THE ADDED VALUE OF QUANTITATIVE METHODS FOR AFTER-SALES SERVICE LOGISTICS MATTHIEU VAN DER HEIJDEN NOVEMBER 25, 2013 CTIT Center for Sustainable Supply Chain Innovation

 Associate professor at University of Twente  Positions before at:  MSc and PhD in Econometrics  Teaching & research: supply chains, transportation, maintenance, operations research  Focus on after-sales service supply chains SOME BACKGROUND November 25, 2013

 Collaboration between Mechanical Engineering and Industrial Engineering and Management  TIME: Joint research projects, joint educational programme (  Unique link between technology and management in the area of maintenance and after-sales service logistics MAINTENANCE AND SERVICE SUPPLY CHAINS WITHIN UNIVERSITY OF TWENTE November 25, 2013

 Capital goods in a B2B (or: B2G) environment  Initial sales versus the aftermarket AFTER SALES SERVICE SUPPLY CHAINS November 25, 2013 CT scanner by Philips Healthcare Naval radar by Thales Netherlands Lithography equipment by ASML

 Market of all products and services related to equipment during its life cycle, after the original sale SERVICE LOGISTICS IN THE AFTERMARKET NETWORK DESIGN, INVENTORY PLANNING, RESOURCE PLANNING,... November 25, 2013 preventive maintenance technical support / training arranging finances conducting repairs spare part supply installing upgrades inspections Servitisation

BUSINESS MODELS FOR SERVICE LOGISTICS November 25, 2013

 Spare part inventory management with repair priorities  Integration of level-of-repair analysis en spare part inventory management  Relation between preventive maintenance planning and spare part inventories  Differentiation for service contract fulfillmant  Last time buy and reuse of spare parts  Dynamic maintenance planning in maritime supply chains EXAMPLES OF RESEARCH PROJECTS November 25, 2013

LEVEL-OF-REPAIR ANALYSIS INTEGRATION WITH SPARE PARTS OPTIMIZATION November 25,  How to influence system uptime and costs of maintenance?  uptime critical → repair-by-replacement of modules → spare parts!  Modules can be expensive → discard or repair by replacement of subcomponents (fast repair → few expensive spares required?)  → decisions for all subcomponents  PLUS: where in the service network?  How many spares at which location? Decisions Uptime

 Spare part inventory optimization given:  Product structure (multi-indenture structure)  Support network (multi-echelon structure)  Item failure rates  Repairable / consumable item  Repair location  Repair shop throughput times  Etc. LEVEL-OF-REPAIR ANALYSIS (LORA) November 25, 2013 Costs of repair vs. discard Availability of resources (Tools, Service engineers,Test equipment,..) Capacities of resources LRU or SRU? LORA: Repair or discard parts? Where to repair? Where to locate resources in the service supply chain? Cost oriented method that does not take into account uptime

LEVEL-OF-REPAIR ANALYSIS (LORA) COMMON WAY OF WORKING November 25, 2013 Collect data LORA Repair / discard per item, repair location per item System upkeep costs without spares Operational availability not included Spare part optimization tool Spare parts to achieve a target operational system availability Tendency: repair upstream in supply chain Tendency: High investment because of long lead times manually modify LORA decisions several trials output insufficient for feedback to LORA

 Characterization solutions (cost index traditional sequential approach = 100) LEVEL-OF-REPAIR ANALYSIS (LORA) SOLUTION: INTEGRAL PLANNING METHOD November 25, 2013 Better to deploy more resources and to reduce spare part inventories

 At some point in time during the system life cycle, part supply is discontinued   last opportunity to procure spare parts for remaining life cycle until the end of service date LTBEnd Of Service June 14, 2013 Remaining time until EOS may be >10 years! LAST TIME BUY AND REIUSE OF SPARE PARTS WORK PACKAGE WITHIN PROSELO PROJECT 2

 Trade-off service level  obsolescence costs (EOS)  Standard inventory models:  Forecast demand  Set safety stock to cover uncertainty in forecasted demand during lead time  But: we cannot order replenishments anymore… DO WE HAVE TO BUY AT LOT? November 25, 2013 Lead time = Time to EOS (many years!) Extremely high uncertainty Very high safety stocks Very high obsolescence Demand forecasting over a product life cycle is very hard

BUT THERE IS ALSO GOOD NEWS WE HAVE MULTIPLE SOURCES OF SUPPLY November 25, 2013 Supply plan Match Repair of failed parts Buy on open market (2 nd hand) Part substitution Asset recovery Stock on hand Stock elsewhere in the supply chain Demand forecast uncertainty

 No uncertainty in demand & supply (timing, quantities)  No explicit cost trade-off between the supply sources  No trade-off service levels  (obsolescence) costs  No explicit safety stocks CURRENT PRACTICE IN INDUSTRY WHEN DECIDING ON THE LTB, COMPANIES CONSIDER… November 25, 2013 Mitigating risks starts with quantifying risks of stockouts of obsolescence Mitigating risks starts with quantifying risks of stockouts of obsolescence.. and for selected interventions: quantify their costs and expected risk reduction We are looking for the best way to mitigate risks during the end-of-service period

WHAT ABOUT THE CURRENT PERFORMANCE November 25, 2013 Severe overforecasting High service level  Overforecasting seems to have more impact than ignoring safety stocks End-of-service (EOS): December 2009 Index: Rough estimate of obsolescence: 40-50% of LTB is scrapped at EOS

 Repair is an attractive option, even if expensive:  cover peaks in demand occurring with low probability  The same applies to other supply alternatives OPTIMIZATION MODEL FOR LTB AND REPAIRS EXAMPLE: IMPACT OF REPAIR COSTS November 25, 2013

SERVICE DIFFERENTIATION FOR SERVICE CONTRACT FULFILLMENT November 25, 2013 How to deal with differentiated targets when providing service to all customers?

November 25, 2013 SERVICE DIFFERENTIATION IN SPARE PART SUPPLY PremiumNon-premiumPremiumNon-premium Option 1: Selective emergency shipments Option 3: Stock reservations (“critical levels”) Option 4: Dedicate stocks Option 2: Selective lateral transshipments

SERVICE DIFFERENTIATION IN SPARE PART SUPPLY November 25, 2013

 Some spare parts are repairable  Repair lead times determine spare part inventories  Opportunities for repair lead time reduction:  In-house, finite capacity: Prioritizing repair jobs reduces lead time, but increases lead time of other repair jobs  Outsourced: lead time reduction comes at a cost  Which parts to prioritize? What is the impact? OTHER EXAMPLES SELECTIVE LEAD TIME REDUCTION OF SPARE PART REPAIRS November 25, 2013 Smart prioritization: expensive slow movers Impact of inventory investment may be high (10-20%)

INTEGRATED MAINTENANCE AND SERVICE LOGISTICS CONCEPTS FOR MARITIME ASSETS MASELMA November 25, 2013 Very complex usage profile:  Many one offs  Moving assets  Varying conditions Opportunities!  Lots of maritime business  Asset owners/OEMs rethink their service chain position Extreme up-time requirements:  Uptime = safety for crews  Downtime = high penalty Current practice:  Reactive maintenance methods  Ultra long supply lead times Resulting in…  >€ in stock  Lack of reliability/availability  Chaotic supply chains Conservative business:  Complex relations  Limited service mentality within OEMS

 WP I Improve the predictability of maintenance  WP IIService logistics planning  WP IIIImprove/extend service logistics cooperation OVERVIEW WORK PACKAGES I-III November 25, 2013

 3D printing of spare parts may cause...  postponement of spare part supply (print-on demand)  huge reduction in spare part inventories and obsolescence  fast repair, because parts can be printed close to (or at) the installed base, even if it is remote  higher system availability  Research proposal  commitment of a.o. NedTrain, Vanderlande, Thales, NLR  ongoing talks with several other parties (including Defence) SINTAS: IMPACT OF 3D PRINTING MAINTENANCE & AFTER-SALES SERVICE SUPPLY CHAINS November 25,