servitization yee mey goh
DESCRIPTION
3rd International Business Servitization Conference 13-14 November 2014 BilbaoTRANSCRIPT
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.