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The purpose of this paper is to reduce impact on profit and supply risk, for strategic part by defining appropriate purchasing strategies using Kraljic portfolio model (KPM) approach.

A five-step approach of KPM is used to identify strategic parts. The attributes affecting impact on profit and supply risk are then identified. A structured questionnaire is prepared for each attribute. Likert scale having weights from 1 to 5 is used to select method of measurement for each response. The overall score of attributes for dimensions “supply risk” and “impact on profit” interpret the position of the part, i.e., “strategic part,” “leverage part,” “bottleneck part” or “non-critical part” in the Kraljic matrix. The part having highest impact on profit and supply risk is to be considered as strategic part for defining the purchasing strategies.

This paper outlines a practical solution to the problem of designing/defining purchasing strategies for development of supplier. Impact on profit is reduced by cost reduction and quality improvement whereas supply risk reduced by improving delivery time and suppliers profile.

The proposed methodology is applied to a boiler industry to identify the strategic parts and focus on strategic suppliers, which are fewer in number but having larger impact on profit and supply risk. The “Body” part of ball valve assembly of the boiler is considered for the study. However, it can be extended for selecting and evaluating of suppliers strategy for other parts of the boiler. Not only boiler industry sector but also other companies also benefit from the results of this study by implementing the purchasing strategies as formulated in the KPM.

The Kraljic matrix has been largely used in many different industries like automobile, manufacturing, construction, oil and gas, etc., as an efficient tool for developing differentiated purchasing strategies. However, its application on parts procured by boiler industry is unknown, as well as the lack of systematical approach on criteria prioritization which is one of the key issues of the methodology. This work explores the output usefulness and its applicability on ball valve assembly projects.

The purpose of this paper is to apply the proposed methodology to develop the product as per customer’s requirements (CRs) and increase customer satisfaction (CS).

The companies are using different methods to identify CRs and translate these requirements into new products. The Kano model is used to identify CRs. The result of the Kano model is used in quality function deployment (QFD) to decide the priority for improvements of CRs and the technical requirements.

The integration of the Kano model and QFD is applied on the mobile phone. The Kano model helps to find out the CRs which affect the CS, such as attractive (A) attribute, one (O)-dimensional attribute and must be (M) attribute. The QFD method helps to translate the CRs into technical requirements so that the designer can decide priority of requirements for improvement or new product development.

The proposed methodology can be applied to different consumers as well as industrial products.

The Kano model has been used in manufacturing as well as service sector. This work explores its usefulness and applicability for consumer and industrial products.

The purpose of this paper is to present a systematic methodology for segmenting customers based on the preferred product features, its cost and worth, so as to facilitate the designer to develop a product that will simultaneously minimize product cost and maximize customer satisfaction.

Post hoc – non-overlapping – non-hierarchical approach has been used for segmentation based on preferred product features by the customers. Allocation of product features to a particular segment is carried out by considering feature cost and customer worth for that feature. Automobile car has been selected as an example to demonstrate the methodology, where features data were collected from dealers and customer worth data were obtained by random generation method.

Methodology facilitates creation of n number of homogeneous segments from a heterogeneous customer group based on the cost and worth of product features. Total product cost decreases though product variety increased due to segmentation.

The proposed approach will help designers in segmenting (grouping) heterogeneous customers based on the preferred product features so that a most compatible (matching) product configuration for each segment, especially during product consolidation stage (beginning of the maturity phase of product lifecycle) can be developed to achieve maximum customer satisfaction.

The purpose of this paper is to propose a multi-criteria decision making method to evaluate factory data collection (FDC) system alternatives.

“Information” in is fundamental resource to the success of any business which is as valuable as capital or people. The factory data (information) collection system (FDC system) consists of the various paper documents, terminals, and automated devices located throughout the plant for collecting data on shop floor operations for compiling and processing the data. In this paper, nine alternatives of FDC methods are evaluated on the basis of eight criteria. The weight of each criterion is determined using Analytic Hierarchy Process, and the same weights are used to evaluate alternatives of FDC system using Grey Relational Analysis – A multi-criterion decision making method.

The methodology facilitates the selection of the best FDC system that will minimize the data entry time and chances of errors. The methodology suggests Radio-Frequency Identification (RFID) system is the most preferred choice (ideal) among the nine alternatives whereas Operation tear strips is the worst solution.

The proposed methodology will provide a useful tool to the decision maker, which may help to eliminate the associated risks during data entry. The selected best FDC system, i.e. RFID is most suitable tool for ERP system to integrate internal (manufacturing) and external (sales and service) management information system.