Servitization in the Capital Goods Industry

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1 Euroma Service Operations Management Forum, Tilburg, 22 Sept Servitization in the Capital Goods Industry Geert-Jan van Houtum Prof. of Maintenance, Reliability, and Quality Eindhoven University of Technology

2 / School of Industrial Engineering 10/15/2014 PAGE 1

3 Key words: System availability Total Cost of Ownership / School of Industrial Engineering 10/15/2014 PAGE 2

4 Size capital goods industry Netherlands Import: 117 billion Euro Export: 117 billion Euro Source: Statistisch Jaarboek, 2009 Concerns: Machines en apparaten, Elektrotechnische machines en apparaten, Transportmiddelen / School of Industrial Engineering 10/15/2014 PAGE 3

5 Size capital goods industry Netherlands Import: 117 billion Euro Source: Statistisch Jaarboek, 2009 Concerns: Machines en apparaten, Elektrotechnische machines en apparaten, Transportmiddelen Export: 117 billion Euro Potential size services: Equally large!!! / School of Industrial Engineering 10/15/2014 PAGE 4

6 Life Cycle Needs and requirements Design Production Exploitation Disposal Big influence on System Availability and TCO One may look at: Modular design More reliable components Building in redundancy Means to facilitate monitoring and diagnosis The maximum system availability is determined in the design phase A large portion of the TCO is made in this phase / School of Industrial Engineering 10/15/2014 PAGE 5

7 Long term trends Maintenance of complex systems becomes too complicated for users themselves Users require higher system availabilities (less downtime) Users look at TCO Maintenance is outsourced to third party or OEM (pooling resources, pooling data, remote monitoring) More extreme: One sells function plus availability Feedback to design (better systems, higher sustainability) / School of Industrial Engineering 10/15/2014 PAGE 6

8 Challenges for the OEM\third party? Definition of the service portfolio / School of Industrial Engineering 10/15/2014 PAGE 7

9 Example of a service portfolio / School of Industrial Engineering 10/15/2014 PAGE 8

10 Challenges for the OEM\third party? Definition of the service portfolio Design of the service supply chain / School of Industrial Engineering 10/15/2014 PAGE 9

11 Service supply chain: Example Supply spare parts Reg. repl.: 1-2 weeks Central Stockpoint Emergency Shipm.: 1-2 days Local Stockpoint Lateral Shipments: A few hours Customers with contracts Customers with contracts Direct sales Reg. repl.: 1-2 weeks Local Stockpoint / School of Industrial Engineering 10/15/2014 PAGE 10

12 Challenges for the OEM\third party? Definition of the service portfolio Design of the service supply chain Design of the service processes Pricing Organization New business models / School of Industrial Engineering 10/15/2014 PAGE 11

13 PURPOSE OF THIS TALK Showing how we can contribute via OM research / School of Industrial Engineering 10/15/2014 PAGE 12

14 CONTENTS 1. Introduction 2. Creation of differentiation while keeping the portfolio effect 3. Effect of product design: Redundancy decision 4. New opportunity: Remote monitoring data 5. General OM research agenda / School of Industrial Engineering 10/15/2014 PAGE 13

15 2. Creation of differentiation while keeping the portfolio effect (Tiemessen, Fleischmann, v.houtum, v.nunen, and Pratsini, EJOR, 2013) / School of Industrial Engineering 10/15/2014 PAGE 14

16 Spare parts network for high-tech equipment HUB Legend Local Warehouse 4hr response area 2hr response area customer (region) / School of Industrial Engineering 10/15/2014 PAGE 15

17 Spare parts network for high-tech equipment Spare parts network Legend HUB Local Warehouse 4hr response area 2hr response area customer (region) Key features: Lateral transshipments Multiple customer classes / School of Industrial Engineering 10/15/2014 PAGE 16

18 Setting Central Warehouse (CW): Infinite stock Multiple Local Warehouses (LW s) Poisson demand processes Cost factors: Cost to fulfill a demand at point x from LW i or from the CW Unit replenishment costs Penalty costs for violating the maximum response time constraints / School of Industrial Engineering 10/15/2014 PAGE 17

19 Planning problems Tactical planning problem: Decision on base stock levels: Given in this study Operational planning problem: We consider two allocation rules Static: Common in practice Dynamic: Exploits real-time stock level information / School of Industrial Engineering 10/15/2014 PAGE 18

20 The two allocation rules Static Allocation (SA-rule): Fulfill demand from nearest LW with positive on-hand stock But, fulfill from CW if cheaper Easy to compute, easy to execute (Markov model) Weak differentiation between customer classes Dynamic Allocation (DA-rule): Estimate near-future effect when fulfilling from one of the LW s with positive on-hand stock (Use of Appr. Dyn. Progr.) Select LW or CW with lowest direct + near-future costs Requires computer support to be applied Better differentiation between customer classes / School of Industrial Engineering 10/15/2014 PAGE 19

21 Computational experiment Large instances: Relative savings of DA-rule compared to SArule: 7.9% Conclusion: Savings relative to the static rule are significant Dynamic rule gives a better way to differentiate between customer classes You get implicitly a kind of dynamic, reserved stock levels for high-priority customers / School of Industrial Engineering 10/15/2014 PAGE 20

22 3. Effect of product design: Redundancy decision (Öner, Scheller-Wolf, and van Houtum, OR, 2013) / School of Industrial Engineering 10/15/2014 PAGE 21

23 Setting Production site Spare parts stock Regular replenishments Emergency supply procedure (in case of stockout) / School of Industrial Engineering 10/15/2014 PAGE 22

24 Model (cont.) Component 1 Component 2 Component 2 Component i Component m Component m Per machine: Multiple critical components Serial structure / School of Industrial Engineering 10/15/2014 PAGE 23

25 Model (cont.) Three possible policies per component 1. No redundancy 2. No redundancy, apply emergency supply procedure when on-hand stock drops to 1 3. Redundancy Optimization problem Min. TCO s.t. system availability constraint / School of Industrial Engineering 10/15/2014 PAGE 24

26 Approach Generation of the efficient frontier for TCO and system availability (via Lagr. Rel.) One curve for case with policy 2 One curve for case without policy 2 One gets an order for which components have to be made redundant / School of Industrial Engineering 10/15/2014 PAGE 25

27 Analysis per component Costs Policy 1 Policy 2 Policy 3 Policy 1 optimal Policy 2 optimal Policy 3 optimal / School of Industrial Engineering 10/15/2014 PAGE 26

28 Efficient frontier Without policy 2 With policy 2 included Availability / School of Industrial Engineering 10/15/2014 PAGE 27

29 Efficient frontier Notice: Without The optimal design policy depends 2 strongly on the required availability So: Multiple customer classes => multiple designs With policy 2 included Availability / School of Industrial Engineering 10/15/2014 PAGE 28

30 4. New opportunity: Remote monitoring data (based on Topan, Dekker Tan, and Van Houtum, WP, 2014) / School of Industrial Engineering 10/15/2014 PAGE 29

31 Monitoring data Condition data: parameters which are directly or indirectly related with the health state of Module X Failure data: failure time Sample Data: Collected at central level, for one critical unit MACHINE NUMBER TIME STAMP VALUE MACHINE TYPE SITE ID CUSTOMER CONTINENT CUSTOMER COUNTRY CUSTOMER NUMBER PARAM ID M Dec T Asia South Korea M Oct T Asia Singapore M Jul T Other Other OT M Jul T Asia South Korea M May T Asia South Korea North M Oct T America USA M Jul T Asia South Korea M Feb T Asia South Korea M Aug T Asia Taiwan M Jan T Europe France M Aug T Asia South Korea M Apr T Asia Taiwan M Sep T Asia Taiwan M Feb T Asia Taiwan M Aug T Asia Taiwan M Dec T Asia South Korea / School of PAGE 30 M Jul T Asia South Korea Industrial /15/ Engineering

32 Imperfect warnings Demand signals produced by the prediction model are imperfect: Prediction model can produce false signals (false positives) Exact time of the failure is uncertain Prediction model may also produce false negatives Sudden, unpredicted failures which cannot be detected in advance by the monitoring system / School of PAGE 31 Industrial 10/15/2014 Engineering

33 Research topic Value of the imperfect warnings for spare parts supply Remark: We do not look at preventive replacements. Setting: Single stockpoint (a local warehouse, gets replenishments from a central warehouse) Single item Imperfect warnings = Imperfect Advance Demand Information (ADI) / School of PAGE 32 Industrial 10/15/2014 Engineering

34 Imperfect ADI Demand demand lead time p: probability that a signal will ever become a demand realization = reliability (false positives) t [ ] l p u [ l, u ] : prediction interval for the demand lead time (timing) Supply t L supply lead time q : ratio of predicted demand to total demand = sensitivity (false negatives) / School of PAGE 33 Industrial 10/15/2014 Engineering

35 Approach 3 scenarios: 1. Optimal cost without ADI (benchmark) 2. Optimal cost with imperfect ADI, but no returns allowed 3. Optimal cost with imperfect ADI, and returns allowed Each scenario: Analyzed by a Markov decision model / School of PAGE 34 Industrial 10/15/2014 Engineering

36 Case study at an OEM 4 parts that OEM supplies its customers all over the world. p, q, [ l, u ] : obtained from prediction model in use c em : transportation cost + high downtime cost c em = Euro c r : transportation cost + pipeline holding cost L : 2 weeks / School of PAGE 35 Industrial 10/15/2014 Engineering

37 Case study at an OEM Part h ( /unit/week) [τ l, τ u ] (week) Part P (p and q are low and information is timely) value of information is high when returning excess inventory is allowed. benefit of returns λ (unit/week) p q c r g NoADI ( /week) g ADINoReturn ( /week) g ADI ( /week) PCR ADINoReturn PCR ADI P 2720 [2,8] % 32.00% T 112 [8,16] % 46.88% X 152 [0,4] % 7.52% W 646 [0,1] % 4.86% Policy with ADI and no returns: Local warehouse carries no stock + a spare part is shipped to the local warehouse only if a warning is issued Part T (p and q are high and information is timely), value of information is high even for without return case. / School of PAGE 36 Industrial 10/15/2014 Engineering

38 Case study at an OEM Part h ( /unit/week) [τ l, τ u ] (week) λ (unit/week) Parts X and W (L > l ), p q c r g NoADI ( /week) g ADINoReturn ( /week) g ADI ( /week) PCR ADINoReturn PCR ADI P 2720 [2,8] % 32.00% T 112 [8,16] % 46.88% X 152 [0,4] % 7.52% W 646 [0,1] % 4.86% value of information is low even when return is allowed => negative impact of timing of information on the value of information / School of PAGE 37 Industrial 10/15/2014 Engineering

39 5. General OM research agenda / School of Industrial Engineering 10/15/2014 PAGE 38

40 Topic: Servitization in the capital goods industry Purpose of this talk: Showing how can we contribute with OM research 1. Creation of differentiation while keeping the portfolio effect 2. Effect of product design: Redundancy decision 3. New opportunity: Remote monitoring data / School of Industrial Engineering 10/15/2014 PAGE 39

41 General OM research agenda Design of service networks and processes Creation of differentiation while keeping the portfolio effect Location strategy For spare parts, service engineers, tools, back office, Where to decompose: For the offered services and between service provider and user Effect of product design decisions in general Redundancy decision, choice of components/suppliers, Commonality across multiple machine types Level of modularity How to deal with modifications Design per type of service contract? / School of Industrial Engineering 10/15/2014 PAGE 40

42 General OM research agenda Exploiting new technologies in general Remote monitoring data Use of 3D printers Sharing of resources/data by multiple companies Service costs and pricing Sustainability issues: Re-use of systems and components CO2 and other emissions / School of Industrial Engineering 10/15/2014 PAGE 41

43 Questions/Discussion / School of Industrial Engineering 10/15/2014 PAGE 42

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