A model for sales forecasting based on fuzzy clustering and Back-propagation Neural Networks with adaptive learning rate

Hicham, Attariuas; Mohamed, Bouhorma; Abdellah, El Fallahi

doi:10.1109/icocs.2012.6458593

Cited by 8 publications

(2 citation statements)

References 12 publications

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“…With the increasing use of image processing applications, it is widely used in analyzing targets in the defense industry [7], [9], [10], [40], developing facial recognition systems in security cameras [52], investigating forensic cases [17], [53], analyzing products in industrial organizations [2], [11], [16], [22], [36], [55], [61], [94], developing medical devices in the field of health, making medical diagnoses [45], and analyzing products in the field of agricuture-grain [49], [67]. In these analyses, the attributes of the objects are determined by performing recognition, classification, and clustering operations [74], [77].…”

Section: Image Processingmentioning

confidence: 99%

Stock Detection of Iron and Steel Products with Image Processing and SDSS Decision Support System

2024

jjmie

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The iron and steel industry is one of the leading sectors contributing to the economic power of countries. It is incorporated into numerous fields, including automotive, construction, manufacturing, agriculture, defense, and healthcare. Today, the increasing supply and demand balance in iron and steel products makes the stock management and control of the products crucial. The large quantity and variety of products in the iron and steel industry make stock management and control difficult. Creating stock for the business is a very costly process and is one of the important elements of the business. Successful stock management can make the business financially advantageous. This study aims to estimate the stock costs of different products and quantities according to different dates found in the business by using data mining. For this purpose, data mining classifier models are used, and estimated costs are found. By establishing a stock tracking system throughout the supply chain, the business should register all inventory movements of the products. It should work in an integrated way with stock management costing. Thus, unforeseen decreases and financial losses in products can be detected.The iron and steel sector, a key player in enhancing competition among nations and shaping the economic landscape, caters to the needs of various industries through its commitment to sustainable steel production. The industry has encountered challenges due to the swift advancements in recent years, impacting the costs associated with iron and steel products and posing issues for the sector. Tackling these challenges is essential for fostering national development and enhancing the competitiveness of businesses. This research delves into the elements influencing the expenses within the iron and steel industry, emphasizing the importance of minimizing factors leading to elevated costs. An automation system was developed using image processing techniques, and iron and steel products were analyzed. In the automation system, k-means and twostep, one of the clustering analysis methods, was applied.A decision support system defined as SDSS (Steel Decision Support System) and BIPS (Burak's Image Processing System)has been developed to determine the inventory costs of iron and steel products, adopting a data mining approach and including clustering methods to reveal similar inventory costs. Measurements with the SDSS-BIPS automation system were 94.4% successful. Withcost levels were determined by matching the products in the iron and steel business database with their costs.

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Section: Image Processingmentioning

confidence: 99%

Stock Detection of Iron and Steel Products with Image Processing and SDSS Decision Support System

2024

jjmie

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“…In [6], the authors define a smart ERP-FCBPN hybrid sales forecasting scheme based on ERP architecture through Delphi, fuzzy clustering, and BPNN with adaptive learning rate. The model is experimented using data come from an industrial company that manufactures packaging in Tangier from 2001 to 2009.…”

Section: Forecasting In Erpmentioning

confidence: 99%

Forecasting in Enterprise Resource Planning (ERP) Systems: A Survey

ElMadany

Alfonse

Aref

2021

Lecture Notes in Networks and Systems

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Using machine learning in ERP enables the organization to store, manage, and analyze their data to get the right decisions and gain valuable insights that were previously unmanageable. There are many industries and lines of business that contain large volumes of data such as manufacturing, finance, and healthcare … etc. One of the most usages of machine learning in ERP is forecasting which refers to the prediction of the future based on past data. This paper surveys different approaches for forecasting in ERP to gain profit for the business process.

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Improved Neural Network Tool: Application to Societal Forecasting Problems

Adamuthe

Vhatkar

2019

Techno-Societal 2018

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A model for sales forecasting based on fuzzy clustering and Back-propagation Neural Networks with adaptive learning rate

Cited by 8 publications

References 12 publications

Stock Detection of Iron and Steel Products with Image Processing and SDSS Decision Support System

Stock Detection of Iron and Steel Products with Image Processing and SDSS Decision Support System

Forecasting in Enterprise Resource Planning (ERP) Systems: A Survey

Improved Neural Network Tool: Application to Societal Forecasting Problems

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