Variability in Supply Chain Planning. Nico Vandaele, GSK Research Chair, KU Leuven PICS Belgium
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1 Variability in Supply Chain Planning Nico Vandaele, GSK Research Chair, KU Leuven PICS Belgium SAS Forum, in Cooperation with PICS Belgium October 9, 2014
2 Agenda NYO Alatus Embracing the variability in your supply chain planning Academic overview by Prof. Nico Vandaele Pharmaceutical case 1 by Carl Verhamme Discrete case 2 by Kris Lieckens Today s reality by Prof. Nico Vandaele Q&A 15: Break Daan Voets and Jos Polfliet, SAS Best practices in using sensor data to increase performance and reliability
3 Planning Performance History 1999 Edelman award with Dana Implementation and further developments at Atlas Copco Continental Tyres Baxter JNJ GSK UCB 2008 Creation of spin-off Nyo Alatus 2012 Partnership NYO & SAS
4 Note: Slides from Carl Verhamme for SAS Forum October 2014
5 Pharmaceutical Co. FlowBalancer used as Improvement and Benchmarking tool besides a planning tool. Value Stream Mapping (VSM) as a starting point during an implementation Test scenarios lead to a future VSM Immediate financial results
6 Capsules Current VSM Suppliers Forecast Order ERP/ MRP Production Control Forecast Order Customers RM s & Components Production Schedule FG s Shipping Schedule I I Weigh Rm. Prod. ID# : Batch Size: Process: 70 min. Setup: 30 min. Cleaning: 0 min. # Oper.: 2 Yield: 100% Avail.: 100% Shifts: 1 B1 Mfg. Prod. ID# : 1434 Batch Size: 200 KG. Process: 585 min. Setup: 270 min. Cleaning: 90 min. # Oper.: 2 Yield: 85% Avail.: 75% Shifts: 2 I B2 Mfg. Prod. ID# : 1436 Batch Size: 155 KG. Process: 1715 min. Setup: 390 min. Cleaning: 90 min. # Oper.: 2 Yield: 80% Avail.: 75% Shifts: 2 QA/QC Prod. ID# : 1436 Batch Size: N/A Process: 480 min. Setup: N/A Cleaning: # Oper.: 2 Yield: N/A Avail.: 100% Shifts: 1 I Capsule Mfg. Prod. ID# : 574C Batch Size: Process: 620 min. Setup: 90 min. Cleaning: 285 min. # Oper.: 2 Yield: 100% Avail.: 85% Shifts: 2 QA/QC Prod. ID# : 574C Batch Size: N/A Process: 480 min. Setup: N/A Cleaning: # Oper.: 2 Yield: N/A Avail.: 100% Shifts: 1 I Bottle Pkg. Prod. ID# : Batch Size: Process: 360 min. Setup: 180 min. Cleaning: 100 min. # Oper.: 5 Yield: 100% Avail.: 75% Shifts: 2 Q.C. Prod. ID# : Batch Size: N/A Process: 960 min. Setup: N/A Cleaning: # Oper.: 2 Yield: N/A Avail.: 100% Shifts: 1 Shipping Customer Orders Batch Size: Process: 240 min. Setup: 0 Cleaning: 0 # Oper.: 11 Yield: 100% Avail.: 100% Shifts: 1 B3 Mfg. QA/QC Prod. ID# : 1435 Batch Size: 192 KG. Process: 1090 min. Setup: 270 min. Cleaning:90 min. # Oper.: 2 Yield: 80% Avail.:75% Shifts: 2 Prod. ID# : 1435 Batch Size: N/A Process: 480 min. Setup: N/A Cleaning: # Oper.: 2 Yield: N/A Avail.: 100% Shifts: 1 I NVA VA VA VA NVA NVA NVA VA
7 Analytical Model - overview Weighing Inbound Test & QA Weighing B1 IM Test & QA B3 ER Test & QA Weighing B2 IR Test & QA Capsules Test & QA Packaging QA Distribution Identify: Productmix Routings Resources (equipment / people)
8 Scenarios Increase of demand Installation of an additional shift Improvement Set-up reduction on the bottleneck Increase MTTF Reduction of variability Optimisation of the batchsize
9 Simulations: Demand Scenario Simulation Parameter BaseCase Simulated % DifferenceCriteria Bead IM Bead ER Capsules Packaging Shipping Total Scenario 0 Base Case WIP Cycle Time Throughput Utilization 88% 88% 59% 68% 88% Scenario 1 Increase demand in finished products by 5% WIP % is too much for the bead Cycle Time Throughput Utilization 93% 93% 62% 71% 93% Diffence WIP Difference Cycle Time Difference Throughput Difference Utilization 5% 5% 3% 3% 5% % WIP 40.56% 49.76% 6.20% 11.29% % Cycle Time 33.87% 42.62% 1.16% 5.98% 24.90% % Throughput 5.02% 5.00% 5.09% 5.09% % Utilization 5.68% 5.68% 5.08% 4.41% 5.68% Scenario Simulation Parameter BaseCase Simulated % DifferenceCriteria Bead IM Bead ER Capsules Packaging Shipping Total Scenario 0 Base Case WIP Cycle Time Throughput Utilization 88% 88% 59% 68% 88% Scenario 2 Reduce available hours per day for packaging 20% WIP Increase of 5% Cycle of Time demand Throughput increases the Cycle Utilization Time by 88% 88% 59% 87% 88% 31 days Diffence WIP Difference Cycle Time Difference Throughput Difference Utilization 0% 0% 0% 19% 0% % WIP 0.00% 0.00% 0.00% 65.56% % Cycle Time 0.00% 0.00% 0.00% 65.53% 2.66% % Throughput 0.00% 0.00% 0.00% 0.00% % Utilization 0.00% 0.00% 0.00% 27.94% 0.00%
10 Simulations: Capacity (extra shift) Scenario Simulation Parameter BaseCase Simulated % DifferenceCriteria Bead IM Bead ER Capsules Packaging Shipping Total Scenario 0 Base Case WIP Cycle Time Throughput Utilization 88% 88% 59% 68% 88% Scenario 4 Increase 50% for hours available for the bead WIP (additional shift) Cycle Time Throughput Utilization 58% 58% 59% 68% 68% Diffence WIP Difference Cycle Time Difference Throughput Difference Utilization % % 0.00% 0.00% % % WIP % % 0.00% 0.00% % Cycle Time % % 0.00% 0.00% % % Throughput 0.02% -0.01% 0.00% 0.00% % Utilization % % 0.00% 0.00% % Cycle Time reduces by 35 days if extra shift on bottleneck is installed
11 Simulations: Utilization (setup, yield) Scenario Simulation Parameter BaseCase Simulated % DifferenceCriteria Bead IM Bead ER Capsules Packaging Shipping Total Scenario 0 Base Case WIP Cycle Time Throughput Utilization 88% 88% 59% 68% 88% Scenario 5 Increase yield in bead 10%, up to 90% in IM, ER WIP Cycle Time Throughput Utilization 88% 88% % Diffence WIP Difference Cycle Time Difference Throughput Difference Utilization 0% 0% 0% 0% 0% % WIP -0.26% 16.14% 0.00% 0.00% % Cycle Time -2.69% 3.24% 0.00% 0.00% 0.50% % Throughput 0.02% 12.50% 0.00% 0.00% % Utilization 0.00% 0.00% 0.00% 0.00% 0.00% Set Up reduction from 6 to 4 on Bottleneck reduces cycle time by 14 days Scenario Simulation Parameter BaseCase Simulated % DifferenceCriteria Bead IM Bead ER Capsules Packaging Shipping Total Scenario 0 Base Case WIP Cycle Time Throughput Utilization 88% 88% 59% 68% 88% Scenario 6 Setup reduction in bead from 6 to 4 in IM, ER WIP Cycle Time Throughput Utilization 84% 84% 59% 68% 84% Diffence WIP Difference Cycle Time Difference Throughput Difference Utilization -4% -4% 0% 0% -4% % WIP % % 0.00% 0.00% % Cycle Time % % 0.00% 0.00% % % Throughput 0.02% 0.00% 0.00% 0.00% % Utilization -4.55% -4.55% 0.00% 0.00% -4.55%
12 Simulations: Batch Size Scenario 2 Reduce available hours per day for packaging 20% WIP Cycle Time Throughput Utilization 88% 88% 59% 87% 88% Scenario 11 Reduce available hours per day for packaging 20% WIP Batch size in packaging from 5 to 7 for 10 mg Cycle Time Batch size in packaging from 10 to 12 for 20 mg Throughput Batch size in packaging from 5 to 7 for 30 mg Utilization 88% 88% 59% 79% 88% Diffence WIP Difference Cycle Time Difference Throughput Difference Utilization 0% 0% 0% -8% 0% % WIP 0.00% 0.00% 0.00% % % Cycle Time 0.00% 0.00% 0.00% % -1.34% % Throughput 0.00% 0.00% 0.00% 0.00% % Utilization 0.00% 0.00% 0.00% -9.20% 0.00% , , , , ,500, ,700, ,000, ,500, ,000, ,000, Packaging cycle time (days) Reduction of available time can be compensated by increase of Batch Size. The optimal Batch Size is 1.7 Mio (10mg) vs current Batch of 0.5 Mio Batch size (# capsules)
13 Capsules Future VSM Suppliers Forecast Order ERP/ MRP Production Control Forecast Order Customers RM s & Components Production Schedule FG s Shipping Schedule I Weigh Rm. Prod. ID# : Batch Size: Process: 70 min. Setup: 30 min. Cleaning: 0 min. # Oper.: 2 Yield: 100% Avail.: 100% Shifts: 1 IM Bead Mfg. Prod. ID# : 1434 Batch Size: 200 KG. Process: 585 min. Setup: 270 min. Cleaning: 90 min. # Oper.: 2 Yield: 85% Avail.: 75% Shifts: 2 Bead Mfg. Prod. ID# : 1435 Batch Size: 192 KG. Process: 1090 min. Setup: 270 min. Cleaning:90 min. # Oper.: 2 Yield: 80% Avail.:75% Shifts: 2 I Bead Mfg. Prod. ID# : 1436 Batch Size: 155 KG. Process: 1715 min. Setup: 390 min. Cleaning: 90 min. # Oper.: 2 Yield: 80% Avail.: 75% Shifts: 2 QA/QC Prod. ID# : 1435 Batch Size: N/A Process: 480 min. Setup: N/A Cleaning: # Oper.: 2 Yield: N/A Avail.: 100% Shifts: 1 ER IR QA/QC Prod. ID# : 1436 Batch Size: N/A Process: 480 min. Setup: N/A Cleaning: # Oper.: 2 Yield: N/A Avail.: 100% Shifts: 1 I I Capsule Mfg. Prod. ID# : 576C Batch Size: Process: 870 min. Setup: 90 min. Cleaning: 285 min. # Oper.: 2 Yield: 100% Avail.: 85% Shifts: 2 QA/QC Prod. ID# : 1436 Batch Size: N/A Process: 480 min. Setup: N/A Cleaning: # Oper.: 2 Yield: N/A Avail.: 100% Shifts: 1 I Decoupling Point Bottle Pkg. Prod. ID# : Batch Size: 4000 Process: 360 min. Setup: 180 min. Cleaning: 100 min. # Oper.: 5 Yield: 100% Avail.: 75% Shifts: 2 Shipping Customer Orders Batch Size: Process: 240 min. Setup: 0 Cleaning: 0 # Oper.: 11 Yield: 100% Avail.: 100% Shifts: 1 NVA VA VA VA NVA NVA
14 Results Reduction of FG inventory by 30 days 6,5 Mio$ in working capital 8,4% reduction in operational costs
15 FlowBalancer A stochastic and analytical planning job shops Powered by
16 Agenda Flow system approach Planning challenges Reason and solution for these challenges: ERP vs ARP Software integration with examples Results from practice
17 Flow System Approach
18 Flow System Job Shops
19 Planning Job Shops Uniform, consistent and advanced decision support tool Monthly sales & operations meeting Capacity-Demand Analysis Lot sizing Improve delivery performance Quickly calculate the impact of decisions during the meeting User friendly interface and powerful visualizations Integration with ERP system!!! Yearly/quarterly process flow design and engineering!! Simulation of new products Simulation of new layouts
20 Planning Job Shops Stochastic nature of the manufacturing system Demand Supply! Seasonality! Product mix changes! Phasing-in/out! Availability of resources changes over the planning horizon Alternative routings, outsourcing, Lot sizes, inventory levels Breakdowns, scrap Unreliable delivery of raw material/ finished good,
21 Planning Job Shops Job Shop Decision Problem Nyo Decision Support Demand Meeting Needs vs Supply Multiple functions Separate tools Complex Interdependent Various goals Intuition Data Warehousing Analytical S&OP System Approach Different views, same world One shared database Modeling Flow Theory Performance measures Software implementation Data Discipline
22 S&OP Job Shops ERP: DETERMINISTIC AND STATIC PLANNING PRODUCT MIX ALTERNATIVE RESOURCES RESOURCE CAPACITY WIP INVENTORY BATCH SIZE PRODUCTION RUN LENGTH RELEASE DATE DUE DATE PROMISING FLOWBALANCER: STOCHASTIC AND DYNAMIC PLANNING Utilization Throughput Inventory Delivery Time + Service Level
23 Job Shops Excessive inventory costs Overdue order costs Volume or Time Sep Oct Nov --- ERP Input Output Target Inventory or Delivery Time Inventory or Delivery Time without Service Level Decisions Production Capacity Production Volume
24 Job Shops Excessive inventory costs Overdue order costs Volume or Time Sep Oct Nov --- ERP Input Output Target Inventory or Delivery Time Inventory or Delivery Time without Service Level Decisions Production Capacity Production Volume
25 Job Shops Balanced buffers: capacity - inventory- time Volume or Time Sep Oct Nov --- Input Target Inventory or Delivery Time Decisions Production Capacity Output Inventory or Delivery Time with Service Level Production Volume
26 Job Shops Wait Set-up Production Process Step 1 Process Step 2 Process Step 3 W S P W P W S P W S P W P W S P Safety Time Actual Planned Delivery Delivery Time Time Release Date Due Date Safety Time is calculated and dynamic Dependent on utilization product mix lot size Performance is consistent: e.g. 90% on-time
27 Job Shops If our standard is a quoted lead time to deliver within 50 hours, our customers will have a service level of 90 % 10% will be too late Expected Time Safety Time Shape of the distribution (Avg and Tail) depends on 'Flow System' S&OP decisions Quoted Lead Time
28 Job Shops KPI (lead time, inventory, ) depends on Utilization Process and setup times Variabilities, disruptions Lot sizes Mix of routings and products
29 Software Job Shops ERP Demand Scenario Builder Aggregate production plan Product mix Lot size Capacity, e.g. working hours Supply Stochastic Analytics Production plan Production system parameters: e.g. lead time offset, lot size, shifts, overtime, Visualization Lead time with service levels Inventory with service levels Utilization details Volume details Time usage details
30 Dashboard - Scenario 1
31 Resource Under/Overcapacity Scenario: Use more/less shifts-overtime
32 Product Volumes Scenario: Assign volume Period 1+3 to Period 2
33 Product Lead Times Scenario: Use alternative routing more
34 Product Lead Times Scenario: Use alternative routing more
35 Positioning FlowBalancer Process Planning Supply Network Planning Years Forecasting Demand Management Strategic Capacity Planning Sales and Operations Planning Months Sales Planning Operations Planning Logistics Manufacturing Services Vehicle Capacity MPS Workforce Capacity Weeks Vehicle Loading MRP Vehicle Dispatching Order Scheduling Workforce Scheduling Days Warehouse Receipt
36 Dana Corporation 1999 Edelman award with Dana Productivity: +27% Manufacturing lead time: -50 to -60% Inventory turns: from 3.5 to 6 Workforce: +41% Sales/FTE: +66% Operating costs: more than -10% S&OP process: 85% of all the improvements with mid term planning Detailed scheduling: the remaining 15% with short term planning
37 Atlas Copco Airtec Implementation and further development at Atlas Copco, Continental Tyres Baxter JNJ GSK UCB Better data quality in ERP system Objective and consistent decision tool Estimate impact of a S&OP decision on KPIs Accurate due date promising Dynamic lead time offsets, up to twice as large than used so far Better customer service & Better delivery performance
38 Questions?
39 We explicitly include the stochastic nature of the production system to allow for variability and disruptions. The mathematical expressions for the expected lead time and lead time variance depend on lot sizes. This enables the plant to minimize lead time and thus optimize lot sizes for all parts simultaneously. If given lead times are unacceptable, adjustments are made to the capacity structure, unloading overloaded equipment or selecting alternative routings. The queuing model responds quickly to these requests, a main reason it has worked. Rony Cremmery Former Supply Chain Spicer-Off Highway, Dana Corp By using these models, our organization has gained insight into non-evident impact factors like uncertainty, variability and stochasticity. This has enabled us to quantify their influence on lead times and associated costs. Noel Janssens Supply Chain Manager Portable Energy Spare Parts, Atlas Copco Airpower NV
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