AWERProcedia Information Technology & Computer

1 downloads 0 Views 403KB Size Report
ERP business productivity evaluation by using multi criteria ... A survey on 78 end users in 42 companies in manufacturing industry took place. Keywords: Enterprise Resource Planning; End-User Satisfaction; End-User Productivity Insight; ...
AWERProcedia Information Technology & Computer Science 1 (2012) 245-249

2nd World Conference on Information Technology (WCIT-2011)

ERP business productivity evaluation by using multi criteria decision making among end users in Turkish manufacturing industry Turan Erman Erkana, Babak Daneshvar Rouyendeghb a,b

Department of Industrial Engineering, Atılım University, Ankara P.O .Box 06836, Turkey

Abstract Information systems play a vital role in improving the competitiveness of organizations. There are numerous enterprise information systems available in the market. One of them is enterprise resource planning (ERP). ERP can provide major improvements in efficiency across a company, but only when implemented correctly and digested by end users perfectly. That is why end user training plays a significant role during and after implementation. In this research, authors try to measure how end users of ERP applications describe the impact of different ERP applications on their business productivity by using Analytic Hierarchy Process (AHP). A survey on 78 end users in 42 companies in manufacturing industry took place Keywords: Enterprise Resource Planning; End-User Satisfaction; End-User Productivity Insight; Perceived Usefulness; Analytic Hierarchy Process Selection and peer review under responsibility of Prof. Dr. Hafize Keser. ©2012 Academic World Education & Research Center. All rights reserved.

1. Introduction As the markets become more competitive, organizations seek for new business opportunities to enhance their competitiveness. Often, organizations focus on improving their agility, such as the speed at which they can respond to consumers, improve service, enhance product quality and improve production efficiency. It is commonly accepted that information technology should be used to fundamentally change the business [1].

a

ADDRESS FOR CORRESPONDENCE: Turan Erman, Erka n , Department of Industrial Engineering, Atılım University, Ankara P.O .Box 06836, Turkey. E-mail address: [email protected] / Tel.: + 90-312-586 83 51; fax: 90-312-586 80 91.

Turan Erman Erkan / AWERProcedia Information Technology & Computer Science (2012) 245-249

Many organizations, therefore, seek to improve their competitiveness by utilizing advanced information technology, such as Enterprise Resource Planning (ERP) systems. Due to competitive pressures resulting from globalization, corporations increasingly need more effective total enterprise solutions like the ERP system. The ERP system enjoys its present popularity because of its apparent capacity to improve operational and business efficiency [2,3]. ERP is a cross-functional enterprise system that integrates and automates many of the internal business processes of a company, particularly those within the manufacturing, logistics, distribution, accounting, finance, and human resource functions of the business. Therefore, typical objectives of ERP system projects are standardization and integration of key business processes across the value chain and the creation of a platform for integrated and enhanced managerial planning, control, and analysis [4–6]. ERP projects have some critical success factors and most of the important ones are: user involvement, frequent communication with the users and a great interface for the end users [7]. An implementation project can achieve technical, budgetary and schedule success, but if the users of the technology do not use it, the intended benefits for implementing that technology are not likely to be obtained. Thus, this study will examine organizational factors that lead to the acceptance and use of the technology. User acceptance of information technology, especially complex technology such as an ERP system, is an extremely important phenomenon that is worthy of study by information systems researchers [8,9]. Moreover the business efficiency [2,3], Shiau et al. [10] have listed a set of possible benefits, including: cost savings from reducing the inventory; reduced workforce, travel costs, and communication costs; increased returns from financial assets; integration of several functional areas for faster retrieval or delivery of information and reports; improving the accuracy or reliability of information; allowing previously unfeasible applications to be implemented; improving organizational processes, management information for strategic planning, and information for management control; speeding up transactions and shortening product cycles; enhancing employee productivity or business efficiency; enabling the organization to respond more quickly to change; changing the way the organization conducts its business; improving customer relations; providing new and/or better products or services to customers; enhancing competitiveness or creating strategic advantage; and enabling the organization to catch up with competitors 2. Method 2.1. Participants This research is a comparative case study of ERP end users’ perceived productivity on the company. It is based on the Iansiti’s research on ERP end users’ productivity perception [11]. Authors of this paper, try to measure how end users of ERP applications describe the impact of Software A and Software B applications on their business productivity. Therefore authors framework of the study coincides with Iansiti’s one. A survey on 78 end users in 42 companies in manufacturing industry took place. Data were collected from 78 end-users were requested to fill AHP Survey. The survey filled on electronic forms in order to test the consistencies. The framework was developed by leveraging a combination of industry standard usability tests and exhaustive research into the roles and responsibilities of end users across Sales & Marketing, Finance and Operations department areas. Usability tests such as the Software Usability Measurement Inventory (SUMI) were evaluated to identify typical factors impacting application usability and end-user business productivity. SUMI is a widely regarded, industry standard usability methodology developed and refined over the past 15 years by software usability experts at the Human Factors Research Group at University College Cork, Ireland. It is designed to evaluate satisfaction and user productivity with software applications and is tool commonly employed in the software development community [11]. This analysis of typical user responsibilities and industry standard usability tests such as SUMI has illustrated that business productivity is a function of six factors. The business productivity framework is comprised of usability, familiarity, transactional efficiency, flexibility, business insight, and collaboration. The scope and context of those six factors are as follows:  Collaboration: Ease of collaboration with colleagues, efficiency of application workflow, ease of 246

Turan Erman Erkan / AWERProcedia Information Technology & Computer Science (2012) 245-249

    

communication with suppliers, partners, customers. Business Insight: Ease of comprehensive reporting, real-time access to information, visibility across departments. Flexibility: Agility in handling unexpected issues, ease of completing infrequent or unusual tasks, system adaptability to business needs. Usability: Ease of use, user ‘command’ of application, user enjoyment with software. Familiarity: Ease of learning, intuitiveness of system, user comfort with application. Transactional Efficiency: User effectiveness in executing repetitive tasks, efficiency of user interface, speed and reliability of system

2.2 Analytical Hierarchy Process (AHP ) The AHP has a special concern with departure from consistency and the measurement of this departure, and with dependence within, and between, the groups of elements of its structure; it has found its widest applications in multi-criteria decision-making in planning and resource allocation, and in conflict resolution. In its general form, the AHP is a non-linear framework for carrying out both deductive and inductive thinking without the use of syllogisms. This is made possible by taking several factors into consideration simultaneously, allowing for dependence and for feedback and making numerical trade-offs to arrive at a synthesis or conclusion [12]. The AHP proposed by Saaty [13] is a flexible, quantitative method for selecting among alternatives based on their relative performance with respect to one or more criteria of interest Boroushaki, et.al [14]. The AHP resolves complex decisions by structuring the alternatives into a hierarchical framework. The hierarchy is constructed through pair-wise comparisons of individual judgments rather than attempting to prioritize the entire list of decisions and criteria simultaneously. This process generally involves six steps Vahidnia et.al [15]: 1. Define the unstructured problem, stating clearly its objectives and outcomes; 2. Decompose the complex problem into decision elements; 3. Employ pair wise comparisons among decision elements to form comparison matrices; 4. Use the eigenvalue method (or some other method) to estimate the relative weights of the decision elements; 5. Calculate the consistency properties of the matrices to ensure that the judgments of decision-makers are consistent; and 6. Aggregate the weighted decision elements to obtain an overall rating for the alternatives. The AHP techniques form a framework of the decisions that uses a one-way hierarchical relation with respect to decision layers. The hierarchy is constructed in the middle level(s), with decision alternatives at the bottom, as shown in Figure 1. The AHP method provides a structured framework for setting priorities on each level of the hierarchy using pair-wise comparisons that are quantified using a 1-9 scale as demonstrated in Table 1.

247

Turan Erman Erkan / AWERProcedia Information Technology & Computer Science (2012) 245-249

Decision Goal

Criterion 1 (C1)

...... ....

Criterion j (Cj)

...... ....

Criterionm(C m)

Alternative 1(A1)

...... ....

Alternative i (Aii)

...... ....

Alternative n (An)

Figure 1. Hierarchy for a typical three-level MCDM problem [16]

Table 1. The 1-9 Fundamental Scale of Absolute Numbers Importanc e intensity 1 3 5 7 9 2,4,6,8

Definition

Explanation

Equal importance Moderate importance of one over another Strong importance of one over another Very strong importance of one over another Extreme importance of one over another Intermediate values

Two activities contribute equally to the objective Experience and judgement slightly favour one over another Experience and judgment strongly favour one over another Activity is strongly favoured and its dominance is demonstrated in practice Importance of one over another affirmed on the highest possible order Used to represent compromise between the priorities listed above

3. Result When the AHP method is applied, the result score is always ‘the-bigger-the-better’. As seen in Table 2, the ERP A (0,66) the top score due to its highest efficiency and performance. The ERP B (0,34) has the lowest score, and is ranked in the second and last place. Table 2: The AHP ranking score ERP Ranking score

4.

Software A 0.66

Software B 0.34

Conclusion

This study serve to focus attention on the interplay between application ease of use and business performance and to highlight the impact applications can have on end user productivity. The business productivity measurement framework used in this study provides a useful mechanism to gauge the perceptions of actual end users. While doing so, AHP-based decision analysis process took place among two ERP softwares. This study provides a framework to assess how well applications meet user needs along six major dimensions: Usability, Familiarity, Transactional Efficiency, Flexibility, Business Insight, and Collaboration by using AHP method. In fact it is a framework for evaluating end user’s satisfaction and productivity on ERP softwares. From

248

Turan Erman Erkan / AWERProcedia Information Technology & Computer Science (2012) 245-249

the surveys that hold on AHP technique among 78 users and 42 firms; ERP A has a score (0,64) and ERP B has (0,36). This study could aid both academicians and industry especially top managers who want to invest on ERP, and this framework proves valuable for those considering application purchases, and that it encourages the industry as a whole to dedicate itself to furthering end-user productivity in all its dimensions. 5.

Future Study

The future study may have another Multi Criteria Decision Making (MCDA), like Fuzzy AHP or a joint comparative one like AHP-FAHP-TOPSIS. The latter one would be preferable because of analyzing so many MCDM tools at the same time and their accuracy. References [1]. T. H. Davenport, Mission critical: realizing the promise of enterprise systems. Boston, MA: Harvard Business School Press (2000) [2]. W. Wei-Wen, Segmenting and mining the ERP users’ perceived benefits using the rough set approach, Expert Systems with Applications 38, pp6940–6948 (2011). [3]. S. W. Chou, & Chang, Y. C.,The implementation factors that influence the ERP benefits. Decision Support Systems, 46(1), 149–157, (2008). [4] V. Botta-Genoulaz, P.-A. Millet, B. Grabot, A survey on the recent literature on ERP systems, Computers in Industry 56 510–522 (2005). [5] D. James, M.L. Wolf, A second wind for ERP, The McKinsey Quarterly 2 (November) 100–107 (2000). [6] K. Maxwell, Executive study assesses current state of ERP in paper industry, Pulp & Paper (October) 39–48 (1999). [7] E. J Umble,. R. R Haft,. and M. M Umble, “Enterprise resource planning: Implementation procedures and critical success factors” European Journal of Operational Research 146 pp. 241–257 (2003). [8] V. Venkatesh, and F. D. Davis, A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies. Management Science, 46(2), 186–204 (2000). [9] Kwasi Amoako-Gyampah, Perceived usefulness, user involvement and behavioral intention: an empirical study of ERP implementation Computers in Human Behavior 23 1232–1248 (2007) [10]. W. L. Shiau, P. Y. Hsu and J. Z Wang, Development of measures to assess the ERP adoption of small and medium enterprises. Journal of Enterprise Information Management, 22(1/2), 99–118 (2009). [11]. M. Iansiti, ERP End-User Business Productivity: A Field Study of SAP & Microsoft, Keystone Strategy (2007). [12]. T.L. Saaty, L.G. Vargas, Decision Making With The Analytic Network Process. Spr. Science, LLC: 1-23 (2006). [13]. T.L. Saaty, The analytich hierarchy process. McGraw-Hill, New York (1980). [14. S.Boroushaki, J. Malczewski, Implementing an extension of the analytical hierarchy process using ordered weighted averaging operators with fuzzy quantifiers in ArcGIS. Computers & Geosciences, 34, 399-410 (2008). [15]. M.H.Vahidnia, A.A Alesheika, A. Alimohammadi, Hospital site selection using AHP and its derivatives. Journal of Environmental Management, 90, 3048-3056 (2009). [16]. Y.M. Wang, J. Liu, T.M.S. Elhag, An integrated AHP-DEA methodology for bridge risk assessment. Com. Ind. Engineering. 1-13 (2007).

249