The Software Vendor Selection Models

Published Date: 02 Nov 2017

INTRODUCTION:

AutoNexus is a company which provide solution to automotive industry. AutoNeus is a company having staff of around 350. The company is growing very fast and the problem company is facing now is the integration of its software for supporting its business operations. AutoNexus is considering implementation of ERP software for its business but the problem is that, there are lot of vendor who provide ERP software. So which vendor’s software is good for the company is the biggest problem. Because the cost of implanting ERP software is not less, they truing to focus on every possible criterion which will affect their company’s future growth. They are planning to do a long term investment on the software which gives them the expected result. They are focusing on software which can be managed by the employees and they can accept that software in their day to day working. AutoNexus also want the participation of its selected employees at the time of ERP implementation so that they know what has been done after the consultant has been gone.

3 BIGGEST VENDORS OF ERP:

SAP ERP: SAP is one of the big names in ERP for decades and credited with founding the technology. Director of marketing for SAP’s business suite believes that his company and oracle are the top names in ERP.

ORACLE: JD Edwards became a part of the Oracle family of applications in 2005. The system is offered in an on-premise and a web-based version and includes human resources, business intelligence and inventory management functionality.

MICROSOFT: Microsoft Dynamics GP supports more than 40,000 customers. It is a feature-rich system with hundreds of industry-specific add-ons from Microsoft partners.

MANAGERIAL DECISION:

While there are number of different ways to evaluate how ERP software matches an organization’s business requirements, we should consider the following things. The whole ERP software selection procedure is characterized by the following steps:

Identifying the characteristics or criteria that the ERP software being purchased must have (internal variables or "WHAT") in order to meet the Company’s Needs (CNs)

Identifying the technical attributes of the ERP software (TAs) relevant to vendor assessment (external variables or "HOW")

Determining the relative importance of the "WHATs"

Determining the "WHAT"-"HOW" correlation scores and constructing the HOQ

Determining the weight of the "HOWs"

Preparing the matrix for correlating the "HOWs"

Determining each potential vendor’s impact on the attributes considered ("HOWs")

Drawing up the final ranking on the FSI (fuzzy suitability index)

SOFTWARE VENDOR SELECTION MODELS:

LITERATURE REVIEW:

Baudry and Vincent 2002 [5]( Baudry, M. and Vincent N. (2002). multi criteria Decision Making using multiattribute utility theory. Retrieved on 4th january 2007 from http:www.univ-tours.fr/ed/edss/comm 2002/baudry,pdf) view decision analysis as a paradigm in which an individual decision maker or decision group contemplates a choice of action in an uncertain environment.

software selection embeds an art of multi criteria decision making in which the best software

package has to be selected from a set of options. In the new global economy, as Hmlinen

et al (2003) [36]( Hmlinen, R. P., Mustajoki, J. and Alanaatu, P.(2003). Smart swaps Smart choices with the even swaps method. Helsinki University of Technology: Computer software,

Systems Analysis Laboratory, Helsinki University of Technology. Retrieved on May,

10th, 2006 from http://www.smart-swaps.hut.fi) found , software selection decision making occurs across multiple physical locations more frequently and it is a very important decision.

According to Gulfem and Gulcin (2007) [31]( Gulfem, I. and Gulcin B. (February 2007). Using a multi-criteria decision making approach to evaluate mobile phone alternatives.Computer Standards & Interfaces ,29 (2): 265-274), Multi criteria decision making (MCDM) refers to screening, prioritising, ranking, or selecting a set of alternatives under usually independent, incommensurate or conflicting criteria. According to Salo et al, (2003) [84]( Salo, A., Gustafsson, T. and Ramanathan, R. (2003). multi criteria methods for

technology foresight. Journal of Forecasting, 22 (2): 235-255.) Chou et al (2004) [18]( Chou Y., Lee, C., and Chung, J. (2004). Understanding m-commerce payment systems through the analytic hierarchy process. Journal of Business Research, 57 (12): 14231430), MCDM concerns breaking a problem down into its constituent parts or components, in the framework of a hierarchy or a feedback network, and establishing importance or priority to rank the alternatives in a comprehensive and general way to look at the problem mathematically

Scoring model is Developed in 1970, the Fair Isaac Corporation introduced a method to measure the ‘creditworthiness’ of an individual. It took into account multiple factors like the length of an individual’s credit history, recent ‘hard’ enquiries, credit usage ratio, etc while calculating the score. The FICO formula and scoring model remains a closely guarded secret to this day.

The NGT is an approach( Potter M, Gordon S, Hamer P. (2004). The Nominal Group Technique: A useful consensus methodology in physiotherapy research. New Zealand Journal of Physiotherapy 32(3) 126-130.) that was first described in the 1960’s as a procedure to facilitate effective group decision-making in social psychological research (Delbecq and van de Ven 1971). Since that time it has been applied in a wide range of felds including;education,health, social service, industry, and government organisations. The three most typical applications have been problem identification, development of solutions, and establishing priorities (Carney et al 1996, Delbecq and van de Ven 1971, Gallagher et al 1993, Jones and Hunter 1995, Justice and Jang 1990, O’Neil and Jackson 1983, Thomas 1983).

SCORING MODEL: Scoring model is a formula that assigns points based on known information to predict an unknown future outcome. The most well known example of a scoring model is the credit score or FICO score. There are many types of scoring models used in the financial service industry. They are,

Credit scoring models

Behaviour scoring models

Collections scoring models

Revenue scoring models

There are certain steps needs to be followed in scoring model for vendor selection. They are,

Preliminary review of all vendor proposals: Before the vendor selection team starts its evaluation and selection process, all proposals must be reviewed for completeness and clarity. Any omissions and ambiguities should be clarified by the submitting vendor. This will ensure that the evaluation and selection process will be through and efficient.

Record business requirements and vendor requirements: On a spreadsheet list the business requirements and then vendor requirements that were compiled in the first step is mentioned. A through and detailed listing of all requirements is essential in order to arrive at a fair and equitable decision.

Assign importance value for each requirements: For each of the requirements assign an "Importance Value" using a scale from one to ten, where 1 is important. If the vendor selection team cannot agree upon an importance value, then accumulate everyone's individual value and calculate an "average" across all members. Use the average score of all submitted values from the team as the Importance Value for that requirement.

Assign a Performance Value for Each Requirement: This step may be the longest and most drawn out process of the entire vendor selection process. The team will need to assign a "Performance Value" that they believe that each vendor performs on each of the requirements. If the team cannot agree upon a performance value, then accumulate everyone's individual value and calculate an "average" across all members. If a team member feels they are not qualified to render an opinion on a certain requirement, they may abstain from submitting a value. Use the average score of all submitted values from the team as the Performance Value for that requirement for that individual vendor. If a requirement is indicated to be "Pass/Fail" and the team agrees that the individual vendor has not met the requirement, that vendor can be immediately removed from further consideration.

Calculate a total performance score: Now you have an "Importance Value" for each requirement and a "Performance Score" for each vendor on each requirement, you can calculate a Total Performance Score for each vendor. Calculate the Total Performance score by multiplying the individual Importance Value by the vendor's Performance Value. Total the sum of all an individual vendor's Performance Score to arrive at a Total Performance Score for the vendor.

Select the winner vendor: The Total Performance Score is not meant to be an absolute value of determination of a vendor's proposal. It is to be used as a guide to highlight differences between vendors and meaningful discussion between team members.

ADVANTAGES:

These models allow multiple criteria to be used for evaluation and decision making including profit/profitability models and both tangible and intangible criteria.

They are structurally simple and so easy to understand and use.

They are a direct reflection of managerial policy.

They are easily altered to accommodate changes in the environment or managerial policy.

Weighted scoring models allow for the fact that some criteria are more important than others.

These models allow easy sensitivity analysis. The trade-offs between the several criteria are readily observable.

DISADVANTAGES:

The output of a scoring model is strictly a relative measure. Project scores do not represent the value or utility associated with a project and thus do not directly indicate whether or not the project should be supported.

Scoring models are linear in form and the elements of such models are assumed to be independent.

The ease of use of these models is conducive to the inclusion of a large number of criteria. Most of which have such small weights that they have little impact on the total project score.

Unweighted scoring models assume all criteria are of equal importance which is almost certainly contrary to fact.

To the extent that profit/profitability is included as an element in the scoring model.

NOMINAL GROUP TECHNIQUE: "A weighted ranking method that allows a group to generate and prioritize a large number of issues within a structure that gives everyone an equal voice." The nominal group technique (NGT) is a decision making method for use among groups of many sizes, who want to make their decision quickly, as by a vote, but want everyone's opinions taken into account (as opposed to traditional voting, where only the largest group is considered)[1] {^ Dunnette M D., Campbell J. D, and Jaastad K., (1963). "The Effect of Group Participation no Brainstorming Effectiveness for Two Industrial Samples, Journal of Applied Psychology, XLVII (February), 30-37.}. The method of tallying is the difference. First, every member of the group gives their view of the solution, with a short explanation. Then, duplicate solutions are eliminated from the list of all solutions, and the members proceed to rank the solutions, 1st, 2nd, 3rd, 4th, and so on.

When to use Nominal Group Technique?

When some group members are much more vocal than others.

When some group members think better in silence.

When there is concern about some members not participating.

When the group does not easily generate quantities of ideas.

When all or some group members are new to the team.

When the issue is controversial or there is heated conflict.

Procedure of NGT

Divide the people present into small groups of 5 or 6 members, preferably seated around a table.

State an open-ended question

Have each Person spend several minutes in silence individually brainstorming all the possible ideas and jot these ideas down.

Have the groups, collect the ideas by sharing them round robin fashion (one response per person each time), while all are recorded in key term, on a flipchart. No criticism is allowed, but clarification in response to questions is encouraged.

Have each person evaluate the ideas and individually and anonymously vote for the best ones (for example, the, best idea gets Points, next best 4 Points, etc).

Share votes within the group and tabulate. A group report is prepared, showing the ideas receiving the most points.

Allow time for brief group presentations on their solutions.

ADVANTAGES:

Generates a greater number of ideas than traditional group discussions.

The nominal group technique is more structured than the traditional focus group approach.

Diminishes competition and pressure to conform, based on status within the group.

Encourages participants to confront issues through constructive problem solving.

Allows the group to prioritize ideas democratically.

You can get a sense of priority concerns represented among the members of the group by using the nominal group technique.

The nominal group technique can be used with small groups as well as with a larger number of participants.  

Reduces the number of issues.

All team members participate.

Rank orders items.

DISADVANTAGES:

Requires preparation.

Is regimented and lends itself only to a single purpose, single-topic meeting.

Minimizes discussion, and thus does not allow for the full development of ideas, and therefore can be a less stimulating group process than other techniques.

While there is a range of group sizes with which the nominal group technique can be used, it is hard to implement it effectively with large audiences unless very carefully planned beforehand.

ANALYTIC HIERARCHY PROCESS: The analytic hierarchy process (AHP) for decision structuring and decision analysis was first introduced by Saaty [23] {[23] T. L. Saaty, The Analytical Hierarchy Process: Planning, Priority Setting, Resource Allocation. New York: McGraw-Hill, 1980.}and is one of the best known and most widely used approaches. Harkerand Vargas [10, p. 1383] states that "AHP is a comprehensive framework which is designed to cope with the intuitive, the rational, and the irrational when we make multiobjective, multicriterion and multifactor decisions with and without certainty for any number of alternatives."{[10] P. Harker and L. Vargas, "The theory of ratio scale estimation: Saaty’sanalytic hierarchy process," Management Science, vol. 33, no. 11, pp.1383–1403, 1987.} It allows users to assess the relative weight of multiple criteria or multiple options against given criteria in an intuitive manner. In case quantitative ratings are not available, policy makers or assessors can still recognize whether one criterion is more important than another. Therefore, pair wise comparisons are appealing to users. Saaty established a consistent way of converting such pair wise comparisons (X is more important than Y) into a set of numbers representing the relative priority of each of the criteria. The analytic hierarchy process (AHP) is a structured technique for organizing and analyzing complex decisions. It has particular application in group decision making,[1]{^ a b Saaty, Thomas L.; Peniwati, Kirti (2008). Group Decision Making: Drawing out and Reconciling Differences. Pittsburgh, Pennsylvania: RWS Publications. ISBN 978-1-888603-08-8.}and is used around the world in a wide variety of decision situations, in fields such as government, business, industry, healthcare, and education.

Hierarchies in the AHP:

Procedure of using AHP:

Structuring a decision problem and selection of criteria.

Priority setting of the criteria by pair wise comparison (weighing).

Pair wise comparison of options on each criterion (scoring).

Obtaining an overall relative score for each option.

ADVANTAGES:

Flexible, intuitive appeal to the decision makers and its ability to check inconsistencies

It decomposes a decision problem into its constituent parts and builds hierarchies of criteria. Here, the importance of each element (criterion) becomes clear.

It helps to capture both subjective and objective evaluation measures. While providing a useful mechanism for checking the consistency of the evaluation measures and alternatives, AHP reduces bias in decision making.

It supports group decision−making through consensus by calculating the geometric mean of the individual pair wise comparisons.

It is uniquely positioned to help model situations of uncertainty and risk since it is capable of deriving scales where measures ordinarily do not exist.

DISADVANTAGES:

This method can be considered as a complete aggregation method of the additive type. The problem with such aggregation is that compensation between good scores on some criteria and bad scores on other criteria can occur. Detailed, and often important, information can be lost by such aggregation.

With AHP the decision problem is decomposed into a number of subsystems, within which and between which a substantial number of pair wise comparisons need to be completed. This approach has the disadvantage that the number of pair wise comparisons to be made, may become very large (n (n−1)/2), and thus become a lengthy task.

FUZZY MULTIPLE CRITERIA DECISION-MAKING METHOD:

A fuzzy goal programming approach is applied in this paper for solving the vendor selection problem with multiple objectives, in which some of the parameters are fuzzy in nature. A vendor selection problem has been formulated as a fuzzy mixed integer goal programming vendor selection problem that includes three primary goals: minimizing the net cost, minimizing the net rejections, and minimizing the net late deliveries subject to realistic constraints regarding buyer's demand, vendors' capacity, vendors' quota flexibility, purchase value of items, budget allocation to individual vendor, etc.

The triangular fuzzy number and linguistic variable are the two main concepts used to assess the preference ratings of linguistic variables, ‘importance’ and ‘appropriateness’. The decision makers can employ an assumed weighting set W＝ {Very Low, Low, Medium, High, Very High} to assess the relative importance of various criteria. And use the linguistic rating set S＝ {Very Poor, Poor, Fair, Good, Very Good} to evaluate the appropriateness of the alternatives versus various criteria. The membership functions of linguistic values in the weighting set W and the linguistic rating set S can be represented by approximate reasoning of triangular fuzzy numbers. It is shown in the following table.

Procedure of fuzzy set theory:

Construction of hierarchical structure

Form a committee of decision makers, and then identify the evaluation criteria and alternative capabilities of company.

Construct the hierarchical structure of company through the concept of balanced scorecard.

Evaluation of the importance weight of each Criterion

Use fuzzy Delphi method to determine the fuzzy number of pooled weight of each criterion.

Construction of linguistic scales for linguistic variables

Choose the appropriate preference ratings for the importance weight of the evaluation criterion

Select the appropriateness ratings for alternatives under sub-criteria.

Aggregation of fuzzy appropriateness indices

Aggregate the weight of sub-criterion to get the aggregated weight Wt.

Pool the decision makers’ opinions to get the aggregated fuzzy rating Sit of alternative Ai under each sub-criterion 2Ct.

Aggregate Sit and 2Wt with respect to each sub-criterion to obtain the fuzzy appropriateness indices Ri for all alternatives.

Computation of fuzzy overall evaluation

Aggregate polled weight（1Wt）of criteria with fuzzy appropriateness indices（Ri）to obtain the fuzzy overall evaluation（Fi）of each alternative.

Defuzzification of fuzzy overall evaluation

Calculate the ranking value UT （Fi ）by defuzzifying the fuzzy overall evaluation through ranking method.

Analysis and decision

Choose the capability of motion picture company with the maximal ranking value

ANALYTIC NETWORK PROCESS METHOD: The Analytic Network Process (ANP) is a general form of the AHP [18]. {[18] L. M. Meade and J. Sarkis, "Analyzing organizational project alternatives for agile manufacturing processes: An analytical network approach," Int. J. Prod. Res., vol. 37, no. 2, pp. 241–261, 1999.}It issued in multi-criteria decision analysis. AHP structures a decision problem into a hierarchy with a goal, decision criteria, and alternatives, while the ANP structures it as a network. Both then use a system of pair wise comparisons to measure the weights of the components of the structure, and finally to rank the alternatives in the decision. The basic structure is an influence network of clusters and nodes contained within the clusters. Priorities are established in the same way they are in the AHP using pair wise comparisons and judgment. Many decision problems cannot be structured hierarchically because they involve the interaction and dependence of higher-level elements in a hierarchy on lower-level elements. Analytic Hierarchy Process (AHP) is more suitable when the hierarchical levels are independent of each other and Analytic network Process (ANP) is used when there are factors, which are dependent of the other factors in the same hierarchical levels or other levels.

Analytic Hierarchy Process (AHP) is more suitable when the hierarchical levels are independent of each other and Analytic network Process (ANP) is used when there are factors, which are dependent of the other factors in the same hierarchical levels or other levels. The ANP has been applied to a large variety of decisions: marketing, medical, political, social, forecasting and prediction and many others. Its accuracy of prediction is impressive in applications that have been made to economic trends, sports and other events.

METHODOLOGY:

Delphi Method: The Delphi method consists of a group of experts’ judgment by means of successive iterations of a given questionnaire. The questionnaire classifying usually ask the participants to use Likert scale. The survey usually takes two or three rounds before getting consensus of participants. The first round focuses on the subject and participants contribute additional information. After collecting the first round questionnaire, the analysis and statistic summary of the responses are needed. The second round or rounds is generated based on the feedback of the last questionnaire. Through the process of reaching an understanding of how the group views an issue, during this phase, the participants are given an analysis and told that most experts’ judgments on this round. According to other experts’ judgments, the participants can reconsider their previous opinions and see if they want to change their opinions or if they have any additional clarifications or new ideas on the question. Because the survey usually takes few rounds, if the consensus is reached clearly, it will be unnecessary to process more rounds.

PROCESS OF ANP:

Model construction and problem formulation.

Questionnaire surveys and expert preference integration.

Establishment of the pair-wise comparison matrixes.

Calculation of the eigenvalue and eigenvector.

Consistency test.

Computations of super matrixes.

Computations of limit super matrixes.

Selection of best alternatives.

NON-LINEAR PROGRAMMING MODELS AND GOAL-PROGRAMMING MODELS:

NON-LINEAR PROGRAMMING MODEL: Optimization problems that involve nonlinearities are called nonlinear programming (NLP) problems. It is the process of solving a system of equalities and inequalities, collectively termed constraints, over a set of unknown real variables, along with an objective function to be maximized or minimized, where some of the constraints or the objective function are nonlinear.[1]{^ Bertsekas, Dimitri P. (1999). Nonlinear Programming (Second ed.). Cambridge, MA.: Athena Scientific. ISBN 1-886529-00-0}.

A general optimization problem is to select n decision variables x1, x2, . . . , xn from a given feasible region in such a way as to optimize (minimize or maximize) a given objective function

f (x1, x2, . . . , xn)

of the decision variables. The problem is called a nonlinear programming problem (NLP) if the objective function is nonlinear and/or the feasible region is determined by nonlinear constraints. Thus, in maximization form, the general nonlinear program is stated as:

Maximize f (x1, x2, . . . , xn),

subject to:

g1(x1, x2, . . . , xn) ≤ b1,

.

.

.

.

.

.

gm(x1, x2, . . . , xn) ≤ bm,

where each of the constraint functions g1 through gm is given.

Unconstrained nonlinear programming is the mathematical problem of finding a vector x that is a local minimum to the nonlinear scalar function f(x). Unconstrained means that there are no restrictions placed on the range ofx.

Nonlinear Programming

Constrained nonlinear programming is the mathematical problem of finding a vector x that minimizes a nonlinear function f(x) subject to one or more constraints.

CHARACTERISTICS OF NLP:

Difficult to solve

Optimal solutions are not necessarily at corner points

There are both local and global optimal solutions

Solution may depend on starting point

Starting point is usually arbitrary

You can solve unconstrained nonlinear programming problems with MATLAB and Optimization Toolbox, which includes the following algorithms:

Quasi-Newton: uses a mixed quadratic and cubic line search procedure and the Broyden-Fletcher-Goldfarb-Shanno (BFGS) formula for updating the approximation of the Hessian matrix

Nelder-Mead: uses a direct-search algorithm that uses only function values (does not require derivatives) and handles no smooth objective functions

Trust-region: used for unconstrained nonlinear problems and is especially useful for large-scale problems where sparsely or structure can be exploited

Optimization Toolbox includes four algorithms to solve constrained nonlinear programming problems:

Interior-point: especially useful for large-scale problems that have sparsity or structure

Sequential quadratic programming (SQP): solves general nonlinear problems and honors bounds at all iterations

Active-set: solves problems with any combination of constraints

Trust-region reflective: solves bound constrained problems or linear equalities only

GOAL PROGRAMMING MODEL: Goal programming is a branch of multiobjective optimization, which in turn is a branch of multi-criteria decision analysis (MCDA), also known as multiple-criteria decision making (MCDM). This is an optimization programme. It can be thought of as an extension or generalisation of linear programming to handle multiple, normally conflicting objective measures. Each of these measures is given a goal or target value to be achieved. Unwanted deviations from this set of target values are then minimised in an achievement function. This can be a vector or a weighted sum dependent on the goal programming variant used. As satisfaction of the target is deemed to satisfy the decision maker(s), an underlying satisficing philosophy is assumed.

Goal programming is a special extension of linear programming {(Charnes &Cooper, 1961; Ijiri, 1965)} that is capable of handling decision problems that deal with a single goal with multiple subgoals as well as problems with multiple goals with multiple subgoals {(Ijiri, 1965)}

Goal programming is used to perform three types of analysis:

Determine the required resources to achieve a desired set of objectives.

Determine the degree of attainment of the goals with the available resources.

Providing the best satisfying solution under a varying amount of resources and priorities of the goals.

ADVANTAGES:

It is simple and easy to use.

It can handle relatively large numbers of variables, constraints and objectives.

There is always a solution to the problem in this model even if some goals are conflicting.

It does not require very sophisticated solution procedure.

Linear goal programming problems can be solved by easily available linear programming routines.