Journal of Materials Processing Technology 76 (1998) 289–294

An optimisation model for information systems

K.A. Reynolds a, C.E.R. Wainwright a, D.K. Harrison b,*a The CIM Institute, Cranfield Uni6ersity, Cranfield, Bedford, MK43 0AL, UK

b Department of Engineering, Glasgow Caledonian Uni6ersity, Glasgow, G4 0BA Scotland, UK

Received 27 May 1997; received in revised form 12 June 1997

Abstract

The previous decade has witnessed major change within the information systems (IS) environment with a correspondingemphasis to the importance of specifying timely and accurate information strategies. Hence, this paper responds to this emphasisvia the development of a model to aid the formulation of information technology (IT) strategy. The model is suitable for allorganisation’s who seek to plan long term investment strategies. The paper initially describes the conceptual nature of the modelprior to a disclosure of the optimising process. The optimisation method uses a linear goal programming (LGP) mathematicalapproach to analyse IT investment projects through the multi-variable optimisation of business goals. The paper concludes witha discussion of the validation of the model. © 1998 Elsevier Science S.A. All rights reserved.

Keywords: Information systems; Business goals; Optimisation; Goal programming

1. Introduction

During the last 20 years the role of informationtechnology (IT) in organisations has shifted from oneof operational gain to one of strategic benefit [1].Investments in computer system technologies are oftenseen as instrumental in sustaining and achieving com-petitive advantage, and therefore, IT strategies are con-sidered fundamental to enable organisations achievetheir strategic business objectives and goals. However,whilst there have been advances in technology andrefinement in most organisations’ structure, informa-tion systems (IS) integration is still extremely complexmainly due to the comprehensive technologies of differ-ent ages owned by different process members. Conse-quently, there are still difficulties prioritising andachieving the expected benefits from IT investment. Arecent survey [2] highlighted the three most importantIT issues facing UK manufacturing:� integration of information;� gaining more benefit from IT;� better control of production.

Often the problem is the identification of potentialinvestment targets that will generate optimum benefit toa whole process. A short term attitude still prevails asorganisation’s select those projects which generatebenefit over short periods, in order to reduce risk offailure. Unfortunately, this attitude is contradictory tothe long term strategic nature of IS and IT strategy.Therefore, there is clearly a need to develop an ap-proach which applies multi-variable optimisation to aidorganisations achieve strategic benefits from IT invest-ment selection.

2. Conceptual model

To satisfy the need to achieve IT integration a pro-cess is proposed which seeks to aid the development ofIT strategies. The process is based upon a three stagemodel in which each stage has individual objectives, yetall three operate as an integrated whole. The processesare: audit, assessment and suggestion. An overview ofthe complete model is given before explaining the opti-misation process in greater detail. Fig. 1 illustrates theconceptual nature of the model and indicates the re-quired inputs and the outputs generated.* Corresponding author.

0924-0136/98/$19.00 © 1998 Elsevier Science S.A. All rights reserved.

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K.A. Reynolds et al. / Journal of Materials Processing Technology 76 (1998) 289–294290

Fig. 1. IT integration conceptual model.

2.1. Audit

The audit process examines the existing engineeringprocess using mathematical analysis techniques. Theseapply the principles of queuing theory to identify andanalyse information flows across the engineering pro-cess. Information arrival and service times are ex-pressed as statistical distributions. The informationservice system is assumed to be a single server queuewith a first come, first served queue discipline.

The three most common statistical distributionswhich describe arrival and service times are the negativeexponential (or random) distribution, the deterministicdistribution and the Erlang distribution. The Erlang isa more general distribution which splits itself into anumber of phases. This can exhibit characteristics of a

deterministic statistical distribution as the number ofphases tend to zero or can exhibit characteristics of arandom distribution as the number of phases tend toinfinity. The Kendall notation [3] for a single serverqueuing system which follows an Erlang distributionfor both its arrival and service times is expressed asEk/El/1, where k is the number of phases in the Erlangarrival time distribution and l is the number of phasesin the Erlang service time distribution. Average waitingtimes in units of average service time can be calculatedusing steady state queuing formulae [4], using appropri-ate values for the utilisation of the system (u), l and k(as defined earlier). Subsequently, values of averagewaiting time for intermediate values of utilisation (u)and the degree of variability in the arrival and servicetime distributions can also be determined based on

K.A. Reynolds et al. / Journal of Materials Processing Technology 76 (1998) 289–294 291

Table 1Benefits appraisal of each project

3 4Project 1 2 5

— 1Reduced material cost (£’000) 2 4—— 2—3Reduced inventory (£’000) 5

22Reduced operating cost (£’000) 3 — 46 —Improved sales volume (£’000) 10 2 3

2—1Improved profit margin (£’000) ——

6 10 3 13Total benefit (£’000) 20

Table 3Selections based on a single objective: to maximise benefit

Cost (£’000)Benefit (£’000) Risk (units)Projects se-lected

1, 2, 3, 4 39 29 1128 101, 2, 3 36

1036 301, 4, 5

products will typically have random information flowcharacteristics as they cannot accurately predict cus-tomer demand or product development life-cycles. Bycomparison an organisation which produces a range ofproducts which are modular in nature can more readilyforecast market demand and product development life-cycles and subsequently has more deterministic infor-mation flows. By using Ingham and Harrington’scategorisation [5], a hypothetical ideal process based ondeterministic and random information flows can beproposed. This gives an ideal process guideline basedon the operational practice of that organisation. Theassessment of IT investments is based on the degree theexisting process is transformed into the ideal process.

Potential IT investments are introduced into the pro-cess appraisal. They are described in terms of proposedarrival and service times and any significant differencein labour hours. These values are used to estimate thebenefit generated by the IT investment. Estimatedbenefits take one of two forms: operational benefit orstrategic benefit. Operational benefit concentrates onproductivity performance and is similar to investmentappraisals based on cost reductions. The assessment ofstrategic benefit is measured by improvements in salesvolume and profit margin. These values can also beused in conjunction with industry sales forecasts thusdetermining the expected impression the investment hason a company’s market growth and market share.

The level of risk associated with particular ITprojects discourages their implementation and so is alsoassessed. By assessing the degree of risk associated withindividual projects, managers can determine whetherthe benefits are worth the risks. The risk level of ITprojects can be assessed and a risk profile created basedon the weighted average of several factors, for example

steady state Erlang queuing distributions (Ek/El/1).Each calculation can be replicated for each individualactivity in the process, thus providing an operationalprofile of the existing engineering process. These valuesare subsequently used in the assessment process.

2.2. Assessment

The assessment process determines an optimum in-vestment solution based on the company’s existing pro-cess, its hypothetical ideal process and the priorities ofbusiness goals. Business goals are created using businessobjectives and proposed target levels for cost, benefitand the risk of each investment project. These goals areprioritised according to business preferences before us-ing algorithms to calculate an optimal solution.

Within the assessment process each investment pro-ject has to be assessed in terms of benefit, cost, risk andthe expected impact to the existing process. A hypothet-ical ideal process is used as a template to assess themerit of the existing engineering process and the impactinvestment scenarios have on the information flowswithin the process. The assessment of costs and benefitsare relative to the existing process, that is they are costsand benefits above the existing process as a result of aparticular IT investment. An appropriate cost of capitalis taken and the discounted cash flows may be deter-mined.

The ideal process proposed is based on the dataprovided in the audit process and engineering companycategorisation [5]. By categorising engineering compa-nies subject to their sales and production policies, theinformation flows in and out of the organisation can beclassified as being random or deterministic in nature.For example, a job shop which produces customised Table 4

Selections based on two objectives

Projects se- Risk (units) Cost (£’000)Benefit (£’000)lected

1, 3 8 30 25263391, 3, 4

9 29331, 5101, 2, 3 36 28

3010 361, 4, 5

Table 2Benefits, risks and costs of each project

1 5Project 3 42

Total benefit (£’000) 10620 1335 2 3Total Risk (units) 1 4

Total cost (£’000) 19 3 6 1 10

K.A. Reynolds et al. / Journal of Materials Processing Technology 76 (1998) 289–294292

project leader experience, project team size, commit-ment of user, familiarity with software and previoussuccess rate of similar projects. Different organisationshave different types of risk and will weight them ac-cordingly. The risk value used in the optimisationmodel is an integer value between one and five, whereone is no relative risk and five is high risk. Thisindicates the likelihood the project will fail.

2.3. Suggestion

The suggestion module uses the optimum selection ofIT projects from the assessment process. The suggestionthen provides a feedback loop into the assessmentmodule thereby allowing the amendment of businessgoals. This allows the simulation of different scenarios,thus identifying the variables which change a goodproject into a poor performer. This can be useful whendeveloping a longer term IT strategies or investigatingsome of the implementation risks.

3. Optimisation algorithms

At the heart of the assessment process is the optimi-sation model’s selection of IT projects based on theprioritisation of business goals. The prioritisation isbased on the mathematical approach of linear goalprogramming (LGP). LGP differs from traditional lin-ear programming (LP) as it addresses multiple objec-tives instead of one single, linear objective function [6].The limitation of a single objective makes LP unsuit-able for the optimisation of business decisions whichare usually made in complex environments involvingmultiple and often conflicting goals and objectives. ALGP model optimises goals rather than objectives. Anobjective usually seeks to maximise or minimise a par-ticular function, whereas a goal seeks to optimise (max-imise/minimise) with respect to an assigned target level.The LGP model also has two types of constraints:system constraints and goal constraints. A system con-straint limits the decision variables and reflects therestrictions imposed on a problem. Goal constraints areobjectives that an organisation seeks to achieve. Increating a LGP model there are several steps:� All objectives are transformed into goals and as-

signed an appropriate target level.� Each goal is ranked in importance. System con-

straints always have the highest priority.� Deviational variables are assigned to each goal.

LGP assigns deviational variables to minimise theunwanted deviations between desired goals and possiblesolutions. The deviations from the objective functiontake two forms, positive deviations (d+ ) and negativedeviations (d− ) These deviation variables are mutually

exclusive, that is (d+ )�(d− )=0. Positive deviation(d+ ) variables represent over-achievement of a goaland negative deviation variables (d− ) represent under-achievement of the goal. If the aim is not to under-achieve the goal, (d− ) should be driven to zero.Likewise if the aim is not to overachieve the goal, (d+ )should be driven to zero.

In the optimisation model, there are N available ITinvestment projects. The objective is to select an opti-mal set of projects so as to achieve the targeted goalssubject to system constraints. Goals include maximisingthe total benefit derived from the selected projects andminimising the total risk of the portfolio of projects.The available financial resources available for invest-ment are the model’s system constraints.

Variables are denoted as xi where I=1, …, N, andthey correspond to the N IT investment projects avail-able for selection, that is xi=1 if project i is selected,and xi=0 otherwise.

The benefit related objective represents the totalbenefit derived from the implemented projects which isto be maximised. The benefit related objective functionhas the form:

Zb= %N

i=1

bixi

The risk related objective represents the total riskderived from the implemented project. This objective isto be minimised. The risk related objective functiontherefore has the form:

Zr= %N

i=1

rixi

The complete LGP model is formulated as the fol-lowing linear 0–1 goal programming problem:

Minimise Z=Pb(db−)+Pr(dr+)

subject to:

%N

i=1

rixi+ (dr+)− (dr−)=Tr

%N

i=1

bixi+ (db+)− (db−)=Tb

%(i�Ai)xi\= �Ai �

j�QA xi�{0, 1} i=1,…, N

where db− , db+, dr−, dr+ are non-negative continu-ous deviation variables; Tb\=0, Tr\=0 are thetarget values assigned to respective benefit and riskgoals; Pb\0, Pr\0 are the priorities assigned to therespective benefit and risk goals; bi\=0 the benefitderived from implementing investment project i alone;ri\=0 the risk of implementing project i ; the sets Aj,QA are subsets of {1, …, N} where N is an integer.

y

K.A. Reynolds et al. / Journal of Materials Processing Technology 76 (1998) 289–294 293

Table 5Selections based on three objectives

Cost (£’000) Increase in sales vol. Additional profitProjects Risk (units) Increase in profit marginBenefit (£’000)(£’000) (£’000)(£’000)

18 1 2001, 2, 3 2810 3630 13 2 1601, 4, 5 10 36

3.1. Application of the optimisation algorithm

To illustrate the workings of the optimisation al-gorithms, a simple example will be described. Assumein a typical process, for example, five investmentprojects may be proposed. The audit process will haveassessed the relative merit of each project, generatingthe results below. The benefit assessment of each pro-ject is outlined in Table 1 with the correspondingproject totals listed in Table 2.

Table 3 shows the results of a linear program whichselects investment projects based on a single objective.The objective is to maximise benefit given the totalbudget must not exceed £30 000. Projects 1, 2, 3 and 4are selected as they generate the greatest benefit.

A LGP differs from a LP as it compares multipleobjectives. For example, Table 4 shows project selec-tions based on two objectives:� maximise benefit so that it is greater than £30 000� minimise risk so that it is less than ten units.

The priority of these objectives affects the optimalselection. If maximising benefit is of greater importancethan reducing risk, projects 1, 2, 3, or projects 1, 4, 5,would be selected. Note that projects 1, 2, 3, 4, wouldnot be selected as the total risk is greater than ten units.However, if minimising risk has priority, projects 1, 3,would be selected, as they generate £30 000 of benefitwhilst minimising risk.

Table 4 shows two corresponding selections (projects1, 2, 3, and projects 1, 4, 5,) have equal values of riskand benefit whilst meeting the constraints. If moreobjectives are included, the selection can be refinedfurther. For example the selection of projects basedupon:� maximising benefit so that it greater than or equal to

£30 000� minimising risk so that it less than or equal to ten

units� maximising profit so that it greater than or equal to

£200 000Table 5 illustrates that projects 1, 2, 3 are the optimal

selection to meet these objectives. If the priority ofobjectives changes the optimal selection often changes.

Additional profit is based on a product price of

£1.00, the existing profit margin is 10% and the existingproduction quantity equals 200 units.

Projects 1, 2, 3= (18�11)+1%�200=200Projects 1, 4, 5= (13�12)+2%�200=160At the current stage of development the process can

select investment projects based on different objectivesand their priorities. Currently these are permutations ofoperational and strategic benefit, risk and cost. Theprocess has enough flexibility to relax system con-straints such as investment budgets in order to assessinvestment projects from a long term perspective. Theimpact of optimal selections for different objectives canthen be linked back to financial performance indicatorssuch as profit and loss account, financial ratios and thebalance sheet illustrating the expected impact differentinvestment scenarios have on business performance. Itis intended to develop the process further so that it canidentify and include mutually exclusive and mandatoryprojects and also examine the benefit and cost interde-pendencies between projects. For example, the reducedcosts of shared hardware and software and the benefitappraisal of infrastructure costs such as networks whenspread over several projects.

4. Conclusions

IT strategies are difficult to formulate as they requirethe integration of comprehensive technologies, whilstbalancing short term benefits with long plans for sus-tainable growth. Although information integrationtechnologies exist, there is a need for a tool whichidentifies potential areas where investment can achievemaximum benefit across the whole process. Conse-quently, this paper has proposed a process which iden-tifies potential investment areas based on operationsresearch principles. When investment projects are pro-posed, different scenarios are simulated according to anorganisation’s business objectives. This is a useful toolas it illustrates the future impact of investments on thefinancial performance of the organisation. This decisionbased tool performs a substantial quantity of calcula-tions, thereby helping managers to assess the develop-ment of their IT strategies, identify potentialimplementation risks and highlight areas to invest.

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