An Overview of the Application of Big Data in Supply Chain Management and Adaptation in Nigeria

That the world is a global village is no longer news through the tremendous advancement in the Information Communication Technology (ICT). The metamorphosis of the human data storage and analysis from analogue through the jaguars-loom mainframe computer to the present modern high power processing computers with sextillion bytes storage capacity has prompted discussion of Big Data concept as a tool in managing hitherto all human challenges of complex human system multiplier effects. The supply chain management (SCM) that deals with spatial service delivery that must be safe, efficient, reliable, cheap, transparent, and foreseeable to meet customers’ needs cannot but employ bid data tools in its operation. This study employs secondary data online to review the importance of big data in supply chain management and the levels of adoption in Nigeria. The study revealed that the application of big data tools in SCM and other industrial sectors is synonymous to human and national development. It is therefore recommended that both private and governmental bodies should key into e-transactions for easy data assemblage and analysis for profitable forecasting and policy formation.

A dynamic optimization model of an integrated coal supply chain system and its application

Various nodes,logistics,capital flows,and information flows are required to make systematic decisions concerning the operation of an integrated coal supply system. We describe a quantitative analysis of such a system. A dynamic optimization model of the supply chain is developed. It has achieved optimal system profit under conditions guaranteeing a certain level of customer satisfaction. Applying this model to coal production of the Xuzhou coal mines allows recommendations for a more systematic use of washing and processing,transportation and sale resources for commercial coal production to be made. The results show that this model,which is scientific and effective,has an important value for making reasonable decisions related to complex coal enterprises.

Three-scale integrated optimization model of furnace simulation,cyclic scheduling,and supply chain of ethylene plants

In order to explore the potential of profit margin improvement,a novel three-scale integrated optimization model of furnace simulation,cyclic scheduling,and supply chain of ethylene plants is proposed and evaluated.A decoupling strategy is proposed for the solution of the three-scale model,which uses our previously proposed reactor scale model for operation optimization and then transfers the obtained results as a parameter table in the joint MILP optimization of plant-supply chain scale for cyclic scheduling.This optimization framework simplifies the fundamental mixed-integer nonlinear programming(MINLP)into several sub-models,and improves the interpretability and extendibility.In the evaluation of an industrial case,a profit increase at a percentage of 3.25%is attained in optimization compared to the practical operations.Further sensitivity analysis is carried out for strategy evolving study when price policy,supply chain,and production requirement parameters are varied.These results could provide useful suggestions for petrochemical enterprises on thermal cracking production.

Optimization of Medical Supply Chains and Forward Store Locations for Recurrent Homecare Patient Demand with Periodic Interruptions

Robust and cost-effective distribution is critical to any home delivery network growing company, both to meet demand under normal conditions and to adapt to temporary disruptions. Home healthcare is anticipated to be a rapidly growing modality of healthcare, itself the largest industry in the US and rife with optimization needs in areas such as logistics, scheduling, and supply chains. We develop two mixed integer programming models to optimize forward storage locations in the supply chain of a national consumable medical supplies company with consistent monthly repeating demand, temporary disruption of facility operations, and remote international manufacturers. Modified p-median single and multi-echelon models are used to determine optimal locations of warehouses and distribution facilities that minimize total transportation cost, with 13% savings in one application (approximately $1.4 million annually). Sensitivity analyses to a range of scenarios suggest that the optimal solution is robust across a number of potential scenarios.

A Proposed Feature Selection Particle Swarm Optimization Adaptation for Intelligent Logistics--A Supply Chain Backlog Elimination Framework

The diversity of data sources resulted in seeking effective manipulation and dissemination.The challenge that arises from the increasing dimensionality has a negative effect on the computation performance,efficiency,and stability of computing.One of the most successful optimization algorithms is Particle Swarm Optimization(PSO)which has proved its effectiveness in exploring the highest influencing features in the search space based on its fast convergence and the ability to utilize a small set of parameters in the search task.This research proposes an effective enhancement of PSO that tackles the challenge of randomness search which directly enhances PSO performance.On the other hand,this research proposes a generic intelligent framework for early prediction of orders delay and eliminate orders backlogs which could be considered as an efficient potential solution for raising the supply chain performance.The proposed adapted algorithm has been applied to a supply chain dataset which minimized the features set from twenty-one features to ten significant features.To confirm the proposed algorithm results,the updated data has been examined by eight of the well-known classification algorithms which reached a minimum accuracy percentage equal to 94.3%for random forest and a maximum of 99.0 for Naïve Bayes.Moreover,the proposed algorithm adaptation has been compared with other proposed adaptations of PSO from the literature over different datasets.The proposed PSO adaptation reached a higher accuracy compared with the literature ranging from 97.8 to 99.36 which also proved the advancement of the current research.

E-Commerce Supply Chain Process Optimization Based on Whole-Process Sharing of Internet of Things Identification Technology

With in-depth development of the Internet of Things(IoT)in various industries,the informatization process of various industries has also entered the fast lane.This article aims to solve the supply chain process problem in e-commerce,focusing on the specific application of Internet of Things technology in e-commerce.Warehousing logistics is an important link in today’s e-commerce transactions.This article proposes a distributed analysis method for RFID-based e-commerce warehousing process optimization and an e-commerce supply chain management process based on Internet of Things technology.This article first introduces the advantages and disadvantages of shared IoT identification technology and the IoT resource sharing platform based on the three-layer abstract data model and representational state transfer(REST)style.Combining actual IoT applications and the characteristics of an existing platform,a REST-based IoT resource sharing platform is proposed.Combined with actual projects,a REST-based IoT resource sharing platform was built,and key technology experiments were conducted for verification.Finally,optimizing the e-commerce supply chain management process under Internet of Things technology and explaining the advantages of optimized e-commerce supply chain management are discussed.Research on this subject provides a theoretical basis for the application of the Internet of Things in e-commerce and has practical significance and practical value for managing service capabilities and service levels in e-commerce.

Development and Comparison of Hybrid Genetic Algorithms for Network Design Problem in Closed Loop Supply Chain

This paper presents four different hybrid genetic algorithms for network design problem in closed loop supply chain. They are compared using a complete factorial experiment with two factors, viz. problem size and algorithm. Based on the significance of the factor “algorithm”, the best algorithm is identified using Duncan’s multiple range test. Then it is compared with a mathematical model in terms of total cost. It is found that the best hybrid genetic algorithm identified gives results on par with the mathematical model in statistical terms. So, the best algorithm out of four algorithm proposed in this paper is proved to be superior to all other algorithms for all sizes of problems and its performance is equal to that of the mathematical model for small size and medium size problems.

Network topology optimization of supply chain information systems

Network topology optimization has been widely researched. Since market competition has gradually developed into competition among the supply chain information systems, the network to- pology optimization of supply chain information systems has been in urgent need. However, the net- work topology optimization of supply chain information systems is still in its early stages and still has some challenges. So a description of typical seven network topologies for various supply chain infor- mation systems has been given. The generic characteristics of each network topology can be summa- rized. To analyze the optimization of network topology optimization of supply chain information sys- tems, a numeric model has been established based on these general characteristics. A genetic algo- rithm is applied in the network topology optimization of supply chain information systems model to a- chieve the minimum cost and shortest path. Finally, our experiment results are provided to demon- strate the robustness and effectiveness of the proposed model.

Optimization Agricultural Supply Chain:A Case Study of Fertilizer Supplier Selection

The 21st century is associated with the IndustrialRevolution 4.0 and the organic agriculture trend,making the utilization of high-quality fertilizers,abundant nutritional content,economical,and no affect to environment pollution.According to the new concept,clean agricultural production and organic agricultural products are not allowed to excessively use synthetic chemicals such as chemical fertilizers,and plant protection drugs,but priority is to use manure,organic fertilizers,and natural mineral fertilizers.Fertilizer must meet the balanced nutritional requirements of crops,maintain,and improve the fertility of the ground,protect the surrounding ecosystem,and leave harmful effects in agricultural products,products with high quality,safe for users and high economic efficiency for producers.To achieve the above goal,the selection of a fertilizer supplier is an important decision,supporting the supply chain’s sustainable development,fertilizer supplier selection is a multicriteria decision making model,the decision maker must assess all qualitative and quantitative factors.In this paper,the author proposed an integer decision making model including Fuzzy Analytic Hierarchy Process(FAHP)and Complex Proportional Assessment of Alternatives(COPRAS)for fertilizer supplier selection.The weightings of the criteria are calculated by using FAHP,COPRAS is then applied for ranking some potential fertilizer suppliers.The efficiency of the proposed models is proved by a case study conducted in a farm located in the south of Vietnam.This research is the first fertilizer supplier evaluation and se-lection model in Vietnam by interviewing experts and reviewing the literature.Re-search result is to provide a case study on evaluating supplier in agricultural supply chain utilizing the model proposed by the combination of FAHP and COPRAS models.

Optimization Analysis of Agricultural Product Supply Chain From the Perspective of Internet

With the development of our agricultural modernization process accelerated, the agricultural products supply chain is also facing the demand of further optimization and improvement. And the development of the Intemet makes it become an important means to optimize the agricultural products supply chain. Firstly, it is based on the concept explanation of ＂Internet ＋＂. Secondly, according to the present situation which includes traditional models dominated, the main bodies on the chain are diversified, farmers＇ position is lower and the problems which include the chain is too long, logistics nodes are scattered, infi＇astructure lags behind, circulation of information is poor, and risk monitoring is difficult of the supply chain of agricultural products are facing in China, clarifying the role of the Internet in the agricultural products supply chain which includes making the agricultural information network more perfect, adjusting and optimizing the supply chain of agricultural products. Finally, authors put forward some measures to optimize the supply chain of agricultural products and make conclusions.

OPTIMAL CONTRACTS IN TWO-ECHELON SUPPLY CHAIN UNDER ASYMMETRIC INFORMATION AND NONLINEAR DEMAND

The challenge for supply chain management is to create appropriate supply chain contracts so as to optimize the system performance. To examine the role of asymmetric information in a two-echelon supply chain system and derive supply chain contracts to deal with existing asymmetric information, a two level supply chain model including one Supplier and one retailer under the demand of price elasticity is developed. By using the principalagent principle and the optimal control theory, three types of supply chain contract, i. e. , a wholesale pricing contract, a two-parameter linear and a two-parameter nonlinear contracts are obtained. In these contracts, the Supplier has asymmetric information about the retailer cost structure. Simulation results show that the two-parameter contracts are more effective strategies to achieve supply chain coordination.

DISRUPTION MANAGEMENT FOR SUPPLY CHAIN COORDINATION WITH EXPONENTIAL DEMAND FUNCTION

The coordination problem of a supply chain comprising one supplier and one retailer under market demand disruption is studied in this article. A novel exponential demand function is adopted, and the penalty cost is introduced explicitly to capture the deviation production cost caused by the market demand disruption. The optimal strategies are obtained for different disruption scale under the centralized mode. For the decentralized mode, it is proved that the supply chain can be fully coordinated by adjusting the price discount policy appropriately when disruption occurs. Furthermore, the authors point out that similar results can be established for more general demand functions that represent different market circumstances if certain assumptions are satisfied.

Remanufacturing closed-loop supply chain network design based on genetic particle swarm optimization algorithm

As the huge computation and easily trapped local optimum in remanufacturing closed-loop supply chain network (RCSCN) design considered, a genetic particle swarm optimization algorithm was proposed. The total cost of closed-loop supply chain was selected as fitness function, and a unique and tidy coding mode was adopted in the proposed algorithm. Then, some mutation and crossover operators were introduced to achieve discrete optimization of RCSCN structure. The simulation results show that the proposed algorithm can gain global optimal solution with good convergent performance and rapidity. The computing speed is only 22.16 s, which is shorter than those of the other optimization algorithms.

Application of Stork Optimization Algorithm for Solving Sustainable Lot Size Optimization

The efficiency of businesses is often hindered by the challenges encountered in traditional Supply Chain Manage-ment(SCM),which is characterized by elevated risks due to inadequate accountability and transparency.To address these challenges and improve operations in green manufacturing,optimization algorithms play a crucial role in supporting decision-making processes.In this study,we propose a solution to the green lot size optimization issue by leveraging bio-inspired algorithms,notably the Stork Optimization Algorithm(SOA).The SOA draws inspiration from the hunting and winter migration strategies employed by storks in nature.The theoretical framework of SOA is elaborated and mathematically modeled through two distinct phases:exploration,based on migration simulation,and exploitation,based on hunting strategy simulation.To tackle the green lot size optimization issue,our methodology involved gathering real-world data,which was then transformed into a simplified function with multiple constraints aimed at optimizing total costs and minimizing CO_(2) emissions.This function served as input for the SOA model.Subsequently,the SOA model was applied to identify the optimal lot size that strikes a balance between cost-effectiveness and sustainability.Through extensive experimentation,we compared the performance of SOA with twelve established metaheuristic algorithms,consistently demonstrating that SOA outperformed the others.This study’s contribution lies in providing an effective solution to the sustainable lot-size optimization dilemma,thereby reducing environmental impact and enhancing supply chain efficiency.The simulation findings underscore that SOA consistently achieves superior outcomes compared to existing optimization methodologies,making it a promising approach for green manufacturing and sustainable supply chain management.

Green Investment Cost Optimization Model in the Supply Chain

The objective of this study is to develop a model that determines the optimal points for investment in green management by defining a mathematical relationship between carbon trading profits and investments in green management using a company’s supply chain information. To formulate this model, we first define and analyze a green supply chain in a multi-dimensional and quantitative manner. The green investment alternatives considering in our model are as follows: 1) purchasing eco-friendly raw materials that cost more than conventional raw materials but whose use in production results in lower CO2 emissions;2) replacing current facilities with new eco-friendly facilities that have the capability to reduce CO2 emissions;and 3) changing modes of transport from less eco-friendly to more eco-friendly modes. We propose a green investment cost optimization (GICO) model that enables us to determine the optimal investment points. The proposed GICO model can support decision-making processes in green supply chain management environments.

Multimodal Fuzzy Downstream Petroleum Supply Chain:A Novel Pentagonal Fuzzy Optimization

The petroleum industry has a complex,inflexible and challenging supply chain(SC)that impacts both the national economy as well as people’s daily lives with a range of services,including transportation,heating,electricity,lubricants,as well as chemicals and petrochemicals.In the petroleum industry,supply chain management presents several challenges,especially in the logistics sector,that are not found in other industries.In addition,logistical challenges contribute significantly to the cost of oil.Uncertainty regarding customer demand and supply significantly affects SC networks.Hence,SC flexibility can be maintained by addressing uncertainty.On the other hand,in the real world,decision-making challenges are often ambiguous or vague.In some cases,measurements are incorrect owing to measurement errors,instrument faults,etc.,which lead to a pentagonal fuzzy number(PFN)which is the extension of a fuzzy number.Therefore,it is necessary to develop quantitative models to optimize logistics operations and supply chain networks.This study proposed a linear programming model under an uncertain environment.The model minimizes the cost along the refineries,depots,multimode transport and demand nodes.Further developed pentagonal fuzzy optimization,an alternative approach is developed to solve the downstream supply chain using themixed-integer linear programming(MILP)model to obtain a feasible solution to the fuzzy transportation cost problem.In this model,the coefficient of the transportation costs and parameters is assumed to be a pentagonal fuzzy number.Furthermore,defuzzification is performed using an accuracy function.To validate the model and technique and feasibility solution,an illustrative example of the oil and gas SC is considered,providing improved results compared with existing techniques and demonstrating its ability to benefit petroleum companies is the objective of this study.

Optimal production lot sizing model in a supply chain with periodically fixed demand considering learning effect

This paper presents an optimal production model for manufacturer in a supply chain with a fixed demand at a fixed interval with respect to the learning effect on production capacity. An algorithm is employed to find the optimal delay time for production and production time sequentially. It is found that the optimal delay time for production and the production time are not static, but dynamic and variant with time. It is important for a manufacturer to schedule the production so as to prevent facilities and workers from idling.

Research on Cultural Supply Chain Integration and Invocation

Supply chain management is an integrated management method for solving these effective problems which were integrated in existing resources with the overall competitiveness and theirs effects. The supply chain integration and innovation models are proposed with the development and current situation for these global Chinese businessmen. The supply chain integration is given in order to optimize for the culture of Chinese business management. It is established that these practice network platform for supply chain management are presented service for Chinese businessmen. And they are realized these integration innovation in the logistics system, financial system, Chinese business culture system, production manufacturing system, and customer service system. These suggestions are provided for the development of Chinese business culture in supply chain theory. These further forward Chinese culture are given some fruitful reference for con- structing a new development for the world Chinese businessmen’s culture management.

Evaluation and Optimization of the Logistics Service of Agricultural Products:an Empirical Analysis Based on Analytic Hierarchy Process

This research,from the theories of management science,supply chain management and logistics engineering,on the basis of extensive investigations,and using the method of analytic hierarchy process(AHP),evaluates the present situation of logistics service of agricultural products.Taking Nanping City(Nanping)as a case,it explores the obstacles existing in current logistics service system and the factors limiting the development of agricultural product logistics service.Combining with the theory of modern logistics system,it reveals the problems in the logistics system and the causes,and then constructs the strategy of optimization for agricultural product logistics service in Nanping.The conclusion of the study can be references for the government to make scientific strategies for the development of the agricultural product logistics service and help logistics enterprises improve their service level.

Improving construction performance through supply chain management

A typical model of construction supply chain (CSC) is presented firstly. Then the characteristics and problems of CSC are identified. The authors also describe the progress towards adoption of supply chain management (SCM) in the construction industry. Traditional construction management approaches (CMAs) are compared with new SCM-based CMAs. The authors suggest that integrated SCM based on cooperating competition is crucial to improve construction performance. Based on relevant results from related research, a framework for integrated construction supply chain management (CSCM) is developed to guide the effective implementation of SCM in construction.