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This paper describes the application of case based reasoning (CBR) to decision support for design managers and engineers during the early phases of new product development projects, in a concurrent engineering environment. The paper discusses the rationale of using CBR, emphasising its suitability for ill‐defined, unstructured problems, in comparison with traditional knowledge‐based systems. The overall research approach is presented, the importance of case collection, case base maintenance and user training is highlighted and the pre‐requisites for effective use of the system are discussed. Finally, the benefits and costs of the CBR system, as perceived by the user companies, are discussed. The experimental nature of the approach is emphasised and it is shown that the industrial environment for which the system is designed and in which it is used has great bearing on its capability.

From knowledge management point of view, business process models and ontologies are two essential knowledge artifacts for organizations that consume similar information sources. In this study, the PROMPTUM method for integrated process modeling and ontology development that adheres to well-established practices is presented. The method is intended to guide practitioners who develop both ontologies and business process models in the same or similar domains.

The method is supported by a recently developed toolset, which supports the modeling of relations between the ontologies and the labels within the process model collections. This study introduces the method and its companion toolset. An explanatory study, that includes two case studies, is designed and conducted to reveal and validate the benefits of using the method. Then, a follow-up semi-structured interview identifies the perceived benefits of the method.

Application of the method revealed several benefits including the improvements observed in the consistency and completeness of the process models and ontologies. The method is bringing the best practices in two domains together and guiding the use of labels within process model collections in ontology development and ontology resources in business process modeling.

The proposed method with its tool support is a pioneer in enabling to manage the labels and terms within the labels in process model collections consistently with ontology resources. Establishing these relations enables the definition and management of process model elements as resources in domain ontologies. Once the PROMPTUM method is utilized, a related resource is managed as a single resource representing the same real-world object in both artifacts. An explanatory study has shown that improvement in consistency and completeness of process models and ontologies is possible with integrated process modeling and ontology development.

The problem that original design manufacturing (ODM) companies encounter with the request for quotation (RFQ) process is that there is no effective and efficient methodology for them to formulate accurate and profitable RFQs. The purpose of this paper is to present a quality function deployment (QFD)‐, concurrent engineering (CE)‐, and target costing‐based methodology for ODM companies to formulate accurate and profitable RFQs.

From eight Taiwan electronics ODM companies, 15 people are interviewed to understand their current methods for formulating RFQs and the problems of these methods. Based on the interview results, it was decided to make use of the merits of QFD, CE, and target costing by integrating them into the proposed methodology.

A case study is presented to illustrate a successful application of the proposed methodology in a case company. The case study shows integrating QFD, CE, and target costing into the proposed methodology allows the authors to effectively and efficiently formulate an accurate and profitable RFQ for the case company.

The interview sample quantity of this study is limited to eight Taiwan electronics companies, which is insufficient to represent all ODM companies in various industries. For the future research, it is suggested researchers collect more samples from different industries in order to verify the effectiveness of the proposed methodology in ODM companies from different industries.

This paper aims to integrate QFD, CE, and target costing to come up with a systematic ten‐step approach that can accurately formulate the three parts of an RFQ: the product specifications proposal, the product price quotation, and the product development schedule. By adopting this methodology, ODM companies can provide accurate and profitable RFQs to ODM customers, thus increasing their chances of obtaining ODM business.

The purpose of this paper is to present a systematic review and analysis of existing research articles on new product development (NPD) published in the 12-year period starting from 1998 to 2009.

To explore the articles related to NPD, four key words namely new product, product design, product development and product innovation were used in combination of title or abstract or keyword of the articles through several knowledge databases. The abstracts of journal papers were read and a decision as to whether article belongs to any NPD research issue or not was made. In total, 1,127 research articles were categorized systematically and then analyzed on various principal NPD information groups.

Analysis of selected articles led to a certain level of dispersion in the publication of NPD research in different journals. It is found that more attention needs to be on knowledge and creativity management, communication and information transfer in any NPD process.

By observing extended literature from authors reviewing articles from various journals, growth in research, and variety of topics covered in NPD, a broad systematic multi journal review of NPD literature is clearly overdue. The authors have developed a comprehensive listing of publications on NPD where they have classified the surveyed papers according to various principal NPD information groups like: published year, NPD research stream, type of organization studied (industrial/consumer/service), level of innovation (high/moderate/low), NPD focus on frameworks, performance perspective (success, failure or both), NPD research design (conceptual/empirical and qualitative/quantitative) and NPD relevant best practice element. Based on the classification scheme, the issues were analyzed from the system's perspective and their implications to NPD research.

This paper conducts a Systematic Literature Review (SLR) of Machine Learning (ML) in Supply Chain Management through bibliometric and network analysis, the authors are able to grasp key features of the contemporary literature. The study makes use of state-of-the-art analytical framework based on a unified approach to reveal insights from the present body of knowledge and the potentials for future research developments.

Unlike standard literature reviews, in SLR, a structured approach is followed. The approach enables utilizing contemporary tools and software packages such as R-package “bibliometrix” and Gephi for exploratory and visual analytics. A number of clustering methods are employed to form clusters. Later, multivariate analysis methodologies are adopted to determine the dominant clusters for the influential co-cited references.

Using contemporary tools from Bibliometric Analysis (BA), the authors identify in an exploratory analysis, the influential authors, sources, regions, affiliations and papers. In addition, the use of network analysis tools reveals research clusters, topological analysis, key research topics, interrelation and authors’ collaboration along with their patterns. Finally, the optimum number of clusters computed for cluster analysis is decided using a systematic procedure based on multivariate analysis such as k-means and factor analysis.

Modern-day supply chains increasingly depend on developing superior insights from large amounts of data available from diverse sources in unstructured and semi-structured formats. In order to maintain a competitive edge, the supply chains need to perform speedy analysis of big data using efficient tools that provide real-time decision-making insights. Such an analysis necessitates automated processing using intelligent ML algorithms. Through a BA followed by a detailed data visualization in a network analysis enabled grasping key features of the contemporary literature. The analysis is based on 155 documents from the period 2008 to 2018 selected using a systematic selection procedure.

The paper aims to propose a novel strategic approach, named a Scorecard‐Markov model, combining an evaluation scorecard and a hidden Markov model (HMM) for new product idea screening (NPIS) decisions.

A scorecard is constructed to evaluate new product ideas on several criteria, including customer needs, marketing strength, competency, manufacturing compatibility, and distribution channels, involving a consideration of risk buy. A HMM is then developed accordingly to predict the overall performance of new ideas in terms of success probability. To implement the model, it is trained and tested by the historical dataset of a world‐class, leading company in the power tools industry through a case study.

The approach is proven to be encouraging and meaningful. The scorecard can serve as a guide for new product idea evaluation to convert experts' linguistic judgments to quantifiable and comparable data, whereas the HMM can determine the success probability of new product ideas to support NPIS decision making based on their computed evaluation performance. The optimal cut‐off value for making either a go or kill decision on each idea can thus be determined. Concerning the case company, a go decision should be made when the probability lies in the interval [0.53, 1].

The model can prevent companies from undertaking risky and failed new product development projects. Further, it is believed that this study can assist decision makers in choosing winning new product ideas towards commercialization in an effective and certain manner, thus enhancing the new product success rate in the innovation industry.

The approach incorporating the scorecard method and HMM is novel. Illustrated by the case study, the application of this approach to NPIS decisions is confirmed to be effective.

Managers' attention is a scarce resource in complex innovation settings. Prior research on the factors to which managers pay attention is mostly based on surveys. The present study aims to address the need for knowledge about the behavior of decision makers based on observations from portfolio meetings. The study seeks to investigate how managers allocate their attention and the role of different factors for their attention. Observations also make it possible to compare prior research and expectations with the actual observed behavior of decision makers.

The present analysis draws on insights from previous research into decision making in product and portfolio management and studies on organizational decision making. The authors frame why the attention of decision makers is so critical in complex situations. Data for this study were collected through direct observation, from a portfolio management system, and from an information quality measurement system in an internationally operating petrochemical company. Observations were transcribed, coded and analyzed with regression analysis using the Proc Lifereg procedure in SAS.

Six potential factors that might explain decision makers' attention are identified. The analysis shows that the quality of information was not significant for explaining variations of decision makers' attention; but, even more surprisingly, differences in project status did not explain variations in attention. Delayed projects did not get significantly more attention than those delivered on time. By controlling for other project characteristics, the newness of projects to the corporate portfolio was found to be the most important parameter.

First, the analysis is based on observations from actual meetings rather than from surveys. Second, several observations contradict prior research on product development, e.g. it has been argued that decision makers should pay special attention to certain phases and projects having trouble meeting expectations towards planned deadlines. It is also new that findings on the different treatment of new projects and ongoing projects have been brought forward in research on product and portfolio management.

Many companies that were once centrally involved in the actual manufacture of products, and the delivery of their supporting services, now find themselves primarily engaged in integrating a number of other organizations, some of which they may own but many of which will be independent, each of which goes to make up a particular supply network. Consequently, continuously tracking performance of suppliers and an appropriate selection mechanism is one of the crucial activities in managing this supply network. This paper presents an intelligent generic supplier management tool (GSMT) using the case‐based reasoning (CBR) technique for outsourcing to suppliers and automating the decision‐making process when selecting them. The development of GSMT and how the CBR technique is applied is then given, followed by an application of GSMT in Honeywell Consumer Products (Hong Kong) Limited.

Getting the right information at the right time is one of the success factors of product development. However, there exist a great variety of data formats which make the bi‐directional data interchange among various development groups inefficient and costly. Due to the lack of adequate product and engineering data, design practitioners may find it difficult to transform the customer’s requirements to a value‐added product. This paper attempts to propose a hybrid database approach (HDA) to support effective data exchange among a case based reasoning knowledge repository and a corporate relational database model, presenting an integrated view of a company’s knowledge sources so as to enable better decision‐making by providing immediate access to key business information. To introduce machine learning capability to the whole system, case based reasoning (CBR) is embraced to capture past experience and knowledge for case matching in terms of product development issues. The significance of this research is the provision of a cross‐platform data exchange system, which is able to facilitate the creation of a conducive product development environment.

In increasingly competitive markets, customer satisfaction is a vital corporate objective. Key elements to increasing customer satisfaction include producing consistently high‐quality products and providing high‐quality customer service. Also, supplier relationship management (SRM) contributes to the supplier selection and increases the competitive advantage of manufacturers. SRM can enhance customer satisfaction and increase market share. Thus the development of a customer‐SRM system in the areas of outsourcing is essential for a company to remain competitive. Discusses an intelligent customer‐SRM system (ISRMS), using case‐based reasoning to help solve problems such as supplier selection and the help desk problem‐solving approach. By using ISRMS, companies can select the most suitable suppliers from the supplier list, as well as establishing a good customer‐supplier relationship between parties.