A multiattribute spreadsheet model for manufacturing technology justification

Computers ind. Engng Vol. 21, Nos l~l, pp. 29-33, 1991 Printed in Great Britain. All rights reserved

0360-8352/91 $3.00+0.00 Copyright © 1991 PergamonPress pie

A MULTIATTRIBUTE SPREADSHEET MODEL FOR MANUFACTURING TECHNOLOGY JUSTIFICATION Adedeji B. Badiru, Bob L. Foote, and Joseph Chetupuzha School of Industrial Engineering University of Oklahoma, Norman, OK 73019

ABSTRACT This paper presents a multiattribute spreadsheet models for the justification of manufacturing technology. The model includes interactive macro modules for AHP, Utility Model, and System Value Model. IblTRODUCTION Manufacturing productivity can be improved by implementing new manufacturing technologies at strategic points in a manufacturing system. Previous failures of advanced manufacturing technologies were caused by improper adoption and implementation strategies. The failure of many Flexible Manufacturing Systems (FMS) projects to perform at projected productivity levels is a good evidence of the need to justify technology before implementation. As pointed out by several authors (Canada and Sullivan 1989; Troxler and Blank 1989; Badiru 1990a), the major impediment to adopting a new technology in the appropriate place is the cost justification requimmant. A reliable methodology for evaluating a manufacturing technology for specific oI~rations is essential to the full exploitation of the recent advances in technology. Figure 1 shows some of the prevailing justification methodologies. PROBLEM BACKGROUND Manufacturing economic analysis is the process of evaluating manufacturing system alternatives. The conventional methods of engineering economic analysis are based on quantitative measures of worth of an alternative. In manufacturing systems, many tangible and intangible, quantitative and qualitative factors intermingle to compound the decision making process. Consequently, a more comprehensive evaluation methodology is needed. Such a methodology would integrate both objective and subjective approaches such as conventional present value analysis, analytic hierarchy process (Saaty 1980), utility models (Keeney and Raiffa 1976), and system value model (Troxlcr and Blank 1989). These integrated evaluation methodologies are simple and yet very powerful. Unfortunately, their implementation in real-life problems is impeded by the tedious computational process they require. It is helpful to have a computer-based tool that can be used to quickly evaluate the various aspects of prospective manufacturing technologies so that a timely decision can be made. DEVELQPMENT APPROACH A multiattribute spreadsheet model has been developed to address the problem stated above. The program contains modules for analytic hierarchy process (AHP), Utility Model, and System Value Model. The multidimensional structure of the spreadsheet model is useful in representing the multitude of attributes that am encountered in the cost justification of manufacturing technology. Spreadsheet models have the following advantages:

FORECONOMIJUSTIFICAllON C

MODELS

f NPV ROI IRR Ptyto~k

Vaduo Anady~

korlng

L AltP

Manlhematk:al

~

Rlank AnaW~

goal Stocha~lProgrammi c ng

Figure 1. Different Justification Methodologies 29

f Todmlad IINInms

Coml~ltlon R&D

30

•

• •

Proceedings of the 13th Annual Conference on Computers and Industrial Engineering They offer an environment for performing sensitivity analysis. They are familiar to most management analysts and decision makers. They have built-in routines for making graphical plots for visual assessment.

The spreadsheet model was developed with Lotus 1-2-3 macro language and can handle up to five manufacturing technology alternatives and five attributes at the same time. It permits the inclusionof both monetary and nonmonetary factors in the analysis. Figure 2 shows the design layout of the system.

Spreadsheet Model

I~"

USERINTERFACE oat, attributes,etc.

l

DecisionParameters

r

Which UtilityModels Model SystemValue To Use? AHP

Analysis ~ Quantltatlve w"-I and Qualitative

Figure 2. Design Layout for the Justification Model SAMPLE RUNS The examples below illustrate the use of the spreadsheet model Suppose we have the objective of selecting the best overall technology out of three feasible alternatives. The alternatives are to be compared on the basis of factors or attributes that the organization considers to be very important. Such factors may be determined based on goals relating to productivity, quality, customer service, and so on (Badiru 1990b). For the purpose of this illustration, five attributes are used. The data is presented below: Alternative h Technology type I Alternative 2: Technology type II Alternative 3: Technology type III

Attribute A: Attribute B: Attribute C: Attribute D: Attribute E:

Training requirements Flexibility Serviceability Quality Productivity

Due to space limitations, only the examples for AHP and Utility Models are presented in this paper. However, the mathematical basis for the system value model is presented. Screen 1 shows the program options: System Value, Utility Model, and AHP.

f £I : INFORMATION

1 ~

UTILITY

£VALUATION

MODEL

OF A L T E R N A T I V E S

AHP P R O C E S S

USING

THE VARIOUS O P T I O N S A V A I L A B L E ARE ................................. GENERAL

SYSTEM

UTILITY

AHP 13~-Jan-91 k..

12:54

INFORMATION

QUIT

SHEET M O D E L S

:

ON THE MODELS

V A L U E MODEL

MODELS

PROCESS PM

SPP~EAD

RETURN

: C h o o s e option l

and hit R e t u r n

key

Screen 1. Methodologies Available for Comparing Alternatives

Badiru et al.: Justification of Manufacturing Technology

31

Analvtic Hierarchv Process The fL,'ststep in the AHP procedure involves developing relative weights for the five attributes with respect to the specified objective. To obtain the relative weights, the attributes are compared pair-wise with respect to their respective contributions to the objective. The pair-wise comparison is done through subjective evaluation by the decision maker(s). After the relative weights of the attributes are obtained, the next step is to evaluate the alternatives on the basis of the attributes. In this step, relative evaluation rating is obtained for each alternative with respect to each attribute. The procedure for the pair-wise comparison of the alternatives is similar to the procedure for comparing the attributes. The attribute weights are combined with the local weights of the alternatives to obtain the overall relative weights of the alternatives as shown below:

% = ~(wikii) where: = o v e r a l l weight of Alternativej

Wi

= relative weight for Attribute i

ko

= local weight for Alternativej with respect to Attribute i

w~kij

= g l o b a l weight of Alternativej with respect to Attribute i.

Screen 2 shows the pairwise comparisons of the five attributes. Table 1 shows the summary of the final AHP analysis for the example. The three technology alternatives are rated on the basis of all five attributes. It is seen that Alternative III is the best choice. g ~40:

U

l

MATRIX

OF P A ~ R E D C O H P ^ R I S O ~ B

.............................................. ATTRIBUTES

A

B

C

D

E

.............................................................

0.33

A

TRAINING

1.00

B

FLEXIBILITY

3.00 .... 1.00

C

SERVICEABILITY

0*2D

0.17

D

QUALITY

0.17

0.14

0.33

E

PRODUCTIVITY

0.20

0.17

4.57

1.8~

~UM 2 l-Jan-91

11:24

l

AM

:

5.00

6.00

5.00

6.00

7.00

6.00

1.00

3.00

1.00

1.00

0.25

1.00 13~3

4.00

1.00

21.00

13.25

l

M

Screen 2. Pairwise Comparison of Attributes

Table 1. Summary of AHP for Decision Aid Alternatives AUribute,s

wi :=~

A

B

C

D

E

i=l

i=2

i=3

i=4

i=5

0.288

0.489

0.086

0.041

0.096

Technologyj

k#

Technology I Technology2 Technology3

0.093 0.211 0.685

0.118 0.243

ColumnSum

1.000

0~)

0.639

0.500 0.250 0.250

0.633 0.106 0.260

0.562 0.375 0.063

0.208 0.244 0.548

1.000

1.000

1.000

1.000

1.000

32

Proceedings of the 13th Annual Conference on Computers and Industrial Engineering

Utility Models Four alternatives and five attributes are used to illustrate the module for utility models. It is assumed that the user already knows the appropriate utility functions (or subjective estimates) for each alternative with respect to the objective and the desired attributes. The utility of any combination of outcomes of n attributes is expressed as:

U(x)

=

U(x~,x 2..... x . )

where: Xi

U(x)

= specific outcome of attribute i (i = 1, 2 ..... n) = hypothesized utility of the set of outcomes to the decision maker

Utility is entered into the program on a scale of 0.0 to 1.0. Screen 3 shows the relative scaling factors assigned to the five attributes and the relative utilities of the alternatives with respect to each attribute. The sum of the particular set of scaling factors for the attributes is 1.57. Thus, the multiplicative utility model rather than the additive model was used. Screen 4 shows the histogram of the result of the utility model example. The overall relative ratings of the four alternatives are 0.791, 0.757, 0.864, and 0.891. It is seen that Alternative 4 is the best choice in this case. f ii01:

0.9

MODEL

SCALING FACTOR

UTILITY FOR ALTERNATIVES

ALT.I ALT.2 ALT.3 ALT.4 ......................................................

~y

0.25

0.95

0.7

0,31

0.6

0.61

0.5

0.85

0.36

0.3

0.56

0.91

0.74

0.40

0.67

0.45

0.65

0.7

0,56

0.6

0.25

22-Jan-91

0.51

0.2

0.7 1

m

06:36 PM

k.

Screen 3. Utilities of Alternatives with Respect to Attributes

G,9

-

0.8-

kl.I

._1 I-D =

0.70.6-

0.50.40.30.20. IOALT. I

ALT.2

ALT.3

ALT.4

ALT.5

Screen 4. Histogram of Relative Utilities of the Alternatives

System Value Model Under the system value methodology, the attributes are first weighted on the basis of their relative importance to the organization. Each alternative is then rated with respect to each attribute. An overall rating for each alternative is then developed by using the model presented by Troxler and Blank (1989): TSV = f ( A p A 2..... Ap) where: TSV = technology system value Ai = vector of quantitative measures or attributes (i= I, 2 ..... p) f(.) = hypothesized function (e.g. simple additive function)

Badiru et al.: Justification of Manufacturing Technology

33

Examples of system attributes are Quality, Performance, Throughput, Capability, Productivity, and Net Pre sent Value. Attributes are considered to be a combined function of "factors," xi, expressed as:

mk A,Ixl,x2 ..... xm,) =

Y. fi(xi) i=1

where

{x~} f,

= set of m factors associated with attribute A k (k = 1, 2 ..... p) = contribution function of factor xi to attribute Ak

Examples of factors are Market Share, Reliability, Flexibility, User Acceptance, Capacity Utilization, Safety, and Functionality. Factors are themselves considered to be composed of "indicators," vi, expressed as: Xi(V 1, V 2 ..... Vn)

where {vj} zi

---- j =~l Zi(Vi)

= set of n indicatorsassociated with factorxl (i= I, 2 .....m) = scaling function for each indicator variable vj

Examples of indicators are debt ratio, responsiveness, lead time, learning curve, and scrap volume. The above variables are combined to obtain a composite measure,f(.), of the value of a technology alternative.

The implementation of advanced manufacturing technology involves large initial investments. Many companies cannot afford this level of investment unless it can be fully justified. Justifying manufacturing technology is a complex task which requires the consideration of several factors both tangible and intangible. To improve the decision making process, it is necessary to include all relevant factors in the justification process. Keeping this in mind, a multi-attribute spreadsheet model has been developed. The model is interactive and user friendly. The program can help improve the utilization of existing analytical tools for making technology investment decisions.

Badiru, Adedeji B., "A Management Guide to Automation Cost Justification," Industrial Engineering, Vol. 22, No. 2, Feb. 1990a, pp. 26-30. Badiru, Adedeji B., "Systems Integration for Total Quality Management," Engineering Management Journal, Vol. 2, No. 3, Sept. 1990b, pp. 23-28. Canada, John R. and William G. Sullivan, Economic and Multiattribute Evaluation of Advanced Manufacturing Systems, Prentice-Hall, Englewood Cliffs, NJ, 1989. Keeney, R. L. and H. Raiffa, Decisions with Multiple Objectives: Preferences and Value Tradeoffs, John Wiley & Sons, Inc., NY, 1976. Saaty, Thomas L., The Analytic Hierarchy Process, McGraw-Hill, New York, 1980. Troxler, Joel W. and Lcland Blank, "A Comprehensive Methodology for Manufacturing System Evaluation and Comparison," Journal of Manufacturing Systems, Vol. 8, No. 3, 1989, pp. 176-183. ABOUT THE AUTHORS Dr. Adcdeji B. Badiru, P.E., is a member of the industrialengineering faculty at the University of Oklahoma. He holds BS and M S in IndustrialEngineering and M S in Mathematics from Tennessee Technological University and PhD in IndustrialEngineering from the University of Central Florida. He is the author of ProjectManagement in Manufacturingand High TechnologyOperations,John Wiley, 1988, Computer Tools,Models,and Techniquesfor Project Management, TAB Books, 1989; and Project Management Tools for Engineering and Management Professionals, liE Press, 1991. He is a member of IIE, SME, PMI, AAAI, ORSA, and TIMS.

Dr. Bob Foote, P.E., is a professor in the School of Industrial Engineering at the University of Oklahoma. He received his BS and MS degrees in Mathematics and his Phi) in Industrial Engineering from the University of Oklahoma. His research interests are in applied operations research, plant and production planning, inventory models/MRP, and quality control/assurance. Dr. Foote is a registered professional engineer in Oklahoma. He is Associate Director of the Oklahoma Center for Integrated Design and Manufacturing. He is a member of liE and ORSA. Mr. Joseph Chetupuzha is a graduate student in the school of industrial engineering at the University of Oklahoma. He received his BE degree in Electrical and Electronics Engineering from Annamalai University, India. His areas of interest include economic analysis and expert systems. He is a member of IIE.