Background: Traditional value-based ABC inventory classification allocates protection according to economic value, overlooking operational risk factors such as demand variability, lead time, and assembly criticality, and it couples safety-stock and replenishment-cycle decisions. Methods: We propose a Hybrid Risk-Value framework that decouples these two decisions: stock-keeping units (SKUs) are segmented by multivariate K-means clustering on operational risk variables to set safety-stock factors (Z), while ABC economic value sets replenishment cycle coverages (d). The framework is validated through stochastic discrete-event simulation on an anonymized dataset of 200 SKUs from an automotive supplier, under base, high-demand-variability, and extended-lead-time scenarios (20 replications each), and is benchmarked against both classic ABC and a coupled ABC-XYZ policy. Results: Across all scenarios, the Hybrid framework reduces average inventory investment and total logistical cost by approximately 26–28% relative to ABC (p<0.001) while maintaining the service level; stockout days remain statistically unchanged except under extended lead times. The coupled ABC-XYZ benchmark performs almost identically to ABC, indicating that the gains arise from decoupling rather than from variability-based segmentation alone. Conclusions: Decoupling safety-stock and replenishment decisions offers a capital-efficient, data-driven alternative to static financial segmentation for resilient industrial inventories.
Modern retail supply chains operate under significant uncertainty due to fluctuating demand, variable lead times, and dynamic cost structures. Traditional inventory management approaches rely on static reorder policies based on historical averages, which fail to adapt to real-time demand patterns and often lead to suboptimal outcomes such as stockouts or excess inventory.This paper presents an ML-driven digital twin framework for joint optimization of inventory management and cash flow in a retail supply chain setting. The proposed system integrates demand forecasting using machine learning models, including Linear Regression and Long Short-Term Memory (LSTM) networks, where comparative analysis shows that simpler models outperform deep learning approaches for the given dataset, with a discrete-event simulation engine that models day-level inventory dynamics, replenishment decisions, and financial transactions.The framework evaluates multiple inventory control policies, including a fixed baseline policy, an adaptive policy driven by ML predictions, and an adaptive policy augmented with statistically derived safety stock. Additionally, Monte Carlo simulation is employed to capture demand uncertainty and quantify risk in terms of stockout rates and cash flow variability.Experimental results on a real-world retail dataset demonstrate that the adaptive policy consistently outperforms the baseline approach by improving service levels and stabilizing cash flow, while the inclusion of safety stock significantly reduces stockout risk at the expense of higher holding costs. The proposed digital twin framework provides a robust decision-support tool for supply chain optimization under uncertainty.
K. Vishal Kumar, Vedansh Garg, Shakeela Sathish· 2026 7th International Confe...· 0 citations
Background: Manufacturing supply chains must increasingly coordinate cost, environmental impact, and service continuity under demand uncertainty and limited capacity. Methods: This study develops a stochastic mixed-integer linear programming framework for carbon-aware production–distribution planning in an automotive supply chain. The model jointly optimizes production quantities, inventory levels, shipments, truck usage, and lost sales over a multi-period horizon. Demand uncertainty is represented through scenarios, while production- and transportation-related emissions are monetized using an internal carbon price. Lost-sales penalties capture service degradation when demand cannot be fulfilled by the focal plant, and a rolling-horizon analysis evaluates planning responsiveness as demand information is updated. The framework is applied to an industrially inspired, capacity-constrained automotive case with multiple products, production lines, destinations, and demand scenarios. Computational experiments assess carbon pricing, lost-sales penalties, demand volatility, deterministic versus stochastic planning, and rolling-horizon replanning. Results: Results show that carbon pricing mainly acts as an economic valuation mechanism under the studied fixed-structure configuration, whereas lost-sales penalties strongly influence service performance. Demand volatility increases unmet demand, and lower emissions may reflect lower fulfilled demand rather than improved efficiency. Conclusions: The study provides a decision-support framework for evaluating cost–carbon–service trade-offs under stochastic demand while acknowledging single-plant and fixed-routing limitations.
Mohammed Machkour, A. El Barkany, Bilal Harras· Logistics· 0 citations
The COVID-19 pandemic exposed critical weaknesses in global supply chains, necessitating strong and sustainable inventory strategies for perishable goods. This study proposes an economic production quantity (EPQ) model for items subject to delayed deterioration under a two-phase production framework with shortages and partial backlogging. The production process is divided into two distinct phases representing different stages of disruption and recovery. During the production period, demand is assumed constant, whereas in the non-production period, demand depends on the available stock level, reflecting post-pandemic consumer behavior in which product visibility influences purchasing decisions. The model incorporates a finite freshness period, after which deterioration begins, making it suitable for products such as meat, bread, and cassava. Shortages are permitted and partially backlogged to capture realistic market responses. The objective is to determine the optimal production cycle and inventory levels that minimize the total system cost, including production, holding, deterioration, shortage, lost sales, and environmental costs. The purpose is to determine the optimal cycle length and inventory level for each cycle to minimize total cost. The necessary and sufficient conditions are provided to show the existence and uniqueness of the optimal solution. Also, the decision rule of the Newton-Raphson method has been used to determine the optimal solutions for the nonlinear optimization problem. Numerical illustrations and sensitivity analyses are presented to examine the impact of key parameters, including demand sensitivity, deterioration rate, and backlog fraction, on optimal decisions. The results demonstrate that the proposed framework provides practical insights for manufacturers seeking to stabilize operations, reduce waste, and enhance profitability in the post-COVID-19 recovery phase.
Abdulmumin Alibaba, Idris Samail· International journal of re...· 0 citations
AI doesn't always translate to better inventory economics, even if the demand forecasts it generates are better, if the rules engineered behind replenishment models are still "legacy" and based on legacy planning conditions. This paper proposes a sequential response surface methodology (RSM) protocol to provide an approach for policy tuning after forecasting in a four-echelon Saudi fast-moving consumer goods (FMCG) simulation. FMCG Co. is a generic placeholder rather than a registered company, and all case data are synthetic, generated using realistic assumptions for the Saudi FMCG sector. The analysis is based on 1,248 weekly demand observations for the three regions and the four SKU families, a static LightGBM forecasting engine, and a periodic-review replenishment policy. These five controllable factors were screened using a Resolution V fractional factorial design, steepest descent, a three-factor central composite design, quadratic response modelling, constrained optimization, Derringer-Suich desirability, and held-back confirmation. For the cost-led response-surface search, safety-stock multiplier, review period and demand-signal smoothing were kept. Total cost showed a significant center-point curvature (F = 80.49, p = .0029) and the fitted quadratic models accounted for over 99.9% of the variance in total cost, fill rate, and bullwhip with no significant lack-of-fit tests. The strict model-based constrained solution (k = 1.775, R = 5.095 days, alpha = 0.343) predicted an annual cost of SAR 46.146 million, a 96.000% fill rate, and a bullwhip ratio of 1.595. The desirability solution with a balance of the three factors (k = 2.253, R = 3.520 days, alpha = 0.3635) predicted better service and stability at higher cost. A previous candidate, based on cost, was rejected as infeasible, while the only directly confirmed feasible candidate was able to deliver 97.605% fill and 1.546 bullwhip at SAR 49.706 million. The results indicate that forecast modernization and replenishment-policy modernization are two different interventions and that the policy re-tuning is important to translate the forecasting capability into system-level performance while maintaining interpretability and awareness of constraints.
Ibrahim H. Aldali· International Journal For Mu...· 0 citations
Sustainable supply chains are increasingly vulnerable to systemic risks, such as geopolitical conflicts at critical trade routes like the Strait of Hormuz or climate disasters, which reveal deep Environmental, Social, and Governance (ESG) weaknesses. Conventional optimization often fails in these “deep uncertainty” contexts, where reliable historical data are often scarce and qualitative factors are paramount. This study introduces a simulation-optimization framework that reframes risk management as a decision process rather than a purely computational one. Portfolios are parameterized across five key characteristics—prevention, vulnerability, resilience, recovery, and detection—to enable a genetic algorithm (GA) to generate a diverse ensemble of high-performing strategies. Instead of providing one “best” answer, the GA allows managers to evaluate multiple options against quantitative tail-risk measures and qualitative institutional factors. The framework produces a “trade-off map,” or Pareto frontier, visualizing the cost of protecting against downside risks. By adjusting the GA’s settings, decision makers can toggle between improving current plans and exploring new, structurally different strategies. The numerical results demonstrate that the GA consistently identifies high-performing portfolios, achieving at least 99.55% of the true optimal performance across all metrics while requiring only 25% of the computational evaluation budget of an exhaustive search space. Furthermore, the framework successfully generates a structurally diverse menu of near-optimal alternatives across all performance metrics, consistently outperforming Monte Carlo sampling in the quality of near-optimal solutions identified, particularly for tail-risk measures such as conditional value-at-risk. Ultimately, this approach integrates the manager’s professional judgment regarding non-quantifiable factors, such as political stability and social responsibility, with simulation data to support the selection of a robust, sustainable portfolio.
Kilhwan Kim, Sungjune Park, Ramessh Kumar· Sustainability· 0 citations
Effective inventory management for Indonesian e-commerce MSMEs is challenged by extreme demand shocks driven by platform-specific promotional events (tanggal kembar). While operations research often suggests adaptive rolling horizon frameworks to handle non-stationarity, these dynamic models frequently rely on standard formulas assuming normally distributed demand. This study evaluates the robustness of the continuous-review inventory control policy under deliberate model misspecification. Using a 6-month daily transactional dataset from a fashion footwear MSME, empirical daily sales were fitted to a Negative Binomial distribution, while lead times followed a stochastic Triangular distribution. A 180-day discrete-event Monte Carlo simulation framework replicated across 2,500 iterations evaluated a global Parametric Static policy against an Adaptive Rolling Horizon policy ( to 60 days). Counter-intuitively, the simpler Parametric Static policy dominated, maintaining a superior Cycle Service Level (CSL) of 96.7% and a Fill Rate of 95.0%. Conversely, the adaptive framework failed to reliably satisfy the 95% target, yielding lower CSL ranges (94.2%–95.5%) and Fill Rates (90.9%–93.1%) due to sampling errors and localized variance instability caused by forcing a symmetric Normal assumption onto highly skewed data. For resource-constrained MSMEs, these findings reveal that dynamic parameter updates can lead to operational self-disruption, indicating that a stable, long-term static policy serves as a highly robust and administratively efficient buffer against promotional volatility.
M. A. Bisma, Amri Yanuar, Tamadara Hilman· Mandalika Journal of Busines...· 0 citations