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Advanced Planning Systems (APS) can contribute to improved decision-making and enhanced efficiency along complex food supply chains. This paper presents a systematic literature review of three increasingly important supply chain planning (SCP) tasks supported by APS, namely Supply Chain Network Design (SCND), Sales & Operations Planning (S&OP) and Production Planning & Scheduling (PP&S). Furthermore, academic literature on the implementation of software tools for SCP practices is investigated. The literature review reveals that multiple models for SCP practices have been developed. Empirical literature including case studies on the implementation of APS is sparse. The findings suggest that developed models for the examined planning tasks are implemented to a limited extent in practice. The study can help practitioners in the food industry to get insights regarding the opportunities by the areas of SCP examined in this paper. A theoretical framework providing research propositions to enhance the understanding of APS implementation is introduced.

In the shift toward sustainable and resource-efficient manufacturing, Artificial Intelligence (AI) is playing a transformative role in overcoming the limitations of traditional production scheduling methods. This study, based on a Systematic Literature Review (SLR), explores how AI techniques enhance Advanced Planning and Scheduling (APS) systems, particularly under finite-capacity constraints. Traditional scheduling models often overlook real-time resource limitations, leading to inefficiencies in complex and dynamic production environments. AI, with its capabilities in data fusion, pattern recognition, and adaptive learning, enables the development of intelligent, flexible scheduling solutions. The integration of metaheuristic algorithms—especially Ant Colony Optimization (ACO) and hybrid models like GA-ACO—further improves optimization performance by offering high-quality, near-optimal solutions without requiring extensive structural modeling. These AI-powered APS systems enhance scheduling accuracy, reduce lead times, improve resource utilization, and enable the proactive identification of production bottlenecks. Especially relevant in high-variability sectors like fashion, these approaches support Industry 5.0 goals by enabling agile, sustainable, and human-centered manufacturing systems. The findings have been highlighted in a structured framework for AI-based APS systems supported by metaheuristics that compares the Industry 4.0 and Industry 5.0 perspectives. The study offers valuable implications for both academia and industry: academics can gain a synthesized understanding of emerging trends, while practitioners are provided with actionable insights for deploying intelligent planning systems that align with sustainability goals and operational efficiency in modern supply chains.

When a new forward contract is signed between a mining company and a customer to hedge the risk incurred by the uncertainty in commodity market, the mining company needs to reoptimize the plans of the entire value chain to account for the change of risk level. A two-stage stochastic mixed integer nonlinear program is formulated to optimize a mineral value chain in consideration of both geological uncertainty and market uncertainty. A heuristic is developed to deal with the complexity incurred by the throughput-and head-grade-dependent recovery rate in the processing plant. Through a series of numerical tests, we show that the proposed heuristic is effective and efficient. The test results also show that ignoring the dynamic recovery rate will result in loss and severe misestimation in the mineral value chains profitability. Based on the proposed model and heuristic, an application in evaluating and designing a forward contract is demonstrated through a hypothetical case study.

This study presents a practical and scalable framework for the mid- to long-term distribution network planning that reflects real-world infrastructure constraints and investment requirements. While traditional methods often rely on simplified network models or reactive reinforcement strategies, the proposed approach introduces an investment-oriented planning model that explicitly incorporates physical elements such as duct capacity, pole availability, and installation feasibility. A linear programming (LP) formulation is adopted to determine the optimal routing and sizing of new facilities under technical constraints including voltage regulation, power balance, and substation capacity limits. To validate the model’s effectiveness, actual infrastructure and load data were used. The results show that the model can derive cost-efficient expansion strategies over a five-year horizon by prioritizing existing infrastructure use and flexibly adapting to spatial limitations. The proposed approach enables utility planners to make realistic, data-driven decisions and supports diverse scenario analyses through a modular structure. By embedding investment logic directly into the network model, this framework bridges the gap between high-level planning strategies and the engineering realities of distribution system expansion.

Purpose – The purpose of this paper is to understand value creation of information technology (IT) in supply chain planning (SCP). The impact of different IT components in SCP remains unclear and requires some thorough research. In addition, an analysis of the optimization dimension provides insights into intra-functional, inter-functional and cross-company optimization. Design/methodology/approach – A survey was conducted among German companies using a continuous production flow. In total, 47 of 193 contacted companies completed the web survey, which corresponds to a response rate of 24 percent. Findings – IT functionality for SCP is widely spread. The value of IT functionality in SCP is tremendous. Implementations in demand fulfillment and available-to-promise (ATP) have the biggest value creation potential. Supply chain performance indicators can be improved by investments in certain functional domains. Packaged standard software is widely distributed and should be considered as the first option. IT functionality to improve intra-functional processes is significantly more often implemented than IT functionality for inter-functional or cross-company process optimization although the realized value is comparable. Research limitations/implications – Respondents of the survey are limited to the German continuous production flow industry. Future research could be interesting in the discrete manufacturing industry. Originality/value – The paper provides empirical insights into the value of IT in SCP where data are less available than in the ERP context. Furthermore, this paper provides first insights into the optimization dimension whether processes are optimized intra-functional, inter-functional or cross-company.

Purpose: The objective of this paper is to provide a systematic literature review of recent developments in methodological frameworks for the modelling and simulation of agent-based advanced supply chain planning systems. Design/methodology/approach: A systematic literature review is provided to identify, select and make an analysis and a critical summary of all suitable studies in the area. It is organized into two blocks: the first one covers agent-based supply chain planning systems in general terms, while the second one specializes the previous search to identify those works explicitly containing methodological aspects.Findings: Among sixty suitable manuscripts identified in the primary literature search, only seven explicitly considered the methodological aspects. In addition, we noted that, in general, the notion of advanced supply chain planning is not considered unambiguously, that the social and individual aspects of the agent society are not taken into account in a clear manner in several studies and that a significant part of the works are of a theoretical nature, with few real-scale industrial applications. An integrated framework covering all phases of the modelling and simulation process is still lacking in the literature visited.Research limitations/implications: The main research limitations are related to the period covered (last four years), the selected scientific databases, the selected language (i.e. English) and the use of only one assessment framework for the descriptive evaluation part. Practical implications: The identification of recent works in the domain and discussion concerning their limitations can help pave the way for new and innovative researches towards a complete methodological framework for agent-based advanced supply chain planning systems. Originality/value: As there are no recent state-of-the-art reviews in the domain of methodological frameworks for agent-based supply chain planning, this paper contributes to systematizing and consolidating what has been done in recent years and uncovers interesting research gaps for future studies in this emerging field.Purpose: The objective of this paper is to provide a systematic literature review of recent developments in methodological frameworks for the modelling and simulation of agent-based advanced supply chain planning systems. Design/methodology/approach: A systematic literature review is provided to identify, select and make an analysis and a critical summary of all suitable studies in the area. It is organized into two blocks: the first one covers agent-based supply chain planning systems in general terms, while the second one specializes the previous search to identify those works explicitly containing methodological aspects. Findings: Among sixty suitable manuscripts identified in the primary literature search, only seven explicitly considered the methodological aspects. In addition, we noted that, in general, the notion of advanced supply chain planning is not considered unambiguously, that the social and individual aspects of the agent society are not taken into account in a clear manner in several studies and that a significant part of the works are of a theoretical nature, with few real-scale industrial applications. An integrated framework covering all phases of the modelling and simulation process is still lacking in the literature visited. Research limitations/implications: The main research limitations are related to the period covered (last four years), the selected scientific databases, the selected language (i.e. English) and the use of only one assessment framework for the descriptive evaluation part. Practical implications: The identification of recent works in the domain and discussion concerning their limitations can help pave the way for new and innovative researches towards a complete methodological framework for agent-based advanced supply chain planning systems. Originality/value: As there are no recent state-of-the-art reviews in the domain of methodological frameworks for agent-based supply chain planning, this paper contributes to systematizing and consolidating what has been done in recent years and uncovers interesting research gaps for future studies in this emerging field.

Modern advanced planning systems offer the technical prerequisites for an allocation of “available-to-promise” (ATP) quantities—i.e. not yet reserved stock and planned production quantities—to different customer segments and for a real time promising of incoming customer orders (ATP consumption) respecting allocated quota. The basic idea of ATP allocation is to increase revenues by means of customer segmentation, as it has successfully been practiced in the airline industry. However, as far as manufacturing industries and make-to-stock production are concerned, it is unclear, whether, when, why and how much benefits actually arise. Using practical data of the lighting industry as an example, this paper reveals such potential benefits. Furthermore, it shows how the current practice of rule-based allocation and consumption can be improved by means of up-to-date demand information and changed customer segmentation. Deterministic linear programming models for ATP allocation and ATP consumption are proposed. Their application is tested in simulation runs using the lighting data. The results are compared with conventional real time order promising with(out) customer segmentation and with batch assignment of customer orders. This research shows that—also in make-to-stock manufacturing industries—customer segmentation can indeed improve profits substantially if customer heterogeneity is high enough and reliable information about ATP supply and customer demand is available. Surprisingly, the choice of an appropriate number of priority classes appears more important than the selection of the ATP consumption policy or the clustering method to be applied.

The rapid growth in computer technology providessupply chain managers with valuable tools to bettercoordinate and control their operations. This bookseeks to describe systems available to give supplychains information system support, demonstratingkey tasks with demonstrated analytic techniques.This second edition provides you with newer cases todemonstrate concepts that will allow to better manageyour supply chain management position in one of thefastest growing fields in our economy.

The rapid growth in computer technology provides supply chain managers with valuable tools to better coordinate and control their operations. This book seeks to describe systems available to give supply chains information system support, demonstrating key tasks with demonstrated analytic techniques. This second edition is basically the same as the first edition, but with newer cases to demonstrate concepts. The target market for this book is practitioners in the supply chain management field, one of the fastest growing fields in our economy.

Purpose - The purpose of this paper is to explore what potential benefits may be achieved by using advanced planning and scheduling (APS) systems in the sales and operations planning (S&OP) process.Design methodology approach - The paper investigates benefits at the S&OP process level by interviewing APS experts and APS users. Several methods have been used; literature review, Delphi study, and a case study at a company in the chemical industry which uses APS system support in the S&OP process.Findings - Three types of potential benefits were found to be achieved when using APS systems in the S&OP process; benefits concerning decision support, planning efficiency and learning effects. The most common type was decision support benefits according to APS users and APS experts. The results from the case company showed that the benefits perceived in the different S&OP activities differed. In the activities concerning the preparation and generation of delivery plans, the perceived benefits mainly concerned learning effects. In the activities concerning the generation of a production plan, the benefits were foremost found in planning efficiency. In the S&OP meeting decision support benefits were highest valued. The reason for the different results can be explained by the aim of the activity, how APS was used in the activity, the user characteristics and the design of the model and access and quality of planning data.Research limitations implications - The focus of this paper is on potential benefits of APS systems in the S&OP process only, not the costs. It has established a typology of potential benefits. No validation in form of statistical analysis has been done. The empirical analysis is mainly based on findings from a single case study.Practical implications - The findings about the types of APS potential will assist companies in understanding the benefits they can expect from its use in the S&OP process. The case study analysis gives further insight into how APS can be employed and what benefits different APS user categories can expect when it is used in an appropriate way.Originality value - The knowledge about which benefits that can be achieved when using APS in the S&OP process is quite unexplored. This paper fills some of these gaps.