1. 1 Last Time Buys and Reuse at Océ A last time buy decision tool for reusable service parts. Anke Verbaarschot 2011, October 19 th

2. 2 Agenda l Introduction & trigger for the project l The supply chain at Océ & the current situation l Model input & the modeling approach l Model output & results l Practical application & Scientific relevance for companies l Conclusions

3. 3 Océ Corporate Profile Océ today: l 22,000 people worldwide l Annual revenue 2.7 billion l Worldwide distribution in 100 countries l Direct sales and services in 30 countries l 10 R&D-sites in 9 countries (1,550 people)

4. 4 Trigger for the project Océ copes with difficulties in case a supplier stops producing a part. Then, Océ has one last opportunity to buy those parts; this is called a Last Time Buy (LTB). l It was difficult to determine the correct quantity to have sufficient service parts until end-of-service (EOS) date  often, the quantity was on the safe side and this resulted in high obsolescence costs. l Océ did not have an integral approach with regards to LTB’s: the service parts department did not take into account the possibility of re-use when making a LTB calculation and the remanufacturing department did not take into account LTB costs when making decisions on re-use.

5. 5 Introduction into Océ 22.000 employees worldwide Revenue 2010: €2.7 billion operating in >100 countries strong together

6. 6 Trigger for the project Océ copes with difficulties in case a supplier stops producing a part. Then, Océ has one last opportunity to buy those parts; this is called a Last Time Buy (LTB). → which quantity should Océ order such that sufficient service parts are available until the end-of-service (EOS) date → an LTB quantity can decrease by reusing parts, but Océ has difficulties with forecasting the quantities and timing of reusable parts → high costs with respect to LTBs were involved last years.

7. 7 Research problem The research objective is to analyze whether and how the forecast of quantities and timing of reusable parts can be improved such that it can be integrated in an appropriate existing LTB calculation model while minimizing costs and obtaining an overall service target

8. 8 Introduction into the supply chain of Océ The Planning and Inventory Control department (PLIC) is responsible for the availability of the service parts until the end-of-service date. The Asset Recovery department (AR) can deliver: l repaired parts from the field l repaired/tested parts from dismantled machines

9. 9 Current situation (1) The current way of working: l PLIC and AR have separate models to determine the LTB quantity and whether reusing parts is interesting, respectively l little communication between PLIC and AR takes place; LTBs and reuse are not combined well last years The current LTB performance: l LTB dates and quantities are not stored accurately in the past l 10% of the inventory value of the central supply centre consists of LTB parts l PLIC scraps on average 53% of the LTB quantity l PLIC has on average for 99,97% of the demand of LTB parts, directly sufficient stock available l PLIC spends €1 million per year due to obsolescence, stock out, and holding costs for LTB parts Océ has opportunities to decrease the involved costs for LTB decisions by combining LTBs with reusing parts; PLIC and AR should together take LTB decisions

10. 10 Current situation (2) An LTB example

11. 11 Problem overview We need to determine the optimal LTB quantity, while considering the uncertainties in the timing and quantities of SF, SD and D minimizing costs obtaining a certain probability that no stock out exists before the end of service date, i.e. 0,97 SF = Supply from the Field SD = Supply from Dismantling D = Demand Q = optimal LTB Quantity

12. 12 Demand forecast

13. 13 Supply from the field forecast SF t =y SF *D t-L_sf

14. 14 Supply from dismantling forecast SD t =y SD *(IB t-L_sd-1 -IB t-L_sd )

15. 15 We want to find the optimal LTB quantity with an existing LTB model that is appropriate for Océ. LTB models in the existing scientific literature assume that: l in each interval, D ≥ SF + SD l for each part, repair (SF) costs < new part price l for each part, dismantle (SD) costs < new part price Since those assumptions cannot be applied to Océ, a model from own ideas and parts of literature is designed. Relevant literature

16. 16 Model input Model output Model overview a heuristic is developed to generate the model output

17. 17 Model output (example)

18. 18 Results With only using the new forecasting method, the LTB buying costs will decrease approximately with 22%. We do not know the cost decrease opportunities precisely in case LTBs would be combined with reusing parts. We noticed for two real examples, that the total expected costs difference between using no reusable parts and using both types of reusable parts, is around 50%.

19. 19 Model recommendations 1.Improve the model by: l monitoring the yield rate of SF and SD l reconsidering the safety stock for each scenario 2.Find an appropriate forecasting method for LTB parts that have an increasing demand → Moore ellipse can only be used for decreasing demand

20. 20 Practical application of the developed model Océ has started using the model from June 2011 to calculate end-of- service demand for LTB’s and stock take-overs l This has helped to reduce the LTB value in several cases l Also, Océ now takes a more integral approach to LTB’s and stock take-overs and there is a structural discussion between PLIC and AR in case of LTB’s l Although the model is not always exactly applicable, the structural integral approach has helped Océ to reduce the LTB value in several cases

21. 21 Scientific relevance for companies The model can be used in a variety of situations: l Calculating LTB’s l Deciding whether reuse from dismantling is beneficial l Deciding whether reuse from field supply is beneficial l Combining and comparing options (LTB / supply from dismantling / supply from field)

22. 22 Practical application of the developed model The model is used currently… Example? The model can be used as a demand forecast to calculate the expected demand until the EOS date. AR can use the model to determine whether reusing a certain part type is interesting.

23. 23 Scientific relevance for companies The model can easily be applied to other companies which deal with LTB decisions or whether reusing parts is interesting. Even if only supply parts from the field or supply parts from dismantling can be used.

24. 24 Conclusions 1.With the designed model, LTBs and the possibilities of reuse can be combined. 2.The new demand forecasting method decreases the LTB buying costs with 22%. 3.The model can be used at other companies too.

25. 25 Questions?

26. 26 Océ specific heuristic Step 1: Determine the desired supply from the field and from dismantling Start at the last interval and work backwards to determine the desired supply from the field and dismantling. This is done such that: l repairing and/or dismantling starts as late as possible l new parts are used first, so holding costs are minimized l in reality, unnecessary repair costs are avoided, in case real demand is lower than expected Step 2: Safety stock Consider the uncertainty in demand and supply by adding a safety stock. Step 3: LTB quantity Determine the optimal LTB quantity by using the total expected demand, the total amount of desired supply, the safety stock, and the inventory on hand. Step 4: Total expected costs Calculate the expected buying, repairing, holding, and obsolescence costs.

27. 27 Research approach 1.Analyzing the current way of working 2.Analyzing the current LTB performance 3.Searching for LTB models in the existing scientific literature 4.Forecasting the quantities and timing of reusable parts 5.Finding a modeling approach 6.Testing whether the developed model is appropriate for Océ 7.Defining an implementation plan

28. 28 Current way of working To determine the LTB quantity, PLIC considers: l to the number of years until the EOS date l the usage of last year l a predefined factor l the proportions between material groups. To determine whether reusing parts is interesting, AR considers: l the expected demand l average historical percentages to forecast the quantities of reusable parts l the involved costs for AR l the involved risks

29. 29 Current situation (2) We had some research difficulties, since Océ l does not register all stock take-over and LTB dates and quantities l does not maintain the status of all parts in SAP l does not store information about stock outs and obsolescence costs accurately We estimate that the total inventory value at the central supply centre (in Venlo) consists of: l 10% LTB parts l 8% Stock take-over parts l 2% Parts of which for a component a LTB is executed After measuring 7 LTB parts with an end-of-service (EOS) date in 2009 or 2010, we estimate that: l PLIC scraps on average 53% of the LTB quantity l PLIC has on average for 99,97% of the demand of LTB parts, directly sufficient stock available

30. 30 Drawbacks of the model (1) For fast decreasing demand: l demand decreases easily to zero l safety stock is relatively high

31. 31 Drawbacks of the model (2) Safety stock: l st. dev of demand in last 12 months * k * √# months l k = safety factor ≈ 2 l is assumed to be the same for each scenario = weak assumption

32. 32 Recommendations 1.Register LTB decisions accurately, store l the LTB date & quantity l the chosen scenario with the desired SF and SD, and the expected demand for each interval 2.Monitor executed decisions → verify whether the real and expected demand, SF, and SD are close to each other 3.Improve the model by: l monitoring the yield rate of SF and SD l reconsidering the safety stock for each scenario 4.Find an appropriate forecasting method for LTB parts that have an increasing demand → Moore ellipse can only be used for decreasing demand

33. 33 Implementation plan Step 1: Define a committee of PLIC, AR, and S&S members → Paul Kengen,.., and.. Step 2: Make the model understandable to the LTB committee Step 3: Register decisions l the part number, material groups and the EOS dates l the LTB date and the chosen scenario l the LTB quantity, the desired number of supply parts from the field and/or dismantling Step 4: Monitor executed decisions The committee should verify for each LTB part whether action is needed, for example AR needs to increase the number of returns from the field. Step 5: Improve the model e.g. start monitoring the yield rates from supply and/or dismantling find a forecasting method for increasing demand

34. 34 Safety stock: chi-square test