[DSC Croatia 22] Manufacturing optimisation & planning using ML Platform - Nino Pozar

[DSC Croatia 22] Manufacturing optimisation & planning using ML Platform - Nino Pozar
Manufacturing optimisation & planning using ML Platform Data Science Conference Croatia 2022 Nino Požar Data Scientist
1. Introduction 2. ML Platform - architecture 3. Working example 4. Use case example 5. Benefits 6. Conclusions Agenda
Introduction Challenge FACTORY 1|DEMAND FORECASTING 2|OPTIMISE PRODUCTION PLANNING 3|SCHEDULE PURCHASE ORDERS
ML Platform overview – pipeline Data Input Sales & Stock • Historical sales, stockouts • Current stock per item • Open orders Production • Raw- and semi- stock levels • Bill of materials for item • Production capacity • Manufacturing planning Forecast Demand Forecast demand per item • Evaluate granularity • ML model per item Quantity Definition Recommend order per item & procedure • Evaluate forecast & current stock • Define production plan for 5w to avoid stockout • Production quantity (line constraints, nominal capacity) Output Output • Sales: sales forecast, open orders, term contracts • Stock: production plan proposal • Production: work order plan, impact on revenue in case of stockout
ML Platform overview – pipeline Data Input Sales & Stock • Historical sales, stockouts • Current stock per item • Open orders Production • Raw- and semi- stock levels • Bill of materials for item • Production capacity • Manufacturing planning Forecast Demand Forecast demand per item • Evaluate granularity • ML model per item Quantity Definition Recommend order per item & procedure • Evaluate forecast & current stock • Define order and production plan for 5w to avoid stockout • Ordering quantity using constraints (item, vendor, packaging) • Production quantity (line constraints, nominal capacity) Output Output • Sales: sales forecast, open orders, term contracts • Stock: order proposal and production plan proposal • Production: work order plan, impact on revenue in case of stockout Production Optimisation Production optimisation • Use recommendation quantities • Schedule work orders per line to maximize profit • Evolutionary algorithm
ML Platform overview – pipeline Data Input Sales & Stock • Historical sales, stockouts • Current stock per item • Open orders Production • Raw- and semi- stock levels • Bill of materials for item • Production capacity • Manufacturing planning Forecast Demand Forecast demand per item • Evaluate granularity • ML model per item Quantity Definition Recommend order per item & procedure • Evaluate forecast & current stock • Define order and production plan for 5w to avoid stockout • Ordering quantity using constraints (item, vendor, packaging) • Production quantity (line constraints, nominal capacity) Output Output • Sales: sales forecast, open orders, term contracts • Stock: order proposal and production plan proposal • Production: work order plan, impact on revenue in case of stockout Production Optimisation Production optimisation • Use recommendation quantities • Schedule work orders per line to maximize profit • Evolutionary algorithm
ML platform GET PARTS BUILD PRODUCTS SELL TO CUSTOMER Working example Let’s build some LEGO’S
FORECAST DEMAND Contextual data Outliers, weather, substitutes, promotions… Enough data Relevant and true data Data Forecast Demand • Daily, weekly, monthly
• F – Finished product • H – Half Product • I – Ingredient F1 F2 F3
• F – Finished product • H – Half Product • I – Ingredient 1 x I1 3 x I2 1 x I3 1 x H1 2 x H2
• F – Finished product • H – Half Product • I – Ingredient 1 x I1 3 x I2 1 x I3 1 x H1 2 x 1 x I3 1 x I4
• F – Finished product • H – Half Product • I – Ingredient F1 F2 F3 H1 H1 H2 H1 I3 I1 I2 I2 I4 I2 I3
Plan Time steps 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 FPL1 FPL2 HPL1 H1 I2 I1 1. cycle F1 H2 I3 I4
Plan Time steps 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 FPL1 FPL2 HPL1 1. cycle F1 H2 I3 I4 F1 I2 I1 F1 F1 H1 F3 lacks H1 & I2
Plan Time steps 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 FPL1 FPL2 HPL1 F1 F2 lacks I2 F1 F3 H1 F1 I2 I1 F1 F3 F3 lacks H1 & I2 1. cycle I3 I4 H2 • We lack H1 & I2
Plan Time steps 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 FPL1 FPL2 HPL1 F1 F2 lacks I2 F1 F3 H1 F1 I2 F2 F3 I3 I1 F2 F1 F3 F3 lacks H1 & I2 1. cycle H2 F2 I4 H1 • We lack H1 & I2
H1 Plan Time steps 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 FPL1 FPL2 HPL1 F1 F2 lacks I2,I3 F1 H1 F3 H1 F1 H2 I2 F2 I3 F2 I1 F2 F1 F3 F3 lacks H1 & I2 H1 I4 2. cycle H1 • We still lack H1 for F3 • We lack I2 & I3
H1 Plan Time steps 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 FPL1 FPL2 HPL1 F1 F2 lacks I2,I3 F1 H1 F3 H1 F1 H2 I2 F2 I3 F2 I1 F2 F1 F3 H1 I4 2. cycle H1 F3 lacks I2 • We still lack H1 for F3 • We lack I2 & I3
Plan Time steps 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 FPL1 FPL2 HPL1 F1 F2 lacks I2,I3 F1 H1 F3 H1 F1 H2 I2 I3 F2 I1 F2 F1 F3 F3 lacks I2 H1 H1 I4 2. cycle H1 F2 Order I2 Order I3 • We lack I2 & I3
Plan Time steps 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 FPL1 FPL2 HPL1 F1 F2 lacks I2 H1 F1 F3 H1 F1 H2 I2 I3 F2 I1 F2 F1 F3 I2 F3 lacks I2 H1 H1 I4 3. cycle H1 F2 I3 Order I2 Order I3 • We still lack I2
Plan Time steps 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 FPL1 FPL2 HPL1 F1 F2 lacks I2 H1 F1 F3 H1 F1 H2 I2 I3 F2 I1 F2 F1 F3 I2 H1 H1 I4 3. cycle H1 F2 I3 Order I3 F3 lacks I2 Order I2 • We still lack I2
Plan Time steps 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 FPL1 FPL2 HPL1 F1 F2 lacks I2 H1 F1 F3 H1 F1 H2 I2 I3 F2 I1 F2 F1 F3 I2 F3 lacks I2 H1 H1 I4 3. cycle F2 I3 H1 Order I2 Order I3 • Global order: F1, H1, I3, I2, F3, F2
Plan Time steps 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 FPL1 FPL2 HPL1 F1 F2 lacks I2 H1 F1 F3 H1 F1 H2 I2 I3 F2 I1 F2 F1 F3 I2 F3 lacks I2 H1 H1 I4 3. cycle F2 I3 H1 Order I2 Order I3 • Global order: F1, H1, I3, I2, F3, F2
Bob Planner ML platform Use case This is Bob…
Bob Planner How can AI help Bob? Manufacturing optimisation with ML Platform • Bob is presented with results – BI application
Bob Planner How can AI help Bob? Manufacturing optimisation with ML Platform • Bob is presented with results – BI application Bob Planner
Benefits? Service level is increased • Less delays in production Increased cash flow due to controlled stock level • Stock levels can be decreased from 25%-65% Increased profit due to higher sales and lower costs • Forecasting ensures product availability • Work orders optimised to satisfy demand • Less waste Automation as the ordering procedure is transferred into algorithms of ML platform • Increases productivity & utilisation; decreases manual work • Work order plan optimised • Quicker learning curve for new planners/purchasers (replacements) Possibility of controlling order frequency per vendor • Date of incoming trucks/containers • Less jams in warehouses Verification of orders through dashboard Advantage in negotiations with vendor Set suitable bonuses for costumers / better promotional activities
Conclusion Automation of orders & planning Controlled and optimised stock Increased profit & cash flow Increased service level Controlled movement in the warehouse At right place Right product At right time Using technology as a tool for crunching large amounts of data unlock benefits: ... so, we are really able to have: across different industries!
Thank you! Nino Požar nino.pozar@be-terna.com +385 91 4007 075

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