Supply chain
Oakville · GTA · Great Lakes · Canada-US
Supply Chain Skills Lab
Hiring-manager path: network optimization on GTA-Great Lakes lanes, then cash and working-capital impact. Modules stay one problem deep. Synthetic demo data only.
Capstone demo for Design + Analytics. Charts and tables use original synthetic North America corridor data.
Network Optimizer Lab
Allocate synthetic demand from Oakville, Mississauga, Hamilton, Buffalo, and Detroit DCs to metro customers under capacity; minimize landed freight with Canada-US border friction on selected lanes.
Optimal flow by DC
Cost vs distance (lanes used)
Shipment plan (top lanes by cost)
| DC | Customer | Tons | $/ton | Total $ | Transit days |
|---|
Method
Capacity-constrained lowest-cost assignment over haversine lane costs with a border friction premium on some
cross-border lanes. Re-run locally with python scripts/generate_data.py.
Five modules · one problem each
Fundamentals → Design → Dynamics → Analytics → Technology. Essays stay collapsed; open a chip for the worked sample.
SCF 1. Fundamentals EOQ · Mississauga DC
Inventory drivers, replenishment math, and turns: vocabulary hiring managers expect before network or cash talk.
EOQ + orders per year (Mississauga CPG and retail SKU)
Statement: Annual demand 24,000 cases. Order and setup cost $180. Unit cost $28; holding rate 16%/yr → H = $4.48/case/yr (≈ $4.50). What EOQ minimizes order + holding cost, and how many orders per year?
Approach: EOQ = √(2DS/H). Orders/year = D/Q. Avg cycle stock = Q/2.
Module essay (skim optional)
Foundations of how goods, information, and cash move: roles (supplier → plant → DC → customer), inventory drivers (cycle stock, safety stock, pipeline), replenishment math (EOQ, reorder point, CSL), ABC prioritization, and turns / days-of-supply. Same patterns transfer across CPG, industrial, retail, and logistics.
SCD 2. Design Aggregate plan · Oakville
Where capacity and inventory absorb seasonality: structural choices that lock in cost-to-serve on Great Lakes corridors.
Aggregate plan for an Oakville plant (level vs chase vs hybrid)
Statement: A GTA plant faces seasonal demand over 6 months. Compare level, chase, and hybrid plans. Which minimizes production + inventory + workforce cost?
Approach: Monthly balance sheets: production, ending inventory, hires/fires or overtime.
Module essay (skim optional)
Structural choices: plant and DC roles, capacity allocation, contracts, and medium-term production rates. Design decisions lock in cost-to-serve for years. Critical for Canada-US Great Lakes networks where border friction and corridor density matter. Network node roles live in the Optimizer above.
SCY 3. Dynamics Bullwhip · GTA corridor
Why a small Toronto retail wobble can look like a Michigan plant crisis, and how visibility dampens it.
Bullwhip on a Canada-US CPG corridor
Statement: End-customer demand at GTA stores is relatively smooth. With 2-week ordering cycles, safety buffers, and delayed POS visibility, how much do DC and plant orders amplify over 16 weeks?
Approach: Simulate chained reorder points; compare CV of demand vs orders at each tier.
Module essay (skim optional)
Demand amplification (bullwhip), lead-time and batching effects, information delay, and resilience under disruption. Same corridor dynamics show up in auto parts and grocery when status latency rises at the border.
SCA 4. Analytics Product-mix LP · Mississauga
Optimization and stocking math that connect service to dollars. Network landed-cost lives in the Optimizer above.
Capacity-constrained product mix (Mississauga plant)
Statement: Product H (margin $80/unit) and Product S (margin $200/unit). Weekly capacity ≤ 110 units. Additive A: 3H+2S ≤ 300; additive B: 1H+3S ≤ 280. Maximize weekly margin.
Approach: Linear program: solve at binding constraints; report optimal mix.
Module essay (skim optional)
Optimization models, newsvendor stocking, inventory analytics, and KPI packs (OTIF, cost/ton, turns). Analytics connects fundamentals and design to measurable P&L and working-capital outcomes.
SCT 5. Technology & Systems OTIF tower · Great Lakes
ERP, WMS, TMS, and tower value only when exception rules match the physical network.
OTIF control tower for Great Lakes lanes
Statement: Leadership wants a single tower for Canada-US lanes. Which KPIs and alert rules catch failure early without flooding planners?
Approach: OTIF by lane + DC, dwell hours at border. Alert on OTIF < 92% or border dwell > 8h for 2 consecutive days.
Module essay (skim optional)
Digital backbone: ERP, WMS, TMS, visibility towers. Technology creates value when master data, process design, and exception workflows match Canada-US handoffs where status latency drives bullwhip and OTIF misses.
Financial impact of supply chain
Working capital and P&L translation for hiring managers who ask “what does this do to cash?” Synthetic GTA distributor numbers.
Illustrative statement-of-account style panels only. Not real company financials.
CCC = DIO + DSO − DPO. Inventory days, receivables, and payables set how long cash is locked in the chain.
Inventory build vs stockout, payment terms, and expediting all move CCC and free cash.
Revenue, COGS, carrying cost, and service / penalty cost show why “lean” is not automatically cheaper.
Same story for manufacturing, retail, CPG, and 3PL: trade service risk for cash release (or the reverse).
Working capital · GTA distributor snapshot
Statement: Annual COGS $48M, sales $72M. Average inventory $6.4M, AR $7.8M, AP $5.1M. What are DIO, DSO, DPO, and CCC? If we cut DIO by 8 days without hurting DSO/DPO, how much cash is released?
Approach: DIO = Inv / (COGS/365); DSO = AR / (Sales/365); DPO = AP / (COGS/365); CCC = DIO+DSO−DPO. Cash release ≈ ΔDIO × (COGS/365).
Scenario · inventory build vs lean (cash vs service)
Statement: Compare a buffer plan vs a lean plan. Which improves annual operating contribution after carrying and service costs?
Approach: Waterfall from revenue → COGS → gross margin → carrying − service/penalty → contribution.