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

Funders and collaborators

Outline

• Research background

• Contract bidding framework development

• Previous work

• Value modelling approach

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

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.

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

Managing uncertainty in contract bidding framework

Probability of winning the contract

New value modelling approach

Pacceptance = P(b > p) = 1 – P(b ≤ p) 𝑃𝑙𝑒𝑎𝑑 = 𝑃 𝑝 < 𝑝_𝐴 . 𝑃 𝑝 < 𝑝_𝐵 . 𝑃 𝑝 < 𝑝_𝐶

VALUE MODELLING Build in intangible attributes that influence the decision

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”

PERGAVE Exemplar Requirement Essential Desirable Aspiriational

AMaintainloiterpositionwithin2kmunderwind

speed

30kts 40kts 50kts

B OperationalLimitsUpto66degN/S

anytimeofyear

Beyond66degN/Sduringwinterfor0

-5weeks

Beyond66degN/Sduringwinterfor

>5weeks

CTimetoachievenewloiter

position<12hours <9hours <6hours

D Power/Propulsion 2kW 10kW 50kW

E EnduranceRequirements Months Year Years

F RecyclingMinimum90%recyclableon

disposal

Minimum95%recyclableon

disposal

100%recyclableon

disposal

G PayloadRequirements 200kg 500kg 1000kg

H Lossrate 1e-5/flyinghour 1e-6/flyinghour 1e-7/flyinghour

I Missionfailurerates <1in5 <1in10 <1in25

J MaintenanceIntervals>5000flyinghours

(6months)

>10000flying

hours(1year)

>20000flying

hours(2years)

Inputs to Value Model

Your Price bid

Elicit Value to

the Customer

Competitors Price Bids

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

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

PERGAVE – 3 ‘hot’ buttons/attributes

Value(£M)

ExpPower(kW) Payload(kg)

MaintenanceInterval(hrs) Min

MostLikely 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 13 155 10 500 20000 12.5 14.5 16.5

6 10 1000 5000 11 13 157 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

Value Model – Map the Relationships Willingness to Pay - Attributes

A1

A2

Val

ue

Min ML Max

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.

Our Offering/Competitor Offering

ProposedOffering

Expertsviewofcustomersbeliefinwhatwewill

deliver

Expertsviewofcustomersbeliefin

whatcompetitorwilldeliver

Min ML Max Min ML Max

Power(kW) 10 8 10 11 8 10 11

Payload(kg) 1000 800 900 1000 800 900 1000

MaintenanceInterval(hours)

5000

4500

5000

5500

5000

6000

7000

Here you could assume the competitors offering is the winner

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

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

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

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

Decision Charts

£M

Pro

bab

ility

%

ANY QUESTIONS? Thank you for listening.