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The purpose of this paper is to develop an effective approach to support and guide production improvement processes utilising online product reviews.

This paper combines two methods: (1) natural language processing (NLP) to support advanced text mining to increase the accuracy of information extracted from product reviews and (2) quality function deployment (QFD) to utilise the extracted information to guide the product improvement process.

The paper proposes an approach to automate the process of obtaining voice of the customer (VOC) by performing text mining on available online product reviews while considering key factors such as the time of review and review usefulness. The paper enhances quality management processes in organisations and advances the literature on customer-oriented product improvement processes.

Online product reviews are a valuable source of information for companies to capture the true VOC. VOC is then commonly used by companies as the main input for QFD to enhance quality management and product improvement. However, this process requires considerable time, during which VOC may change, which may negatively impact the output of QFD. This paper addresses this challenge by providing an improved approach.

The purpose of this paper is to introduce a solution to the problem of changing priorities of customer needs (CNs) in quality function deployment (QFD). Customer preferences and priorities are not very stable and they may change before products are ready for the market. Therefore, finding CNs accurately is a key to reach a higher level of customer satisfaction through improving products.

In the proposed model, a Markov chain is employed to model the changing priorities of CNs. The Markov chain finds a pattern of future CNs, the main inputs of QFD. The QFD method is applied to translate CNs into product requirements (PRs). The analytic network process (ANP) is attached to QFD to ensure that all the relations among the elements, inner and outer, are taken into consideration during the translation process. Thus, CNs are received and adjusted by a Markov chain.

The application of Markov chains for an ANP-QFD model develops an adequate method of finding a pattern of changing priorities of CNs. This pattern enables the ANP-QFD method to work independent of the initial CNs, and originates a Markovain ANP-QFD.

This study originates a stochastic ANP-QFD model. There have been several papers employing various tools and techniques such as the ANP or analytic hierarchy process for QFD to find accurate relations between PRs and CNs. While there are a few papers applying Markov chains to predict the future of the relations of QFD, there is no study which traces the changes in priorities of the CNs during the improvement process. This is addressed by applying a Markovian ANP-QFD. The model is validated through a case study.

The purpose of this paper is to use game theory and ambiguity theory to show how “economically rational” vendors will behave in a procurement process that runs over more than one period. In light of that behavior, we have proposed “economically rational” counter-strategies on the part of purchasers.

Based on a perception–expectation framework, a unique game-based approach is designed. The authors have proposed “economically rational” counter-strategies on the part of purchasers, which are premised on the theory of rational agency.

Ambiguity in the procurement process is a bane for procuring principals and a boon for suppliers – for the former, it is an issue to be managed, and for the latter it provides an opportunity to extract “insurance rents” from the principals. The authors show that, under certain conditions, the contracting principal can be exploited by a rational, rent-extracting vendor. In particular, they show that there is an incentive for a vendor to delay the resolution of ambiguities in the contract until late in the procurement process, when the insurance rents are at a maximum.

This study contributes to the current literature by highlighting an existing problem in the procurement process and describing it using decision theory under ambiguity in a game-like setting. Specifically, the authors use game theory in a unique way to deal with imperfect information coupled with ambiguity.