🤖 AI Summary
This study addresses the challenge of dynamic food pricing, which can enhance retailer profits but often raises consumer fairness concerns due to inadequate integration of sales forecasting and price equity in existing approaches. The authors propose a novel framework that uniquely embeds consumer price index (CPI)-based fairness constraints directly into the sales prediction model. Leveraging a log-log ARDL specification to estimate price elasticity, the framework combines linear programming with simulated annealing to solve both single-item and multi-item pricing problems. Empirical results demonstrate that unconstrained optimization frequently drives prices to their upper bounds, whereas CPI-anchored pricing yields more conservative solutions that balance sales objectives with consumer affordability. Furthermore, CPI deflation reveals that apparent positive nominal elasticities are largely driven by inflation effects, underscoring the transparency and enhanced fairness perception of the proposed mechanism.
📝 Abstract
Pricing food products to balance profitability with consumer welfare is a central challenge for retailers. Dynamic pricing is widely used to maximize revenue, yet most pricing models optimize business objectives while overlooking consumer fairness. This paper studies the risk of consumer exploitation under dynamic food pricing in Canada and proposes a methodology that embeds fairness constraints directly into retail sales forecasting. We model total retail trade sales with a log--log Autoregressive Distributed Lag (ARDL) specification, in which the coefficient on a product price is a sales elasticity, and pose the pricing problem as maximizing forecast sales subject to price bounds anchored to the Consumer Price Index (CPI). We solve this problem with both Linear Programming (LP) and Simulated Annealing (SA), under single-product and multi-product configurations. A key finding is that the fitted nominal elasticities are positive. As a result, an unconstrained sales-maximizer would push every price to its upper bound, and the CPI ceiling is the safeguard that prevents this. Simulated Annealing instead settles on conservative, interior prices that lower consumer cost while still meeting the sales target. We benchmark forecast accuracy against naive, seasonal-naive, ARIMA, and SARIMA baselines, and a CPI-deflated re-specification shows that the positive nominal elasticities are largely an inflation-driven artifact. The result is a transparent, fairness-aware pricing framework.