[DSC Europe 22] Riddles in Supply Chain Management - How AI solves them - Nino Pozar
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In the talk, we will look at how the use of artificial intelligence can help supply chain management – from optimising inventory levels and production planning to the ordering process itself, applied to a distribution centre and a manufacturing company. The talk will go through the challenges of Supply Chain Management, and we will present what artificial intelligence methods are, and how they are used to predict a demand for products on the market and optimise the production and purchasing of products or raw materials.
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[DSC Europe 22] Riddles in Supply Chain Management - How AI solves them - Nino Pozar
- 1. Riddles in Supply Chain Management - How AI solves them? DSC Europe 2022 Nino Požar Data Scientist, BE-terna +385 91 4007 075 nino.pozar@be-terna.com https://www.linkedin.com/in/nino-pozar SOLVING MODERN SUPPLY CHAIN CHALLENGES
- 2. 1. Supply chain planning challenges 2. Demand forecasting using regression 3. Challenges in forecasting 4. Solving SCM challenges using forecasting 5. Financial impact & business benefits Agenda
- 3. Data & AI potential in Supply Chain
- 4. Data & AI potential in Supply Chain
- 5. Data & AI potential in Supply Chain
- 6. The challenges in supply chain
- 7. • Scheduling purchase orders according to forecasted demand • Schedule work orders to satifsfy market demand Introduction The challenges
- 8. • Scheduling purchase orders according to forecasted demand • Schedule work orders to satifsfy market demand • Send items to retail stores and wholesale customers Introduction The challenges
- 9. • Scheduling purchase orders according to forecasted demand • Schedule work orders to satifsfy market demand • Send items to retail stores and wholesale customers • Replenishment between stores 2| AUTOMATED & OPTIMISED ORDERS 1| DEMAND FORECASTING 3| PRODUCTION PLANNING OPTIMISATION 4| STORE TRANSFERS Introduction The challenges
- 10. • Scheduling purchase orders according to forecasted demand • Schedule work orders to satifsfy market demand • Send items to retail stores and wholesale customers • Replenishment between stores 2| AUTOMATED & OPTIMISED ORDERS 3| PRODUCTION PLANNING OPTIMISATION 4| STORE TRANSFERS 1| DEMAND FORECASTING Introduction The challenges
- 12. Forecasting (regression) Statistical approach: Sales data Forecast sales t0 ttoday 100 200 300 400 0 Average Statistical methods Sales in last period True value
- 13. Forecasting (regression) Machine Learning approach: Sales data Forecast sales t0 ttoday 100 200 300 400 0 ML model Average Statistical methods Sales in last period True value
- 14. DIFFERENT DATA SOURCES CLEAN DATA BUILDING FEATURES DIFFERENT ML MODELS • ERP, BI, CRM, external data • True and relevant data • Outliers, stock-outs, promotions • Substitute data • Date & sales fetures • Contextual features • External features • Different algorithms • Granularity - daily, weekly, monthly • Forecasting per item & location How does it and why does it work
- 15. How does it and why does it work
- 16. What are the riddles in demand/sales forecasting?
- 17. What are the riddles in demand/sales forecasting? How to deal with seasonality of sales?
- 18. What are the riddles in demand/sales forecasting? How to deal with seasonality of sales? How to handle stock-out periods?
- 19. What are the riddles in demand/sales forecasting? How to deal with seasonality of sales? How to handle stock-out periods? How to forecast promotional periods?
- 20. What are the riddles in demand/sales forecasting? How to deal with seasonality of sales? How to handle stock-out periods? How to forecast promotional periods? How to manage outliers in sales?
- 21. What are the riddles in demand/sales forecasting? How to deal with seasonality of sales? How to handle stock-out periods? How to forecast promotional periods? How to manage outliers in sales? How to account for price elasticity?
- 22. What are the riddles in demand/sales forecasting? How to deal with seasonality of sales? How to handle stock-out periods? How to forecast promotional periods? How to manage outliers in sales? How to account for price elasticity? How to solve external impact?
- 23. What are the riddles in demand/sales forecasting? How to deal with seasonality of sales? How to handle stock-out periods? How to forecast promotional periods? How to manage outliers in sales? How to account for price elasticity? How to solve external impact? How to estimate forecast of new items?
- 24. What are the riddles in demand/sales forecasting? How to deal with seasonality of sales? How to handle stock-out periods? How to forecast promotional periods? How to manage outliers in sales? How to account for price elasticity? How to solve external impact? How to estimate forecast of new items?
- 25. How to deal with seasonality of sales? How to forecast promotional periods? How to manage outliers in sales? How to account for price elasticity? How to solve external impact? Riddles in Demand forecasting How to handle stock-out periods? How to estimate forecast of new items?
- 26. How to deal with seasonality of sales? How to forecast promotional periods? How to manage outliers in sales? How to account for price elasticity? How to solve external impact? Riddles in Demand forecasting How to handle stock-out periods? How to estimate forecast of new items?
- 27. Riddles in Demand forecasting How to deal with seasonality of sales? How to forecast promotional periods? How to manage outliers in sales? How to account for price elasticity? How to solve external impact? How to handle stock-out periods? How to estimate forecast of new items?
- 28. Riddles in Demand forecasting How to deal with seasonality of sales? How to forecast promotional periods? How to manage outliers in sales? How to account for price elasticity? How to solve external impact? How to handle stock-out periods? How to estimate forecast of new items?
- 29. Riddles in Demand forecasting How to deal with seasonality of sales? How to forecast promotional periods? How to manage outliers in sales? How to account for price elasticity? How to solve external impact? How to handle stock-out periods? How to estimate forecast of new items? Price = 0.29€ Price = 0.49€ Price = 0.57€ Highest sales HIGHEST MARGIN Lowest sales 0.29€ 0.49€ 0.57€
- 30. Riddles in Demand forecasting How to deal with seasonality of sales? How to forecast promotional periods? How to manage outliers in sales? How to account for price elasticity? How to solve external impact? How to handle stock-out periods? How to estimate forecast of new items?
- 31. Riddles in Demand forecasting How to deal with seasonality of sales? How to forecast promotional periods? How to manage outliers in sales? How to account for price elasticity? How to solve external impact? How to handle stock-out periods? How to estimate forecast of new items? NEW ITEM SIMILAR ITEMS
- 33. Results presented in BI app Recomm. purchase qty Production recomm. Store transfers Integrated into ERP
- 34. Results presented in BI app Recomm. purchase qty Production recomm. Store transfers Integrated into ERP
- 35. Results presented in BI app Recomm. purchase qty Production recomm. Store transfers Integrated into ERP
- 36. Results presented in BI app Recomm. purchase qty Production recomm. Store transfers Integrated into ERP
- 37. Results presented in BI app Recomm. purchase qty Production recomm. Store transfers Integrated into ERP
- 38. Financial impact & business benefits
- 39. Benefits Automation Manual procedures are transfered into algorithm ~ 50% less manual work Cash flow increase Stock contains items that will sell soon Stock level is decreased from 25% - 65% Stock out events are decreased Increased utilisation of equipment The work orders are in optimised sequence Increased service level Less delays in production Increased profit Forecast ensures product availability Optimised work orders satisfy demand & decrease energy consuption Less waste Verification through dashboard Verification of production plan Verification of orders Control Control over frequency of production Control over frequency of orders Control over unexpected events
- 40. Conclusion Using technology as a tool for crunching large amounts of data unlocks benefits: At right place Right product At right time ... so, we are really able to have: …across different industries: Automationof orders& planning Controlledandoptimisedstock Increased profit & cash flow Increased service level Controlledmovementinthewarehouse
- 41. Thank you! Nino Požar +385 91 4007 075 nino.pozar@be-terna.com https://www.linkedin.com/in/nino-pozar „Great things in business are never done by one person. They are done by a team of people” -Steve Jobs
Editor's Notes
- Application of AI in SCM – from purchasing, forecasting demand/sales, optimising production processes, distributing to retail stores/wholesales, etc. And we will see what are challenges in SCM and how AI solves them
- 1 Tell about challenges in supply chain from our experiences with clients 2 Present insights about demand and sales forecasting 3 What callenges occur during forecasting 4 What challenges can be solved in SCM with well done forecasting 5 Present benefits with focus on financial benefits of one system like this for company involved in SCM
- We will show how works operating processes of a company working distribution (retail & wholesale), manufacturing and challenges it faces
- distribuira u trgovine i veleprodaju i nešto imaju svoje proizvodnje Moramo znati kolika je potreba/demand tj prodaja Naručujemo od dobavljača Postavljamo radne naloge shodno tome kolika je potreba Predviđamo prodaju po trgovinama i šaljemo u njih
- Isto i za veleprodaju
- Problem ako je razlika u dinamici trgovina Poslati tamo gdje je veći potencijal prodaje Vidi se kompleksnost procesa Točke primjene umjetne inteligencije 1,2,3,4
- Everything comes down to forecasting
- The importance of visualizing to get trust with end user
- Integracija s ERP
- Listaj sve stranke (Salus, mass, zoo hobby, Gavrilovic, marche, DACH clients) Auto dijelovi Retail - fashion Retail Proizvodnja – hrane Distribucija – hrane Farma/big farma Kemijska
- We work in cloud or on prem Thank you