Optimsing Supply Chain Management for Indian Retail Success
Sarthak Chandwadkar1 , Yuvraj Agarkar2 , Devashish Potdar3 , Aishwarya Patil4 , Prof. Gayatri Asalkar5
1,2,3,4UG student, Department of Computer Engineering, JSPM’s Rajarshi Shahu College of Engineering, Tathawade, Pune
5Professor, Department of Computer Engineering, JSPM’s Rajarshi Shahu College of Engineering, Tathawade, Pune
Abstract - India's supply chains have been susceptible for a long time. Unexpected events such as COVID-19 and international conflicts present obstacles for supply chain management. Supply chain management is an important component of company that has encountered challenges due to increased demand and restricted supply. According to PwC's 26th Global Annual CEO Survey: India Perspective, about half of Indian CEOs are concerned about supply chain disruptions, while approximately 67% are changing supply chains. The goal of optimizing supply chain management for Indian retail success is to revolutionize demand forecasting, demand planning, and inventory optimisation through the use of artificial intelligence and machine learning tools. The goal is to implement solutions that will lower operational expenses while increasing customer satisfaction. Optimising supply chain management for Indian retail success describes a smart supply chain management system that uses machine learning and artificial intelligence to improve efficiency, save costs, and promote sustainability [4]. This user-friendly desktop tool enables merchants to tailor solutions to their individual needs by leveraging sales data and other relevant characteristics.
Divided into three phases -
Demand Forecasting
Demand Planning
Inventory Optimisation
Optimising supply chain management for Indian retail success describes a smart supply chain management system that uses machine learning and artificial intelligence to improve efficiency, save costs, and promote sustainability [11]. This user-friendly desktop tool enables merchants to tailor solutions to their individual needs by leveraging sales data and other relevant characteristics.
Key Words: : Indian Supply Chain, Machine Learning, Supply Chain Management, Artificial Intelligence, Retail Distribution.
I.INTRODUCTION
In recent years, the Indian retail sector has experienced unprecedented growth and transformation, fueled by technological advancements, changing consumer preferences, and increasing competition. As the retail landscape evolves, businesses must navigate a complex web of supply chain dynamics to maintain a competitive edge. Effective supply chain management (SCM) is critical for ensuring product quality and availability, which directly impacts customer satisfaction and brand loyalty. However, the journey from suppliers to retailers presents numerous challenges, including accurately forecasting consumerpreferencesandmanaginginventoryeffectively. These challenges underscore the need for innovative solutions that can optimize supply chain processes and enhanceoperationalefficiency.
Thisthesisfocusesspecificallyonoptimizingsupplychain management within the Indian retail sector, with an emphasis on developing an intelligent system to address the multifaceted challenges faced by businesses. By exploring the current state of supply chain practices in India, we aim to identify gaps and opportunities for improvement that can lead to enhanced product quality and customer satisfaction. The scope of this research will encompass the analysis of existing SCM practices, identification of key challenges, and the development of a proposed intelligent system designed to streamline operationsandimprovedecision-making.
Fig1:LogisticInfrastructureofIndia
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This research is relevant not only to Indian retailers but also to the broader field of supply chain studies. While existing literature has extensively explored supply chain optimization in various global contexts, there is a conspicuouslackoffocusedstudiesaddressingtheunique challenges faced by the Indian retail sector. By contributing to this niche area, the research aims to fill a critical gap in the literature and provide actionable insights for practitioners seeking to enhance their supply chainstrategies.
Theprimaryquestionsguidingthisresearchinclude:
1.WhatarethekeychallengesfacedbyIndianretailersin managingtheirsupplychainseffectively?
2.Howcananintelligentsystembedevelopedtoimprove forecasting accuracy and inventory management in the Indianretailcontext?
3. What strategies can be implemented to ensure product qualityfromsupplierstoretailers?
To address these questions, the research will employ a mixed-methods approach, incorporating both qualitative andquantitativemethodologies.Thismayincludesurveys andinterviewswithindustrystakeholders,casestudies of successful supply chain implementations, and data analysis to assess the impact of proposed intelligent systemsonsupplychainperformance.
The structure of this thesis is organized into several key sections,eachcontributingtotheoverallaimofoptimizing supply chain management for Indian retail success. The first chapter will provide a comprehensive literature review, establishing a theoretical framework and contextualizing the research within existing studies. The secondchapterwilloutlinethemethodology,detailingthe research design, data collection methods, and analytical techniques employed. The third chapter will present the findings from the research, including insights from stakeholder interviews and case studies. The fourth chapter will discuss the implications of these findings for supply chain management practices in India, while the final chapter will conclude the thesis with recommendations for future research and practical applications.
Through this structured approach, the thesis aims to contribute valuable knowledge and tools to enhance the effectiveness of supply chain management within the Indian retail sector, ultimately driving success in an increasinglycompetitivemarketplace.
Assuring product quality from suppliers to retailers is important for enterprises looking to stay competitive. However, this is a challenging process! There are numerous obstacles, including accurately forecasting
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client preferences and effectively managing inventory. To tackle these issues and improve the delivery process, we intendtodevelopanewintelligentsystem.Themethodis intendedtohelpstoresworkmoreefficiently,savemoney, andbemorecustomer-friendly.
This smart supply chain management system is intended to improve efficiency, save prices, and promote the longterm development of India's retail sector. It employs cutting-edge technology like machine learning and artificial intelligence to handle critical aspects of product quality, such as demand, demand planning [8], and productavailabilityoptimization.Furthermore,thedesign is user-friendly for store owners. They can readily access data,trackinventory,andmanagefacilities,personnel,and other critical elements. The system will assess this data andprovideappropriatesolutions.
The task will be separated into three stages: anticipating purchasers, planning the best products, and tracking the remaining products. Each component is specifically intended to solve the issues that Indian retailers encounter. The overall goal is to assist Indian retailers in improving their inventory management, operations, and performanceforallstakeholders.
Demand forecasting is the process of projecting future demand for a product or service and then advising businesses on the projected quantity that consumers will buyinatimelymanner.Accurateforecastingiscriticalfor organizations to plan production and meet client demands. Overestimating or underestimating client demand can result in overstocking or a scarcity of supplies,amongotherdifficulties.Thedemandforecasting phasewillincludeanexaminationofseasonaltrendsusing historical data and trends. Improve business decisionmaking by providing cannibalization-based solutions that takeintoaccountthecurrentcircumstances,ensuringthat theorganizationispreparedforitsnextmove.
Demand forecasting is the technique of projecting future demand for a product or service and then advising businesses on the projected quantity that customers will purchase right now. Accurate forecasting is critical for organizations to plan production and meet client demands. Overestimating or underestimating client demand can result in overstocking or a scarcity of supplies,amongotherdifficulties.Thedemandforecasting phasewillincludeanexaminationofseasonaltrendsusing historical data and trends. Improve business decisionmaking by providing cannibalization-based solutions that takeintoaccountthecurrentcircumstances,ensuringthat the organization is prepared for its next move. Check the product'svalidity.
Inventory optimization is the act of controlling a company's products and stock levels (SKUs) in order to increase efficiency and save costs while satisfying e-ISSN:2395-0056
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consumerdemands.Itentailsexamining elementssuch as demand variations, uptimes, service objectives, and transportation costs to identify the optimal quality and inventorylocationinthesupplychain.Thegoalistostrike a balance between retaining enough stock to meet consumer demand and avoiding overstocking, which affects cash flow and raises storage expenses. This phase entails developing algorithms to improve the pricing and quality of various items, as well as providing locationbasedservicestomakebetteruseofwarehousespaceand idealproductionfacilitiestoassureorderfulfillment.
II. LITERATURE SURVEY
[1] Benjamin T. Hazena, Joseph , Skipperb, Jeremy D. Ezelld,Christopher,Boone,conductedastudyonBigdata and predictive analytics. Their paper aims on finding out how big data and predictive analytics (BDPA) impact environmentalandsocialsustainabilityinsupplychains.It reviews eight theories to provide a framework for understanding BDPA's influence on sustainability beyond just economic performance. The goal is to encourage a more comprehensive understanding of BDPA's role in promotingsustainablesupplychainpractices.
[2] In this research “Green supply-chain management: A state-of-the-artliteraturereview”,thispaperistodevelop a comprehensive framework for green supply-chain management (GrSCM) by integrating and classifying existing literature. It addresses the lack of a broad perspectiveonGrSCM,particularlyfromareverselogistics viewpoint, and aims to provide a structured overview of GrSCM concepts, methodologies, and mathematical tools. The paper summarizes key findings, highlights research issues,andidentifiesopportunitiesforfutureresearchand practice.
[3] Houtian Ge , Richard Gray , James Nolan paper titled “Agriculturalsupplychainoptimizationandcomplexity: A comparisonofanalyticvssimulatedsolutionsandpolicies” this research is to develop and compare analytic and simulationmodelsoftheCanadianwheathandlingsystem to identify effective wheat quality testing strategies. By
examining both models, the study aims to address complex optimization issues in the supply chain, considering both analytical tractability and the realistic behaviorsofindividualsinvolved,withtheultimategoalof enhancingfoodsecuritythroughimprovedqualitycontrol inwheathandling.
[4] Halima Bousqaoui, Said Achchab, Kawtar Tikito Research on “Machine learning applications in supply chains: An emphasis on neural network applications”this paper is to explore how machine learning algorithms can be applied to enhance various supply chain processes. By examining different algorithms and their applications, the paper aims to demonstrate how machine learning can improve productivity and decision-making in supply chainsthroughbetterforecastingandpatternrecognition.
[5] Qi Zhang , Nilay Shah , John Wassick , Rich Helling , Peter van Egerschot conducted study on to develop and apply a multi-objective optimization framework to enhance the sustainability performance of supply chains. By considering cost, GHG emissions, and lead time, the project aims to identify and manage trade-offs among these factors, enabling businesses to make informed decisions that balance economic, environmental, and socialimpactswhileimprovingoverallsustainability.
[6]PhilippN.Köhler,MatthiasA.Müller,JürgenPannek, FrankAllgöwer research on exploreandimprovesupply chain efficiency using distributed model predictive control. By proposing and analyzing two different algorithms for sharing predictive information on delivery and demand, the project aims to determine how varying approaches to information exchange can impact system performance. Ultimately, it seeks to quantify the value of predictive information in optimizing supply chain operations.
III. METHODOLOGY
The initial step involves data collecting and processing. Thisincludesgatheringinformationaboutwarehouseand sales locations, employee numbers and salaries, vehicle details, and associated costs. The categorical value encodingmethodswillbeemployedduringprocessing.
The initial stage focuses on demandforecasting [23]. This necessitates predictive analytics, which utilizes historical data to forecast future client demand for goods and services.Thisupdatewillbeimplementedusingarangeof AI and machine learning techniques, including real-time analysis (TSA), regression modeling, and deep learning. TSA is the study of data points collected at similar time periodsusingalgorithmslikeARIMA.
The second way is regression modeling, which uses algorithms like Random Forest and XGBoost to forecast future requirements. Random Forest combines the e-ISSN:2395-0056
Fig2:LogisticCostasaPercentageofGDP
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outcomes of several decision trees into a single result, whereas XGBoost employs gradient-boosted trees with extra efficiency and speed optimizations. In our scenario, XGBoost outperformed Random Forest. Deep learning modelsalsoincludeartificialneuralnetworks(ANNs)and short-distancemodels(LSTMs).LSTMsaregenerallymore effective than ANNs at simulating the behavior of biological systems like neurons, and LSTMs are used to process,predict,andclassifydata.
For object association, the Apriori algorithm for association rule mining is used. This algorithm searches for active elements in the dataset based on the relationshipandassociationbetweenelements.
Demand planning as the name implies is the process of determining if an organization can meet the demands forecasted by demand forecasting model. This includes arrangingmaterialsorsuppliersforthespecificproduct.
The third phase is inventory optimisation, which will be implemented using techniques such as K - meaning clustering, justin-time (JIT) compilers and time series algorithms. K-Means groups unsigned datasets into different groups and the JIT compiler performs optimisationwhilewritingthebytecodeintothemachine language to improve Java-based execution time and thus ensuregoodhandling.
IV. ARCHITECTURE
IV. Architecture
5:Flowofoperations
DataCollection:
Thissectioncollectsimportantdatafromavariety of sources, including the company's internal systems, external databases, web services, and so on. The data can be in several formats, including PDF, CSV, and XML. The datacollectionmoduleextractsinformationfromPDFfiles using a PDF parser and stores it in an organized fashion. Thedatagatheringmodulealsohasauserinterfacewhere users can upload their firm information such as name, address, and contact information. The user interface also allows users to register as administrators and view their dashboard.
DataPreprocessing:
Thissectionisinchargeofanalyzingthecollected data andapplying various machinelearning algorithms to produce insights, forecasts, and recommendations. The data processing module employs a model trainer to train and test various machine learning models such as regression, classification, and clustering. The data processing module also includes APIs that allow users to access and interact with the data processing findings and models.
Datavisualisation:
This section is in charge of visualizing the processed data and presenting it in an interactive and understandable format. It displays the data using a data visualisation template, which generates numerous charts, graphs, tables, and other graphics. The data visualisation module alsohas a user interface where users may upload salessheetsandbuildchartsbasedonsalesdata.Theuser interface also enables users to view and download charts andreports.
Flowchart
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DataOptimisation:
This section is in charge of improving the company's supply chain by leveraging insights, projections, and suggestions created by the data processing module. The data optimisation module employs a variety of optimisation approaches, including linear programming, integer programming, and genetic algorithms, to identify the optimal solutions to supply chain problems such as inventory management, demand forecasting, transportation planning, and so on. The data optimization module also includes a user interface where users may examine and edit optimization findings and settings
V. APPLICATIONS
1.Enhanced Accuracy of Demand ForecastingExplanation: Retailers can enhance their accuracy in forecasting future demand by employing sophisticated statistical models and AI/ML algorithms. This lowers the possibility of overstocking or understocking, which improvesinventorycontrolandlowersexpenses.
2. Improved Inventory Turnover - Details: Retailers can reduce surplus inventory and increase turnover rates by optimizing inventory levels based on demand estimates. This helps retailers maintain the proper amount of stock. Better cash flow and less capital invested in unsold items resultfromthis.
3. Decreased Stockouts - Justification: Retailers may reduce the likelihood of stockouts and guarantee that products are available when customers need them by precisely forecasting demand and optimizing inventory. Customerloyaltyandsatisfactionincreaseasaresult.
4. Dynamic Pricing Strategies - Retailers can use supply chain optimization to develop dynamic pricing based on demand data that is updated in real time. By modifying prices in accordance with current market conditions and inventorylevels,thishelpsmaximizerevenue.
5.Reduction of Supply ChainCost - Explanation: Retailers cancutexpenses onstorage, shipping,andstock handling by optimizing demand planning and inventory management. A leaner and more economical supply chain resultsfromthis.
6.EnhancedSupplierCooperation-Elucidation:Enhanced demand planning [8] and forecasting facilitate improved supplier collaboration, resulting in more precise order placementsandshorterleadtimes.Asa result,thesupply chain is more dependable and supplier relationships are strengthened.
7.MaximizedInventoryControl-Details:Byguaranteeing that stock levels correspond with demand, precise
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demandforecastingandinventoryoptimizationcontribute to the optimization of warehouse operations. This lowers storage expenses and boosts the effectiveness of order fulfillment.
8. Reduced Deadstock - Details: Retailers can minimize dead stock items that remain unsold and take up precious storage space by managing inventory based on demandestimates.Thisaidsincuttingdownonwasteand markdowns[7].
9. Enhanced Management of Cash Flow - Explanation: Effective inventory control makes ensuring that money isn't locked up in extra inventory, which enhances cash flow. This enables retailers to put money back into other facets of their company, including marketing or adding newproductlines.
10. Improving Product Lifecycle ManagementJustification: Retailers may more efficiently manage product lifecycles by launching and discontinuing products in accordance with demand patterns thanks to demand planning and forecasting. By doing this, obsolescence risk is decreased and profitability is increased.
11. Increased Client Contentment - Elucidation: Supply chainoptimizationraisescustomersatisfactionbymaking sure the proper products are available when they're needed. Content customers are more likely to patronize thebusinessagainandreferotherstoit.
12. Adaptive Reaction to Market Shifts - Elucidation: Retailers that have effective inventory management and demand planning are able to react rapidly to changes in the market, including new trends or changes in the economy. Having this agility allows one to keep a competitiveedge.
13. Reduction of Waste and Sustainability - Explanation: Effective inventory control minimizes waste by cutting back on excess inventory and overproduction. This supports sustainability initiatives and assists shops in connecting with customers that care about the environment.
14. Networks for Optimised Distribution - Explanation: Distribution networks that are more effective can ensure thatgoods are delivered to stores in the most economical and timely way possible through supply chain optimization. This expedites delivery times and lowers transportationexpenses.
15. Enhanced Profit Margins - Highlight: Retailers can improve their profit margins through cost reduction, inventory turnover, and demand forecasting. Operations become more profitable and efficient when supply chain processesareoptimized. e-ISSN:2395-0056
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These examples highlight the many advantages of supply chain optimization for the Indian retail sector, especially when it comes to demand forecasting, planning, and inventorycontrol.
VI. ADVANTAGES
1. The project's goal is to create low-cost solutions that will allow enterprises to fulfill their objectives without incurring excessive costs, hence increasing earnings and ensuring long-term financial security. The program enables businesses to more efficiently manage cash and invest in areas that promote growth and innovation by employing appropriate techniques and resources.
2. This project involves the supply of warehousing services, which enable businesses to optimize their logistics operations by constructing warehouses near large retailers or distribution facilities. This not only decreases transportation costs and time, but also improves product efficiency, resulting in higher customer satisfactionandretention.
3. By studying seasonal conditions, the initiative enables businesses to forecast variations in demand and alter product quantities and marketing strategies accordingly.Thismethodcanhelpbusinessescapitalizeon peakseasonswhileloweringsurplusinventoryduringoffpeak periods, thereby increasing sales, minimizing waste, andenhancingperformance.
4. The program aims to improve business decisionmaking by providing organizations with meaningful information and data-driven skills to help them make strategic choices. Companies that use sophisticated analytics and predictive modeling technologies can maximize resources, find growth possibilities, and decrease risks to create better and more sustainable businessresults.
The initiative emphasizes the benefits of lowering competition, which can help organizations mitigate the negative impact of launching new products or services thatcompetewith existing offerings. The projectanalyzes market and consumer behavior to generate strategies for product differentiation, cost-effectiveness, and loyalty, ultimatelyimpactingbusinessandprofitability.
5. The ability to study time series enables businesses to obtain a better knowledge of past data and patterns, as well as make more accurate predictions and decisions, according to the initiative. Companies that analyze historical performance and find recurrent trends can better predict future work, optimize resource allocation, and reduce resource event time, boosting efficiencyandcompetitiveadvantage.
6. The program analyzes product connections to offer sales statistics, establish relationships, and understand client preferences. This enables businesses to tailor their marketing campaigns, product assortments, and pricing strategies to fit client demands and preferences, resulting in increased sales growth and development.
7.EconomicEffectiveness-Perk:Throughthereductionof surplus inventory, lower transportation costs, and increased warehouse efficiency, optimized supply chains save operating expenses. Significant cost savings are achievedthroughoutthesupplychainasaresult.
8. Higher Profitability - Benefit: Retailers can increase profit margins by optimizing sales and eliminating losses due to stockouts or overstock by implementing improved inventorymanagementanddemandforecasts.
9. Improved Customer Contentment - Benefit: Clients are more likely to locate the things they require at the appropriate time, resulting in heightened contentment, recurringbusiness,andpatronage.
10. Improved Forecast Accuracy - Advantage: More precise forecasts of consumer demand enable improved planning and decision-making while lowering the risk of errors. This is achieved through the use of advanced forecastingmethodologies.
11. Faster Response to Market Changes- Advantage: Retailers that haveoptimizedtheirsupply chains are able to react swiftly to unanticipated disruptions, emerging trends, andchanges in marketdemand, which helps them remainrelevantandcompetitive.
12. Decreased Inventory Holding expenses - Advantage: Retailers can free up resources for other investments by keeping appropriate inventory levels, which dramatically lowerstheexpensesassociatedwithmanagingandstoring excessinventory.
13. Enhanced Supplier Connections - Perk: Stronger relationships with suppliers and more dependable supply chains result from more precise and timely orders made possiblebyimproveddemandplanningandforecasting.
14. Reduction in Stockouts - Benefit: Reduced stockouts result in fewer missed sales opportunities and an improved consumer shopping experience, both of which canboostprofits.
15. Reduced Waste- Benefit: By avoiding overproduction and unsold goods, optimized inventory management reduces waste, supports sustainability initiatives, and lessensitsnegativeenvironmentaleffects.
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16. Efficient Use of Resources - Advantage: More effective useofcapital,labor,andwarehousespaceresultsinmore streamlined processes and increased productivity all around.
17. Improved Cash Flow- Advantage: Businesses can increase their cash flow by investing in expansion possibilities or other strategic initiatives, as they won't haveasmuchcapitallockedupinunsoldgoods.
18. Enhanced Competitive Advantage - Advantage: Retailers with well-functioning supply chains have an advantage over their competitors in the market due to theircapacitytoprovidebetterprices,fasterdelivery,and increasedproductavailability.
19. Advantage - Scalability A more scalable supply chain enables companies to grow and expand their operations without encountering major logistical obstacles or higher expenses.
20.Enhanced Risk Handling - Perk: Retailers can mitigate the risks associated with supply chain interruptions, such as supplier delays or market volatility, by precisely anticipatingdemandandoptimizinginventory.
21. Everlasting Development - Benefit: Long-term sustainable growth is supported by an efficient supply chain that lowers costs, increases productivity, and complieswithsocialandenvironmentalrequirements.
VIII. ACKNOWLEDGEMENT
We are grateful to our HOD Sir for giving us this opportunitytodemonstrateourskillsandtakepartinthis significant project. Your guidance and assistance have beenvitaltooursuccess.
We also want to express oursincere gratitude toCreative Spirits forgiving us this project. Your beliefinourtalents and faith in us has inspired us to pursue excellence. We are grateful for the resources and creative freedom that haveenabledustoinvestigatenovelsolutions.
WeextendourheartfeltgratitudetoProf.GayatriAsalkar, RSCOE Program Mentor (JSPM), for her invaluable help andsteadfastsupportduringtheentireundertaking.Your sage advice, tolerance, and guidance have been tremendouslyhelpfulingettingusovereveryobstacleand achievingourgoals.
We also want to thank our team members whose commitment,diligence,andteamworkmadethisprojecta success. This project was built on your dedication and effort, and it has been an honor to work with such gifted people.Finally,wewouldwanttoexpressourgratitudeto our family and friends for their unwavering support and tolerance. Wehavefoundstrengthinyoursupportduring thisjourney.
IX. CONCLUSION
Our academic and professional journeys have reached a critical turning point with the successful completion of this project. We learned a great deal about supply chain optimization during the process, especially in the context ofthevibrantIndianretailindustry.Throughinvestigating demandplanning,forecasting,andinventoryoptimization, we were able to have a thorough grasp of the procedures that propel economy, efficiency, and client happiness in retailoperations.
Optimization of the supply chain is essential, particularly in a market as competitive and dynamic as India's. By creatingtacticsspecifictothismarket,ourprojectaimsto handle its particular issues. Accurate demand forecasting was one of the most important lessons we learned. We developed models that helped merchants maintain ideal stock levels, cut waste, and limit stockouts all of which improved customer pleasure and loyalty by utilizing cutting-edgeanalyticalmethodologies.
An additional critical component of our approach was inventory optimization. We looked at ways to improve inventory control procedures, reduce holding costs, and raiseturnover rates. Our results highlightthesignificance of a well-rounded strategy that takes into account the expenses associated with inventory storage as well as the possible revenue loss from stockouts. Supply chains can become more flexible and adaptable to changes in the marketbyputtingthesetechniquesintopractice.
REFERENCES
1. Navaneet Deshpande, Dr. P Nithya Priya (2023) “GreenSupplyChainManagement”
2. Daphne Lopez, Abraham Paul (2023) “ A Study of Evolution and Future of Supply Chain Management withAgileSupplyChains”
3. Raphael Albert Darius (2023)“ E-Supply Chain ManagementSupplyChain(2023)
4. Lena Ellitan (2023)Supply Chain Management and SupplyChainPerformance:TheCaseofMcdonald’s”
5. Mohamed-IliasseMahraz,AbdelazizBerrado,Loubna Benabbou(2022)“MachineLearninginSupplyChain Management:ASystematicLiteratureReview”
6. Hebah H.O. Nasereddin, (2020) “BIG Data technologiesinsupplychainmanagement:
7. Opportunities,challengesandfuturetrends”.
8. MA.M.H.N Atgapaththu, D.U.S Illeperumarachchi, H.M.K.U Herath, H.K Jayasinghe, W.H Rankothge, Narmadha Gamage (2020) Supply and Demand
Volume: 11 Issue: 09 | Sep 2024 www.irjet.net
Planning for Water: A Sustainable Water ManagementSystem”.
9. Bernd Hellingrath, Sandra Lechtenberg (2019) “ApplicationsofArtificialIntelligenceinSupplyChain Management and Logistics: Focusing Onto RecognitionforSupplyChainExecution”.
10. Halima BOUSQAOUI, Said ACHCHAB, Kawtar TIKITO (2017) “Machine Learning applications in supply chains”.
11. I.U.Munasinghe, Thashika D Rupasinghe (2016) “A Supply Chain Network Design Optimization Model from the perspective of a Retail Distribution Supply Chain”.
12. Ilias Vlachos (2016) “ Managing uncertainity in regionalsupplychains”.
13. YaweiLiang1,LaixiLi(2007)“Integration of
14. IntelligentSupplyChainManagement(SCM)
15. System.”
16. Samir K. Srivastava (2007) “ Green supply-chain management:Astateoftheartliteraturereview”.
17. Martin Verwijmeren (2003) “ Software component architectureinsupplychainmanagement”.
18. Bellmann,K.andKhare,A.(2000).Economicissuesin recyclingend-of-lifevehicles. Technovation, 20,677–690.
19. Perspectives and relationships in Supply Chain Simulation:Asystematicliteraturereview.
20. AModelpredictivecontrolforsimultaneousplanning ofcontainerandvehicleroutes.
21. Sustainable supply chain optimisation: An industrial casestudy.
22. Agricultural supply chain optimization and complexity: A comparisonof analytic vs simulated solutionsandpolicies.
23. Distributed economic model predictive control for cooperative supply chain management using customerforecastinformation.
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