1. Factors to consider System component costs Methods
Selection Methods
Factors to consider
System component costs
Methods

2. FACTORS Cost: often hard to estimate Intangibles
If technology enabled without modifications, easier to estimate development costs
Still hard to know benefit impact
Intangibles
Market share, better customer service
Better corporate image
Better access to data

3. Hidden Outcomes Organizational power shifts Imposition of work methods
Get more done with fewer people
Big impact on how people work
Subject to technology changes

4. Hinton & Kaye (1996) Two capital budgeting approaches:
Capital investment
Apply rigorous cost/benefit analysis
Revenue enhancement
Disregard cost/benefit details
IT tends to be treated as a capital investment
Maybe it shouldn’t be
If critical to firm strategy
If needed to keep up with competitors

5. IT Budgeting Techniques
FINANCIAL
Discounted cash flow
Long term – reflects time value of money
Cost-benefit analysis
Do not need to include time value of money
Payback
Quick and dirty
Usually sufficient

6. IT Risk Factors Project manager ability (often hire consultant)
Experience (probably won’t have in ERP)
Experience with programming
Availability of critical equipment, software
Probably not a problem in ERP
Project team completeness
Make it complete
Personnel turnover
Especially after adoption of ERP
Project team size (make sure enough)
Relative control of project manager over project team

7. Criteria
Type
% companies
% projects
Rank by value
Business objectives
Managerial
88%
57% 1
Support decisions
29% 7
Required (legal)
71% 13
Implicit objectives
69%
44% 3
Budget constraint
Financial
68%
64% 8
Payback
61%
51% 5
Competition
28% 6
IRR
54% 2
NPV
48%
58% 4
Rate of return
16%
47% 10
Profitability index 8% 14

8. IS/IT Project Evaluation Technique Use Bacon [1992]: Financial
Criteria
% Companies
% Projects
Budget constraint 68 64
Payback 61 51
IRR 54
NPV 48 58
Rate of return 16 47
Profitability 8

9. IS/IT Project Evaluation Technique Use Bacon [1992]: Managerial
Criteria
% Companies
% Projects
Explicit objectives 88 57
Support decisions 29
Legal requirements 71 13
Implicit objectives 69 44
Competition 61 28
Probability of gain 46 63

10. IS/IT Project Evaluation Technique Use Bacon [1992]: Developmental
Criteria
% Companies
% Projects
Technical requirements 79 25
Learn New Technology 60 13
Probability of completion 31 62

11. Criteria
Type
Rank-project value
Explicit business objectives
Managerial 1
IRR
Financial 2
Implicit business objectives 3
NPV 4
Payback 5
Response to competition 6
Support decision making 7
Budgetary constraint 8
Probability of achieving objectives 9
Accounting rate of return 10
Probability of project completion
Developmental 11
Technical/system requirements 12
Legal requirements 13
Profitability index 14
Learn new technology 15

12. Cost Benefit Example Adopt a small ERP ($3 million) 1st year BPR
Internal team doubles in cost in year 2
Consulting costs double in year 2
Year 2 – more hardware
Year 3 – finish implementation
Operate ERP begins year 4
Cost of capital 20% per year

13. Cost/Benefit Example Year Internal Consult Software Hardware Training1
500,000
200,000 2
1,000,000
3,000,000
2,000,000 3
1,500,000 4
4,000,000 5
2,200,000
300,000
5,200,000 6
2,420,000
6,760,000 7
2,662,000
8,788,000 8
2,928,200
11,424,400 9
3,221,010
14,851,720 10
3,543,122
19,307,236

14. NPV – (two benefit growth rates)
Year
Invest
Operate
Ben30%
NPV30
Ben10%
NPV10 1
1,200,000
-1,000,000 2
7,500,000
-5,208,333 3
5,000,000
-2,893,519 4
3,000,000
4,000,000
482,253 5
2,500,000
5,200,000
1,085,069
4,400,000
763,567 6
2,420,000
6,760,000
1,453,457
4,840,000
810,453 7
2,662,000
8,788,000
1,709,654
5,324,000
742,915 8
2,928,200
11,424,400
1,975,945
5,856,400
681,006 9
3,221,020
14,851,720
2,254,108
6,442,040
624,255 10
3,543,122
19,307,236
2,545,992
7,086,244
572,234
NPV
2,404,627
-4,425,168

15. Payback – 30% Benefit Growth
Year
Invest
Operate
Benefits
Net
Cumulate 1
1,200,000
-1,200,000 2
7,500,000
-7,500,000
-8,700,000 3
5,000,000
-5,000,000
-13,700,000 4
3,000,000
4,000,000
1,000,000
-12,700,000 5
2,500,000
5,200,000
2,700,000
-10,000,000 6
2,420,000
6,760,000
4,340,000
-5,660,000 7
2,662,000
8,788,000
6,126,000
466,000 8
2,928,200
11,424,400
8,496,200 9
3,221,020
14,851,720
11,630,700 10
3,543,122
19,307,236
15,764,114

16. Payback – 10% Benefit Growth
Year
Invest
Operate
Benefits
Net
Cumulate 1
1,200,000
-1,200,000 2
7,500,000
-7,500,000
-8,700,000 3
5,000,000
-5,000,000
-13,700,000 4
3,000,000
4,000,000
1,000,000
-12,700,000 5
2,500,000
4,400,000
1,900,000
-10,800,000 6
2,420,000
4,840,000
-8,380,000 7
2,662,000
5,324,000
-5,718,000 8
2,928,200
5,856,400
-2,789,800 9
3,221,020
6,442,040
431,220 10
3,543,122
7,086,244

17. Value Analysis DSS benefits usually very nebulous
Keen (1981)
DSS benefits usually very nebulous
Unfair to apply cost-benefit analysis
benefit estimates unreliable
Costs - identify as in cost-benefit
Benefits - leave in subjective terms
Managerial decision: are you willing to pay this much for that set of benefits?
Value analysis was proposed by Keen (MIS Quarterly, 5:1, 1981, pp. 1-16).
It assumes that benefits cannot be quantified in monetary terms, but that costs can. Therefore, the problem is converted to a consumer-purchase decision: Is the manager willing to pay this price for this set of subjective benefits? (Are you willing to pay $40,000 for a Lexus?)

18. SMART Simple Multi-attribute Rating Technique
develop hierarchy, score alternatives, weight
value =  weight x score

19. terminology objectives - what you want to accomplish
attributes - features of a thing
criteria - measures of things of value
tradeoffs - one alternative better on one attribute, the other better on another attribute
Vendor fast, has better perceived quality, higher price
ASP fast, but less control
In-House slow, risky, but best fit

20. SMART technique 1. identify person whose utilities are to be maximized
2. identify the issue or issues
3. identify the alternatives to be evaluated
4. identify the relevant dimensions of value for evaluating alternatives (attribute scales)
5. rank the dimensions in order of importance
6. rate dimensions in importance, preserving ratios
7. sum the importance weights, & divide by total(wi)
8. measure how well each alternative does on each dimension(sij)
9. U =  wi sij

21. points in Step 4, limit criteria
there are only so many things a human can keep track of at one time
8 plenty
if weight extremely low, drop

22. methodology Step 4: Vendor, ASP, In-house Step 5: rank order criteria
cost > quality > control
Step 6: rate dimensions
least important = 10 control = 10
quality = 35
cost = 50

23. methodology Step 7: sum, divide by total
cost = 50/95 = .526
quality = 35/95 = .368
control = 10/95 = .105
SWING WEIGHTING (check)
give most important 100, others proportional
cost = 100, quality = 60, control = 25
cost = 100/185 = .541, quality = .324, control = .135
maybe average: cost .53; quality .35; control .12

24. methodology purpose of swing weighting
the input is admittedly an approximation
giving values based on a different perspective
additional check
should yield greater accuracy

25. scores Step 8: score each alternative on each criterion
need as objective a scale as you can get
doesn’t have to be linear
COST: maximum feasible = 0
minimum available = 1.0
Vendor about 0.8
Outsource 1.0
In-House 0

26. scores QUALITY: Vendor excellent 1.0 ASP less 0.4 In-House good 0.8
CONTROL Vendor average 0.3
ASP low 0.1
In-House maximum 1.0

27. recommends the Vendor system
calculation of value
U =  wi sij
COST QUAL Control
weights
scores: TOTALS
Vendor
ASP
In-House
recommends the Vendor system

28. SMART
provides a very workable means to implement the principles of MAUT
in fact, it can be MORE accurate than MAUT
(more realistic scores, tradeoffs)
identify criteria
develop scores over criteria
identify alternatives available, measure scores
simple calculation