Strategic system selection with linguistic preferences and grey information using MCDM

Highlights

• Grey theory based decision making.

• Multi criteria decision making.

• Strategic system selection with linguistic preferences and grey information using MCDM.

• New technology selection.

Abstract

In this article, grey based theory is used to grasp the ambiguity exists in the utilized information and the fuzziness appears in the human judgments and preferences. Grey theory can produce satisfactory results, and hence stimulates creativity and the invention for developing new methods and alternative approaches. This article is a very useful source of information for fuzzy grey and decision making using more than one decision makers in fuzzy environment. A case study on system selection comprised of 12 attributes and 7 alternatives is constructed and solved by the proposed method and the results are compared with the results obtained from QSPM, TOPSIS and SAW approaches for analysis purposes.

Introduction

Picture a community hospital struggling to pay monthly payments on time and there are no too many ways for competing with the big hospitals around. This community hospital has to make decision in getting into the radio frequency identification (RFID)-based systems for better managing the patients and those drugs that are tagged already. This is the reality that more producers are getting into the RFID tags and fewer goods are going to be barcode in the near future and so the appetite for using RFID would increase. On the other hand, since more larger hospitals and healthcare centers are getting into the RFID based systems and more producers are taking steps to go ahead in using tags instead of barcodes therefore it is the force of industry that make this community hospital decide on using RFID-based systems, sooner rather than latter.

Putting forward a strategy named “stepwise strategy of accepting new technology” this community hospital may survive and fight for its long term existence. The reality is that the hospital owner cannot spend a large amount of money on RFID based systems but can get into a partially RFID-based system. This is especially true when a community hospital owns more than one branch in its surrounding community. In this case, one or two of these branches can come to picture for the RFID systemization.

The stepwise strategies can be stated as below: “accept the new technology preferably not in full at first, rather in steps, until it is fully completed”. To implement this stepwise strategy the question that might be asked is with what sort of system a small family owned hospital or similar health centers should start? Generally speaking, a new technology based system can be broken into a mix of RFID and Barcode type systems and named as 10%RFID, 20%RFID, …, 100%RFID where 20%RFID system means a system that has 20% RFID capability with 80% barcode capability. It is obvious enough that an organization cannot fully accept RFID in all branches of its business at once but it can manage to partially equip some of its branches or locations. Does a mix of RFID-Barcode-based system help to manage a community hospital better?

The aim of this research is to employ grey theory approach for evaluating RFID-based systems and determining the most appropriate system among them. The practicality of the proposed model is demonstrated using a sample case study. To check the results obtained by the proposed approach, data are collected and the quantitative strategic planning matrix (QSPM) decision making technique for strategy selection is employed. For model validation purposes, TOPSIS and Simple Additive Weighting (SAW) multi criterion decision making techniques are employed.

The rest of this paper is structured as follows: Section 2 is about the background on multi criteria decision making, strategy prioritization, RFID-based systems, and grey theory. Section 3 describes research methodology while preliminaries which include grey theory and grey number comparison is described in Section 4. Section 5 discusses the case study used to show the model implication in real world situations. Study validation is the topic of Section 6. Author's discussion and conclusion is given in Section 7.