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The purpose of this paper is to examine the information gathering methods used during enterprise system (ES) selection among Polish organizations and to have IT consulting experts evaluate these methods, which will result in identification of the preferred approach for the information‐gathering task during system selection.

The research has a qualitative exploratory design with grounded theory being the main research method. A mixed quantitative‐qualitative approach to data collection was used: an e‐mail‐based survey was used as an introductory stage to gather data on information‐gathering methods. The results of the survey were evaluated by ES experts during unstructured interviews. The interviews were then transcribed and coded according to the grounded theory coding techniques.

The evaluation of selection approaches revealed that the approach to information gathering should depend on the level of configurability of the systems subject to selection. Two generic approaches were identified: requirements driven – for highly configurable systems, and system functionality driven – for the systems offering limited configurability. Interaction between the customer and the bidder was also identified as the main condition for preparing the unbiased offer by the bidders.

Regarding the survey study, as the survey sample was small and the sample selection process was not random, the results should not be generalised to the whole population of Polish enterprises. It would be also beneficial to test the validity of the findings from the grounded theory study on the big population of ES bidders with the use of statistical methods.

For highly configurable systems, a model that includes a business process analysis, detailed requirements' specification and dedicated system functionality presentation is the preferred approach. A workshop is preferred to obtaining a ready requirements list. For systems offering limited configurability, an approach centred on system presentation is more suitable. In this case the requirements should be gathered during the presentation on a “gap‐fit” basis.

There is a significant lack of research that explores the selection process, with the stress on organizational needs and system functionality information gathering and makes indications for improvement of this process from the consulting enterprise (bidder) point of view. The paper fills this gap by presenting the results of a survey among Polish enterprises regarding selection routines and the evaluation of these routines by expert ES professionals, resulting in the development of the preferred set of information‐gathering methods.

The purpose of this paper is to assist facility design practitioners, industrial managers, and expert systems (ES) developers identify important issues to consider and actions to follow in the selection of material handling (MH) equipment, overcome limitations of existing equipment selection approaches, and design complete MH systems.

A framework that specifies the steps that have to be taken in the selection of MH equipment is suggested. It identifies categories of equipment based on all handling‐related functions performed in a facility, which allows consideration of various categories of equipment, and selects equipment classes and types from among candidates based on user requirements and objectives.

The suggested framework is capable of producing a wide range of equipment that performs various functions in a facility, and satisfies requirements and objectives without the need for using large data bases. Applying it requires cooperation of facility managers and designers.

Some of the steps of the suggested framework require further investigation and research.

Facility design practitioners, industrial managers, and developers of ES can make better selection among a large number of MH equipment and design complete systems by relying on functions performed in a facility, requirements, and objectives.

A framework for selection of MH equipment is lacking in the literature. This paper fills a gap by presenting design issues and organized steps that practitioners, managers, and expert system developers working on selection of MH equipment have to address and follow in order to design complete MH systems.

ERP selection is a multi-faceted process and needs to be successful in dealing with high uncertainty. The purpose of this paper is to propose a novel multi-criteria decision making (MCDM) approach, titled as cloud-based design optimization (CBDO), for ERP selection problem to handle high uncertainty with a computationally effective way.

CBDO has been utilized as an alternative method to fuzzy set theory and stochastic programming, and proposes robust findings for worst case scenario. In order to assess the proposed methodology, a numerical study is conducted by taking into account existing state-of-the-art study on the ERP selection problem for the small medium enterprises. The outputs of the existing state-of-the-art study are assumed as uncertain and varying across time as it is expected in real life; therefore, different scenarios are created in order to reveal the effect of uncertainty on decisions.

In the methodology, the results given under uncertain conditions are compared with the results obtained under stable conditions. It is clearly seen that ERP system selection problem area has high sensitivity to the uncertain environment, and decision makers should not undervalue the unsteadiness of criteria during the ERP system selection process, especially within volatile economies.

This study contributes to the relevant literature by utilizing CBDO as a MCDM tool in the selection of the ERP software as a first time, and validating the impact of unsteadiness on the ERP selection procedure. It is the first CBDO-based study that validates the effect of distributional differences on uncertainties in the ERP selection processes.

This paper aims to analyse the valuation of cultural products and explores what this process means for organizations involved in their production and marketing.

The authors develop the arguments using a number of mini-cases and industry examples.

The main thesis is that the meaningfulness and value ambiguity of cultural products shift the focus of valuation away from the products themselves towards how certain agents in the socio-cultural environment identify and certify these products. This paper discuss how valuation takes place via selection systems and how the nature of cultural products drives the dominance of one selection system over others.

Theories on value creation needs to take consideration of the critical role played by the selection system instead of just the firms that produce these products.

Organizations engaged in producing highly symbolic products need to manage selection systems and related industry dynamics to establish an enduring competitive advantage.

Value creation is a collective social efforts. Every member of the society can play a central role in this process. Better engaging various member of the society to enable them actively participate in the value creation process is what organizations today need to consider, instead of just treating individuals in the society as a “customer” who only passively consume. This research calls for the true empowerment of every member of the society to facilitate collective creativity and participation in the value creation endeavour that benefits the entire society as a whole.

It is the first paper that has created a conceptual link between the type of selection system and product categories. In other words, it takes existing literature on value creation and selection system one step further by creating the alignment or match between types of selection system and types of product categories. Therefore, it offers academics and practitioners a much detailed understanding on how value creation is conducted across different product categories.

Presents a model of the machining system selection process that is focused on capital intensive, complex machining systems that are intended to provide service over a long time horizon. This model was developed based on interviews with both machine tool suppliers and buyers. The systems considered here increasingly face potentially conflicting demands such as: the ability to be quickly and inexpensively upgraded and reconfigured in order to have quick new product change‐over and ramp‐up time; and high product variety at close to mass production costs. This new “reconfigurability” capability increases the importance of the supplier‐buyer relationship after the machining system has been selected. We also remark that the selection process can serve as the basis for internal consensus and team building within the buyer firm and for enhancing supplier base quality.

Today, the high cost of e-learning systems’ implementation and the difficulty of managing the infrastructures motivate educational institutions toward application of cloud-based e-learning systems. This new system should be aligned with the academics’ aims and pedagogical principles to be beneficial for learners and instructors. Therefore, the vendor selection of learning systems is one of the most important processes to migrate toward cloud-based e-learning. The purpose of this paper is to develop a new framework to facilitate the vendor selection of cloud-based e-learning systems in the cloud market.

To identify the initial criteria as to the vendor selection of cloud-based e-learning services, a literature review is done. To enrich the initial criteria, a focus group of experts is investigated, and the framework developed; then, a survey analysis is conducted to validate the proposed framework. The extracted criteria and sub-criteria are weighted and prioritized using best-worst method (BWM).

The results indicate that the main dimensions of vendor selection framework as regards cloud-based e-learning systems are managerial, technological and pedagogical factors. The rank orders and weights of the mentioned aspects and their sub-criteria are calculated using the BWM.

The proposed framework helps managers to get a big picture of requirements as to cloud-based e-learning and more effectively to select appropriate vendors in this initiative. In the vendor selection process, managers must pay attention to technological issues as well as managerial and pedagogical considerations.

Cloud-based e-learning systems are getting increasingly essential to offer training courses more efficiently in educational institutions. Although the intersection between cloud computing and e-learning has increasingly grown in both practical and academic contexts, there are little studies on how educational institutions and organizations could be able to select appropriate cloud-based e-learning systems. This paper explores the ignored but critically important subject of cloud-based e-learning. The main contribution of this paper is to propose a novel and integrated framework containing the important aspects of vendor selection in cloud-based e-learning services. The proposed framework comprises managerial, technological and pedagogical aspects simultaneously as well as sub-criteria denoting each aspect.

A Hong Kong‐based study of factors affecting construction industry performance levels confirms that selection methodologies and decisions are critical, both at the upstream formulation of procurement and operational systems themselves as well as the downstream selection of various project participants. Needs are identified for a holistic approach to broader procurement choices that incorporate all procurement sub‐systems, an integration with synergistic operational sub‐systems and more informed and intelligent participant choices. The many interacting variables and potential information overload point to the consequential need for harnessing IT/IS (information technology/information systems) to enable smarter selections at all stages of the construction supply chain. Frameworks and sample modules of the proposed knowledge‐based decision aids are presented in the case of overall procurement system selection and contractor selection.

This paper describes the design and development of a system for the selection and construction of vertical and horizontal machining center packages with a base machine and options format subject to budgetary constraints. Options are categorized and divided into groups depending on their utilization, and their selection is based on a priority setting. Options are categorized as those providing the capability for high speed, enhanced levels of productivity, high levels of machining complexity, high levels of machining accuracy, and potential for the inclusion of automation to facilitate and satisfy production requirements. A user‐friendly and object‐oriented computer0based decision support software system was developed incorporating real data from a machine tool sales organization.

A key task in the material handling system design process is the selection and configuration of equipment for material transport and storage in a facility. Material handling equipment selection is a complex, tedious task. However, there are few tools other than checklists to assist engineers in the selection of appropriate, cost‐effective material handling equipment. This paper describes the development of an intelligent material handling equipment selection system called MHESA (Material Handling Equipment Selection Advisor). The MHESA is composed of three modules: a database to store equipment types with their specifications; a knowledge‐based expert system for assisting material handling equipment selection; and an analytic hierarchy process (AHP) model to choose the most favorable equipment type. The concept proposed in this paper can automate the design of material handling equipment selection system, and provides artificial intelligence in the decision‐making process.

Availability of accurate quantity of materials, at correct place and at right time is extremely critical for increasing production effectiveness of any manufacturing organization. This can be achieved through employing an appropriate material handling equipment (MHE) capable of performing the desired operation. Therefore, choosing a right MHE from the available options is a key concern for the success, growth and competitiveness of a manufacturing organization. The purpose of this paper is to describe the design and development of an expert system based on quality function deployment (QFD) methodology in Visual Basic 6.0 for selecting the most appropriate industrial truck which is a commonly practiced MHE in any manufacturing organization.

A QFD-based approach is adopted to incorporate customers’ needs into the evaluation criteria on the basis of which industrial truck selection is carried out. The applicability of the developed expert system in solving industrial truck selection problems is demonstrated using two illustrative examples.

While applying this QFD-based model, CPCD 80x manufactured by Heli is recognized as the most suitable forklift truck for transporting unitized loads within a manufacturing unit with some spatial constraints, and for loading/unloading packages/boxes/cartons and place them at the desired locations in a manufacturing unit, ETV 216 manufactured by Jungheinrich evolves out as the most suitable reach truck.

Till date, numerous research articles have been published suggesting the applications of various mathematical models, multi-criteria decision-making methods and knowledge-based systems for solving MHE selection problems, and it is intriguing to note that none of the previously adopted methods has proposed a systematic procedure for selection of the evaluation criteria and interrelated the needs of customers with the technical specifications of MHEs while identifying the best alternative for performing a specified operation. These issues can be addressed through application of this developed QFD-based expert system, which can translate customers’ needs into organizational functions that are implementable in the decision-making/selection procedure.