed using cluster analysis where the companies are classified in four strategic manufacturing groups: reactive enterprise, neutral enterprise, active enterprise and proactive enterprises. Competitive priorities, order winners and critical success factors are identified for these companies after rigorous discussions with the managers. Based on strategic manufacturing issues, the manufacturing competence index for the companies has been worked out. A framework for manufacturing strategy is also proposed based on experiences of the case companies.     

Fuzzy expected value modelling approach for determining target values of engineering characteristics in QFD

Quality function deployment (QFD) is a planning and problem-solving tool that is renowned for translating customer requirements into the technical attributes of a product. To deal with the imprecise elements in the development process, fuzzy set theory is incorporated into QFD methodology. A novel fuzzy expected value operator approach is proposed in this paper to model the QFD process in a fuzzy environment, and two fuzzy expected value models are established to determine the target values of engineering characteristics in handling different practical design scenarios. Analogous to stochastic programming, the underlying philosophy in the proposed approach is based on selecting the decision with maximum expected returns. Furthermore, the proposed approach considers not only the inherent fuzziness in the relationships between customer requirements and engineering characteristics, but also the correlation among engineering characteristics. These two kinds of fuzzy relationships are aggregated to give the fuzzy importance of individual engineering characteristics. Finally, an example of a quality improvement problem of a motor car design is given to demonstrate the application and performance of the proposed modelling approach.     

Choices in manufacturing strategy decision areas in batch production system – six case studies

The configuration of a production system can be described by the choices a firm makes in its manufacturing decision areas. Manufacturing strategy literature lacks empirical research in manufacturing decision areas. The current paper is an exploratory study using six case companies on alternative configurations that can exist in a batch production system. Choices made in decisions such as layout, shop floor control, etc., were found to be similar for all six companies that use batch process. However, there were a number of decisions that were found to be non-process specific and are influenced by product complexity, important competitive priorities, strategic orientation of manufacturing, top management decisions and the size of the company. The paper concludes with managerial implications and future research directions.     

An industrial workstation characterization and selection using quality function deployment

In flexible manufacturing, the need to evaluate various process design concepts that can survive and deliver better performance through many families and generations of products becomes more urgent. It is important to derive characteristics of the assembly station that are measurable and controllable. These characteristics should satisfy process and products needs. In this paper, we report on the findings of a cross-functional team, representing Product Development and Manufacturing Engineering, that was assigned the design of the advanced assembly station required to load magnetic heads suspended by actuator arms in between a stack of media disks in the assembly of direct access storage devices (DASD) also known as hard disk drives (HDD). The core of our work was based on a Quality Function Deployment (QFD) approach that included the construction of a house of quality (John Hauser and Don Clausing, ‘The house of quality’ Technical Report, Operations Department, Harvard Business School, Boston, MA 02163; Bob King, ‘Better Designs in Half the Time, Implementing Quality Function Deployment in America, 3rd edn, Goal/QPC Publishing, MA, 1989) which treated this process step as a system. We present results of the team efforts and describe the progressive stages that took place from the gathering of the station customers’ wants and needs to the selection of a station design. © 1997 John Wiley & Sons Ltd.     

Fuzzy multiple objective decision making approach to prioritize design requirements in quality function deployment

Quality function deployment (QFD) is a customer-oriented design tool for developing new or improved products to increase customer satisfaction by integrating marketing, design engineering, manufacturing, and other related functions of an organization. QFD focuses on delivering value by taking into account customer needs and then deploying this information throughout the development process. Although QFD aims to maximize customer satisfaction, technology and cost considerations limit the number and the extent of the possible design requirements that can be incorporated into a product. This paper presents a fuzzy multiple objective programming approach that incorporates imprecise and subjective information inherent in the QFD planning process to determine the level of fulfilment of design requirements. Linguistic variables are employed to represent the imprecise design information and the importance degree of each design objective. The fuzzy Delphi method is utilized to achieve the consensus of customers in determining the importance of customer needs. A pencil design example illustrates the application of the multiple objective decision analysis.     

基于扩展QFD模型的包装机械质量成本分析

在QFD质量屋基础上提出扩展QFD模型,引入质量保证成本评价指标、质量损失成本评价指标及技术要求综合评价指标,对顾客需求转化而成的技术需求进行成本分析,为包装机械产品开发及设计人员选择和确定实际的技术特征提供科学的依据.包装机设计的应用实例表明,利用扩展的QFD模型能够快速、有效的进行产品质量成本分析.