Ops Under Pressure — Empaques del Trópico

A four-round, advanced operations and supply-chain simulation set inside Empaques del Trópico S.A., a Costa Rican sustainable-packaging manufacturer (USD 86M revenue, ~13% EBITDA margin ≈ USD 11.2M, 3 lines / 2 shifts at 78% utilization, 55% of revenue tied to a volatile agricultural-export calendar). A demand whipsaw — a +38% January surge, on-time delivery collapsing to 81% vs a 96% target, USD 240,000 in late-delivery penalties, then USD 3.1M of excess inventory (≈52 days vs a 30-day target) from a panic overcorrection — collides with a static spreadsheet plan, a single-sourced compostable-resin supplier (6-week lead time, 3-week delay flagged), and a USD 14M two-year contract that demands +20% capacity. Playing the new operations manager with a USD 6M modernization budget and two quarters to deliver, you (1) diagnose the system, not the symptom — separating true external volatility from self-inflicted variability and naming the root cause behind the bullwhip / overcorrection loop; (2) set capacity under volatile demand — go/no-go on the USD 14M contract and the permanent-versus-flexible capacity mix, sized to a demand distribution and a service level rather than last month's headline; (3) choose the digitalization investment — allocating the digital slice across demand-sensing/S&OP, MES + line sensors, an automated warehouse, or a supplier control tower, recognising that information-centric Industry 4.0 tools often beat throughput-centric ones for a volatility problem; and (4) redesign inventory buffers and defend the integrated operating plan — statistical, item-differentiated safety stock toward the 30-day target, a response to the single-sourced resin risk, and a KPI scorecard. Grounded in Roy Zúñiga's (INCAE) behavioral-operations and system-dynamics research, the math rewards damping the bullwhip, flexible capacity, information-first digital spend and risk-differentiated buffers — and punishes the five classic errors: a recency-driven permanent-capacity bet, over-producing into the glut, signing the contract before fixing variability, buying throughput when the constraint is decision quality, and a flat safety-stock policy that ignores the upstream supply risk. Final KPIs track On-Time Delivery, Inventory days, Capacity Utilization and the cumulative cost/P&L impact in USD.