SUPPLY CHAIN OPTIMIZATION

Artificial intelligence (AI) reshapes services by supporting or fully performing tasks and thus becomes a major source of service innovation (Huang & Rust, 2018). AI can automate and personalize services, support human decision-making, and, in the future, when intuitive, social and empathetic capabilities of AI are developed, may even humanize services (Heinonen et al., 2020). The influence of AI on services in general has gained research attention, and Huang and Rust (2018) identified two research streams. While the service and technology literature discuss the positive influence of AI allowing the quick analysis of big amounts of data, the economic literature focuses on the effect of AI on jobs. However, a key challenge is leveraging AI to create business value. The literature on this topic is still scarce (Vidgen et al., 2017). Therefore, this chapter aims to shed light on the challenges of implementing AI-based services using a case study of the Polish high-tech company Signal. In our case study, we analyze predictive maintenance as an AI-based service that helps scheduling the maintenance of machines and other equipment using machine learning algorithms, statistical models, and historical data to predict when equipment will likely fail. Predictive maintenance aims to perform maintenance tasks only when necessary, rather than on a fixed schedule or when equipment breaks down, allowing maintenance teams to take proactive measures to prevent downtime, reduce costs, and improve operational efficiency. This maintenance approach has become increasingly popular, as technologies like sensors, big data, and AI allow data collection and analysis in real time, enabling organizations to make better informed decisions about maintenance and optimize their operations (Mobley, 2002). Transitioning from preventive maintenance on a fixed schedule to AI-based predictive mainte nance in a company requires historical data and algorithms (Carvalho et al., 2019). A transition also leads to organizational changes that affect employees and how they work. In this chapter, we aim to answer the following research question: What are the challenges to realizing business value with a predictive maintenance service? To answer our research question, we first provide a theoretical background on big data, AI, and their role in maintenance services. Then, we outline the methodology and present the findings of our case study by analyzing how Signal developed its maintenance service from corrective to predictive maintenance. Based on interviews and document analysis, we identify the value created for the customer, how it is delivered, and how the supplier and customer capture it. Furthermore, we highlight the challenges of managing the development of AI-based predictive maintenance. Finally, we discuss how a service initially perceived from a security and maintenance value perspective was developed toward much broader functionalities thanks to the analytical power of AI.

This study investigates key factors affecting supply chain efficiency and delivery times from 2020 to 2024 using regression analysis. The primary objective is to identify the impact of transportation management, inventory optimization, and supplier reliability on delivery performance. A quantitative research approach was adopted, analyzing data from manufacturing, retail, and logistics sectors. Multiple regression models revealed a strong negative correlation between inventory levels and delivery times (r =-0.89, p < 0.01), confirming that efficient inventory management accelerates deliveries. Supplier lead times significantly influenced delivery consistency (β =-0.65, p < 0.05), while transportation cost reductions correlated with improved efficiency (β =-0.58, p < 0.05). The overall correlation coefficient for key supply chain factors and delivery times was r =-0.82, indicating a strong relationship between operational improvements and delivery efficiency. The study concludes that optimizing supply chain components enhances efficiency, reducing delivery times from 7.5 days in 2020 to 6.3 days in 2024. Recommendations include AI-driven predictive analytics for demand forecasting, supplier performance management strategies, and real-time transportation tracking for improved logistics.

The pulp and paper industry depends on a long and integrated supply chain. It starts in forest harvest areas as trees and ends as multiple products used in all persons daily usage. The lead time from the first step to the last is long and it involves many steps operated by several companies and organizations. In this overview paper we describe the overall supply chain, its participants and the planning problems arising along the chain. We divide the planning problems into strategic, tactical and operative in a supply chain matrix and describe their characteristic and provide applications as illustrations. We discuss the need for information and decision support for planners in each of these areas. This relates to planning within a single company as well as integrated planning across several. A number of tailor-made systems has been developed and published in the literature and we describe these tools/systems together with their characteristics and results. To conclude with a discussion around current issues and outline future research areas.

One key aspect of disruptive technology in logistics is the integration of automation. From autonomous vehicles to robotic warehouses, the automation revolution involves streamlining operations and reducing human intervention . Automated systems not only enhance speed and accuracy in order fulfillment but also minimize errors and operational costs. The efficiency gains are particularly evident in tasks such as inventory management, where real-time tracking and automated replenishment systems optimize stock levels, preventing both overstock and stockouts. "Artificial intelligence and the Internet of Things" are driving the development of smart logistics, leading to increased service efficiency, cost reduction, and business model innovation (Wang, 2021). In the era of big data, the logistics sector is leveraging advanced analytics to glean actionable insights. Data analytics facilitates predictive modeling for demand forecasting, route optimization, and risk management. By harnessing the power of data, organizations can make informed decisions, anticipate market trends, and proactively address challenges. This strategic use of data contributes to more responsive supply chains, enabling businesses to adapt swiftly to changing consumer demands and market dynamics. AI in logistics and supply chain management improves decision-making and automation but does not replace human logistics planners; it enhances their capabilities (Boute & Udenio, 2021). Artificial intelligence, specifically machine learning algorithms, plays a major role in enhancing the predictive capabilities of logistics and distribution management. Disruptive technologies require businesses to evolve their business models to maximize opportunities, and research in logistics and supply chain management can help shape the next generation of competition . These algorithms analyze historical data patterns to forecast demand, optimize routes, and improve scheduling. Machine learning models continuously learn from real-time data, refining predictions and recommendations over time. This adaptability is particularly valuable in an environment where factors influencing logistics are constantly evolving. The integration of machine learning not only enhances efficiency but also empowers organizations to address challenges, creating a more resilient supply chain. As disruptive technology and AI continue to develop, logistics and distribution face both challenges and opportunities. Cybersecurity concerns, the need for a skilled workforce, and ethical considerations surrounding AI are potential hurdles. However, the potential benefits, including enhanced efficiency, cost savings, and improved customer satisfaction, position organizations to thrive in an increasingly competitive market. AI will lead to qualitative changes in the labor market,

Renewable energy resources have received increasing attention due to environmental concerns. Biomass, one of the most important renewable energy resources, is abundant in agricultural-based countries. Typically, the biomass supply chain is large due to the huge amount of relevant data required for building the model. As a result, using a standard optimization package to determine the solution for the biomass supply chain model might not be practical. In this study, the focus is on developing and applying advanced methodologies that can be used to determine a solution for the biomass supply chain model efficiently. The decisions related to plant selection, and distribution of biomass from suppliers to plants require optimization. The methodologies considered in this research are based on stochastic programming, parameter search, and simulation-based optimization. Computational results and managerial insights based on case studies from different regions of Vietnam are provided. The re...

This study applies Actor-Network Theory (ANT), Resource-Based View (RBV), and Closed-Loop Supply Chain (CLSC) Theory to investigate the integration of Internet of Things (IoT) and  blockchain  technology  for  sustainable  waste  management  in  cold  chain  food  supply chains. Using  a  case  study  of  Company,  A  supported  by  datasets  such  as  the  Time-Temperature Data and the Google Cloud Blockchain Dataset, the research evaluates how IoT sensors  and  blockchain  systems  monitor  waste  and  enhance  operational  efficiency  across supply  chain  stages.  Findings  reveal  that  IoT-enabled  real-time  monitoring  significantly reduces waste by addressing temperature deviations, while blockchain enhances traceability and  accountability  through  immutable  record-keeping  and  smart  contracts.  The  combined application  of  these  technologies  demonstrates  substantial  waste  reduction potential, particularly in transportation and distribution stages, aligning with sustainability objectives such as the UN Sustainable Development Goals. This research provides both theoretical and practical  contributions  to  the  adoption  of  digital  tools  insustainable  supply  chain management.

The e-commerce sector experiences remarkable expansion and advancement in global business practices, which positively influences food security in developing nations. The integration of digital technology into agriculture enhances food security regarding accessibility, affordability, and availability (FAO, 2021). This study evaluates the role of e-commerce in mitigating food insecurity by examining its effects on the inefficiencies of conventional agricultural markets, transaction costs, and market reach (Reardon et al., 2020).-The adoption of e-commerce in developing economies is frequently hindered by structural barriers such as insufficient technology, lack of access to financial services, inadequate regulatory frameworks, and subpar logistical arrangements (UNCTAD, 2022). Furthermore, the deficiency in digital competencies and skepticism towards online transactions impede the adoption of e-commerce within the food distribution sector (Aker & Ghosh, 2016). The integration of mobile payment technologies, blockchain logistics, and AI agricultural analysis can significantly enhance food accessibility via digital commerce (World Bank, 2023).-This study integrates quantitative and qualitative methodologies through a case study approach, utilizing e-commerce data analysis to assess food security indicators. Evidence from Sub-Saharan Africa and South Asia indicates that digital agricultural marketplaces mitigate post-harvest losses and enhance price stabilization (Lutz and Tadesse, 2017). Findings suggest that effective governance of e-commerce ecosystems can enhance food security in developing nations and other resource-constrained regions. The research concludes with recommendations for enhancing digital infrastructure development, regulatory frameworks, and trade policies that facilitate engagement in the digital economy.

Abstract - Food waste constitutes a significant global challenge with profound economic, environmental, and social ramifications. The Food and Agriculture Organization (FAO) reports that almost one-third of all food produced, totaling 1.3 billion tons, is wasted each year, resulting in substantial financial losses estimated at $940 billion yearly and worsening food poverty (FAO, 2019). The environmental impact is worrisome, with food waste contributing around 8–10% of global greenhouse gas emissions (UNEP, 2021). In the age of digital transformation, e-commerce and digital platforms present a viable opportunity to reduce food waste via technical advancements, enhanced supply chains, and consumer interaction methods.

This study examines the impact of e-commerce on reducing food waste through the utilization of artificial intelligence (AI), blockchain technology, and the Internet of Things (IoT). AI-driven demand forecasting and predictive analytics allow online retailers to enhance inventory management, consequently minimizing overstock and unsold perishables (Accorsi et al., 2020). Blockchain technology improves transparency and traceability in supply chains, facilitating real-time tracking of food goods from farm to consumer, hence minimizing spoiling (Kamilaris et al., 2019). IoT-enabled smart packaging enhances the shelf life of perishable items by monitoring storage conditions and delivering automated freshness notifications (Gustavsson et al., 2022).

In addition to technological progress, e-commerce enables novel business models that aid in the reduction of food waste. Direct-to-consumer (DTC) and farm-to-table platforms eradicate inefficiencies in conventional distribution routes, hence decreasing transit time and minimizing spoilage (Xu & Liu, 2021). Surplus food marketplaces and rescue platforms allow enterprises to sell surplus or near-expiration products at reduced costs, preventing edible food from being discarded and improving affordability for customers (Ciulli et al., 2020). AI-driven dynamic pricing solutions enhance inventory turnover by modifying prices according to demand patterns, expiration dates, and supply variations (Khan et al., 2023).

Policy frameworks and regulatory incentives are essential in promoting a food waste-aware e-commerce environment. Global governments are enacting regulations that promote food donation, provide financial incentives for firms that decrease waste, and standardize expiration date labeling to limit the wasteful disposal of edible food (Parfitt et al., 2020). Public-private partnerships have augmented these initiatives, fostering sustainable corporate practices and ethical consumer conduct in the digital economy.

This research highlights the revolutionary capacity of e-commerce in mitigating food waste through the integration of technical solutions, innovative business models, and legislative initiatives. Nonetheless, obstacles including logistical limitations, consumer awareness, and regulatory adherence must be confronted to optimize efficiency. The results indicate that digital transformation within the food supply chain can improve operational efficiency, generate cost savings, and support global sustainability objectives.

Millions of people still suffer from hunger and malnutrition, thus food security remains a major issue even if Africa has great agricultural potential. Weak and ineffective food supply chains, inadequate infrastructure, climate change, and large postharvest losses help to explain some of the continuation of food poverty. Many times, farmers find it difficult to get their goods to markets on time; occasionally, inadequate storage causes waste and spoiling. This paper explores how more robust and efficient food supply networks might assist to better ensure increased food availability and access across Africa, hence overcoming these problems. Scholarly papers and case studies as well as trustworthy sources such Food and Agriculture Organization (FAO, 2021) and World Bank (2020) are included in this paper. It looks at doable fixes including better rural roads, building of cold storage facilities, and greater market interactions for farmers. Particularly the African Continental Free Trade Area (AfCFTA), the goal of regional trade agreements is especially underlined as a means to lower trade obstacles and streamline the flow of food across borders. Our research on focused investments in modern transportation systems, climate-resilient technologies, and cold chain infrastructure shows that they could greatly lower food losses and offer supply line dependability. Though financing and farmer education have issues that have to be addressed, inexpensively priced technologies like hermetic storage bags, solar-powered cooling units, and community-based warehouses are also listed as important tools. The report also underlines how government, corporate investor, farmer group, and development partner cooperation help to increase food security. Policies supporting smallholder farmers, invention stimulation, and financial access enhancement may assist food systems to be more resilient and inclusive.

The management of food waste has become a critical challenge in urban areas, encompassing environmental, socioeconomic , and governance concerns. The increasing volume of food waste produced in cities is significantly influenced by rapid urbanization, inefficient supply chains, and changing consumption patterns (FAO, 2019). This paper critically examines the current challenges and sustainable solutions associated with food waste management in urban environments, with a particular emphasis on systemic inefficiencies that exacerbate environmental degradation and economic losses. The study identifies principal barriers, such as inadequate infrastructure, low public awareness, poor waste segregation at source, and insufficient regulatory frameworks, through a comprehensive review of scholarly literature, case studies, and policy documents (Gustavsson et al., 2011; Aschemann-Witzel et al., 2015). The results indicate that numerous urban centers, particularly those in developing countries, are devoid of effective sourcelevel segregation mechanisms, which impede the potential for recycling and decomposition (Adewumi et al., 2020). In addition, the necessity for targeted education and behavior change strategies is underscored by the significant role of consumer behavior, which is influenced by socio-cultural and psychological factors, in household-level food wastage (Papargyropoulou et al., 2014). The paper suggests a variety of sustainable solutions to address these multifaceted challenges, including the establishment of food redistribution networks, such as food banks and surplus-sharing platforms, which have been successful in reducing both waste and food insecurity in cities such as London and New York (Mourad, 2016). Additionally, the paper suggests the adoption of circular economy approaches, such as composting and anaerobic digestion, to convert food waste into renewable energy and bio-fertilizers, as effectively implemented in cities such as Stockholm and Seoul (Jensen et al., 2017; Song et al., 2015). By emphasizing the necessity of integrated collaboration among governments, private sectors, and communities to cultivate sustainable urban ecosystems, this study concludes by advocating for the alignment of urban food waste management strategies with Sustainable Development Goal 12, which promotes responsible consumption and production.

This research explores the transformative role of Internet of Things (IoT)-based predictive maintenance in promoting energy efficiency and operational excellence within the manufacturing sector. With rising operational demands and environmental responsibilities, industries are shifting from reactive and time-based maintenance to proactive, data-driven systems. This study presents a fictional case rooted in realistic industrial practices to demonstrate how predictive maintenance reduces energy usage, operational downtime, and long-term costs. The findings indicate that when supported by IoT infrastructure, predictive maintenance serves as a key enabler of sustainable manufacturing practices, strategic agility, and industrial resilience in the age of digital transformation.

Supply chain management and optimization is a critical aspect of modern enterprises and an expanding area of research. Modeling and optimization are the traditional tools of supply chain management. The techniques have been used by many companies for planning,
manufacturing, and other decision areas in supply chains. Current study is motivated by the fact that optimization studies in supply chain management have mostly considered network optimization. Supply chain management however, requires alignment between the supply chain partners at the tactical level. As a first step towards achieving this goal, current study presents a model that incorporates the activity level planning at the focal firm in a supply chain. This paper presents a new mixed integer programming model that incorporates optimization of production planning at the focal firm while optimizing the strategic alignment of the supply chain entities. The model represents a four step, multi-echelon supply chain including supplier, warehouse, manufacturer, and retailer. The manufacturer in this network represents the focal firm. This model is an attempt to integrate the production planning decisions in the network optimization decisions.

Biofuel industry has attracted much attention due to its potential to reduce dependency on fossil fuels and contribute to the renewable energy. The high levels of uncertainty in feedstock yield, market prices, production costs, and many other parameters are among the major challenges in this industry. This challenge has created an ongoing interest on studies considering different aspects of uncertainty in investment decisions of the biofuel industry. This study aims to determine the optimal design of supply chain for biofuel refineries in order to maximize annual profit considering uncertainties in fuel market price, feedstock yield, and logistic costs. In order to deal with the stochastic nature of parameters in the biofuel supply chain, we develop two-stage stochastic programming models in which Conditional Value at Risk (CVaR) is utilized as a risk measure to control the amount of shortage in demand zones. Two different approaches including the expected value and CVaR of the profit are considered as the objective function. We apply these models and compare the results for a case study of the biomass supply chain network in the state of Iowa to demonstrate the applicability and efficiency of the presented models.

Biofuel industry has attracted much attention due to its potential to reduce dependency on fossil fuels and contribute to the renewable energy. The high levels of uncertainty in feedstock yield, market prices, production costs, and many other parameters are among the major challenges in this industry. This challenge has created an ongoing interest on studies considering different aspects of uncertainty in investment decisions of the biofuel industry. This study aims to determine the optimal design of supply chain for biofuel refineries in order to maximize annual profit considering uncertainties in fuel market price, feedstock yield, and logistic costs. In order to deal with the stochastic nature of parameters in the biofuel supply chain, we develop two-stage stochastic programming models in which Conditional Value at Risk (CVaR) is utilized as a risk measure to control the amount of shortage in demand zones. Two different approaches including the expected value and CVaR of the prof...

Renewable fuel is playing an increasingly important role as a substitute for fossil based energy. The US Department of Energy (DOE) has identified pyrolysis based platforms as promising biofuel production pathways. In this paper, we present a general biofuel supply chain model with a Mixed Integer Linear Programming (MILP) methodology to investigate the biofuel supply chain facility location, facility capacity at strategic levels, and biofuel production decisions at operational levels. In the model, we accommodate different biomass supplies and biofuel demands with biofuel supply shortage penalty and storage cost. The model is then applied to corn stover fast pyrolysis pathway with upgrading to hydrocarbon fuel since corn stover is the main feedstock for second generation biofuel production in the US Midwestern states. Numerical results illustrate unit cost for biofuel production, biomass, and biofuel allocation. The case study demonstrates the economic feasibility of producing biofuel from biomass at a commercial scale in Iowa.

Food is essential for human survival. Some of the most crucial factors to think about are minimising food waste, optimising the supply chain, and enhancing food logistics, delivery, and safety. The achievement of these goals is greatly aided by the application of AI and ML. Proliferation of ever-more-powerful and pervasive computer networks has enabled modern logistical and industrial systems. Within these networks, there is a continual influx of fresh data from many sources, generated by sensors, equipment, systems, intelligent devices, and humans. Thanks to computers' ever-improving capabilities, we can now analyse Big Data more quickly, thoroughly, and in-depth than ever before. These developments have rejuvenated and ushered in a new era called Industry 4.0 or the Smart Factory, which has increased the importance of artificial information technology (AI). Machine learning and artificial intelligence are discussed in this article within the context of the food industry and business. Important uses of this technique include supply chain optimisation, crop selection, logistics, food distribution, and processing plant predicting maintenance.

As global supply chains become more intricate, the significance of supply chain risk management has significantly increased. This research delves into the utilization of artificial intelligence (AI) in managing supply chain risks, analyzing cutting-edge advancements, obstacles, and potential areas for further exploration. Through a comprehensive review of literature sources like Google Scholar, Web of Science, EI, and Scopus, this study investigates how AI methods such as machine learning, deep learning, neural networks, fuzzy logic, genetic algorithms, and evolutionary algorithms can help mitigate supply chain risks. These AI technologies have demonstrated remarkable efficacy in mitigating various risks, including forecasting, anomaly detection, image recognition, text mining, logistics optimization, and emergency response tactics. Apart from AI-driven approaches, optimization solvers and algorithms play a critical role in tackling complex supply chain dilemmas. Mathematical programming solvers, including linear programming (LP), mixed-integer programming (MIP), and quadratic programming (QP), are commonly used to model and optimize supply chain networks by considering factors like cost, capacity, and demand fluctuations. Finetuning solver parameters and strategies to enhance computational efficiency-known as solver tuning-has been crucial in enhancing solution quality and decreasing computation time for large-scale supply chain issues. Heuristic solvers like genetic algorithms, simulated annealing, and ant colony optimization are frequently employed to resolve conflicts in supply chains. These solvers offer practical solutions to problems where exact methods are computationally impractical, allowing for swift responses to disruptions such as production delays or spikes in demand. Pyramid classification, a hierarchical approach, further refines decision-making by categorizing risks and aligning response strategies based on priority and severity. Furthermore, the integration of AI technologies and solvers enables advanced conflict resolution techniques, including scenario-based modeling and multi-objective optimization. These approaches enable decision-makers to weigh trade-offs between conflicting objectives like minimizing costs versus maximizing service levels in real-time. The study underscores that AI technologies and optimization solvers significantly bolster risk management in supply chains. Nonetheless, challenges such as data privacy concerns, security vulnerabilities, technical intricacies, and implementation obstacles pose critical barriers to widespread adoption. The study offers practical suggestions for businesses and decision-makers while pinpointing key areas for future exploration, such as devising hybrid models that merge heuristic solvers with AI for adaptive and scalable risk management strategies.

This study provides a systematic review of the challenges and solutions in
electric vehicle (EV) manufacturing, addressing key areas such as
technological advancements, workforce readiness, supply chain
optimization, sustainability practices, and lean manufacturing principles.
A total of 86 peer-reviewed articles published between 2015 and 2023
were analyzed following the Preferred Reporting Items for Systematic
Reviews and Meta-Analyses (PRISMA) guidelines to ensure rigor and
transparency. The findings reveal that technological innovations,
including automation, artificial intelligence (AI), and digital twin
technologies, significantly improve production efficiency and resource
optimization, though their adoption remains hindered by high
implementation costs and infrastructural incompatibilities. Workforce
challenges, including skill mismatches and labor shortages, were
identified as critical barriers, particularly in emerging economies where
education and training initiatives remain insufficient. Supply chain
inefficiencies, driven by raw material dependency and geopolitical risks,
continue to disrupt production, despite the potential of predictive analytics
and digitalization to enhance resilience. Sustainability practices, such as
circular economy strategies and energy-efficient manufacturing
processes, were found to reduce environmental impacts and operational
costs, though scalability remains a challenge. Finally, lean
manufacturing principles emerged as effective solutions for improving
productivity and reducing waste, though their success depends on
organizational commitment and workforce adaptability.

The purpose of this editorial is to introduce a special issue "Optimization and Simulation Models in Supply Chain Management" that looks at optimization and simulation applications in supply chain management. In addition to introducing the papers in this issue and summarizing their major contributions, we give a brief overview of the current practice in supply chain optimization and the prospects for future research in this field.

This paper explores the potential for optimizing the Internet of Things by integrating machine learning and computer vision technologies and its implications for U.S. national security and economic competitiveness. First, the application of machine learning in IoT device optimization is introduced, emphasizing its ability to improve system intelligence and efficiency. Secondly, the critical role of computer vision technology in monitoring and reacting to changes in the physical environment is discussed, especially in security applications, such as the protection of national infrastructure and border security. Finally, the strategic significance of integrating these technologies in national security strategy and economic development is analyzed, and the direction and challenge of future research are put forward. .

In this paper, a new metaheuristic algorithm called Orangutan Optimization Algorithm (OOA) is designed,
which imitates the behaviors of Orangutans in nature. The fundamental inspiration of OOA is the foraging strategy of
Orangutans and the skills of these animals in nesting. The theory of OOA is explained and then the implementation
steps of OOA in two phases of exploration and exploitation are mathematically modeled. The performance of OOA in
dealing with real-world applications is evaluated on twenty-two constrained optimization problems from the CEC
2011 test suite. The optimization results show that the proposed OOA approach, by balancing exploration and
exploitation during the search process, is able to provide suitable solutions for the benchmark functions. Also, in order
to measure the quality of OOA, the results obtained from the proposed approach are compared with twelve well-known
metaheuristic algorithms. Analysis of the simulation results shows that OOA has provided superior performance by
providing better results in 100% of the benchmark functions compared to competitor algorithms.

This study explores the feasibility, readiness, and benefits of implementing blockchain technology within humanitarian pharmaceutical supply chains, with a specific focus on the International Rescue Committee (IRC). Humanitarian organizations, such as the IRC, face persistent challenges in ensuring the traceability, transparency, and integrity of essential medical supplies. Blockchain technology offers a transformative solution to address these issues by providing a decentralized, secure, and immutable system for tracking and managing supply chain operations.

The research adopts a mixed-methods approach, utilizing both quantitative and qualitative data gathered from 44 IRC personnel. Key objectives include evaluating the awareness and preparedness of supply chain staff regarding blockchain technology, identifying potential benefits and limitations, and analyzing regulatory and infrastructural barriers. Findings reveal that while blockchain technology can enhance traceability, inventory management, and data security, significant challenges remain, such as infrastructure limitations, gaps in technical expertise, and regulatory complexities.

The study proposes a phased approach to blockchain integration, starting with targeted training initiatives, donor engagement, and pilot projects to test the technology in a controlled environment. The research underscores the importance of stakeholder collaboration and resource allocation in overcoming barriers to adoption. Ultimately, this work highlights the potential of blockchain to revolutionize pharmaceutical supply chains in humanitarian contexts, enhancing operational efficiency, transparency, and accountability.

This thesis contributes to the growing body of knowledge on blockchain applications in supply chains and provides actionable insights for humanitarian organizations seeking to implement innovative technologies to improve their operations.

Intelligent automation (IA) is transforming supply chain management by integrating advanced technologies such as artificial intelligence (AI), machine learning (ML), robotics, and the Internet of Things (IoT). This paper explores how IA optimizes supply chain processes, enhances operational efficiency, and drives strategic decision-making. By analyzing the impact of intelligent automation on various supply chain functions, including inventory management, logistics, and demand forecasting, this research highlights the critical role of IA in achieving agility and responsiveness in increasingly competitive markets. Additionally, the paper discusses the challenges organizations face in implementing IA solutions and provides insights into best practices for successful integration. The findings underscore the importance of leveraging intelligent automation as a key driver of supply chain optimization in today's digital landscape.

Biofuels, for reasons such as the high demand of fossil fuels, the need for energy security and concerns over greenhouse gas emissions, have attracted the attention of researchers. Production of biofuels can be costly. On the other hand, the environmental benefits of biofuels depend on the technique they are produced, transported and used. This article studies a mathematical programming model for optimal planning of a biofuel supply chain based on existing facilities. The developed model is based on multi-objective linear programming (MOLP) and considers the economic, environmental and social concerns related to the proposed biofuel supply chain. The model takes into account feedstock availability, biofuel demand, existing biofuel plants and processing technologies. The proposed model is used to evaluate biodiesel production from palm oil and jatropha oil based on existing biodiesel plants in Malaysia. From developed model, the ε-constraint method is applied to solve the optimisation problem. Solving of the proposed model determines the optimal quantity of biomass to be harvested, transportation schedules and the quantity of biodiesel produced at biorefineries as well.

The study explores how Geopointe, a route optimization tool, affects the effectiveness of industrial supply chains and proposes a novel method for route optimization that is used in the IT industry. Outdated solutions have been retired as Google Maps and its API have been improved. Route optimization is the process of determining the fastest and most economical path for your distribution network between nodes. By improving routes, both the productivity and environmental impact of mobile workers will increase. Along with route optimization, these two-technique based API will be compared to other methods, like Salesforce and Geopointe tool, etc., to evaluate performance, expense, and carbon emission. Numerous optimization technologies, including Geopointe, Map Anything, Amazon, etc., are contrasted in the article to investigate the delivery and cost function difficulties. All sectors must have a strong supply chain, and route optimization technologies, which are utilized by many diffe...

Supply chain planning aims to maximize the chain's profit and find an effective way to integrate production and distribution. Mathematical and simulation-based optimizations are two common disciplines. This study integrates both of them together to consolidate their advantages. A mathematical model is formulated to find an optimal production-distribution plan. Then, the result is fed into a simulation model operating under uncertainty to verify the feasibility of the plan. The integrated approach tries to find a feasible plan that satisfies both required customer service level and makespan limitation where safety stock is used to hedge against uncertainties, and lateral transshipment is used for emergency measures against excessive fluctuation of customer demand. A case study that optimizes the profit of an entire chain is used to demonstrate the algorithm. The outcomes of the study show that the proposed approach can yield feasible results (with near or even optimal solution) with much faster computational time as compared to the traditional simulation-based optimization.

Supply chain planning aims to maximize the chain&#39;s profit and find an effective way to integrate production and distribution. A mathematical and simulation-based optimizations are two common disciplines in which this study integrates both of them together to consolidate their advantages. A mathematical model is formulated to find an optimal production-distribution plan. Then, the result is fed into a simulation model operating under uncertainty to verify the feasibility of the plan. Our integrated approach tries to find a feasible plan that satisfies both required customer service level and makespan limitation where safety stock is used to hedge against uncertainties, and lateral transshipment is used for emergency measures against excessive fluctuation of customer demand. A case study that optimizes the profit of an entire chain is used to demonstrate the algorithm. The outcomes of the study show that our proposed approach can yield feasible results (with near or even optimal s...

This paper studied the material ordering and inventory control of supply chain systems. The effect of controlling policies is analyzed under three different configurations of the supply chain systems, and the formulated problem has been solved by using an evolutional optimization method known as Differential Evolution (DE). The numerical results show that the coordinating policy with the incentive scheme outperforms the other policies and can improve the performance of the overall system as well as all members under the concept of supply chain management.

The textile industry faces significant challenges in supply chain management, including increasing pressure to reduce costs, improve operational efficiency, and minimize environmental impacts. This paper explores the role of data analytics in optimizing supply chains within textile manufacturing, focusing on how advanced technologies such as Artificial Intelligence (AI), Machine Learning (ML), and the Internet of Things (IoT) can improve efficiency and sustainability. By integrating Lean Manufacturing and Six Sigma methodologies, the study highlights how textile manufacturers can streamline production processes, reduce waste, and enhance product quality. Furthermore, the paper discusses sustainability practices, emphasizing the importance of reducing carbon footprints and optimizing resource management. Through a combination of case studies and data-driven insights, the paper provides practical examples of how these strategies can be implemented in textile supply chains to drive efficiency and environmental responsibility.

Biofuels provide an attractive alternative for satisfying energy demands in a more sustainable way than fossil fuels. To establish a biorefinery, an optimal plan must be implemented for the entire associated supply chain, covering such aspects as selection of feedstocks, location, and capacity of biorefineries, selection of processing technologies, production amounts and transportation flows. In this context, there are several parameters, including the availability of biomass, product demand, and product prices, which are difficult to predict because they might change drastically over the different seasons of the year as well as across years. To address this challenge, this work presents a mathematical programming model for the optimal planning of a distributed system of biorefineries that considers explicitly the uncertainty associated with the supply chain operation as well as the associated risk. The potential of the proposed approach is demonstrated through its application to the production of biofuels in Mexico, considering multiple raw materials and products.

Supply chain networks are increasingly complex due to evolving customer demands and geopolitical uncertainties. The integration of SAP Transportation Management SAP TM and Business Network for Logistics BN4L can significantly enhance logistics operations. This paper explores the integration of SAP TM with BN4L, highlighting their benefits, integration strategies, and overall impact on supply chain efficiency. It aims to provide insights for industry practitioners and researchers on leveraging BN4L addons like Project 44 for improved supply chain visibility and predictive analytics. Key research questions addressed include the impact on different industries, challenges and opportunities, effective use of predictive analytics, and return on investment ROI of the integration. In this paper, we will explore the following research questions: 1) How does the integration of SAP TM and BN4L impact different supply chain industries (e. g., retail, transportation)? 2) What are the key challenges and opportunities associated with implementing and maintaining the integration? 3) How can predictive analytics be effectively leveraged within the SAP TM and BN4L framework to improve decisionmaking? 4) What is the return on investment (ROI) of integrating SAP TM and BN4L? By addressing these questions, this paper aims to provide a comprehensive understanding of the transformative potential of SAP TM and BN4L integration.

This article presents a tactical procurement plan for an aromatic coconut manufacturer located in Thailand using a mathematical model. Procurement planning is complex because many uncertainty factors are dependent upon the delivery schedule and season, which affect the price of the coconut and the quantity of the order. There were two alternatives for procurement: farmers and coconut collectors. The model used in this research study compared four scenarios: three deterministic models in which every parameter is constant under the worst-case, best-case, and average-case scenarios and a stochastic model simulation. For the deterministic models, the mixedinteger linear programming (MILP) was formulated in a spreadsheet and solved using the Frontline Premium Solver in Excel. In the second model, a Monte Carlo simulation-based MILP with three random variables-demand, price, and the number of coconuts-was solved optimally. The solution indicates how many coconuts will be purchased from farmers and collectors and which truck will be used to deliver the order to maximize total annual profit. The results were compared among four scenarios that could help decision-makers consider the range of profit. The results showed that the stochastic model resulted in less profits than the deterministic model. In the worstcase scenario, profit was lost; in the best-case scenario, profit was gained. In the stochastic model, profits were increasing, except in July. In summary, procurement planning helps factories and farmers realize the price, supply quantity, and demand uncertainties and organize to respond optimally. Proper farm management could increase the productivity of the farm and lessen the supply shortage, resulting in a higher profit. The findings of this research could be applied to assist coconut processors' supply planning efforts; moreover, proper farm management could increase the farm's productivity and lessen the supply shortage.

Sustainable Lot Size Optimization is an important challenge of Supply Chain Management as it seeks to balance the economic goals of minimizing costs with environmental and social objectives, ensuring efficient production and inventory management while reducing environmental impact and enhancing social responsibility. Metaheuristic algorithms play a crucial role in solving Sustainable Lot Size Optimization problems by efficiently exploring large and complex search spaces to find near-optimal solutions that balance economic, environmental, and social objectives, often outperforming traditional optimization methods in terms of flexibility and scalability. With this attitude, in this paper, a new metaheuristic algorithm called Sales Training Based Optimization (STBO) is designed to solve Sustainable Lot Size Optimization applications. The fundamental inspiration in the design of STBO draws upon human behaviors observed during sales training. The theoretical framework of STBO is thoroughly described, and its implementation process is mathematically formulated in two distinct stages: the exploration phase and the exploitation phase. The efficiency of STBO to address Sustainable Lot Size Optimization applications has been evaluated on 10 study scenarios. The optimization outcomes reveal that STBO consistently delivers highly effective solutions by seamlessly integrating exploration with exploitation throughout the search. Furthermore, a thorough comparison was conducted, revealing how STBO's results stack up against those from twelve widely recognized metaheuristic algorithms. The simulation findings conclusively demonstrate that the STBO approach consistently outperforms competitors, achieving superior performance across all study scenarios. These insights confirm that the STBO approach serves as a highly reliable and potent optimization tool, capable of addressing a wide range of optimization challenges in diverse applications.

The production of biofuels using second-generation feedstocks has been recognized as an important alternative source of sustainable energy and its demand is expected to increase due to regulations such as the Renewable Fuel Standard. However, the pathway to biofuel industry maturity faces unique, unaddressed challenges.

This comprehensive article explores the transformative impact of Artificial Intelligence (AI) on the freight industry, examining its applications, benefits, challenges, and prospects. It delves into how AI is revolutionizing key areas such as logistics optimization, predictive maintenance, demand forecasting, and inventory management. The article discusses the emergence of autonomous vehicles and drones in freight transport, highlighting their potential to reshape last-mile delivery and longhaul operations. Additionally, it analyzes the role of AI in enhancing customer service and communication through chatbots, virtual assistants, and advanced analytics.
The article also addresses the significant challenges in AI integration, including data privacy concerns, cybersecurity risks, and the need for workforce adaptation. Looking towards the future, the article explores emerging technologies like block chain enabled smart contracts and AI-optimized warehousing, offering insights into how these innovations may further transform the industry. Through a critical examination of current implementations and future possibilities, this article provides a comprehensive overview of AI's role in shaping the future of global freight operations, emphasizing its potential to drive efficiency, sustainability, and innovation in the sector.

Utilizing blockchain technology and circular supply chain management can bring about significant changes in achieving sustainability and enhancing resource utilization. This study highlights the significant role of tokenization in promoting sustainable resource management within blockchain-based circular supply chains. Tokenization provides a secure and efficient way to digitize assets, creating incentives, and facilitating efficient exchange within these supply chains. The paper explores in detail the benefits of tokenization, including faster transaction settlement, cost savings, democratization of access, transparency enhancement, and reduced infrastructure costs. It emphasizes that tokenization is a pivotal tool for promoting the transition towards a circular economy model. The study also acknowledges the challenges and limitations of tokenization, including regulatory issues, interoperability, and standardization challenges, among others, and provides recommendations for effective implementation. Overall, the paper highlights the potential of tokenization to revolutionize how we exchange ideas, information, and money through blockchain-based circular supply chains and emphasizes its significance in promoting sustainable resource management.

Industrial automation is experiencing a significant transformation with the integration of edge computing technologies, which bring computational power closer to data sources, enabling real-time processing and intelligent decision-making at the network edge. This paradigm shift optimizes data analytics and enhances system responsiveness in industrial settings, addressing critical issues such as latency, bandwidth limitations, and reliability concerns associated with traditional cloud-based systems. The paper explores edge computing's applications in industrial automation, including real-time monitoring and control, predictive maintenance, and quality assurance, highlighting benefits like improved operational efficiency, reduced downtime, and enhanced product quality. While implementation challenges such as security concerns, interoperability problems, and data governance issues exist, the potential of edge computing to reshape industrial automation is immense. As technology advances and industry standards evolve, edge computing is poised to unlock new levels of efficiency, reliability, and scalability in industrial processes. The integration of edge computing with emerging technologies like 5G and artificial intelligence promises to further revolutionize the industrial landscape, despite the hurdles that need to be overcome.

Chemical production from crude oil represents a substantial percentage of the yearly fossil fuel use worldwide, and this could be partially offset by renewable feedstocks such as woody biomass and energy crops. Past techno-economic and environmental analyses have been conducted for isolated feedstocks on a regional or national scope. This study encompasses complete supply chain logistics analysis, delivered cost financial analysis, national availability, and environmental life cycle assessment (LCA) for 18 selected cellulosic feedstocks from around the world. A biochemical conversion route to monomeric sugars is assumed for estimated sugar yields and biosugar feedstock cost analysis. US corn grain was determined to have the highest delivered cost, while rice hulls in Indonesia resulted in the lowest cost of the feedstocks studied. Monomeric sugar yields from literature ranged from 358 kg BDMT-1 for US forest residues to 700 kg BDMT-1 for corn syrup. Environmental LCA was conducted in SimaPro using ecoinvent v2.2 data and the TRACI 2 impact assessment method for mid-point impacts cradle-to-incoming biorefinery gate. Carbon absorption during biomass growth contributed most substantially to the reduction of net global warming potential. Rice hulls and switchgrass resulted in the highest global warming potential, followed closely by corn and Thai sugarcane bagasse. Contribution analysis shows that chemical inputs such as fertilizer use contribute substantially to the net environmental impacts for these feedstocks.

Economic damage due to the supply chain turmoil in the past few years has been more severe than the pandemic, labor shortages, and domestic conflict combined. The primary cause of such a crisis is that the current supply chain analysis tool, relying heavily on static optimization, is insensitive to non-eligible changes such as policy changes due to the pandemic. As a result, such analysis needs to be conducted regularly whenever there is a change in the economic environment, which dramatically increases the computational cost. In this paper, the main purpose is to achieve agile sustainability supply chain management through dynamic system modeling and control for production processes of supply chain networks (SCNs), which involves both theoretical and numerical analysis. In particular, we first formulated a chain-like dynamic system to represent the daily production process, which is a discrete-time dynamic system from the control engineering perspective. Then, an optimal control problem can be developed for decision-making on production. Several numerical cases are presented in this paper to demonstrate the applicability of this developed dynamic system and further discuss the potential optimal production.