Week 11 | Session 5: Network Optimization with Capacity Constraints & Supply Chain Control Tower
Course: Supply Chain Digitization — Module 4: Digital Infrastructure
Session Agenda
Section titled “Session Agenda”Part 1 — Network Optimization with Capacity Constraints
Section titled “Part 1 — Network Optimization with Capacity Constraints”1. Model Modification — Replacing Big M with Actual Capacity
Section titled “1. Model Modification — Replacing Big M with Actual Capacity”In Session 4, factories and DCs had unlimited capacity represented by M (a very large number). This session replaces M with actual facility capacity — making the model more realistic.
Constraint Modification
Section titled “Constraint Modification”Session 4 (unlimited capacity):
Σj xij ≤ M × yi ∀iThis session (realistic capacity):
Σj xij ≤ CapFac_i × yi ∀i (CapFac_i = actual capacity of factory i)Σk qjk ≤ CapDC_j × zj ∀j (CapDC_j = actual capacity of DC j)- If yᵢ = 0 (factory closed): RHS = 0 → cannot ship anything.
- If yᵢ = 1 (factory open): RHS = CapFac_i → limited to actual production capacity.
2. Capacity Constraint Applied — Aurangabad Factory
Section titled “2. Capacity Constraint Applied — Aurangabad Factory”Capacity constraint added only at Aurangabad; Nashik and both DCs remain unconstrained.
| SKU | Min Throughput/yr (units) | Max Throughput/yr (units) | Nashik Capacity |
|---|---|---|---|
| SKU 1 | 10,000 | 25,000 | Unlimited (no constraint) |
| SKU 2 | 10,000 | 25,000 | Unlimited (no constraint) |
| SKU 3 | 10,000 | 25,000 | Unlimited (no constraint) |
| SKU 4 | 10,000 | 25,000 | Unlimited (no constraint) |
Purpose: study how adding a capacity constraint at one facility changes the optimal network.
3. Results — With vs Without Capacity Constraint
Section titled “3. Results — With vs Without Capacity Constraint”| Aspect | Without Constraint (Session 4) | With Constraint (This Session) |
|---|---|---|
| Capacity constraint | M (unlimited) for all facilities | Aurangabad: min 10,000 / max 25,000 units per SKU/year |
| Nashik factory | Not opened | Opened — needed to supplement Aurangabad’s limited capacity |
| Aurangabad factory | Opened — sole factory | Opened — but constrained |
| VAPI DC | Opened | Opened |
| V1D DC | Not opened | Not opened |
| Product flow | Aurangabad → VAPI → all 4 markets | Aurangabad + Nashik → VAPI → all 4 markets |
| Total Profit | $5.9 million | $1.4 million |
| Profit impact | — | Drop of ~$4.5 million due to capacity limit |
Why Did Both Factories Open?
Section titled “Why Did Both Factories Open?”- Aurangabad is capped at 25,000 units/SKU/year — not enough to serve all doubled demand alone.
- To meet the full demand at all 4 markets, the model must open Nashik factory as well.
- Nashik has higher production cost ($7/unit vs $5/unit) → reduces profit significantly.
Part 2 — Supply Chain Control Tower
Section titled “Part 2 — Supply Chain Control Tower”4. What is a Supply Chain Control Tower?
Section titled “4. What is a Supply Chain Control Tower?”Definition: a centralized hub that provides real-time visibility and command over every facet of the supply chain.
- Enables businesses to oversee and govern extensive SC activities through a unified platform.
- End-to-end integrated with all information management systems (ERP, WMS, TMS, IoT, etc.).
- Connects: raw material suppliers → sub-suppliers → manufacturers → distributors → retailers → consumers.
5. Control Tower — Use Cases
Section titled “5. Control Tower — Use Cases”| Use Case / Industry | Challenge → How Control Tower Helps |
|---|---|
| FMCG — Snacks (Sunflower Oil SC) | Multiple geographically spread suppliers, seasonal demand, varying lead times, transport disruptions. CT provides: real-time tracking of raw material from supplier to plant. CT action: truck delayed due to accident → CT auto-reroutes shipment or expedites another → prevents production stoppage. Result: reduced transport + holding costs; better procurement agility. |
| FMCG — Dairy / Perishable Foods | National DC network + refrigerated truck fleet; complex routing; no real-time visibility. CT integrates AI-ML + real-time traffic data → optimizes delivery routes + schedules. Considers traffic, weather, order volume → on-time delivery + minimum fuel consumption. Dispatchers can track shipment location, condition, provide accurate ETA. |
| Paint Manufacturing (Multi-plant) | Multiple plants across regions, inventory optimization across plants + DCs, varied supplier lead times. CT provides: real-time visibility of production + inventory at ALL facilities simultaneously. Production managers + central planners collaborate to address breakdowns, material shortages. CT extends to suppliers: monitors raw material availability → auto-adjusts production plan. |
6. Control Tower — Key Capabilities
Section titled “6. Control Tower — Key Capabilities”| Capability | What it Does |
|---|---|
| Real-time Visibility | End-to-end view of entire SC — location, inventory, shipments, production — all in real time |
| Data Integration | Pulls data from ERP, WMS, TMS, IoT sensors, GPS, supplier systems into one unified platform |
| Process Automation | Automates repetitive SC processes — order generation, dispatch scheduling, alerts |
| Data Analytics & Insights | Analyzes SC data → actionable insights for managers (dashboards, KPI reports) |
| Demand & Supply Forecasting | Predicts future demand and supply using AI/ML → enables proactive planning |
| Production Planning & Control | Determines what to produce, where, how much — across multiple plants — optimally |
| Inventory Optimization | Finds optimal inventory levels at each factory and DC — balances understocking vs overstocking |
| Exception Management | Detects disruptions (road block, supplier delay, equipment failure) → auto-triggers recovery action |
| KPI Tracking | Continuous monitoring of key performance indicators — flags underperforming areas immediately |
| Decision Support | Real-time analytical decision-making support — not just visibility, but recommendations |
Exception management example: truck en route hits a road blockage → CT detects → auto-reroutes to fastest alternate path → customer receives on time.
7. Benefits of Supply Chain Control Tower
Section titled “7. Benefits of Supply Chain Control Tower”- Improved service level: deliveries on time, demand met, fewer stockouts.
- Reduced cost: optimized routes, reduced excess inventory, less disruption-related loss.
- Revenue maximization: demand captured, fewer lost sales due to stockouts or delays.
- Increased efficiency: automation of non-value-added tasks, better resource allocation.
- Better collaboration: shared visibility with SC partners across regions and time zones.
- Compliance & governance: monitors regulatory requirements; signals violations automatically.
- Continuous improvement: KPI tracking identifies weak areas; enables data-driven improvement cycles.
- Disruption response: auto-identifies alternate supplier or route when primary fails.
8. Technologies Needed to Build a Control Tower
Section titled “8. Technologies Needed to Build a Control Tower”| Technology | Role in Control Tower |
|---|---|
| Data Integration Platforms | ERP, WMS, TMS, IoT sensors, GPS — collect data from all SC nodes into one view |
| Collaboration & Communication Tools | Seamless communication + information sharing across all SC stakeholders (different regions, time zones) |
| Blockchain | Enhances transparency, traceability and security in SC transactions and data exchanges |
| Analytics & Visualization | Dashboards that visualize KPIs in real time; analytics tools for insights and automated decisions |
| AI & ML Algorithms | Forecasting (demand + supply), disruption recovery plans, inventory optimization, production scheduling |
| Optimization Algorithms | Compute optimal production plans, inventory levels, delivery routes — the prescriptive layer of the CT |
| Cloud Infrastructure | Stores vast SC data; enables real-time access from anywhere; supports collaboration + AI/ML compute |
Week 11 — Full Summary
Section titled “Week 11 — Full Summary”| Session | Key Topics |
|---|---|
| S1 | DT introduction: definition, 5 types (product → network of networks), 4 enabling technologies, 3 DT components |
| S2 | GFA case: optimization model, 3 distance formulas, Excel Solver (GRG Non-linear). Result: DC at Mumbai. |
| S3 | GFA hands-on: Excel formula chain (d_long→d_lat→A→C→dist), SUMPRODUCT, AnyLogistix multi-SKU demo. |
| S4 | Network Optimization: 4 decision variables (yᵢ, zⱼ, xᵢⱼ, qⱼₖ), maximize profit model, Big M, flow balance. Result: Aurangabad + VAPI, profit $5.9M. |
| S5 | Network Optimization with capacity constraints: replace M with actual capacity. Result: both factories needed, profit drops to $1.4M. + Supply Chain Control Tower. |
Session Summary
Section titled “Session Summary”- Capacity constraint modification: replace M with CapFac_i (or CapDC_j) in capacity constraints. One change; big impact.
- Result with capacity: Aurangabad capped → Nashik must also open → profit drops $5.9M to $1.4M.
- Managerial insight: capacity constraints force more expensive alternatives — real-world trade-off of facility sizing.
- Control tower: centralized hub → real-time end-to-end visibility + automated decision support + KPI monitoring.
- 3 use cases: FMCG raw material (sunflower oil) | Dairy perishables + routing | Paint multi-plant inventory.
- 10 capabilities: visibility, integration, automation, analytics, forecasting, production planning, inventory, exception mgmt, KPI tracking, decision support.
- 7 technologies: Data integration | Collaboration | Blockchain | Analytics + visualization | AI/ML | Optimization | Cloud.