Logo Leibniz Universität Hannover
Logo: IFA - Institut für Fabrikanlagen und Logistik
Logo Leibniz Universität Hannover
Logo: IFA - Institut für Fabrikanlagen und Logistik
  • Zielgruppen
  • Suche
 

QuantiLoPe - Quantitative analysis and evaluation of the causes of low logistical performance along the company's internal supply chain

Duration:01.12.2016 - 31.12.2018
Funded by:BVL, AiF, IGF, BMWi
Contact:haertelifa.uni-hannover.de
Web:www.ifa.uni-hannover.de/quantilope

Bild QuantiLoPe - Quantitative Analyse und Bewertung der Ursachen einer geringen logistischen Performance entlang der unternehmensinternen Lieferkette

starting position:

Achieving high logistical performance is becoming increasingly important for companies, as it is becoming a key factor in the customer's purchasing decision, along with price, quality and functionality. At the same time, the realization of a high level of logistical performance offers companies the opportunity to position themselves optimally against their competitors in a competitive environment and thus to succeed in the market in the long term.

Ensuring high logistical performance, however, is a major challenge for SMEs in particular, as it depends on a valid quantitative analysis of the primary cause of low logistical performance. Frequently, selected measures lead to an insufficient increase in logistical performance, since many companies only correct the symptom due to the complexity of logistical interrelationships. However, the actual causes are usually found in completely different areas of the supply chain than expected. In this way, companies miss the opportunity to eliminate the real cause of the problem and thus achieve a real increase in logistical performance.

project goal:

The aim of the research project is therefore to enable SMEs in particular to independently conduct a logistical analysis of logistics performance along the supply chain, to derive improvement measures and to sustainably increase logistics performance. In order to achieve these goals, the following sub-targets are aimed for:

    • Establishment of generally valid symptom-cause-relationships for a transparent representation of the logistical interrelationships within complex internal supply chains consisting of the areas of procurement, production with (intermediate) stock and dispatch.

      • Development of a procedure model for data-based quantitative analysis using appropriate logistic models

    • Derivation of a comprehensive catalogue of measures to eliminate the identified primary causes

      • Implementation of the procedure in a software demonstrator that supports employees in the systematic analysis of logistical performance and the derivation of suitable measures within the framework of a workshop concept to be developed.

 

 

back to list