Operations & Supply-Chain Notes – Forecasting, Bottlenecks & Capacity in the Samosa Case

Demand Forecasting – Principles & Initial Numbers

• Definition & purpose
  • Demand forecast = informed estimate of future sales; always an approximation, never exact.
  • Used to align Marketing, Finance, Operations & Supply-Chain decisions.

• Sakshi’s Samosas initial top-down forecast
  • Monthly demand assumed: $220\text{ samosas / month}$.
  • Simplifying assumption: $4\text{ weeks / month}$ → $55\text{ samosas / week}$.
  • 3 operating days / week ⇒ $18\text{–}19\text{ units / day}$.

• Why it is conservative
  • Based on only $5\%$ of dorm (McNutt) residents trying the product.
  • Assumes only a couple of repeat purchases per month.
  • Minimal capital at risk → over-forecasting carries limited financial penalty.

• Key lesson
  • Always create contingency plans: What if we sell far more? What if we sell far less?
  • Example from retail: video-game launches (Battlefield 6); a ±$10\%$ error on $100,000$-unit forecast is considered “good”.

Core Business Model Elements for the Samosa Venture

Key Activities

• Cooking process (80 % of work)
  • Mixing dough & spices
  • Forming/filling samosas
  • Frying
  • Cooling
  • Packing

• Supporting tasks
  • Taking orders / point of sale (Shopify)
  • Advertising & social-media marketing
  • Washing dishes / cleanup
  • HR: hiring, scheduling
  • Compliance: dorm approvals, permits

Key Resources

• Physical: shared kitchen, stove, fryer, mixer, packaging supplies

• Human: Sakshi (founder/chef), potential hire Ash

• Intellectual: grandmother’s recipe (IP)

• Financial: borrowed funds from friends / family

Key Partnerships

• Dorm administration (facility access)

• Student customer base & word of mouth

• Advertising platforms

• Shopify for e-commerce transactions

• Analogy: lemonade stand requires parents + HOA permissions – partners can "keep you in business or take you out."

Operations Foundations – Transforming Inputs to Outputs

• Inputs: raw food materials, money, data

• Transformation: 5-step cooking process adds value → saleable samosa

• Outputs: finished food + customer experience

• Analysis goals: identify bottlenecks & continuously improve throughput, cost & quality.

Real-World Supply-Chain Illustrations

• Target
  • Turned local stores into mini-warehouses to compete with Amazon’s same-day promise.

• Ulta Beauty
  • Only some stores have sufficient backroom space to double as distribution nodes.

• Outsource vs Insource
  • Apple’s decision to bring certain component production in-house for reliability.

• Tesla Cybertruck mis-pricing
  • Promised $\$39{,}999$, launched at $\$60{,}000+$ → result of siloed decision making.

• Bottom line: Marketing, Ops & Finance must iterate together to align price, cost & capability.

Process-Analysis Toolkit Introduced

• Cycle time: time to execute an individual task.

• Capacity rate: output per unit time (e.g.
  $\text{rate}=1/\text{cycle time} \;\text{units/min}$).

• Flow time (throughput time): total time for a unit to travel the complete process.

• Gantt chart: left-to-right visual of task timing & resource utilization.

• Bottleneck: stage/resource that limits overall system capacity.

• Process-capacity formula:

  (Time available - Time first unit) / Time bottleneck + 1

Case Study – Baseline (Solo Sakshi)

Step times

1. Mix dough & prep filling – $6\,\text{min}$

2. Fill samosa – $2\,\text{min}$

3. Fry – $10\,\text{min}$

4. Cool – $5\,\text{min}$

5. Pack – $2\,\text{min}$

• Flow time (first unit): $6+2+10+5+2 = 25\,\text{min}$.

• With one person, Sakshi herself is the bottleneck (she touches every step for the full $25\,\text{min}$).

• 4-hr shift $\Rightarrow T_{\text{available}} = 240\,\text{min}$.

• Capacity:
  $\frac{240-25}{25}+1 = 9.6 \approx 9 \text{ batches}$
  → ~$23\text{ samosas/week}$ (3 production days).

• "0.6" of a batch = partially fried, unsellable units – illustrates importance of interpreting fractional outputs.

Process Improvement – Hiring Ash & Letting the Fryer Run Unattended

• New resource allocation
  • Sakshi: Steps 1-2 (total $8\,\text{min}$)
  • Fryer: Step 3 (unmanned, $10\,\text{min}$)
  • Ash: Steps 4-5 (total $7\,\text{min}$)

• Overlapping production now possible – multiple batches in process concurrently.

• Class poll on when to start Batch #2 (minute 9, 10, 11)
  • Earlier start (9) = front-load → buffer against breakdowns.
  • Later start (11) = just-in-time → maximum freshness, minimal WIP.

Updated Bottleneck & Capacity

• Bottleneck shifts to Fryer (longest single task, always busy).

• First-unit flow time still $25\,\text{min}$, but ongoing units gated every $10\,\text{min}$.

• Capacity per 4-hr shift:
  $\frac{240-25}{10}+1 = 22.5 \approx 22 \text{ batches}$.

• Weekly capacity (3 days): $22 \times 3 = 66$ samosas.

Strategic Question – Should We Hire Ash?

Pros

• Meets & exceeds forecast (66>55) giving sales upside.

• Shorter customer wait times.

• Allows founder to take breaks / avoid burnout.
  Cons

• Added labor cost; profitability depends on unit margin.

• Risk of overproduction if sales remain at conservative level.

• Must analyze contribution margin:
  • If unit margin (\text{Price} - \text{Variable Cost})<0, higher volume increases total loss!
  → Finance session on Monday will compute exact breakeven & ROI.

When NOT to Elevate the Bottleneck

• Additional capacity may be too expensive (e.g., $5,000 espresso machine).

• Scarcity of skilled labor/unique IP (expert scientist, proprietary tech).

• Demand uncertainty – investing upfront may create unused capacity.

Conceptual Takeaways

• Forecasts should always be stress-tested against operational reality.

• Bottleneck dictates system output; relieving it can transform capacity.

• Cross-functional integration (Marketing–Ops–Finance) prevents costly mismatches (e.g.
  Cybertruck price fiasco).

• Just-in-Time vs Buffering involves trade-offs among freshness, risk & capital tied up in WIP.

• Never accept raw numbers without interpretation (the “0.6 samosa” lesson).

• Decision to expand capacity (hire, buy equipment) must include cost/benefit, not just technical feasibility.

Next Steps / Upcoming Class Links

• Monday’s Finance lecture will:
  • Quantify unit contribution margin.
  • Calculate breakeven volumes with & without Ash.
  • Evaluate ROI of hiring decision.

• Catch up on asynchronous Top-Hat modules; revisit process-mapping terminology.