Design And Development Of Digital Workshop Management System

The current society is in a golden age of digital transformation,and the "14th Five-Year Plan" provides clear guidance and instructions on this.Digitization has become a national strategic goal.In the development and transformation of enterprises,the digitalization of production and operation processes is an inevitable trend.Generic digital systems often cannot adapt to the production and operation modes of different enterprises.Based on the production and business characteristics of this enterprise,this thesis designs and develops a digital workshop management system,with the following main work and innovation points:1.Fully investigated the production and operation modes of various departments such as sales,production,procurement,and warehousing in the enterprise,and sorted out the business processes of each department.In-depth analysis of the reasons for difficulties in quality traceability,tedious manual scheduling,and insufficient internal information sharing.Based on this,the functional requirements of the enterprise for the management system were clarified.2.In order to improve the accuracy of sales forecasting,this thesis proposes an improved sales forecasting algorithm,ARIMA-CART algorithm,based on the ARIMA algorithm.The ARIMA algorithm can capture the linear part of the data well and has good trend prediction ability,but its handling of non-linear components is mediocre,leading to large result errors.To solve this problem,the CART model is used to process the non-linear part of the data and reduce result errors.Experimental results show that the improved algorithm not only has good trend prediction ability but also has higher prediction accuracy.3.In response to the problems with the K-Means algorithm,an improved algorithm,DSKMeans algorithm,is proposed.The algorithm uses the similarity between samples to constrain the selection of initial clustering centers and determines the search range based on the density of samples.This overcomes the problem of the initial centers being randomly selected in the K-Means algorithm,which easily leads to results being affected by noise,reduces accuracy,and increases the number of iterations.Experimental results show that the improved algorithm has good accuracy,number of iterations,and running time,and performs well in customer classification and rating tasks.4.The system is based on a browser/server architecture,using a front-end and back-end separation development model,RBAC dynamic allocation of permissions,and one-code traceability technology,reducing the difficulty of system development and maintenance and ensuring the security of system data.Based on this,a digital management system is designed,which includes modules such as sales,production,procurement,warehousing,customer/supplier,personnel,and system management.In addition,the system also includes data analysis and mining functions,realizing sales forecasting and customer classification,providing business insights and trends for enterprises,and providing decision support and directions for business improvement.After a year of trial operation,the system has now been successfully deployed online,effectively solving problems such as non-standard data management,tedious scheduling work,insufficient internal information sharing,and difficult quality traceability in enterprises.It has also reduced production and management costs and improved production efficiency.