and Control approaches, key issues Professor Dr. Frank Herrmann Innovation and Competence Centre for

Transcription

1 Production Planning and Control State-of-the-art the art approaches, key issues Professor Dr. Frank Herrmann Innovation and Competence Centre for Production Logistics and Factory Planning (IPF) University of Applied Sciences Regensburg Postfach 20327, Regensburg Expert Day Simulation, Optimisation, Scheduling, Systema GmbH, Dresden, January 24 th 203

2 Improvement of procedures and parameters for planning in ERP systems used in industrial practice Procurement Project shop Job shop FMS Flow shop JIT production... Project shop Job shop FMS Flow shop JIT production... Distribution

3 Distribution Planning hierarchy Procurement Project shop Job shop FMS Flow shop JIT production... Project shop Job shop FMS Flow shop JIT production...

4 Distribution Planning hierarchy Procurement Project shop Job shop FMS Flow shop JIT production... Project shop Job shop FMS Flow shop JIT production...

5 Impact of capacity case study /2 Lot-sizing problem for one product. Demand 0, 70, 55, 5 pieces within four consecutive days. Setup expenses: 250 and storage costs: 2 per unit. Optimal solution without regarding capacity: Lot in period about 235 units (covers demand in periods to 3). Lot 2 in period 4 about 5 units (covers demand in period 4). Total costs: 860.

6 Impact of capacity case study 2/2 Capacity: 5 units per day. Avalaibility at the end of a period Delay of 2nd demand Delay of st demand! Sales Total costs: 860. commitment 235 units 5 units days Just in time production Total costs: units 70 units 55 units 5 units days

7 Stages of semiconductor manufacturing Diameter of wafer: 000 identical chips of wafer, 40 layers Raw wafers Electrical tests advanced technologies. Put in appropriate package Ensure quality

8 Stages of semiconductor manufacturing Diameter of wafer: 000 identical chips of wafer, 40 layers Raw wafers Electrical tests advanced technologies. Put in appropriate p package Entire manufacturing gprocess: up to 700 single process steps and up to 3 months to produce. Ensure quality Performance measures (planning): machine uitilization (machine account for around 70% of the costs - $ 5 billion US).

9 Operations in a wafer fab (front-end) Most difficult planning problem!

10 Operations in a wafer fab some characteristics ERP systems: Simple or even missing bill of materials T production planning functionality of ERP systems is used only to a small extent. Production Control Systems: wafer fabwide scheduling systems are generally not in use, problematic - selection: Each process flow: process steps on > 00 stations. Same basic process T close to flow-shop, but reentrant: wafers at different manufacturing stages: compete with each other for stations. Duration of process steps: 5 min or less to over 2 h. High amount of batch process - one-third of the wafer fab operations. Different planning problems in productions segments like etch.

11 Enterprise-wide information system architecture

12 Planing for semiconductor (wafer Fab) Capacity Planning long planning horizon and monthly time periods. Master Planning assign production quantities to different facilities, horizon of several months with weekly time periods. Operational Production Planning Master Production Planning Lot-Sizing Short-term capacity planning industrial practise: spreadsheet model: Calculate expected utilization of stations (groups; e.g. etch station group (containing eight stations )) under some amount of demand or loading. Result: expected out dates for products/jobs T lots; CLSP. Stations (groups): regarded as production system building a (flat) bill of materials; MLCLSP.

13 Methodological procedure in ERP-Systems Master Production Planning Material Requirements Planning Component Component k + Component k Net requirements Lotsizing Order scheduling Capacity Planning short term Workload adjustment Scheduling

14 More complex case study /2 20 products and 3 resources (R, R2 and R3) R R R R R R 2 3 R2 7 R3 4 R2 8 R3 0 R 2 R 2 6 R R2 3 5 R2 9 R3 7 R 2 R2 6 R3 20 R3 Bill of materials

15 More complex case study 2/2 Demand of 2 final products: k/t Capacity per period: 000 Lead times : Final product: 0 All other: period Feasible schedule: no demand in sum of lead times (over components).

16 Material Requirement Planning with Silver-Meal-Heuristic for lotsizing ( product) /3 Period P P P P P P P P P P P P P P P P P P P P Results

17 Material Requirement Planning with Silver-Meal-Heuristic for lotsizing ( product) 2/3 j/t resource loads

18 Material Requirement Planning with Silver-Meal-Heuristic for lotsizing ( product): resource loads and delays 3/3 Resource Ressurce 2 [unit] Load [unit] Load Period Period Resource 3 Lo oad [unit] Delays: 24 of 04 orders are delayed 6 of orders for both final products have a delay of period Period

19 Optimal solution /3 Period P P P P P P P P P P P P P P P P P P P P Results

20 Optimal solution /3 Period P P2 Significant deviation P3 of standard PPS P4 solution: e.g. shift of P5 product 20, other lots P6 Not detectable by P7 experienced worker! P P P P P P P P P P P P P Results

21 Optimal solution 2/3 j/t Resource loads

22 Optimal solution 3/3 Ressource Ressource 2 Load [unit] Load [u unit] Period Period Ressource 3 Lo oad [unit t] Resource loads Period

23 Methodological procedure in ERP-Systems Master Production Planning Master Production Planning Material Requirements Planning Capacity Planning Component Component k + Component k Calculate net requirements Lotsizing Order scheduling Multi-Level Capacitated Lot Sizing Problem (MLCLSP) Workload adjustment Scheduling Scheduling Used in commercial ERP-Systems Actually to solve!

24 Fluctuation of lead times /6 Simulation Experiment: Structure of products and processes: 40 products/operations on 6 low-level codes, and demand of final products: dynamic and fluctuating (period: day with 2 shifts). (Standard) PPS successive planning concept - with fix lead times (for each product): Release of orders based on material requirements planning. 4 days before the expected due date, and 7d days before the expected ddue date. MLCLSP: Deterministic minimum lead time of period for each product/operation. Simulation of scheduling by a deterministic job shop simulation model.

25 Fluctuation of lead times 2/6 Frequ uency days before the expected due date Lead time (operation) [shifts]

26 Fluctuation of lead times 3/6 2 Frequ uency days before the expected due date Delay (operation) [shifts]

27 Fluctuation of lead times 4/6 Frequ uency days before the expected due date 7 days before the expected due date Lead time (operation) [shifts]

28 Fluctuation of lead times 5/6 2 Frequ uency days before the expected due date 7 days before the expected due date Delay (operation) [shifts]

29 Fluctuation of lead times 6/6 Frequ uency PPsuccessive planning concept / material requirements planning: calculates with fix lead times (for each product). Optimum: calculates lead times (dynamic) days before the expected due date 7 days before the expected due date Optimum Lead time (operation) [shifts]

30 Operations in a wafer fab

31 Wafer fab: main issues with commercial ERP- Systems Cycle time is the time needed for a job (lot of wafers), to travel through the semiconductor manufacturing system including queue time, processing time, and transit time. Facts about cycle time: Cycle time is load dependent! Queueing theory: cycle time increases nonlinearly with resource utilization. Causes some problems in model formulations for production planning: cycle time information serves as a parameter of the models and production planning approaches determine the load of the resources by determining release quantities.

32 Output t (X) Approach: clearing functions, primarily for semiconductor wafer fabrication facilities industry Constant Proportion Fixed Capacity Effective Clearing Function WIP Inventory Effective CF : Clearing Functions express the expected output (throughput) of a resource (may be production system) over a given period of time as a function of some measure of the system workload over that period.

33 Usage of clearing cunction some examples Kacar, and Uzsoy in 200 developed a clearing function for a wafer fabrication facility from empirical data simulation model to generate the data and evaluate the performance. Alternative to MLCLSP (optimal solution): Order release (originally developed in 980s Kettner and Bechte, than Wiendahl) nowadays by optimisation models; e.g. Missbauer 20: clearing function models stochastic properties of the material flow. Improves commercial PPS successive planning significantly; again for wafer fabrication facility. Planning production in the face of uncertain demand (robust planning) e.g. by Ravindran, Kempf and Uzusoy in 20 or by Tarik and Uzsoy in 202; much better results than with safety stock calculations lations (inventory management); again for wafer fabrication facility.