Abstract
In this study, we address the inventory decision problem of ameliorating goods by explicitly incorporating a demand spillover effect between product categories - an interaction that has received little attention in operations management. We first empirically demonstrate the existence of this spillover using multi-year sales data from 11 Port wine brands across 86 markets. Building on these insights, we integrate the spillover effect into a stochastic inventory decision model for a (Port) wine seller who must decide whether to sell existing inventory or continue aging it to offer higher-quality products in the future. The problem is formulated as a Markov Decision Process and solved using a forecast-based Deterministic Lookahead (DLA) approach and a Proximal Policy Optimization (PPO) algorithm. Our results show that accounting for the spillover effect can increase profits by up to 1.31%, and that both proposed solution methods outperform the myopic strategy currently applied by producers. While the DLA policy performs best under high forecast accuracy, the PPO algorithm proves more robust when uncertainty is high. The study contributes to bridging marketing and operations perspectives by quantifying the economic impact of spillover effects and providing decision-support tools for managing aged inventory under demand uncertainty.

Abstract
In perishable categories, assortment design entails a three-way trade-off among variety, profitability, and waste. Because products deteriorate over time, effective assortment management requires not only selecting the right product mix but also ensuring that items meet consumers' freshness expectations at the point of purchase. Although assortment optimization has been extensively studied, existing models typically ignore shelf life or treat it as a static availability constraint, overlooking its dynamic effect on demand, substitution, and the selling window. We address this gap by developing an assortment optimization framework that incorporates remaining shelf life (RSL) directly into consumer utility and captures RSL-driven substitution across products. Using transaction-level data from the non-dairy yogurt category of a major European grocery retailer, we estimate an RSL-aware multinomial logit model and evaluate alternative assortment strategies via simulation. Relative to a freshness-agnostic specification, the RSL-aware approach yields a 3.3% increase in expected profit under a profit maximization objective. Under a relative waste minimization objective, it reduces food waste by 0.7 percentage points. To ensure scalability in larger categories, we introduce two simple, objective-specific heuristics that use ranking rules to restrict the candidate assortment set. Despite their computational simplicity, these heuristics deliver assortments whose profit is, on average, within 1.42% of the exhaustiveenumeration reference profit and whose waste differs by only 0.04 percentage points. Overall, the results establish product freshness as a critical determinant of assortment performance. The framework links freshness-sensitive consumer behavior to operational decisions and provides retailers with practical guidance for improving both profitability and sustainability in perishable categories.

Abstract
Problem Definition: Grocery retailers increasingly fulfil online orders from existing stores rather than from dark stores (dedicated warehouses/fulfilment centers). Theory and practice debate whether this omnichannel strategy reduces or exacerbates food waste. There is a lack of empirical evidence on the impact on waste of introducing fulfillment to brick-and-mortar stores. Our study provides the first causal estimate of this impact and traces the operational levers behind it.&nbsp; <br><br>Methodology/results:Exploiting the staggered roll-out of store-basd online fulfilment at a European grocer, we apply difference-indifferences estimators to a 48-month panel of 27 stores, and six fresh categories. The conceptual framework decomposes the waste ratio (waste-to-sales) into inventory planning (inventory-to-sales) and inventory execution (waste-to-inventory) components. Introducing online fulfilment increases the waste ratio by 0.5 percentage points-about 15 percent relative to stores that are do not serve online customers. This rise is mostly explained by higher inventory-to-sales levels driven by a 25 percent jump in demand variability; inventory execution does not deteriorate significantly.&nbsp;<br><br>Managerial Implications:Fulfilling online orders from brick-and-mortar stores can backfire on waste unless retailers mitigate added demand variability via prioritizing tailored inventory management strategies and selecting stores that may mitigate demand variability and maximize overall sales. Our findings reconcile conflicting predictions in the literature by demonstrating that pooling benefits can be overwhelmed by variability-driven stock increases when service-level targets remain unchanged.

Abstract
Self-service technologies (SSTs) that replace regular checkout are widely deployed in grocery retailing to reduce customer frictions and labor needs, yet their impact on consumer purchasing behavior remains unclear. We study a specific type of these SSTs: mobile app 'scan &amp; go' technologies. Mobile app scan &amp; go technologies may lower customer time and effort spent during a shopping trip by eliminating queuing and double handling. However, they also shift scanning effort to customers, which may change attention and shopping patterns. We explore how customers adopt mobile app scan &amp; go technologies in practice, and study the causal effect of adoption on their purchasing behavior. We partner with a European grocery retailer that introduced a mobile app scan &amp; go technology in their physical stores and analyze a large transactional dataset spanning more than 7 million purchases from nearly 60,000 customers. Leveraging the staggered adoption timing and matched non-adopters, we estimate the effect of adoption using difference-in-differences designs with customer and time fixed effects and modern staggered-DiD estimators. We find that adoption increases customers' monthly purchase frequency and total monthly spending, with little change in average basket value. We also find that adoption is not 'all-or-nothing': most adopters use the scan &amp; go technology selectively for particular shopping trips, and about one-third try it once and then discontinue using it. The observed spending and frequency gains are concentrated among customers who use the technology repeatedly. These findings suggest that mobile app scan &amp; go technologies can strengthen customer retention, but only when they reliably reduce customer friction. Retailers should promote mobile app scan &amp; go usage for 'major' grocery trips and design onboarding and in-store support to reduce first-use learning costs - because repeated usage, not mere adoption, is what drives performance benefits.

Abstract
In the pharmaceutical industry, noncompliance with any good manufacturing practice (GMP) leads to deviation, resulting in potential retention of finished product batches, reprocessing, or rejection-consequently increasing lead time and cost. This study aimed to outline a strategy to define, classify, and mitigate recurrent deviations occurring more than once within 12 months. This research followed an action research methodology, carried out within a Portuguese pharmaceutical company. A transversal analysis of the deviation management process was conducted across three phases: recording, investigation, and conclusion. The intervention included defining objective recurrence criteria, developing investigation models based on structured problem-solving, and redesigning the deviation management information system. The implementation decreased recurrent deviations by 78 percent, and a new process was established, facilitated by the participation and involvement of everyone in the organization. This article introduces pioneering contributions to the pharmaceutical industry by presenting novel criteria for assigning recurrence to recorded deviations and integrating Good Manufacturing Practices (GMP) with big data and analytics. Our approach enhances decision-making and manufacturing processes by structurally incorporating all types of causes beyond the human factor, emphasizing recurring deviations over extended periods. It defines conditions for correct deviation classification and constructs a decision matrix for investigation models. Additionally, it presents workshop management, providing analysis templates and a prototype information system, and outlines key steps to mitigate deviations, highlighting research limitations and future directions.

Abstract
Quality performance in manufacturing has a direct influence on efficiency, generated waste, and costs. In collaboration with a textile manufacturer as a case study, this paper develops an automated defect detection system for a weaving process and evaluates its impact on operational performance. The system identifies defects immediately at their onset and prevents their propagation to subsequent fabric and production stages. A deep learning image classification model is developed, with six well-established network architectures being compared, leveraging a non-invasive image acquisition method that averts machinery disturbances for data collection. Based on the best-performing model, key indicators of operational performance are estimated using Markov Chain modelling, addressing a gap in linking model performance to operational impacts. Notable operational gains are demonstrated, namely a cost reduction of 1.3% and over 90% of waste reduction. A sensitivity analysis guides the definition of the image acquisition frame rate to minimise false alarms and shows that different operational indicators are impacted differently by different predictive performance metrics, affecting model selection. This research not only underscores the potential of integrating deep learning into textile production but also guarantees the effective communication of its impact to industry stakeholders, thus offering valuable practical insights to enhance operational performance.