Week 12 | Session 5: Industry 4.0 Case Studies — Robotics, AR/VR, Additive Manufacturing & Blockchain Across SC Functions
Course: Supply Chain Digitization — Module 4: Digital Infrastructure
Session Agenda
Section titled “Session Agenda”1. Session Context
Section titled “1. Session Context”Week 12 covered: Digitization pyramid → Industry 4.0 (9 pillars) → Blockchain → I4.0 × SC function mapping → IoT case studies (Session 4).
This final session completes the case study set with Robotics, AR/VR, Additive Manufacturing, and Blockchain across different SC functions.
Note: all company names are hypothetical — cases are developed for learning purposes only.
2. Robotics — Case Studies Across 4 SC Functions
Section titled “2. Robotics — Case Studies Across 4 SC Functions”Robotics covers both physical robots (AMRs — Autonomous Mobile Robots) and software automation (RPA — Robotic Process Automation).
| SC Function | Company & Context | Challenge → Solution → Outcome |
|---|---|---|
| Sourcing & Procurement | BioCarrel Pharma (Drug Manufacturer) | Challenge: slow, error-prone manual sourcing and supplier management. Solution: RPA (software bots) — real-time market analysis, automated vendor onboarding, price negotiation, order processing. Outcome: procurement cycles reduced, stockouts minimized, cost savings from better negotiation + fewer errors. |
| Production & Manufacturing | Yani (Ukulele Maker) | Challenge: limited production capacity due to reliance on scarce skilled luthiers — demand exceeds supply. Solution: robotic arm with sensors + tools for repetitive tasks (wood cutting, initial assembly). Outcome: higher output + consistent quality from robots. Luthiers freed to focus on intricate craftsmanship → best of both: robotic precision + human expertise. |
| Inventory Management | Bulb Boutique (Bulb Retailer) | Challenge: large warehouse + fragile bulbs → slow, damage-prone order fulfillment. Solution: robotic picking system using AMRs with vision sensors — scan shelves, identify and retrieve bulbs precisely. Outcome: picking times significantly reduced, damage to fragile bulbs minimized → faster, more accurate fulfillment → company expanding product range. |
| Distribution & Logistics | Bookworm Central (Used Books) | Challenge: slow processing → unsatisfied customers. Solution: robotic sorting system (AMRs): identify, sort, and photograph books; auto-upload all data → speeds up listing. Outcome: processing time drastically reduced, customer satisfaction improved. |
| Customer Service | Shelf Reliance (Book Store Chain) | Challenge: long customer wait times for in-store online order pickup — manual retrieval from tall shelves is slow. Solution: robotic retrieval system (AMRs + digital maps + grippers) — swiftly locate and retrieve books. Outcome: wait times reduced significantly → better customer experience; staff freed from retrieval → now assist customers with browsing + personalized recommendations. |
Robotics — Key Observations
Section titled “Robotics — Key Observations”- RPA (software) handles procurement data tasks. AMRs (physical) handle warehouse/logistics movement. Both are ‘robotics’ under Industry 4.0.
- Consistent theme: robots take over repetitive tasks → humans freed for higher-value, judgment-intensive work.
3. AR / VR — Case Studies Across 3 SC Functions
Section titled “3. AR / VR — Case Studies Across 3 SC Functions”AR (Augmented Reality) overlays digital information on the real world. VR (Virtual Reality) creates a fully digital immersive environment. Often used together.
| SC Function | Company & Context | Challenge → Solution → Outcome |
|---|---|---|
| Sourcing & Procurement | ACME Furniture (Furniture Maker) | Challenge: international suppliers — cannot travel for quality control; traditional remote inspection is insufficient. Solution: procurement officers use AR headsets for virtual tours of supplier workshops + remote production line/material inspection. On-site inspectors use AR tablets for real-time quality checks highlighting key dimensions. Outcome: travel costs significantly reduced; supplier management more effective; better quality control and more informed sourcing decisions. |
| Production & Manufacturing | Honey Ale Aerospace (Aircraft Components) | Challenge: wiring installation in complex aircraft — traditional paper-based manuals lead to misinterpretation errors and delays. Solution: technicians wear AR glasses overlaying digital wire paths + connection points on actual aircraft structure. VR simulations let engineers walk through aircraft virtually to identify maintenance challenges and optimize access points. Outcome: wiring errors minimized, assembly speed improved, safety protocols followed, long-term maintainability enhanced. |
| Customer Service | Mika (Global Furniture Giant) | Challenge: customers make impulse purchases without visualizing furniture in their home → high return rates. Solution: AR showroom — customers place 3D furniture models in their own space using smartphones or tablets, from home. Outcome: better purchase decisions → higher customer satisfaction, sales boosted, returns significantly reduced. |
AR/VR — Key Observations
Section titled “AR/VR — Key Observations”- AR bridges the physical/digital gap for complex tasks (wiring installation, remote quality inspection) — reduces errors without requiring physical presence.
- For customers, AR eliminates uncertainty about how a product will look in context → reduces returns.
4. Additive Manufacturing — Case Study
Section titled “4. Additive Manufacturing — Case Study”| SC Function | Company & Context | Challenge → Solution → Outcome |
|---|---|---|
| Production & Manufacturing | Robopulse (Toy Maker) | Challenge: malfunctioning gear in star robot toy. Traditional sourcing/manufacturing = weeks of lead time. Holiday season demand cannot wait. Solution: engineers designed and 3D-printed a replacement gear in high-strength nylon within days — only necessary material used, directly printing the complex design. Outcome: production resumed within days (not weeks). On-time delivery preserved. Significant financial loss prevented. Waste minimized — only material required was used. |
Additive Manufacturing — Key Observations
Section titled “Additive Manufacturing — Key Observations”- Speed advantage: days vs. weeks — critical when lead time is the constraint.
- No tooling needed: the complex design is printed directly → no molds or special machinery.
- Material: high-strength nylon — demonstrates AM handles performance materials, not just prototyping.
- Minimal waste: only the exact material required is used.
5. Blockchain — Case Studies Across 2 SC Functions
Section titled “5. Blockchain — Case Studies Across 2 SC Functions”| SC Function | Company & Context | Challenge → Solution → Outcome |
|---|---|---|
| Sourcing & Procurement | Oceanic Limited (Seafood Supplier) | Challenge: customers want verification that tuna is sustainably sourced — complex paper trails and potential fraud make this hard. Solution: blockchain platform — each catch recorded with origin, vessel ID, and fishing method → immutable, transparent ledger accessible to all participants. Outcome: consumers gain confidence in sustainable practices. Paper trails streamlined. Costs reduced. Transactions expedited. Fraud risk eliminated. |
| Distribution & Logistics | Rainforest Raindrops (Tea Retailer) | Challenge: company wants to bring transparency to the full journey of tea from harvest to shelf; existing system opaque. Solution: blockchain-based tracking — every step from harvest to packaging recorded on a secure, shared digital ledger. Outcome: customers can trace tea’s origin, verify fair trade prices, confirm ethical sourcing → customer trust enhanced, farmer relationships strengthened, trade more sustainable and ethical. |
Blockchain — Key Observations
Section titled “Blockchain — Key Observations”- Both cases use blockchain for traceability (track the physical journey) and transparency (make records visible and trustworthy).
- Blockchain is particularly powerful when multiple parties (fishermen, distributors, retailers, consumers) need to trust the same data without a single controlling authority.
6. Complete Case Study Reference — All 16 Cases (Sessions 4 & 5)
Section titled “6. Complete Case Study Reference — All 16 Cases (Sessions 4 & 5)”All case studies from Sessions 4 and 5 — full exam-ready reference.
| Tech | SC Function | Company | Core Challenge | Solution → Key Outcome |
|---|---|---|---|---|
| IoT | Planning & Forecasting | Omega Foods | Spoilage & backorders for perishables | Smart sensors (temp + stock) → predict consumption rate + shelf life → adjust schedules → spoilage reduced |
| IoT | Sourcing & Procurement | Acme Pharma | Stockouts due to manual inventory tracking | Shelf sensors → real-time stock → auto-trigger POs → stockouts eliminated |
| IoT | Production & Manufacturing | Newton Instruments | Humidity fluctuations cause violin damage | Humidity sensors → smart vents auto-adjust → consistent quality, less waste |
| IoT | Inventory Management | Vendor Guitars | Guitars lost during shipping | Location sensors in cases → real-time visibility → lost items eliminated |
| IoT | Distribution & Logistics | Best Pharmaceuticals | Medication spoilage due to inconsistent cold chain | Container sensors (temp + humidity) → route + storage optimization → quality preserved |
| Robotics | Sourcing & Procurement | BioCarrel Pharma | Slow, error-prone manual procurement | RPA bots: market analysis, vendor onboarding, order processing → cycles reduced, cost savings |
| Robotics | Production & Manufacturing | Yani (Ukulele Maker) | Production capacity limited by scarcity of skilled luthiers | Robotic arm for cutting + assembly → higher output, consistent quality; luthiers focus on craftsmanship |
| Robotics | Inventory Management | Bulb Boutique | Order fulfillment slow; bulbs fragile in large warehouse | AMRs + vision sensors: scan shelves, retrieve bulbs precisely → faster picking, less damage |
| Robotics | Distribution & Logistics | Bookworm Central | Slow processing, unsatisfied customers (used books) | Robotic sorting (AMRs): sort + photograph books, auto-upload data → faster listing, better satisfaction |
| Robotics | Customer Service | Shelf Reliance | Long wait times for in-store online order pickup | Robotic retrieval (AMRs + maps + grippers): locate + retrieve swiftly → less wait; staff free for service |
| AR / VR | Sourcing & Procurement | ACME Furniture | Quality control of international suppliers without travel | AR headsets: virtual supplier tours + remote inspection; AR tablets for on-site QC → travel cost cut |
| AR / VR | Production & Manufacturing | Honey Ale Aerospace | Wiring errors in complex aircraft assembly | AR glasses overlay wire paths; VR simulations for engineers → errors reduced, assembly speed up |
| AR / VR | Customer Service | Mika (Furniture Giant) | High returns; customers can’t visualize furniture at home | AR showroom: place 3D models in own space via smartphone → better decisions, fewer returns |
| Additive Mfg | Production & Manufacturing | Robopulse (Toy Maker) | Malfunctioning gear delays holiday production; traditional sourcing takes weeks | 3D print gear in high-strength nylon within days → on-time delivery, loss prevented, waste minimized |
| Blockchain | Sourcing & Procurement | Oceanic Limited (Seafood) | Fraud risk + complex paper trails for sustainable tuna verification | Blockchain: each catch recorded (origin, vessel, fishing method) → immutable ledger → fraud eliminated |
| Blockchain | Distribution & Logistics | Rainforest Raindrops (Tea) | Lack of transparency in tea’s journey from harvest to shelf | Blockchain tracking: every step on secure shared ledger → customers verify origin + fair trade → trust built |
7. Course Summary
Section titled “7. Course Summary”Modules 1–3 (Earlier Weeks)
Section titled “Modules 1–3 (Earlier Weeks)”- SC strategy, procurement (Kraljic matrix), SC network design (break-even, CoG, heuristics, LP)
- SC analytics: K-means clustering, DEA, SC network optimization (CPLP, LP with Big M)
- Product tracking and traceability: barcode, QR, RFID, wireless sensors, packaging levels
- Information systems: ERP, WMS, TMS — definitions, modules, integration, implementation
Module 4 (Weeks 11–12)
Section titled “Module 4 (Weeks 11–12)”- Digital twin: 5 types, 3 components, 7 use cases, GFA + network optimization using AnyLogistix
- Digitization pyramid, Industry 4.0: 4 industrial revolutions, 9 pillars in detail
- Blockchain: distributed ledger, 3-step process, 5 network types
- I4.0 × SC function matrix: which technology applies where
- Case studies: IoT (5) + Robotics (5) + AR/VR (3) + Additive Manufacturing (1) + Blockchain (2) = 16 total
Session Summary
Section titled “Session Summary”- Robotics (5 cases): BioCarrel (RPA for procurement) | Yani (robots + luthiers for production) | Bulb Boutique (AMRs for inventory picking) | Bookworm Central (sorting for distribution) | Shelf Reliance (retrieval for customer service)
- AR/VR (3 cases): ACME Furniture (remote supplier QC) | Honey Ale Aerospace (wiring installation) | Mika (customer furniture visualization)
- Additive Manufacturing (1 case): Robopulse — 3D-printed gear in days to resume holiday production vs. weeks for traditional sourcing
- Blockchain (2 cases): Oceanic (sustainable tuna traceability) | Rainforest Raindrops (tea journey transparency)
- Consistent pattern: identify SC pain point → select right I4.0 technology → implement → achieve: cost reduction, quality improvement, speed increase, or trust building