Intelligent Slotting for the Warehouse

2016 ◽  
pp. 326-342
Author(s):  
Narayanan Padmanabahan Iyer ◽  
Ramesh Jayal
Keyword(s):  
Machine Learning ◽  
Supply Chain ◽  
Business Growth ◽  
Market Basket Analysis ◽  
Market Basket ◽  
Rapid Changes ◽  
Future Supply

In the current and future Supply Chain landscape, we need to ensure that we keep the warehouse ship sailing amidst the turbulent waters of dynamic business growth and rapid changes in technology. There are several challenges to be overcome, and many opportunities to be embraced on the path to achieving this. In this chapter, the authors detail one of the key problems facing the warehouse and that is Slotting. They look at the various business drivers, and technological drivers impacting Slotting. They propose a solution to tackle this problem by using Market Basket Analysis and Machine Learning.

Market Basket Analysis Approach to Machine Learning

2021 ◽  
Author(s):  
Abu Hasnat Patwary ◽  
Md Tamim Eshan ◽  
Prazzal Debnath ◽  
Abdus Sattar
Keyword(s):  
Machine Learning ◽  
Analysis Approach ◽  
Market Basket Analysis ◽  
Market Basket

scholarly journals Approach to Kirana Store Product Arrangement Using Machine Learning

2021 ◽  
pp. 72-83
Author(s):  
Ajita Patel ◽  
Krishna Kumar Tiwari
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Domain Knowledge ◽  
Market Basket Analysis ◽  
Market Basket ◽  
Detection Techniques ◽  
Store Product ◽  
Shopping Basket ◽  
Objective Detection ◽  
Better Than

Market Basket Analysis (MBA) is a method for determining the association between entities, and it has often been used to study the association between products in a shopping basket. Trained Computer vision models are able to recognize objects in photos so accurately that it can even outperform humans in some instances. This study shows that combining objective detection techniques with market basket analysis can assist Stores/Kirana in organizing the products effectively. With the use of MBA and Object detection, we formulated recommendations for store arrangements along with putting a recommendation engine on top to help shoppers. After deploying this to local Kirana stores, the Kirana store was able to see an increase of 7% in the sale. The recommendation engine performed better than just the domain knowledge of the kirana store.

MODEL SISTEM INFORMASI PEMASARAN SEBAGAI PENUNJANG KEPUTUSAN PROMOSI PRODUK MENGGUNAKAN ALGORITMA MAX -MINER

ICIT Journal ◽  
2020 ◽  
Vol 6 (1) ◽  
pp. 94-104
Author(s):  
Fernando Siboro ◽  
Capri Eriansyah ◽  
Muhammad Adi Sofyan
Keyword(s):  
Data Mining ◽  
Life Cycle ◽  
Market Basket Analysis ◽  
Market Basket

Teknologi informasi saat ini terus berkembang semakin cepat, membuat pola berfikir manusia berubah, dengan proses pertumbuhan yang seperti ini, generasi akan datang diharuskan mempunyai keahlian yang lebih baik di bidang pemanfaatan teknologi informasi. Kebutuhan adanya kemudahan dari segi pemasaran, saat ini dirasa sangat penting, terutama bagi perusahaan yang bergerak dibidang penjulan atau distributor guna menunjang meningkatkan akurasi dan kualitas pemasaran itu sendiri. Namun pada kenyataanya, sistem yang berjalan masih tergolong kurang efektif dan efesien dalam melayani kebutuhan pelanggan, hal ini dikarenakan sistem pemasaran produk hanya bisa diakses secara manual, dan belum adanya media informasi seputar produk yang ditawarkan, oleh sebab itu dibuatlah suatu perancangan sistem informasi yang mengatur pemasaran produk dan dapat menjadi bahan dalam pembuatan laporan sistem penunjang keputusan. Dalam perancangan ini menggunakan metode data mining market basket analysis dan Max-Miner sebagai algoritma. Serta menggunakan metode penerapan sistem waterfall atau sering dinamakan siklus hidup klasik (classic life cycle). Dengan demikian rancang bangun sistem informasi ini, mengacu kepada bagaimana cara agar pemasaran produk dapat di akses dengan mudah, cepat, dan akurat dimanapun dan kapanpun, calon customer dapat mengakses tanpa terkendala waktu dan tempat, serta menjadi wadah dalam pengambilan keputusan oleh perusahaan. Metodologi desain menggunakan uml yang melimuti usecase, activity, squence dan untuk pengelolaan basis data menggunakan mysql. Sistem ini diharapkan mampu dijadikan salah satu penunjang keputusan untuk kebutuhan promosi produk. Kata Kunci: Penunjang pemasaran, promosi produk, algoritma Max-Miner

Post COVID-19 Potentials and Innovation: The Future Supply Chain

2020 ◽  
Vol 11 (SPL1) ◽  
pp. 1054-1057
Author(s):  
Bindu Swetha Pasuluri ◽  
Anuradha S G ◽  
Manga J ◽  
Deepak Karanam
Keyword(s):  
Mental Health ◽  
Decision Making ◽  
Supply Chain ◽  
Supply Chains ◽  
World Health Organization ◽  
World Wide ◽  
World Health ◽  
Future Supply ◽  
Entire World ◽  
Health Organization

An unanticipated outburst of pneumonia of inexperienced in Wuhan, , China stated in December 2019. World health organization has recognized pathogen and termed it COVID-19. COVID-19 turned out to be a severe urgency in the entire world. The influence of this viral syndrome is now an intensifying concern. Covid-19 has changed our mutual calculus of ambiguity. It is more world-wide in possibility, more deeply , and much more difficult than any catastrophe that countries and organizations have ever faced. The next normal requires challenging ambiguity head-on and building it into decision-making. It is examined that every entity involved in running supply chains would require through major as employee, product, facility protocols, and transport would have to be in place. It is an urgent need of structuring to apply the lessons well-read for our supply chain setup. With higher managers now being aware of the intrinsic hazards in their supply chain, key and suggestions-recommendations will help to guide leader to commit to a newly planned, more consistent supply chain setup. Besides, the employees’ mental health is also a great concern.

Classification of Operational and Financial Variables Affecting the Bullwhip Effect in Indian Sectors: A Machine Learning Approach

2019 ◽  
Vol 12 (3) ◽  
pp. 171-179 ◽  
Author(s):  
Sachin Gupta ◽  
Anurag Saxena
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Product Life Cycle ◽  
Consumer Preference ◽  
Bullwhip Effect ◽  
Machine Learning Techniques ◽  
Chain Management ◽  
Financial Variables

Background: The increased variability in production or procurement with respect to less increase of variability in demand or sales is considered as bullwhip effect. Bullwhip effect is considered as an encumbrance in optimization of supply chain as it causes inadequacy in the supply chain. Various operations and supply chain management consultants, managers and researchers are doing a rigorous study to find the causes behind the dynamic nature of the supply chain management and have listed shorter product life cycle, change in technology, change in consumer preference and era of globalization, to name a few. Most of the literature that explored bullwhip effect is found to be based on simulations and mathematical models. Exploring bullwhip effect using machine learning is the novel approach of the present study. Methods: Present study explores the operational and financial variables affecting the bullwhip effect on the basis of secondary data. Data mining and machine learning techniques are used to explore the variables affecting bullwhip effect in Indian sectors. Rapid Miner tool has been used for data mining and 10-fold cross validation has been performed. Weka Alternating Decision Tree (w-ADT) has been built for decision makers to mitigate bullwhip effect after the classification. Results: Out of the 19 selected variables affecting bullwhip effect 7 variables have been selected which have highest accuracy level with minimum deviation. Conclusion: Classification technique using machine learning provides an effective tool and techniques to explore bullwhip effect in supply chain management.

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