Using quality function deployment to conduct vendor assessment and supplier recommendation for business-intelligence systems

doi:10.1016/j.cie.2014.10.005

Computers & Industrial Engineering

Volume 84, June 2015, Pages 24-31

https://doi.org/10.1016/j.cie.2014.10.005 Get rights and content

Highlights

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A QFD (quality function deployment) based framework is presented.
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Evaluation criteria consist of marketing requirements and technical attributes.
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Various fuzzy MCDM (multi-criteria decision making) schemes are fused.
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Vendor assessment and supplier recommendation are accomplished.
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Supplier recommendation is conducted in an unsupervised way.
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BI (business intelligence) vendors are visualized to generate managerial insights

Abstract

Business intelligence (BI) has been recognized as an important enterprise information system to help decision makers achieve performance measurement and management. Generally, typical BI users consist of financial analysts, marketing planners, and general managers. However, most of them are not familiar with BI’s core technologies. In order to help corporate executives better assess BI vendors, evaluation criteria are separated into marketing requirements (MRs) and technical attributes (TAs), respectively. In particular, a fuzzy MCDM (multi-criteria decision making) based QFD (quality function deployment) is proposed as follows: (1) fuzzy Delphi is used to aggregate the performance scores of BI vendors, (2) fuzzy DEMATEL (decision making and trial laboratory) is conducted to recognize the causalities between MRs and TAs, and (3) fuzzy AHP (analytical hierarchy process) is employed to recommend optimal BI systems. For better benchmarking, the strengths and weaknesses of three competitive BI vendors (i.e. SAP, SAS, and Microsoft) are concurrently visualized through displaying a line diagram (in terms of TAs) and a radar diagram (in terms of MRs). More importantly, experimental results demonstrate that supplier assessment and supplier recommendation have been successfully accomplished.

Introduction

In recent years, rapid advances in information technologies, such as data warehousing and data mining, coupled with urging requirements on performance management and corporate diagnosis embarks the popularity of business intelligence (Chen, Chiang, & Storey, 2012). Different from the wave of “operational” enterprise resource planning (ERP), “strategic” business intelligence (BI) started to emerge as an umbrella in mid 1990s to cover software-enabled business planning, business analytics and integration with the area of big data. Specifically, the need to adopt ERP results from business process reengineering (BPR) while the main reason to implement BI originates from the concept of decision support systems (DSS). Referring to Eckerson (2003), the main benefits of adopting BI for an organization are summarized in Fig. 1 for reference.

According to Gartner’s report (Ravi, 2012), Fig. 2 demonstrates the top five key players in the BI market, including SAP (21.6%), Oracle (15.6%), SAS (12.6%), IBM (12.1%) and Microsoft (10.7%). Obviously, different players have their relative strengths and weaknesses on handling large volumes or high-dimensional big data, dealing with data velocity, data variety (structured and unstructured), and data visualization (dashboards and scorecards). As we know, SAP and Oracle already owns a huge market base in the ERP (enterprise resource planning) field. In addition, SAS is a well-known statistics package provider and Microsoft is the dominant player in the operating systems of personal computers. Today, owing to huge investment on enterprise resource planning (ERP), supply chain management (SCM), customer relationship management (CRM), and product lifecycle management (PLM), enterprise software selection has become much more important than before (Turban, Aronson, Liang, & Sharda, 2007). In particular, choosing software platform is quite different from buying products or services in many ways because software needs to be “maintained”, “updated”, and “repaired” (Büyüközkan and Feyzioğlu, 2005, Motwani et al., 2005).

In choosing an enterprise software package and planning for the overall project, managers or executives need to answer the following questions (Ngai et al., 2008, Tsai et al., 2012a, 2012b): (1) Why do you want to implement BI? (2) What are your business requirements? (3) What is your expected ROI (return on investment)? However, during the process of software implementation and customization, they are often frustrated in integrating legacy systems, identifying key performance indicators, and constructing a causal system to perform corporate diagnoses. Therefore, Turban, Sharda, Aronson, and King (2008) suggested considering the following questions prior to implementing the BI systems: (1) reporting what happened in the past, (2) analyzing why it happened, (3) monitoring what is happening now, (4) indicating which actions should be taken and (5) predicting what will happen in the future.

Needless to say, technical features are more easily measured than non-technical (marketing) features when assessing software/platform vendors. For convenience, a brief comparison between various information technologies is described in Table 1. In reality, typical BI users involve financial analysts, marketing planners, and general managers (Elbashir, Collier, Sutton, Davern, & Stewart, 2013). Usually, most of them may not have sufficient MIS/IT backgrounds. Based on the theory of TAM (technology acceptance model), software users do not care about whom they buy from, but they concern more about perceived usefulness and ease-of-use (Amoako-Gyampah, 2007, Chang et al., 2014). In order to highlight the importance of non-functional features, a QFD (quality function deployment) based framework is implemented in this context to consider two distinct aspects: marketing requirements (MRs) and technical attributes (TAs).

More importantly, this paper presents an integrated framework to help business planners conduct vendor assessment, supplier selection and product (software) recommendation. In particular, several critical issues are addressed as follows:

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By taking the interdependences between MRs and TAs into account, the importance weights of MRs and TAs are derived accordingly,
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To carry out supplier selection, the relative strengths and weaknesses of the competitive BI vendors are visualized and displayed in terms of MRs and TAs,
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User preferences for MRs are incorporated to conduct supplier recommendation in an unsupervised manner for accommodating the inexperienced BI users.

The remainder of this paper is organized as follows. Section 2 introduces vendor evaluation based on quality function deployment. Section 3 introduces the proposed framework composed of fuzzy DEMATEL, fuzzy Delphi, and fuzzy AHP. A real example to benchmark three representative BI vendors is illustrated in Section 4. Conclusions and future works are drawn in Section 5.

Section snippets

QFD based supplier assessment and software recommendation

By means of the quality function deployment (QFD), this study attempts to conduct supplier evaluation and recommendation in terms of two aspects, including marketing requirements and technical attributes. Quality function deployment (Akao, 1990) originated in Japan has been widely applied to numerous areas for product development, concept evaluation, service design, and competitor benchmarking. Generally, the QFD is characterized by a set of marketing requirements (MRs) associated with

The proposed framework

As it was mentioned earlier, evaluation criteria for assessing BI solution vendors are separated into two aspects, including marketing requirements (voice of customers) and technical attributes (voice of engineering). Thus, the framework of QFD is adopted in this study. In order to accommodate human linguistic properties (see Table 3), fuzzy MCDM schemes are incorporated into the conventional QFD and details of the presented framework are described as follows (see Fig. 4 and Table 2):

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Initially,

An illustrated example

Referring to Fig. 2 again, the top five BI vendors are sequentially listed as SAP, Oracle, SAS, IBM, and Microsoft (Ravi, 2012). By considering the status of Taiwan’s most companies, three vendors including SAP, SAS, and Microsoft are selected to conduct vendor assessment. After consulting IT experts, evaluation criteria composed of five MRs and twelve TAs is demonstrated in Table 4. In order to enhance the reliability of this survey, more than half questionnaires were sent to the IT/MIS

Conclusions

Today, business analytics and business intelligence has become a popular enterprise information system to significantly improve information quality and decision timeliness. Typical BI users involve financial analysts, marketing planners, and general managers. Unfortunately, most of them may not have sufficient IT backgrounds. In order to help these users communicate with MIS executives, this study presents a systematic framework to connect marketing requirements with technical attributes. In

References (37)

K. Amoako-Gyampah
Perceived usefulness, user involvement and behavioral intention an empirical study of ERP implementation
Computers in Human Behavior
(2007)
C. Araz et al.
Supplier evaluation and management system for strategic sourcing based on a new multicriteria sorting procedure
International Journal of Production Economics
(2007)
J. Bobadilla et al.
Recommender systems survey
Knowledge Based Systems
(2013)
J. Chai et al.
Application of decision-making techniques in supplier selection: A systematic review of literature
Expert Systems with Applications
(2013)
C.T. Chen et al.
A fuzzy approach for supplier evaluation and selection in supply chain management
International Journal of Production Economics
(2006)
I. Erol et al.
A methodology for selection problems with multiple conflicting objectives and both qualitative and quantitative criteria
International Journal of Production Economics
(2003)
X. Geng et al.
An integrated approach for rating engineering characteristics’ final importance in product-service system development
Computers and Industrial Engineering
(2010)
W. Ho et al.
Multi-criteria decision making approaches for supplier evaluation and selection: A literature review
European Journal of Operational Research
(2010)
D.J.-F. Jeng et al.
Social influence on the use of clinical decision support systems: Revisiting the unified theory of acceptance and use of technology by the fuzzy DEMATEL technique
Computer and Industrial Engineering
(2012)
M. Kumar et al.
A fuzzy goal programming approach for vendor selection problem in a supply chain
Computers and Industrial Engineering
(2004)

Y.T. Lin et al.

A novel hybrid MCDM approach for outsourcing vendor selection: A case study for a semiconductor company in Taiwan

Expert Systems with Applications

(2010)

D.R. Liu et al.

Hybrid approaches to product recommendation based on customer lifetime value and purchase preferences

The Journal of systems and Software

(2005)

J. Motwani et al.

Critical factors for successful ERP implementation: Exploratory findings from four case studies

Computers in Industry

(2005)

E.W.T. Ngai et al.

Examining the critical success factors in the adoption of enterprise resource planning

Computers in Industry

(2008)

M.N. Shaik et al.

Comprehensive performance measurement and causal-effect decision making model for reverse logistics enterprise

Computer and Industrial Engineering

(2014)

T.N. Tsai

Selection of the optimal configuration for a flexible surface mount assembly system based on the interrelationships among the flexibility elements

Computer and Industrial Engineering

(2014)

W.H. Tsai et al.

Selecting management systems for sustainable development in SMEs: A novel hybrid model based on DEMATEL, ANP, and ZOGP

Expert Systems with Applications

(2009)

C.H. Wang et al.

Using quality function deployment for collaborative product design and optimal selection of module mix

Computers and Industrial Engineering

(2012)

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Using quality function deployment to conduct vendor assessment and supplier recommendation for business-intelligence systems

Highlights

Abstract

Introduction

Section snippets

QFD based supplier assessment and software recommendation

The proposed framework

An illustrated example

Conclusions

Computers in Human Behavior

International Journal of Production Economics

Knowledge Based Systems

Expert Systems with Applications

International Journal of Production Economics

International Journal of Production Economics

Computers and Industrial Engineering

European Journal of Operational Research

Computer and Industrial Engineering

Computers and Industrial Engineering

Expert Systems with Applications

The Journal of systems and Software

Computers in Industry

Computers in Industry

Computer and Industrial Engineering

Computer and Industrial Engineering

Expert Systems with Applications

Computers and Industrial Engineering