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Cost Modelling Linda Newnes, Head of Costing Research University of Bath

Cost Modelling Linda Newnes, Head of Costing Research University of Bath. Presentation Outline. Present the IdMRC what we do. Overview of the work undertaken at Bath in cost modelling. Costing for availability Product Costing Availability Contracting Service Costing

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Cost Modelling Linda Newnes, Head of Costing Research University of Bath

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  1. Cost ModellingLinda Newnes, Head of Costing ResearchUniversity of Bath

  2. Presentation Outline • Present the IdMRC what we do. • Overview of the work undertaken at Bath in cost modelling. • Costing for availability • Product Costing • Availability Contracting • Service Costing • Costing for Availability

  3. Innovative design and Manufacturing Research Centre (IdMRC) • One of 16 specialised research centres. • Focus of the activity at Bath is the integration of Design and Manufacturing. • IdMRC leads a 3 year Grand Challenge in through life information and knowledge (KIM project – Knowledge Information Management). The vision is to be an internationally leading research Centre in the synthesis of design, manufacture and product verification

  4. IdMRC – Four themes to realise our vision • Constraint Based Design and Optimisation. (CBDO) • Advanced Machining Processes and Systems. (AMPS) • Metrology and Assembly Systems and Technology. (MAST) • Design Information and Knowledge (DIAK)

  5. Through Life Costing • Our focus is on concept design through to disposal. • Emphasis on knowledge information and management for cost modelling. • Importance of ‘servitisation’ cost modelling (PSS). • Current collaborators include companies such as; Ministry of Defence, BAE Systems, GE Aviation, Airbus UK, Spirax Sarco. The overall aim is to provide methods and tools for managing TLC from concept design to in-service/disposal.

  6. Why is cost modelling important • NAO (2008) highlights key areas • MOD projects late delivery and over budget • 20 largest projects on average 96 months late and £205M over budget • Deloittes (2008) • Cost overruns in defence and aerospace in next 10 years 26% increase • Equates to 46%. • MOD DES • 2007/8 the in-service costs were £10b • Availability contracting

  7. Through Life Costing Feedback to inform future design

  8. Some cost modelling techniques • Synthetic Cost Modelling Here the ‘experts’ do their best ‘guestimate’ on what the Cost Estimating Relationships (CER) are. • Generative Cost Modelling (as design progresses detail improves and cost models e.g. material, processes etc – lots of detail required). • Parametric Cost Modelling (using past knowledge to predict cost e.g. weight of material used in aerospace and injection moulding). In otherwards you find a CER.

  9. Uncertainty in cost modelling • Different options: • Sell the product and spares • Lease the product (computers) • Availability contracting • Future contracting of some aerospace/defence products – availability contracts. How do you cost for through life availability?

  10. Why is the utilization phase important? Up to 75%

  11. Costing for Availability – current modelling • Cost modellers have focused on costing products. • Commercial systems are in general still product based. • Little evidence of in-service/utilisation modelling. • However product and services debated since 1776 by Smith* • Availability contracts already in place, however benefitted with transition between products and service. *Smith, Adam. (1776). The Wealth of Nations, Books I-III, Chichester: Wiley.

  12. Key Changes in the Definition of Goods (G) & Services (S) Smith (1776) clarified labour in terms of productive (e.g. Goods) and non-productive(e.g.Services) Senior (1863)classified G as an object and S as a performance/act Shostack (1977)among the first to argue that intangibility can no longer be the distinction to separate S from G Say (1803)first introduced the concept of Materialability Hicks (1942)Identified another key G&S characteristics Smith (1776) identified unique G&S characteristics Delaunay&Gadrey (1987, cited in Araujo&Spring, 2006)formed producer-user interaction as the basis to distinguish between G&S Axelsson & Wynstra (2002) Rather than distinguishing services and goods explicitly, it would be more helpful to organising & managing firms with complex product-service offerings Araujo & Spring (2006) The current trend is to integrate manufacturing into service to provide solutions throughout the lifecycle of the product Hill (1999)among the first to recognised the ambiguity to separate S from G based on Perishability

  13. Aim of availability research To provide an approach or solution to the challenge of modelling and analysing the provision of through-life costing for a service • To analyse the differences and similarities between product cost estimating techniques and service cost estimating techniques • To design and evaluate an appropriate cost model for product service systems by identifying in-service activities and applying estimating rules • To provide a framework for the provision of service through-life-costing

  14. Challenge How do you cost for through life availability or capability? • How can industry estimate the TLC for their products? In particular, • How can you predict the in-service (utilization/operations support) costs for the products at the concept design stage to enable informed decision making? • Model the cost of decisions e.g. last time buy – how many parts are stored in warehouses from last time buys • Uncertainty modelling to aid decision making

  15. Through Life Cost • Decision making for the acquisition, development and ongoing support of complex engineering systems with extended life • Most meaningful at early stage but highly uncertain • Quantitative and objective estimating of TLC taking into account of uncertainty in estimate • Use uncertainty modelling for decision making through the supply chain

  16. The Framework

  17. Uncertainty and Decision Making • Aleatory uncertainty • Irreducible randomness associated with the physical system or the environment • e.g. repair time, failure rates • Decision making under risk • Epistemicuncertainty • Reducible uncertainty due to a lack of knowledge of quantities or processes of the system or the environment • e.g. future decisions • Decision making under uncertainty • Important to make a distinction • Different theories and methods to model these uncertainties • Can be updated as knowledge is accumulated

  18. Current Approach Cost Model • Probabilistic cost risk analysis • Three-point estimate is common, most likely, min and max to describe triangular distributions • Subjective probability is often used to represent uncertainty • Commercial software incorporate probabilistic modelling technique • Monte Carlo with various types of distributions (e.g. uniform, triangular, normal)

  19. Cost Models Uncertainty More important to characterise epistemic uncertainty More important to characterise aleatory uncertainty

  20. Cost Data Uncertainty

  21. Scenario Uncertainty • A scenario is a conceptual model that is developed (with assumptions) to approximate the actual TLC of the artifact • new technologies or legislation, supply chain disruptions, design changes • Typical scenarios are the most likely, worst and best cases, and the outcomes of these are then used as 3-point estimates • No distinction between risk and uncertainty

  22. Imprecise probability 1 0.8 0.6 0.4 Probability Bounds 0.2 0 0 5 10 15 20 25 30 35 • When both variability and imprecision is present, e.g. uncertain about the distribution parameters e.g. reliability PDF CDF

  23. Probability vs Imprecise Probability There is 90% probability that in service cost is within £23M. F(x) _ F(x) There is 90% probability that cost is within £22M-£24M.  Use this information to set contingency budget, best case £22M worst case £24M. F(x)

  24. Conclusions • Uncertainty important in the context of decision making in TLC • Separating epistemic and aleatory uncertainty in costing • Can update the model when imprecision is reduced/eliminated, e.g. deciding on alternative • Allows reuse of objective data e.g. repair time independent of the probability of repair events • More transparent decision making under uncertainty and risk

  25. References Goh, Y.M, Newnes L.B., Mileham A.R., McMahon, C.A and Paredis, C. A framework for considering uncertainty in quantitative life cycle cost estimation. ASME 2009, San Diego, 30th August – 2nd September 2009.

  26. Current Requirements • We know the literature and approaches of commercial systems. • We have some industrial views of how they model in-service/utilisation costs. • Need further industrial feedback. • Complete questionnaires to ascertain the type of modelling you undertake.

  27. Any Questions

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