Who? When? How much? Decision sciences modeling support for ADC manufacturing. Arijit Chakravarty 10/20/15

Transcription

1 Who? When? How much? Decision sciences modeling support for ADC manufacturing Arijit Chakravarty 10/20/15

2 Decision Science Application of model-based analytics to business problems Fit-for purpose approach Focus is on insight (not traditional financial forecasting) Oncology Procurement Clinical trial supplies Manufacturing & CMC Decision Science Finance Commercial & Marketing Health economics Pricing & NPP 2

3 Heavy-duty math provides a competitive edge in many industries Top-line benefits: Better product Market penetration Alternative business model Bottom-line benefits: Cost-control Efficiency Flexibility & robustness In business, seemingly simple questions can have very complex answers!

4 Moneyball changed baseball scouting forever Business pain point: Who has the talent? Sabermetrics (objective analytics) made intuition and gut-based decisions obsolete in baseball scouting

5 Uber fare Uber is using a model-based approach to disrupt the transport industry Business pain point: Is there a better business model? Dynamic pricing model cab fare Time An example of model-based processing of Uber data (microeconomics models set dynamic pricing for cab fares) Other examples: dispatch (matching drivers to passengers); anticipating passenger demand

6 Model-based analytics power Zara s unique selling proposition Business pain point: How do we make fast fashion a reality? Other examples: inventory allocation; system-level optimization; batch-size optimization

7 Chevron uses model-based approaches for everything Business pain point: How do we run more efficiently? Seismic Data Processing Reservoir design Well management CO2 emissions Government negotiations Investment decisions

8 What are some of the challenges in ADC manufacturing Who: In-house or outsource? When: Supply chain management in manufacturing How much: Demand forecasting

9 In-house or outsource? Given a finite internal capacity to do projects, how do you pick the best combination of projects to get the most out of your fixed internal spend, thereby minimizing the cost of outsourcing? (Fairly standard) algorithms can be used to solve this problem robustly Other criteria can be used as well in the algorithms (multi-criteria optimization) Knapsack algorithm Knapsack value Run time (s) Percentage of Resources Used Arbitrage % Beam %

10 Next year, should we expand in-house or outsource? Project demand each year is often unknown Outsourcing of projects can relieve internal constraints in high demand years Internal resources have finite capacity with a consistent cost External capacity is more costly than internal but offers high flexibility The newsvendor problem is a commonly used methodology in demand planning to determine order quantity

11 How many papers a day should the Utilize the ratio of the cost and profit to maximize profits. newsvendor buy? Overage Cost (C o ): The cost of ordering too many is the purchase cost as each paper must be thrown out. Underage Cost (C u ): The cost of under ordering is lost profits The optimal ratio, R, defines the optimal point in the distribution R C u Cu C o

12 Applying the newsvendor model to ADC outsourcing problems Problem: Internal: $3M per project External: $8M per project Yearly projects: μ=10, σ=3 What should internal capacity be? Solution: C o =$3M C u =(8-3)=$5M R=5/(5+3)=0.625 Use an inverse function to determine the point of the normal distribution the probability of the demand being under the order point is 63% The optimal capacity based on this model is 11 projects, and internal budget should be $33M. Demand under order: idle internal resources 7 Optimal Capacity: Demand over order: Missed sales

13 Applying the newsvendor model to ADC outsourcing problems Simulated the newsvendor model at a range of internal capacities to show that the newsvendor prediction holds. 10,000 simulations performed for yearly cost at various internal capacities Internal Capacity: 8 Projects Internal Capacity: 11 Projects Internal Capacity: 14 Projects

14 Applying the newsvendor model to ADC outsourcing problems Lowest total cost is at the internal capacity predicted by newsvendor model Savings at optimal level depends on distribution of projects and cost of capacity

15 What are some of the challenges in ADC manufacturing Who: In-house or outsource? When: Supply chain management in manufacturing How much: Demand forecasting

16 ADC supply chain management is complex and costly Antibody Drug ADC Conjugation Linker

17 Mar-15 Sep-15 Mar-16 Sep-16 Mar-17 Sep-17 Mar-18 Sep-18 Mar-19 Sep-19 Mar-20 Sep-20 Mar-21 Sep-21 Mar-22 Sep-22 Mar-23 Sep-23 Mar-24 Sep-24 Mar-25 Sep-25 Mar-26 Doses Better demand forecasting is critical for supply chain management Developed an alternative that is: Easier configure in hours, not weeks Cheaper eliminate fees Better focus on insight and understanding, not configuring software Clinical supply simulation (current solution vs. DS simulator, 3 trial arms) DS simulator Current Solution Can predict supply needs for ~100 trials in under 5 seconds Date Confidential and Proprietary Business Information. For Internal Use Only.

18 Pooling: How far along the supply chain should we hold protocol non-specific supplies? Attack problem in two ways: Stochastic agent-based simulation Mathematical optimization Look at cost savings of pooling at depot vs. sub-depot vs. sites Factory Depot Sub-Depot Sub-Depot Clinical Sites Clinical Sites Trials Depot Sub-Depot Clinical Sites Answer may differ based on program and therapeutic area 18 DDMMYY Confidential and Proprietary Business Information. For Internal Use Only.

19 Problem: Oncology clinic sales arise from a complex ecosystem Takeda Payers $ $ Drug Oncology Clinic Oncologists: Buy drug from Takeda Distribute to patients Collect from payers Sales driven by patient demand + clinic inventory decisions Payers Drug Dose demand drivers: Mix of indications Mix of regimens Persistence is stochastic and indication dependent Compliance Patients

20 Monte Carlo simulation of single clinic dose demand, inventory, and sales Single clinic patient demand (simulated, 260 weeks) Next Step: Persistence driven by indication specific evolutionary tumor dynamics Direct connection to clinical data - Four indications - Stochastic patient uptake + persistence - Perfect compliance Use model as a test-bed to explore dependence of clinic sales on persistence, compliance, indication, and inventory decisions 20 DDMMYY

21 Many potential pain points can be modeled using agent-based simulations = Agents = Interactions Oncology Clinic Payers Goal: easily model any situation with identifiable entities (e.g. patients, clinical sites, depots) that interact with each other Confidential and Proprietary Business Information. For Internal Use Only.

22 Pooling and lag behaviors of ADC manufacturing supply chains are tractable to modeling Antibody Drug ADC Conjugation Linker

23 Take-homes We re in a manufacturing industry Supply chain and logistical behaviors can be deceptively complex Modeling is your friend

24 Credits Decision Science: Peter Marschel Evan Larson Doug White Anqi Jiang Modeling and Simulation: Jackson Burton Matt Cohen William Antebi (Finance) Nenad Grmusa (Finance) Wen Chyi Shyu 24 DDMMYY Confidential and Proprietary Business Information. For Internal Use Only.