Abstract
New technologies, increasing competition, and changing consumer preferences in the food manufacturing sector have forced companies to generate customized products in dynamic demand and thus remain competitive in the market. As a result, companies have had to rethink their processes and product designs to optimize their manufacturing operations. In addition, moving from a conventional production model to processes supported by intelligent systems to generate efficiency improvements in the demand planning and productivity in their activities is necessary. This paper aims to introduce the development of an integrated model of lean 4.0 practices, demand forecasting using SARIMAX and DSS in a manufacturing SME. In addition, a literature review allowed identifying the variables that would be affected, such as inventory, waste, obsolete products, and productivity. Finally, a case study in the food manufacturing sector is considered to validate the model. The results will be presented through a visual analytics dashboard to streamline plant team decision-making.
| Original language | American English |
|---|---|
| Pages (from-to) | 139-150 |
| Number of pages | 12 |
| Journal | Proceedings of the Conference on Production Systems and Logistics |
| DOIs | |
| State | Indexed - 2023 |
| Externally published | Yes |
| Event | 5th Conference on Production Systems and Logistics, CPSL 2023 - Stellenbosch, South Africa Duration: 14 Nov 2023 → 17 Nov 2023 |
Bibliographical note
Publisher Copyright:© 2023, Publish-Ing in cooperation with TIB - Leibniz Information Centre for Science and Technology University Library. All rights reserved.
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 9 Industry, Innovation, and Infrastructure
Keywords
- Demand Forecasting
- Food Manufacturing Sector
- Lean 4.0
- Machine Learning
- Productivity
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