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Cork big data_analytics-de_puy_synthes_datsci_cit
- 1. Cork Big Data & Analytics Group The Road to DatSci 2017
- 2. Agenda • Introduction • Path of Analytics in DePuy • Supply Network Planning • Solution • Demonstration • Importance of Prototyping • Q&A
- 3. FAMILY OF CONSUMER COMPANIES MEDICAL DEVICE COMPANIES HIP SOLUTIONS Pinnacle®, Corail® KNEE SOLUTIONS Attune®, Sigma® EXTREMETIES Shoulders, Ankles
- 4. Data Science Group Mick Phelan, PhD. – Head of Data Science Group 14 yrs. experience in planning & analytics across academic, electronics, service & medical device sectors Jimmy Hennessy – Lead Data Scientist: MAKE 6 yrs. experience in real time analytics. Eamonn O’Toole, PhD. – Lead Data Scientist: PLAN 9 yrs. experience in consultancy and academia Alice Harte – Graduate Data Scientist Mags Wildor – Graduate Data Scientist Aidan Cleere – Student Data Scientist
- 5. Why do we need analytics? Become an insight / analytics driven organisation The Bi-Modal Supply Chain ? WHY? do we need data science & analytics?
- 6. Forecasting, Scenario Planning Product Clustering & Segmentation Demand Plan Asset Utilisation Capacity Planning Inventory Entitlement Scenario Planning Production Plan Production Schedule Optimisation Dynamic Routing Operations Anomaly Detection – SPC Anomaly Detection – X-Ray Quality Predictive Project Analytics Capital Portfolio Optimisation Predicative Asset Maintenance OEE Engineering WHERE? do we need to get to? E2E Supply Chain Analytics
- 7. Outreach Programs Engaging with students on the analytics opportunities in health care industry / STEM Supervising advanced analytics student projects Industry representation on college course boards Citizen Data Scientist Developed & delivering course content on postgrad advanced analytics program Offering onsite courses statistics & programming for potential Citizen Data Scientists Graduate Program Delivering bespoke 36 week graduate data science course for new grads in the team Projects Predictive Asset Maintenance Entitlement Modelling Portfolio Optimisation Service Optimisation Anomaly Detection NOW? where are we?
- 8. Citizen Data Scientist Core Data Scientists Democratization of Data Democratization of Models • Embed data science into our DNA, CDS employ the models & data to aid faster data driven decision making across org • Utilize the data and models at scale • Make data available for descriptive & advanced analytics • Level 2 Historians, Level3 MES, Level 4 ERP • Copy to data lake away from production • Develop models with E2E SC focus • Develop the data science toolkit appropriate to an organization • Hub & Spoke model • Deploy analytical models employing the data to optimize business processes • Genetic Algorithms, simulation, forecasting, anomaly detection, PdM HOW? will we get there?
- 9. WHY? WHERE? NOW? HOW?
- 10. Supply Network Planning Manufacturing Level Distribution Level Market Level US made product EMEA made product
- 11. Inventory Entitlement Cycle Stock Pipeline Stock Safety Stock Risk Mgmt Events SLOB Unentitled Stock Base Entitlement Total Inventory Entitlement Total Inventory Unplanned inventory Depletes with demand and is replenished with supply Transport and manufacturing network flow Protects against variation in supply and demand Protects against expected but abnormal supply or demand Supports a specific known requirement Slow moving or obsolete inventory
- 12. Inventory Entitlement • Benefits: – Improved customer service – Right inventory, right place, right time – Standardised process across PP&L organisation – Reduced supply variability – Lower inventory levels & costs Parameters • Lot size • Period between review • Order frequency • Demand forecast • Demand variation • Lead time • Supply variation • Etc., Business rules • Service level targets • Min/max stock thresholds
- 13. As-Is Process SS requirement at Secondary DP Forecast over lead-time Net on Hand at Secondary On Order + In transit to Secondary =+ - - Entitlement Model Lot Size Order Frequency P.B.R Entitlement Settings Other Inputs Historical Demand Forecast Deployment Requirement Plan 1. Entitlement setting 2. Deployment Calculation 3. Pick, Pack and Ship How much inventory should I hold? Do I need to deploy? Make deployment
- 14. Supply Network Planning: Problem Statement • Reduce financial cost of the deployment of inventory from Primary to Secondary Hubs – ≈ 80% of deployments 1 or 2 items • Parameters (Default of 1) – Lot size • Potential: 1 to 250 – Period between review (PBR) • Potential: 1 to 10 – Order Frequency • Potential: 1 to 5 Costs • Inventory holding • Logistics and transportation • Administration • Lost sales (Back orders)
- 15. Requirements for solution 1. Speed – Excel entitlement model too slow 2. Understand potential impact – What effect will different parameter values have? 3. Optimize target parameters – How to select the best values? 4. Usability – The solution must be accessible to end users
- 16. Supply Network Planning Optimisation SS requirement at Secondary DP Forecast over lead-time Net on Hand at Secondary On Order + In transit to Secondary =+ - - Entitlement Model Lot Size Order Frequency P.B.R Entitlement Settings Other Inputs Historical Demand Forecast Deployment Requirement Plan 1. Entitlement setting 2. Deployment Calculation 3. Pick, Pack and Ship Entitlement Model Lot Size Order Frequency P.B.R Genetic Algorithm Simulated Demand Historical Demand Simulated Deployment Plan Agent Inventory Cost Deployment Cost
- 17. Genetic Algorithm - Workflow Initialise population Terminate ? Evaluate fitness Elitism Parent Selection Crossover Mutation Desired size reached START END Entitlement Model Plan Agent Simulated Demand Simulated Deployment Cost Y Y N N
- 18. Importance of Good Coding Principles • Keep your code modular. • Remember any small inefficiencies will become a more serious issue when dealing with larger data sets. • There is no need to reinvent the wheel. Use a repository to save your code so others can re-use. • If it looks too complicated it probably is a bad idea. • Only print to the screen if it is essential.
- 19. The Blend of Data and Computer Science • There is a good argument to having 1 full stack developer in your data science team • It is extremely important to prototype • Showing an application functioning is better than any explanation • Decide on a code stack, and try to stick to it as much as possible