TY  - CONF
A1  - von Stamm, John
A1  - Müller, Jonas
A1  - Hoeborn, Gerrit
A1  - Lassen, Julian
A1  - Stich, Volker
A2  - Herberger, D.
A2  - Hübner, M.
A2  - Stich, Volker
T1  - Using Data-Centric Platforms To Improve Demand Forecasting And Capacity Utilization For Less Digitized Multi-Site Quarrying Businesses
T2  - Proceedings of the Conference on Production Systems and Logistics: CPSL 2023
N2  - The quarrying industry, which largely consists of less digitized SMEs, is an integral part of the German economy. More than 95% of the primary raw materials produced are used by the domestic construction industry. Quarrying companies operate demand-oriented with short planning horizons at several locations simultaneously. Due to the low level of digitization and the reluctance to share data, untapped efficiency potential in data-based demand forecasting and capacity planning arises. The situation is aggravated by the fact that SMEs have a heterogeneous mobile machinery so as not to become dependent on individual suppliers, and that transport distances of over 50 kilometers are uneconomical due to high transport costs and low material values. Within the research project PROmining a data-centric platform which improves demand forecast accuracy and multi-site capacity utilization is developed. One of the core functionalities of this platform is an industry-specific demand forecasting model. Against this background, this paper presents a methodology for establishing this forecasting model. To this end, expected demands of secondary industry sectors will be analyzed to improve mid-term volume-forecasting accuracy for the local quarrying industry. The data-centric platform will connect demand forecasting data with relevant key performance indicators of multi-site asset utilization. Following this methodology, operational planning horizons can be extended while significantly improving overall production efficiency. Thus, quarrying businesses are enabled to respond to fluctuating demand volumes effectively and can increase their personnel and machine utilization across multiple quarry sites.
KW  - Demand Forecasting
KW  - Data-Centric Platform
KW  - Capacity Utilization
KW  - Quarrying Industry
KW  - Digitization of SMEs
KW  - Efficiency Improvement
KW  - Key Performance Indicators
KW  - rev
Y1  - 2023
UR  - https://epub.fir.de/frontdoor/index/index/docId/2533
UR  - https://www.repo.uni-hannover.de/handle/123456789/13572
SP  - 438
EP  - 447
PB  - publish-Ing.
CY  - Hannover
ER  -