Predictable inventory management within dairy supply chain operations

被引:6
作者
Huerta-Soto, Rosario [1 ]
Ramirez-Asis, Edwin [2 ]
Tarazona-Jimenez, John [3 ]
Nivin-Vargas, Laura [3 ]
Norabuena-Figueroa, Roger [4 ]
Guzman-Avalos, Magna [5 ]
Reyes-Reyes, Carla [2 ]
机构
[1] Univ Cesar Vallejo, Postgrad Sch, Trujillo, Peru
[2] Univ Senor Sipan, Dept Business Sci, Chiclayo, Peru
[3] Univ Nacl Santiago Antunez Mayolo, Dept Econ, Huaraz, Peru
[4] Univ Nacl Mayor San Marcos, Dept Stat, Lima, Peru
[5] Univ Nacl Santiago Antunez Mayolo, Dept Nursing, Huaraz, Peru
关键词
Dairy supply chain management; Retail and distribution; PRISMA; P-SVM; Dairy technology; BIG DATA ANALYTICS; ARTIFICIAL-INTELLIGENCE; LOGISTICS; PERFORMANCE;
D O I
10.1108/IJRDM-01-2023-0051
中图分类号
F [经济];
学科分类号
02 ;
摘要
PurposeWith the current wave of modernization in the dairy industry, the global dairy market has seen significant shifts. Making the most of inventory planning, machine learning (ML) maximizes the movement of commodities from one site to another. By facilitating waste reduction and quality improvement across numerous components, it reduces operational expenses. The focus of this study was to analyze existing dairy supply chain (DSC) optimization strategies and to look for ways in which DSC could be further improved. This study tends to enhance the operational excellence and continuous improvements of optimization strategies for DSC managementDesign/methodology/approachPreferred reporting items for systematic reviews and meta-analyses (PRISMA) standards for systematic reviews are served as inspiration for the study's methodology. The accepted protocol for reporting evidence in systematic reviews and meta-analyses is PRISMA. Health sciences associations and publications support the standards. For this study, the authors relied on descriptive statistics.FindingsAs a result of this modernization initiative, dairy sector has been able to boost operational efficiency by using cutting-edge optimization strategies. Historically, DSC researchers have relied on mathematical modeling tools, but recently authors have started using artificial intelligence (AI) and ML-based approaches. While mathematical modeling-based methods are still most often used, AI/ML-based methods are quickly becoming the preferred method. During the transit phase, cloud computing, shared databases and software actually transmit data to distributors, logistics companies and retailers. The company has developed comprehensive deployment, distribution and storage space selection methods as well as a supply chain road map.Practical implicationsMany sorts of environmental degradation, including large emissions of greenhouse gases that fuel climate change, are caused by the dairy industry. The industry not only harms the environment, but it also causes a great deal of animal suffering. Smaller farms struggle to make milk at the low prices that large farms, which are frequently supported by subsidies and other financial incentives, set.Originality/valueThis paper addresses a need in the dairy business by giving a primer on optimization methods and outlining how farmers and distributors may increase the efficiency of dairy processing facilities. The majority of the studies just briefly mentioned supply chain optimization.
引用
收藏
页码:1 / 17
页数:17
相关论文
共 47 条
[1]   A systematic review of machine learning in logistics and supply chain management: current trends and future directions [J].
Akbari, Mohammadreza ;
Do, Thu Nguyen Anh .
BENCHMARKING-AN INTERNATIONAL JOURNAL, 2021, 28 (10) :2977-3005
[2]   Predicting supply chain risks using machine learning: The trade-off between performance and interpretability [J].
Baryannis, George ;
Dani, Samir ;
Antoniou, Grigoris .
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, 2019, 101 :993-1004
[3]   Supply chain risk management and artificial intelligence: state of the art and future research directions [J].
Baryannis, George ;
Validi, Sahar ;
Dani, Samir ;
Antoniou, Grigoris .
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH, 2019, 57 (07) :2179-2202
[4]   Retail logistics service quality: a cross-cultural survey on customer perceptions [J].
Bouzaabia, Rym ;
Bouzaabia, Olfa ;
Capatina, Alexandru .
INTERNATIONAL JOURNAL OF RETAIL & DISTRIBUTION MANAGEMENT, 2013, 41 (08) :627-+
[5]   Supply chain data analytics for predicting supplier disruptions: a case study in complex asset manufacturing [J].
Brintrup, Alexandra ;
Pak, Johnson ;
Ratiney, David ;
Pearce, Tim ;
Wichmann, Pascal ;
Woodall, Philip ;
McFarlane, Duncan .
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH, 2020, 58 (11) :3330-3341
[6]   The self-thinking supply chain [J].
Calatayud, Agustina ;
Mangan, John ;
Christopher, Martin .
SUPPLY CHAIN MANAGEMENT-AN INTERNATIONAL JOURNAL, 2019, 24 (01) :22-38
[7]   When blockchain meets social-media: Will the result benefit social media analytics for supply chain operations management? [J].
Choi, Tsan-Ming ;
Guo, Shu ;
Luo, Suyuan .
TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW, 2020, 135
[8]   Supervised machine learning for theory building and testing: Opportunities in operations management [J].
Chou, Yen-Chun ;
Chuang, Howard Hao-Chun ;
Chou, Ping ;
Oliva, Rogelio .
JOURNAL OF OPERATIONS MANAGEMENT, 2023, 69 (04) :643-675
[9]   Influence of governance instruments on supply chain quality: a qualitative investigation in the dairy industry [J].
de Souza, Osvaldo ;
Machado, Marcio C. ;
Correa, Victor Silva ;
Telles, Renato .
BENCHMARKING-AN INTERNATIONAL JOURNAL, 2023, 30 (08) :2608-2633
[10]   Exploring the relationship between leadership, operational practices, institutional pressures and environmental performance: A framework for green supply chain [J].
Dubey, Rameshwar ;
Gunasekaran, Angappa ;
Ali, Sadia Samar .
INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS, 2015, 160 :120-132