Bi-objective Sales Planning using Machine Learning for Industrial Valves
Main Article Content
Abstract
Accurate prediction and forecasting of industrial products' consumption, enables up-to-date and efficient supply, replacement of worn-out items, and prevention of resource wastage. Planning and forecasting the usage of industrial products can help often based on historical years' performance and environmental factors, using either traditional methods or smart systems. However, the instability of some sales behavior in certain products and the lack of previous data for new products or sales offices can create problems in intelligent systems. In this paper, we present a hybrid and bi-objective model in the form of a business intelligence system that first fits an appropriate function to the products, providing a new estimated combination for the type and sales amount of all products, while taking into account the profit margin. This new intelligent system allows for flexible planning for the company, generating a special scenario for each new input strategy.
Furthermore, using machine learning and based on similarity measurements and the company's previous data, we predict the sales behavior for new products and sales offices in their first year of operation. Finally, the model announces the sales trend of each product in different time periods, separately for each sales office, taking into account the previous two stages. The current investigation outlines the integration of the proposed model into the business intelligence system of Mirab Valves Company, a reputable manufacturer of industrial valves, and its subsequent effective application as an exemplar. The model's efficacy in forecasting sales of new products and sales offices is established at 79% and 92%, respectively.
Downloads
Article Details
Copyright (c) 2023 Bigham BS, et al.
This work is licensed under a Creative Commons Attribution 4.0 International License.
Licensing and protecting the author rights is the central aim and core of the publishing business. Peertechz dedicates itself in making it easier for people to share and build upon the work of others while maintaining consistency with the rules of copyright. Peertechz licensing terms are formulated to facilitate reuse of the manuscripts published in journals to take maximum advantage of Open Access publication and for the purpose of disseminating knowledge.
We support 'libre' open access, which defines Open Access in true terms as free of charge online access along with usage rights. The usage rights are granted through the use of specific Creative Commons license.
Peertechz accomplice with- [CC BY 4.0]
Explanation
'CC' stands for Creative Commons license. 'BY' symbolizes that users have provided attribution to the creator that the published manuscripts can be used or shared. This license allows for redistribution, commercial and non-commercial, as long as it is passed along unchanged and in whole, with credit to the author.
Please take in notification that Creative Commons user licenses are non-revocable. We recommend authors to check if their funding body requires a specific license.
With this license, the authors are allowed that after publishing with Peertechz, they can share their research by posting a free draft copy of their article to any repository or website.
'CC BY' license observance:
|
License Name |
Permission to read and download |
Permission to display in a repository |
Permission to translate |
Commercial uses of manuscript |
|
CC BY 4.0 |
Yes |
Yes |
Yes |
Yes |
The authors please note that Creative Commons license is focused on making creative works available for discovery and reuse. Creative Commons licenses provide an alternative to standard copyrights, allowing authors to specify ways that their works can be used without having to grant permission for each individual request. Others who want to reserve all of their rights under copyright law should not use CC licenses.
Leo B, Jerome F, Richard O, Charles S. Classification and regression trees. Wadsworth Int Group. 1984; 37:237-251.
Verstraete G, Aghezzaf ElH, Desmet B. A leading macroeconomic indicators’based framework to automatically generate tactical sales forecasts. Computers & Industrial Engineering. 2020; 139: 106169.
Spyros M. Forecasting: its role and value for planning and strategy. International Journal of Forecasting. 1996; 12:513-537.
Remus W, Simkin MG. The handbook of forecasting: a manager’s guide. 2nd ed. New York, USA: Wiley’. 1987; 17.
Stein JC. Internal capital markets and the competition for corporate resources. The Journal of Finance. 1997; 52:111-133.
Crittenden VL, Gardiner LR, Stam A. Reducing conflict between marketing and manufacturing’, Industrial Marketing Management. 1997; 22: 299-309.
Boone T, Ganeshan R, Jain A, Sanders NR. Forecasting sales in the supply chain: Consumer analytics in the big data era. International Journal of Forecasting. 2019; 35:170–180.
Mentzer JT, Bienstock CC. Sales forecasting management: understanding the techniques, systems and management of the sales forecasting process. SAGE Publications, Incorporated. 1998.
Trappey CV, Wu HY. An evaluation of the time-varying extended logistic, simple logistic, and Gompertz models for forecasting short product lifecycles. Advanced Engineering Informatics. 2008; 22:421-430.
Sohrabpour V, Oghazi P, Toorajipour R, Nazarpour A. Export sales forecasting using artificial intelligence’, Technological Forecasting and Social Change. 2021; 163:120480.
Sualihu MA. Financial Planning and Forecasting in the Oil and Gas Industry. The Economics of the Oil and Gas Industry: Emerging Markets and Developing Economies. 2023.
Ansuj AP, Camargo ME, Radharamanan R, Petry DG. Sales forecasting using time series and neural networks’, Advanced Engineering Informatics. 1996; 31:421-424.
Alon I, Qi M, Sadowski RJ. Forecasting aggregate retail sales: a comparison of artificial neural networks and traditional methods’, Journal of retailing and consumer services. 2001; 8:147-156.
Faccia A, Pandey V. Business planning and big data, budget modelling upgrade through data science. Proceedings of the 6th International Conference on Information Systems Engineering. 2021.
Rohaan D, Topan E, Catharina CGM. Using supervised machine learning for B2B sales forecasting: A case study of spare parts sales forecasting at an after-sales service provider. Ex- pert systems with applications. 2022; 188: 115925.
Ohlson NE, Jenny B, Maria R. Artificial Intelligence-enhanced Sales & Operations Planning in an Engineer-to- order context. PLAN Utvecklings konferens. 2021.
Ray S. Comparative Analysis Of Conventional And Machine Learning Based Forecasting of Sales In Selected Industries. 2023.
Frank C, Garg A, Sztandera L, Raheja A. Forecasting women’s apparel sales using mathematical modeling’, International Journal of Clothing Science and Technology, MCB UP Ltd. 2003.
Aburto L, Weber R. Improved supply chain management based on hybrid demand forecasts. Applied Soft Computing. 2007; 7: 136–144.
Au KF, Choi TM, YY. Fashion retail forecasting by evolutionary neural networks. International Journal of Production Economics. 2008; 114: 615-630.
Pan Y, Pohlen T, Manago S. Hybrid neural network model in forecasting aggregate US retail sales’, Advances in business and management forecasting, Emerald Group Publishing Limited. 2013.
Dwivedi A, Niranjan M, Sahu K. A business intelligence technique for forecasting the automobile sales using Adaptive Intelligent Systems (ANFIS and ANN). International Journal of Computer Applications. 2013; 74.
Sajawal M. A Predictive Analysis of Retail Sales Fore- casting using Machine Learning Techniques. Lahore Garrison University Research Journal of Computer Science and Information Technology. 2022; 6:33-45.
Aye GC, Balcilar M, Gupta R, Majumdar A. Forecasting aggregate retail sales: The case of South Africa. International Journal of Production Economics 2015; 160:66-79.
Ramos P, Santos N, Rebelo R. Performance of state space and ARIMA models for consumer retail sales forecasting’, Robotics and computer-integrated manufacturing. 2015; 34:151-163.
Kolassa S. Evaluating predictive count data distributions in retail sales forecasting. International Journal of Forecasting. 2016; 32:788-803.
Ma S, Fildes R, Huang T. Demand forecasting with high dimensional data: The case of SKU retail sales forecasting with intra-and inter-category promotional information. European Journal of Operational Research. 2016; 249:245-257.
Mazyar ZS, Gholam-Reza J, Josef J, Sedighe A. A new Monte Carlo procedure for complete ranking efficient units in DEA models. Numerical Algebra, Control and Optimization. 2017; 7.
Zahedi-Seresht M, Mehrabian S, Jahanshahloo GHR. A new method for ranking distribution companies with several scenarios data by using DEA/MADM. International Journal of Applied Operational Research. 2016; 6:11-24.