A multicriteria decision making model for assessment and selection of an ERP in a logistics context Teresa Pereira and Fernanda A. Ferreira
Citation: AIP Conference Proceedings 1863, 050004 (2017); doi: 10.1063/1.4992201 View online: http://dx.doi.org/10.1063/1.4992201 View Table of Contents: http://aip.scitation.org/toc/apc/1863/1 Published by the American Institute of Physics
A Multicriteria Decision Making Model for Assessment and Selection of an ERP in a Logistics Context Teresa Pereira1,a) and Fernanda A. Ferreira2,b) 1
Algoritmi Center, Minho University, Campus Gualtar, 4710-057 Braga, Portugal and Polytechnic Institute of Porto, CIDEM, ISEP, Rua Dr. António Bernardino de Almeida, nº431, 4200-072, Porto, Portugal
2
Polytechnic Institute of Porto, Applied Management Research Unit (UNIAG), Rua D. Sancho I, 981, 4480-876 Vila do Conde, Portugal a)
Corresponding author:
[email protected] b)
[email protected]
Abstract: The aim of this work is to apply a methodology of decision support based on a multicriteria decision analyses (MCDA) model that allows the assessment and selection of an Enterprise Resource Planning (ERP) in a Portuguese logistics company by Group Decision Maker (GDM). A Decision Support system (DSS) that implements a MCDA – Multicriteria Methodology for the Assessment and Selection of Information Systems / Information Technologies (MMASSI / IT) is used based on its features and facility to change and adapt the model to a given scope. Using this DSS it was obtained the information system that best suited to the decisional context, being this result evaluated through a sensitivity and robustness analysis.
INTRODUCTION Business information systems have come in recent years to establish itself as an essential tool in the increasingly competitive market where companies operate. Information Systems (IS) Management is clearly one of the major challenges facing companies currently derived from the pressure to achieve higher levels of individual and collective productivity, with consequent optimization of existing processes and structural changes needed. Differentiation, constant innovation, the demand for value-added service and provide customer experience are key factors in the ability to ensure the viability and sustainability of business enterprises. Systems and Technologies of Information tailored to the needs of the companies have an active role in creating the conditions necessary for businesses to become much stronger and more competitive. Information technology (IT) has evolved a lot in recent years and is increasingly present in the day-to-day lives. Businesses have also been affected by these developments. The IS have the power to change the way businesses work, making the most prepared organizations to operate in a competitive market. Every day brings new applications and solutions that organizations can use to improve their efficiency and productivity. The aims of this paper is to developed an IS/IT selection model for logistics scope using the MMASSI/TI decision Support System (DSS) to support the selection of an Enterprise Resource Planning (ERP) identified as necessary to improve the organization's processes and customer service logistics operation in a Logistics Services Portuguese company.
THEORICAL FRAMEWORK AND PRIOR RESEARCH MCDA is a problem solving methodology that organizes and synthesizes the information regarding a given decision problem in a way that provides the decision maker with a coherent overall view of the problem. MCDA methods assist decision maker in the process of identifying the most preferred action(s), from a set of possible alternative actions (explicitly or implicitly defined), when there are multiple, complex, incommensurable and often conflicting objectives (e.g. maximize quality and minimize costs), measured in terms of different evaluation criteria. The alternative actions distinguish themselves by the extent to which they achieve the objectives, since usually none of the alternatives has the best performance for all objectives [1]. There are several different MCDA methods all support by DSS. The most worldwide known is the AHP [2] from American school and PROMETHEE [3] and ELETRE families [4] from European schools. Different in methodology nature, all presenting vantages and disadvantages in DM process, [5].
International Conference of Numerical Analysis and Applied Mathematics (ICNAAM 2016) AIP Conf. Proc. 1863, 050004-1–050004-4; doi: 10.1063/1.4992201 Published by AIP Publishing. 978-0-7354-1538-6/$30.00
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The MMASSI / IT is a group decision support system (GDSS) that aims to support decision making in the selection of IS / IT towards alternatives in complex cases by conflicting goals [5,7], which differs from other software of this type, because of the consistent and complete set of features / attributes that characterize a set of alternatives predefined. Despite this methodological feature is a GDSS that allows flexibility as it allows making a change to a different scope context by adjust of the criterion set to a coherent and consistent family of criteria. The number of criteria and sub-criteria, despite being already defined for the given context, in a comprehensive and coherent way, is not limited. It is allowed the not selection, modification and addition of new criteria [6, 7].
MODEL, METHODOLOGY AND DATA The MMASSI/TI is a multicriteria methodology to support the alternatives selection and choice that has been designed to be easy to understand and use, without a specific support of a decision making analyst, to offer the GDM an effective support decision-making tool and to act as enhancer of the specification accuracy. This purpose the methodology intends to be simple so that the GDM can be lead through it considering the fallowing steps [7]: First step: define the consistent and coherent family of criteria in consensus by the GDM (to do that, full description and specification of the decision scope must be done to reduce the criterion to a set of relevant, measurable, independent and concise set of criteria named as coherent and consistent family of criteria to the scope problem). This step is time consuming due the alternatives’ gather information need. Second step: analyse/add and validate each criterion description and how to measure it by the GDM until a consensus understanding be reached. Third step: set up the definition of a “neutral” IS and of a “better” IS in the business and organizational context of the analysis, being the alternatives’ reference levels. Fourth step: set up the collective relative importance criterion weight assigned by the GDM, in accordance with swing weight procedure. Fifth step: define a continuous scale, defined as Sj, with seven semantic correspondence levels (S3-: Much Worst, S2-: Worst, S1-: Slightly Worst, S0: Neutral, S1+: Slightly Better, S2+: Better and S3+: Much Better). Two of them are reference levels to evaluate each alternative on each criterion: the Neutral level and the Better level. The Neutral and Better levels fully definition by GDM is mandatory. This interval scale is fully defined by the GDM. It is a fixed scale that will be applied to all alternative’s evaluation on each criterion. Sixth step: adjust the “Neutral” IS and “Better” IS definition for each criterion. Seventh step: assess each alternative on each criterion. To do so, first the GDM must be aware of the existing information about each alternative per criterion consensually, the GDM must attribute a semantic level to each alternative, taking into considerations the two reference levels, and assign a collective value in accordance with the previously defined continuous semantic scale. Eighth step: use an additive model to rank each alternative global score. Finally, MMASSI/TI presents the IS ranking order and respective score value. Ninth step: perform sensitivity and robustness analysis. Both are predefined to: set all criterion weight equal on both phases; set all criterion equal in a phase and maintain the criterion assign weight in the other; vary each criterion value independently Tenth step: view or print methodology report and sensitivity and robustness report with the coherent and consistent family of criteria, it full description, criterion weight procedure, scale definition. Neutral and better IS reference levels description, alternatives’ values on each criterion, additive model and alternatives global score. And results of robustness analysis and each sensitivity analysis.
Data Collection and Model Development Logistics is a shared service, the activities of this department are to manage stock levels in the warehouse, orders to suppliers, the reception and expedition of materials, management of serial equipment (new, recycled and returned), management of payroll and invoiced materials, partners stock management, suppliers stock management, Quality of Service (QOS’s) indicators management defined by the customer such as time limits for storing and packaging equipment, management of new-damaged equipment and non-conformities. The scope of this project arose from the inability of the current IS meet the needs of the activities listed. Add that the existing IS shows a little compatibility with client systems, little flexibility to develop new features and limited to a growing database
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emerged the project of selecting an information system to respond to the increased volume of business, data complexity, requirements in the processing of information and process reengineering. The methodology was applied to real logistics services Business Company that have a four months project to assess and select a new ERP to support its business and operational context. The decision depends on three macro factors: Operational, pertaining to the activities of logistics and repercussions in the back office of each client; Technical, involving the intervention of computer parameterization and interconnection of relevant internal and external systems; Financial, evaluated and validated by senior management taking into account the strategic planning of the company and pre-defined budget aspect.
Evaluation Criteria Based on pre-defined criteria, decision makers were asked to adapt it to the decisional context of the company with validation and customization of these criteria and sub-criteria, giving rise to Table 1. Code A2 A3 A4 A8 A9 A10 A11 A14 A16
A17 A18
TABLE 1. Criteria and Sub-criteria for Validated Decisional Context Sub criteria or Remarks Operationalization A2,1 - financial health of the supplier; Qualitative scale. (measures the A2,2 - Technological trends. technological innovation and risk on maturity) Cost Number of licenses; Value per year or contract Cost of adding module / individual module. Maintenance Annual cost of the same; Ratio: maintenance cost / Base company Analysis of Contract. (billing) Ability to integrate Measured by the index of shared entities to Qualitative scale data. (redundancy total entities; versus exploitation) Assessment of integration into customers. Training Training users; Training those responsible for Ratio: Quality / cost x no trainees forming requirements process improvement. Upgradeability Need: open system. Qualitative scale Needs development / Measured by time / specialist; Cost technician hour x number of hours x adaptation Consider predicting the evolution of the number of technical business development necessary to quantify. Facility External (WEB; EDI, etc.); Qualitative scale communication Internal (most common protocols). Portability (porting Qualitatively measures the degree of If the higher level previously specified capacity of IS / IT) integration (scale); Standards that enable value requires the same platform portability of data between different IS / IT (eg DDE, DBC, etc.) Language Pre-selection of software with equivalent Qualitative scale language to stakeholders. Implementation time Estimate in hours given by the supplier. No. of hours / technical Downtime of employees. Criteria Coefficient of risk
Presentation of Results Table 2 summarizes the results obtained by applying the additive aggregation model. The presented values were calculated automatically by the GDSS based on the inputs of the decision makers. The ERP option that corresponds to the requirements of the decision makers was the PRIMAVERA followed by PHC, SAP and finally NAV. Since some steps of the MCDA process can be permeated by subjectivity and uncertainty, the results were validated by performing a sensitivity and robustness analysis in order to determine how the final ranking of alternatives changes under different criteria weighting schemes. By the sensitivity analysis performed it is concluded that, regardless of the variation of the order, selection criterion remains the same. The robustness analysis result shows the same order of alternatives that the sensitivity analysis. This result allows increased confidence in the model developed.
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Criteria Relative value PRIMAVERA PHC SAP NAV
A3 0,15 50 40 -55 -50
A11 0,13 30 -65 -60 -60
A8 0,12 -30 -20 70 50
TABLE 2. Results of aggregation model A18 A14 A10 A2 A4 A16 0,12 0,10 0,09 0,07 0,07 0,06 50 50 30 48 40 40 40 25 45 45 30 50 -80 50 45 100 25 -40 -90 30 40 78 20 -60
A17 0,05 20 10 -30 -40
A9 0,03 -45 30 -25 -60
Global value 1 30,202 16,180 -3,525 -13,628
CONCLUSIONS The selection of an IS represents a paradigm shift for the processes toward information control and operational excellence. Currently there is a lot of investment in this area, fostering competition among peers, since in the present economic climate businesses need IT support to develop automated systems that reduce waste and thereby increase profit margins that foster their sustainable economic growth. Various IS support the logistics available on the market were analysed. This characterization allowed to gather essential pre-selection of products that can be considered for implementation in the company such as functionality, compatibility, limitations, technical support and other information. The evaluation, selection and validation of the criteria required the monitoring of the various processes involved in logistics for four months and was performed by the three decision makers in the presence and monitoring responsible for this project. At this stage, the IT department had a key role in the verification of technical aspects and ensuring the performance of the interface with the systems used in internal back office and by the client operational process. To obtain results of application of multicriteria model it was selected the MMASSI/IT GDSS due to its affordability, flexibility and adaptation to the decisional context. After the study of the ten steps of this application, it was triggered a sensitivity and robustness analysis to ensure the accuracy of the results, which direct to the implementation of PRIMAVERA because it has a more favorable cost-benefit ratio for the company. However the implementation of this software can bring some implications for the ability to customize the evolution of the organization and also the level of integration / compatibility with client software.
ACKNOWLEDGMENTS The authors thank Polytechnic Institute of Porto for its financial support.
REFERENCES 1. 2. 3. 4. 5. 6. 7.
J. Dodgson, M. Spackman, A. Pearman, and L. Phillips, Multi-criteria analysis: a manual (Technical report, Department of the Environment Transport and the Regions London, United Kingdom, 2000). Y. Dong, G. Zhang, W.-C. Hong and Y. Xu, Consensus models for AHP group decision making under row geometric mean prioritization method, Decision Support Systems, 49(3), 281-289 (2010). R. Vetschera and A. T. Almeida, A PROMETHEE-based approach to portfolio selection problems, Computers & Operations Research, 39(5), 1010-1020 (2012). N. Bojković, I. Anić, and S. Pejčić-Tarle, One solution for cross-country transport- sustainability evaluation using a modified ELECTRE method, Ecological Economics, 69(5), 1176-1186 (2010). Marcia Oliveira, Dalila B.M.M. Fontes and Teresa Pereira, Multicriteria Decision Making: A Case Study in the Automobile Industry. Annals of Management Science (AMS). Volume 3, Number 1, (2014), May 2014. Teresa Pereira, “Metodologia Multicritério para Avaliação e Seleção de Sistemas Informáticos ao Nível Industrial” PhD Thesis, University of Minho, 2003. Teresa Pereira and D.B.M.M Fontes, Group Decision Making for selection of an Information System in a Business Context. DA2PL’2012: From Multiple Criteria Decision Aid to Preference Learning. Mons, Belgic, November 15-16, 2012. Conference Proceedings, 74-82 (2012).
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