This document summarizes research on developing a framework to manage uncertainty in contract bidding for complex service offerings. It presents a value modeling approach to quantify a bidding company's probability of winning a contract against competitors by estimating the customer's willingness to pay for different product attributes. The approach involves building a value model mapping attributes to willingness to pay, eliciting expert opinions on competitors' bids and customer perceptions, running Monte Carlo simulations to estimate expected values, and calculating the probability of winning based on delivering greater value than competitors within the customer's budget. An example using a persistent green aircraft contract is provided to demonstrate applying the five-step value modeling process.

1. 3rd International Conference on Business Servitization Price to Win Through Value Modelling for Service Offering
Dr Linda Newnes – University of Bath
Dr Yee Mey Goh – Loughborough University
Benjamin Lee, William Binder and Christiaan Paredis – Georgia Institute of Technology

2. Funders and collaborators

3. Outline
•Research background
•Contract bidding framework development
•Previous work
•Value modelling approach

4. Academic Background
•High level of uncertainty due to novelty of process and long-term nature of services
•Bidding company needs to determine appropriate price bid to win against competitors

5. Definition of uncertainty
Uncertainty
a potential deficiency in any phase or activity of the process, which can be characterised as not definite, not known or not reliable
Risk
is the possible (positive or negative) effect of a certain event or situation.

6. Development of framework
•Academic literature
•Three experimental studies
1.Visualisation of uncertainty
2.If price bid changed with/without competitors
3.Interviews with decision-makers to ascertain factors that influenced the price bids
4.Workshops with stakeholders
•Developed Uncertainty Framework

7. Managing uncertainty in contract bidding framework

8. Probability of winning the contract
New value modelling approach
Pacceptance = P(b > p) = 1 – P(b ≤ p)
푃푙푒푎푑=푃푝<푝_퐴.푃푝<푝_퐵.푃푝<푝_퐶

9. VALUE MODELLING
Build in intangible attributes that influence the decision

10. Probability of winning
•Demonstrate using a public domain exemplar from BAE Systems MA&I - PERsistent Green Air Vehicle (PERGAVE)
•Our value modelling approach to establish customer’s “willingness to pay”

11. PERGAVE Exemplar
Requirement Essential Desirable Aspiriational A Maintain loiter position within 2km under wind speed 30kts 40kts 50kts B Operational Limits Up to 66deg N/S any time of year Beyond 66deg N/S during winter for 0 - 5 weeks Beyond 66deg N/S during winter for >5 weeks C Time to achieve new loiter position < 12 hours < 9 hours < 6 hours D Power / Propulsion 2 kW 10 kW 50 kW E Endurance Requirements Months Year Years F Recycling Minimum 90% recyclable on disposal Minimum 95% recyclable on disposal 100% recyclable on disposal G Payload Requirements 200kg 500kg 1000kg H Loss rate 1e-5 / flying hour 1e-6 / flying hour 1e-7 / flying hour I Mission failure rates < 1 in 5 < 1 in 10 < 1 in 25 J Maintenance Intervals > 5000 flying hours (6 months) > 10000 flying hours (1 year) > 20000 flying hours (2 years)

12. Inputs to Value Model
Your
Price bid
Elicit
Value to
the Customer
Competitors
Price Bids

13. Five Step Process
1.Value model creation
2.Customer perception of the offerings
3.Quantification of the price bids
4.Establish expected values of your own and the competitor offerings
5.Estimate the probability of winning the contract *Assume the customer is rational

14. Step 1 – Value model creation
•Build the model mapping customers willingness to pay as a function of important attributes.
•Aim is to quantify the experts uncertainty in what the customer would be willing to pay
•Need to balance the number of questions you ask
•We use Taguchi orthogonal arrays to minimise the elicitation questions

15. PERGAVE – 3 ‘hot’ buttons/attributes
Value (£M)
Exp
Power
(kW) Payload (kg)
Maintenance Interval
(hrs) Min
Most
Likely Max
1 2 200 5000 6 8 10
2 2 500 10000 10 12 14
3 2 1000 20000 13 15 17
4 10 200 10000 11 13 15
5 10 500 20000 12.5 14.5 16.5
6 10 1000 5000 11 13 15
7 50 200 20000 13 15 17
8 50 500 5000 11.5 13.5 15.5
9 50 1000 10000 13.5 15.5 17.5
Power (2, 10, 50)kW Payload (200, 500, 1000)kg Maintenance (5000, 10000, 20000)hrs
Taguchi standard arrays – 3 attributes, 3 levels is an L9 array
Elicit experts views on the perceived value to the customer

16. Value Model – Map the Relationships Willingness to Pay - Attributes
A1
A2
Value
Min ML Max

17. Step 2 – Customer’s perception
•The customers belief in the proposed offerings – will you deliver what you state!
•Quantifies any intangible risk factors that are assigned against individual bids, e.g. trust in the contractor’s capability, past experiences etc.

18. Our Offering/Competitor Offering
Proposed
Offering
Experts view of customers
belief in what we will
deliver
Experts view of
customers belief in
what competitor will
deliver
Min ML Max Min ML Max
Power (kW) 10 8 10 11 8 10 11
Payload (kg) 1000 800 900 1000 800 900 1000
Maintenance
Interval
(hours)
5000
4500
5000
5500
5000
6000
7000
Here you could assume the competitors offering is the winner

19. Step 3 – Quantification of price bids
•Our price bid – we can assess a range of values
•Competitor price bids – experts opinion for baseline
•Can build in further Game Theoretic rules – e.g. loss leaders

20. Step 4 – Establish expected value
•Monte Carlo simulations are used to propagate uncertainties in attributes through the value model
•Using the mapping in what the value of the offering to the customer
–Not what the contractors are stating
–Value of what customer believe each contractor will deliver

21. Step 5 – Estimate probability of winning
•Probability of acceptance against customer budget
–Same as before
•Probability of winning against competition
–Value to the customer ≥ price bid
–Offer greater value surplus than competitors

22. Decision Matrix
Price bid (£M)
5
5.5
6
6.5
7
7.5
8
8.5
9
9.5
10
10.5
11
11.5
12
Probability of making a profit %
0
3
13
28
50
86
94
98
100
100
100
100
100
100
100
Expected Profit (if contract is won)
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
Probability of winning % (FROM VALUE MODEL)
Competitor's Price = £9M
100
100
100
100
100
100
100
87
35
3
0
0
0
0
0
Competitor's Price = £8M
100
100
100
100
100
86
21
0
0
0
0
0
0
0
0
Probability of acceptance % (customer budget)
Upper
100
98
96
92
86
77
68
59
48
37
25
16
10
5
0
Lower
100
98
96
92
85
74
64
53
42
31
21
13
8
4
0

23. Decision Charts
£M
Probability %

24. ANY QUESTIONS?
Thank you for listening.