Supply chain digitalization means integrate digital technologies and systems from Industry 4.0 into various aspects of the supply chain, with efficiency, visibility and cooperation enhancement as the main target. This paper will overview steps, advantages and drawbacks of introducing and optimizing a ML-based Demand Planning Software, that exploit statistical methods to forecast future demand, in a leading European steel company, AFV Beltrame Group. The aim of this project is to improve forecasting Accuracy minimizing MAPE, MAEP and RMSEP error indicators. In order to achieve this, it is needed to be found: the most suited spaces in which the software should calculate the forecasts and which algorithms guarantee the best outcomes. It will also describe change management challenges on introducing this advanced tool in the AFV organization.
Supply chain digitalization means integrate digital technologies and systems from Industry 4.0 into various aspects of the supply chain, with efficiency, visibility and cooperation enhancement as the main target. This paper will overview steps, advantages and drawbacks of introducing and optimizing a ML-based Demand Planning Software, that exploit statistical methods to forecast future demand, in a leading European steel company, AFV Beltrame Group. The aim of this project is to improve forecasting Accuracy minimizing MAPE, MAEP and RMSEP error indicators. In order to achieve this, it is needed to be found: the most suited spaces in which the software should calculate the forecasts and which algorithms guarantee the best outcomes. It will also describe change management challenges on introducing this advanced tool in the AFV organization.
Supply Chain Digitalization in AFV Beltrame Group. The implementation and optimization of a ML-based Demand Planning Software.
PERMUNIAN, MARTA
2022/2023
Abstract
Supply chain digitalization means integrate digital technologies and systems from Industry 4.0 into various aspects of the supply chain, with efficiency, visibility and cooperation enhancement as the main target. This paper will overview steps, advantages and drawbacks of introducing and optimizing a ML-based Demand Planning Software, that exploit statistical methods to forecast future demand, in a leading European steel company, AFV Beltrame Group. The aim of this project is to improve forecasting Accuracy minimizing MAPE, MAEP and RMSEP error indicators. In order to achieve this, it is needed to be found: the most suited spaces in which the software should calculate the forecasts and which algorithms guarantee the best outcomes. It will also describe change management challenges on introducing this advanced tool in the AFV organization.File | Dimensione | Formato | |
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https://hdl.handle.net/20.500.12608/55162