<abstract xmlns="http://www.w3.org/1999/xhtml"><p>This article describes the design of a simple forecasting system and its practical application to predict the sporadic needs for a spare part. The article shows new approach already implemented in the special servicing and production company in Slovakia and its results during a short period of performance after its implementation. Such a proposed model can be a part of the purchase planning of spare parts within the company’s logistics system. In some companies, the material flow of spare parts is dominant element in terms of logistics costs. Their management is therefore important for cost optimization, customer satisfaction and market sustainability in a competitive environment. The article, in its introductory part, provides an overview of similar practical solutions within the research of this topic, but many models are designed to be applied in a global market environment and predict the amount of spare parts needed in different industries. However, these models are difficult to use for the needs of a small enterprise, because the main problem lies in the time of a spare part demand rather than its quantity. If there is a need for a specific spare part, which costs several hundred or thousands of euros, but the consumption is only a few pieces per year or more than a year, the time prediction of required spare parts is therefore crucial.</p></abstract>

This article describes the design of a simple forecasting system and its practical application to predict the sporadic needs for a spare part. The article shows new approach already implemented in the special servicing and production company in Slovakia and its results during a short period of performance after its implementation. Such a proposed model can be a part of the purchase planning of spare parts within the company’s logistics system. In some companies, the material flow of spare parts is dominant element in terms of logistics costs. Their management is therefore important for cost optimization, customer satisfaction and market sustainability in a competitive environment. The article, in its introductory part, provides an overview of similar practical solutions within the research of this topic, but many models are designed to be applied in a global market environment and predict the amount of spare parts needed in different industries. However, these models are difficult to use for the needs of a small enterprise, because the main problem lies in the time of a spare part demand rather than its quantity. If there is a need for a specific spare part, which costs several hundred or thousands of euros, but the consumption is only a few pieces per year or more than a year, the time prediction of required spare parts is therefore crucial.

The Design of Forecasting System Used for Prediction of Electro-Motion Spare Parts Demands as an Improving Tool for an Enterprise Management

Management Systems in Production Engineering

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

This article describes the design of a simple forecasting system and its practical application to predict the sporadic needs for a spare part. The article...

The Design of Forecasting System Used for Prediction of Electro-Motion Spare Parts Demands as an Improving Tool for an Enterprise Management

Pubblicato online: 04 dic 2019

Pagine: 242 - 249

Ricevuto: 01 mag 2019

Accettato: 01 ago 2019

DOI: https://doi.org/10.1515/mspe-2019-0038

Parole chiave
forecast, spare parts, time series, order, model

© 2019 Peter Kačmáry et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

The Design of Forecasting System Used for Prediction of Electro-Motion Spare Parts Demands as an Improving Tool for an Enterprise Management

Pubblicato online: 04 dic 2019

Pagine: 242 - 249

Ricevuto: 01 mag 2019

Accettato: 01 ago 2019

DOI: https://doi.org/10.1515/mspe-2019-0038

Parole chiaveforecast, spare parts, time series, order, model

© 2019 Peter Kačmáry et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Parole chiave
forecast, spare parts, time series, order, model