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- 1. A Framework for Supply Chain Planning in Make-to-forecast Environments Master Thesis defense By M.D. (Marten) Kraaij October 13, 2016
- 2. Presentation outline 1. Introduction to FEI and this research 2. Main research question 3. Framework for SCP 4. Gaps between ideal situation & FEI 5. Detailed planning approach 6. Reflection on the research: conclusions and recommendations Master thesis defense Marten Kraaij 10/13/16 Slide 2 of 29
- 3. Introduction to FEI and this research
- 4. Before we start: as of September 2016, FEI is part of Thermo Fisher scientific. Master thesis defense Marten Kraaij 10/13/16 Slide 4 of 29
- 5. In Eindhoven, FEI produces high-end transmission electron microscopes (TEM)… Master thesis defense Marten Kraaij 10/13/16 Slide 5 of 29
- 6. … in order to make ground-breaking research possible. Master thesis defense Marten Kraaij 10/13/16 Slide 6 of 29
- 7. FEI’s business environment is challenging… Product complexity and diversity Unique microscope; Long production lead time; Uncertain lead times Volatile market conditions Short requested customer lead time; Customers subject to volatile markets and/or government subsidies Rapid technological developments Product or component life cycles as short as a year Master thesis defense Marten Kraaij 10/13/16 Slide 7 of 29
- 8. When FEI starts building a TEM… • … there is no customer order. • … it is not known how the actual microscope should be (its configuration). • … it is not known how long it will take to produce. • … it is not known what the actual variant of components is going to be when the microscope finishes. Crucial decisions are required early! “How to decide as good as possible?” Master thesis defense Marten Kraaij 10/13/16 Slide 8 of 29
- 9. FEI experiences planning nervousness • Product complexity and diversity • Demand uncertainty • Business strategy • Planning and control • Supply chain coordination Master thesis defense Marten Kraaij 10/13/16 Slide 9 of 29
- 10. Main research question How to use a framework for supply chain planning and control, buffers against uncertainty and control mechanisms, in a Make-to-forecast environment, under the objective of improving responsiveness and stability? Master thesis defense Marten Kraaij 10/13/16 Slide 10 of 29
- 12. Supply Chain Responsiveness (Reichart & Holweg) External requirements Internal determinants Responsiveness Master thesis defense Marten Kraaij 10/13/16 Slide 12 of 29
- 14. Production strategies and the CODP Traditional: make-to-stock, make-to-order, assemble-to-order FEI: make-to-forecast (Meredith & Akinc) Master thesis defense Marten Kraaij 10/13/16 Slide 14 of 29
- 15. The material flow in a Make-to-forecast manufacturing process Master thesis defense Marten Kraaij 10/13/16 Slide 15 of 29
- 16. Framework for supply chain planning
- 17. Ideal hierarchical planning framework Master thesis defense Marten Kraaij Strategic planning Aggregate planning / S&OP Supply chain operations planning Production unit control Order acceptance 10/13/16 Slide 17 of 29
- 18. We can see more detail in an operational planning concept (OPC) Master thesis defense Marten Kraaij 10/13/16 Slide 18 of 29
- 19. Case study
- 20. We investigated the current situation at FEI Supply Chain Planning at FEI Company Director Sales&Service Director Operations S&OPPrincipalSVPOperationsPlanning Operational procurement OrderDeskGlobalSourcing Operations Engineering Weekly/Daily operational planningMonthly Sales & Operations PlanningYearly/Quarterly Strategic Planning Create ship request Analyze supply feasibility Ship request agreement Approveshipment plan Adapt Master Production Schedule Global Local (EHV) Change and/or release work order and procurement order Change and/or release purchase order Production planning Send forecast to supplier Approveand prioritize order Accept order configuration Initiate order shipment E1 F1 F2 G E2 J K L H M N O Adapt Master Production Schedule J Parameter setting (assemblies/final systems) D Parameter setting (purchase materials) C2 Strategic Long range Planning (SLRP) A Define supplier contracts C1 Design for Manufacturing B Resourceplanning I Master thesis defense Marten Kraaij 10/13/16 Slide 20 of 29
- 21. As a result, we identified five gaps Supplier agreements Order acceptance Forecasting in S&OP Parameter setting Aggregate planning and MPS Master thesis defense Marten Kraaij No integrated methodology for parameter setting Aggregate planning processes are not completely defined 10/13/16 Slide 21 of 29 Focus on direct material cost price Expression of hopes and objectives Limited workload control and capacity management
- 23. Operational planning concept Master thesis defense Marten Kraaij 10/13/16 Slide 23 of 29 Input: • S&OP forecast • Customer orders Output: • Order releases • System starts • Inventory • Projected demand
- 24. Key characteristics of the new approach Master thesis defense Marten Kraaij MPS (product family level) Module requirements PBOM Material requirements MRP MPS (product family level) Material requirements PBOM MPS for decoupled modules Product Mix Remaining module requirements MRP MRP “Common parts set” MRP Master production scheduling: two-level approach and decouple modules. Shipment Plan MPS MRP Exception Messages Corrective operational actions Shipment Plan MPS MRP Exception Messages Constraints Constraint-based planning: algorithm for material availability planning (MAP). 2. Multiple-option forecast operations 1. Family-generic operations 3. Order-specific operations 4. Pre-defined MTO operations & order-specific operations Order entry CODP Family-specific planned lead time Minimum customer-order lead time Order match Categorization of material; differentiated planning & control. 10/13/16 Slide 24 of 29
- 25. Reflection
- 26. Conclusion – Back to the main research question How to use a framework for supply chain planning and control, buffers against uncertainty and control mechanisms, in a Make-to-forecast environment, under the objective of improving responsiveness and stability? Master thesis defense Marten Kraaij 10/13/16 Slide 26 of 29
- 27. Scientific contribution • Relation of planning nervousness and responsiveness concepts • Framework for SCP in MTF environments • Application SCR framework • Innovative planning approach • Extension of MAP algorithm Master thesis defense Marten Kraaij 10/13/16 Slide 27 of 29
- 28. Recommendations to FEI Master thesis defense Marten Kraaij Supplier agreements Order acceptance Forecasting in S&OP Focus on flexibility and lead time reduction Focus on direct material cost price Actual expected volatile behavior Expression of hopes and objectives Explicit capacity management and customer lead time Limited workload control & continuous order changes New planning approach Differentiated control & material feasible Unconstrained & indifferentiated 10/13/16 Slide 28 of 29
- 29. Directions for further research • Lead time reduction • Safety stock setting • Material control with repairs and maintenance Master thesis defense Marten Kraaij • No organizational change theory applied • No quantitative proof that we improve responsiveness. Limitations 10/13/16 Slide 29 of 29
- 30. End of presentation Please feel free to ask questions!
Editor's Notes
- I’m going to try to tell you what I have discovered in the past 7 months.
- Thermo Fisher serves science. Create everything found in a lab, from complex mass spectrometry systems to plastic bottles.
- On the left, you see a low base microscope. Electrons emitted from the top. A 2D image produced as result. Left: Krios; similar system inside. Cooling mechanism in order to enable to see viruses. Microscopes can be worth 5 million dollars. Manufacturing lead time anywhere between 6 and 12 months.
- In all of these sectors or research areas, FEI’s microscopes can be used.
- Product complexity and diversity Each microscope is unique Long manufacturing lead time which is also uncertain Volatile market conditions Requested customer lead times are short – half of the orders < 3 months. Customers subject to volatile markets and/or government subsidies Rapid technological developments Product and/or component life cycles might be as short as a year
- Voorbeeldje voor planning & Control en SCcoordination When I entered within FEI, the problem they stated was planning nervousness. I commenced the project by making a cause analysis diagram, to identify the various causes for this problem. Areas are product complexity, demand uncertainty, business strategy, planning and control, supply chain coordination Some things we can’t do anything about, some things we can
- How FEI should plan accordingly in order to reduce planning nervousness, we can not take from literature directly. What I did is create such a process. That is: we should start by laying the grounds, the basics for further research. And as a result, we came with the main research question, such as stated here. We see three key elements here in the main research question. All research questions for a general case: What is planning nervousness? What kind of optimal framework? Decoupling points and buffers? Specific MTF complexities? For FEI specific: Key features at FEI? How can we close the gaps?
- MTS: simple supermarket MTO: nice closet (not from IKEA) that is crafted from wood. ATO: Dell. MTF: start building at some point, you know what the order is configure to order Modifications, materials suddenly needed. M&A do not address those issues yet.
- At FEI, it is a little more complicated. So, what does FEI, more or less do: Start production of some modules separately. Put these modules all together to produce a generic system. This is a basic setup of an exemplary product family, but it should be a working TEM already. Otherwise, we cannot go testing it. Some modules are only assembled in a later stage. As well, when the customer order is known, some modules should be disassembled, or e.g. alignments changed/added. This late stage is called configuration. And, as you might have noticed, there is a KANBAN inventory underneath. These are materials used in various part of manufacturing, mainly screws & bolts.
- Vier niveaus van beslissing maken. Strategisch, aggregaat, SCOP/order acceptance. But planning is only really defined with an operational planning concept. Here we state exactly what decisions are made, when, triggered how, with what inputs and what outputs.
- I've just introduce the framework in an hierarchical manner. Here we see it in process shape. We are not going to work through all of these OPCS’s. It’s more important that we see a few crucial processes.
- 20 minuten mag je hier bezig zijn, dan haal je de rest makkelijk.
- Eerst vertellen dat bovenste drie uiteindelijk buiten de scope liggen. The first three gaps are not completely solved, but merely opportunities for improvement. We address both of these two gaps with a novel methodology for planning in a MTF environment. We will not go into much detail here, but we will address some of the concepts that are implemented in this approach.
- Input Yz up left --> forecast and orders Output: demand in a period, released work orders, start ups and inventories We address both of these gaps with a novel methodology for planning in a MTF environment. We will not go into much detail here, but we will address some of the concepts that are implemented in this approach.
- Categorize material Different control mechanisms Different parameter setting methods Master production scheduling: two-level approach and decouple modules Constraint-based planning: algorithm for material availability planning (MAP)
- 1 Defined concepts 2. Provided framework for MTF 3. Took into account responsiveness requirement, 4. Applied framework to FEI, 5. Provide planning approach and support development of buffering and parameters This research answers the research question by: i) defining the concepts responsiveness and stability based on accumulated empirical evidence in literature in Chapter 1; ii) providing an adapted framework for SCP in a Make-to-Forecast business environment, based on a case study at FEI and consistent with other theories in the field of SCP in Chapter 2; iii) taking into account the objectives of responsiveness and stability in this framework; iv) applying this framework to a real world case in Chapter 3; v) providing a planning approach that supports the development of control mechanisms and the placement of buffers against uncertainty in Chapter 4.
- Logically, lead time reduction is the largest enabler of responsiveness.