Simulating Supply and Demand of Dialysis Filters Using Vensim: A Step-by-Step Tutorial for IFB315TC

Introduction to Supply Chain Modelling for Medical Devices

In the healthcare industry, reliable supply chains are critical. For Braun Melsungen AG, a leader in hemodialysis, ensuring a steady supply of filtration devices (filters) is essential. This tutorial guides you through building a Vensim PLE simulation for the IFB315TC Supply Chain Modelling and Optimisation assignment. We'll focus on the non-adoptive make-to-stock (MTS) strategy, using a simplified model to explore inventory dynamics, hiring delays, and demand fluctuations. By the end, you'll understand how to create stock-and-flow diagrams, write Vensim equations, and interpret strip graphs—skills directly applicable to the case study.

Understanding the Business Context

Braun Melsungen AG produces dialysis machines and filters. The company experiences periods of low capacity utilization followed by production surges. The assignment suggests that demand is more stable than supply, hinting at internal issues. Key facts from the case:

• Only in-house production supplies filters.
• Sales go exclusively to dialysis clinics.
• Inventory only increases with fabrication.
• Delivery requires inventory in warehouse.
• Current backup inventory: 300,000 filters.
• Employee training/termination takes 2 months.
• A trained employee produces 1,000 filters/month (ideal) but actual productivity is 6.4 hours/day, yielding 800 filters/month (since 6.4/8 * 1000 = 800).
• Currently 125 employees, fixed demand 100,000 filters/month.

This setup mimics real-world challenges like those in AI-driven logistics or just-in-time manufacturing seen in tech supply chains.

Part 1: Non-Adoptive Stock and Flow Diagram

In the non-adoptive MTS strategy, production happens before demand is known. The stock-and-flow diagram in Vensim includes:

• Stock: Inventory of filters (units).
• Flow: Production rate (filters/month) and Shipment rate (filters/month).
• Variables: Desired production, workforce, hiring rate, etc.

The diagram shows that production adds to inventory, while shipments subtract from it. Since demand is fixed at 100,000 filters/month and current workforce is 125, production = 125 * 800 = 100,000 filters/month, so inventory remains constant at 300,000 initially. However, because the system is non-adoptive, production does not adjust to changes in demand—it stays at 100,000 unless workforce changes are made (which take 2 months). This leads to inventory imbalances when demand shifts.

Part 2: Vensim Equations for Non-Adoptive Model

For the scenario with demand changes every 2 years (24 months) over 120 months, we define:

• Demand = STEP(100000,0) + STEP(20000,24) + STEP(-50000,48) + STEP(40000,72) + STEP(0,96) (simplified; actual implementation uses multiple STEP functions or a lookup table).
• Workforce = 125 initially. Hiring/firing occurs only at the start of each 2-year period to match desired workforce: Desired workforce = Demand / 800. But due to 2-month delay, actual workforce changes gradually.
• Production = Workforce * 800.
• Shipment = MIN(Inventory, Demand) (assuming no backorders).
• Inventory = INTEG(Production - Shipment, 300000).

These equations capture the non-adoptive behavior: production is based on workforce decisions made infrequently, not on real-time inventory.

Part 3: Strip Graphs and Sensitivity Analysis

After running the simulation for 120 months, strip graphs show:

• Inventory fluctuates: when demand rises to 120,000 (months 24-47), inventory drops because production lags; when demand falls to 70,000 (months 48-71), inventory builds up.
• Workforce changes stepwise every 24 months, but with a 2-month delay, causing temporary mismatches.

Sensitivity analysis for each 2-year interval might vary parameters like hiring delay or productivity. For instance, reducing hiring delay from 2 months to 1 month could stabilize inventory faster—a lesson for real-world supply chains.

Connecting to Current Trends

Just as AI models need constant retraining (like hiring delays), supply chains must adapt. Consider how semiconductor shortages affected gaming consoles and EVs—similar to filter shortages here. Using Vensim, you can test “what-if” scenarios, like increasing productivity through automation (a trend in Industry 4.0).

Conclusion

This tutorial covered the basics of modelling a non-adoptive MTS supply chain for dialysis filters. By mastering stock-and-flow diagrams, equations, and sensitivity analysis, you're prepared for the IFB315TC assignment. Remember to use Vensim PLE (free) and follow formatting guidelines: Arial 12, 1.5 spacing, variables in italic/blue. Good luck!