Assignment 2 BD309 – Otniel Feliks Putra Wahyudi – 2481417024

4 Cases Study Linier Equations.docx

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Case 1 – Recalibrating Retail Pricing in Banten: What Pace Are Prices Really Setting?

In mid-2025, national retail chains operating across Banten Province confronted a familiar yet delicate decision: whether to take a universal price increase on packaged foods and day-to-day essentials. Jakarta headquarters proposed a modest, across-the-board markup to protect gross margins. Regional managers in Kota Tangerang and Kota Tangerang Selatan (Tangsel) pushed back, noting that shoppers in their catchment areas—many still rebuilding household buffers post-pandemic—had become more sensitive to certain categories than others. Food and transport drew particular scrutiny at store level; housing-related costs were rising but in fits and starts, with promo periods and platform discounts muddying the water.

The CFO wanted a single, defensible message for the next 90 days. The analytics team built a straightforward workbook from public price indices: monthly headline inflation for Banten and the same index split by COICOP consumption groups (food and non-alcoholic beverages, transport, housing/utilities, etc.), covering 2020–2025. The goal was not to win an econometrics prize but to translate five years of monthly observations into something store managers could act on: “What is the general speed of price change, and which categories, if any, are persistently outrunning the overall basket?”

They charted headline and category indices across the pandemic dip, the reopening rebounds, and the steadier rhythm of 2024–2025. Two patterns mattered for decisions. First, the baseline drift in the headline index provided a clean, intuitive anchor for a province-wide markup that wouldn’t shock customers. Second, relative movements in key categories told them where blunt markups would be most likely to trigger basket-switching or volume loss. Food experienced occasional burst- festival seasons, supply glitches, fuel pass-throughs—but these bursts didn’t always last beyond a quarter. Transport oscillated with fuel policy and mobility patterns. Housing and utilities tended to move more slowly, but once they moved, they rarely reversed quickly.

The team distilled this into a concise playbook: (1) adopt a modest provincial baseline increase in line with the overall pace of prices; (2) flag a short list of staples that had repeatedly run “hot” versus the basket and require pricing discretion (e.g., step the markup or time it after promo cycles); (3) institute a 90-day review keyed to the next three monthly releases, so stores could adjust without losing credibility. The message to operations was intentionally simple: let the overall index set the center of gravity, and let consistent category deviations justify targeted exceptions.

The CFO signed off, balancing margin protection and customer trust. In town-halls, store leaders appreciated that the guidance was grounded in official statistics they could explain to staff and to increasingly savvy shoppers. The looming risk, everyone agreed, was a policy or supply shock that would yank category paths away from the basket again; the 90-day checkpoint existed precisely for that reason.

Discussion questions

• Write a simple linear formula that uses time or the headline index as the driver for a province-wide pricing baseline and use it to predict the next 12 months.
• Based on your formula, which two categories would you exempt from a blunt markup in the next quarter, and why? State the operational considerations (promo calendars, supply lead times, festival seasonality, supplier terms).
• If a fuel-price adjustment occurs next month, how would you update your formula or assumptions without overreacting.

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