Optimal Supply Chain Design despite Uncertainties

In cooperation with Henkel, Bergische Universitaet Wuppertal and the University of Erlangen-Nuremberg, Axxom analyzed the impact of fluctuating parameters on production and logistics networks. In a recent, successfully finished two-year research project the involved parties identified and developed new approaches and concepts for considering volatility in supply chain design.

The new approaches allow a robust supply chain optimization, which considers uncertainties in input parameters for the supply chain design. For this, the project partners analyzed different optimization algorithms and developed them to be able to cope with volatility and uncertainty. The solutions found shall be as good as possible even for unfavorable input parameters.

“Using the new algorithms we gained important insights into coping with uncertain input parameters”, explains Axel Richter, Project Manager at Henkel. “Especially important for our daily business is the impact of fluctuating demands on inventory and ability to deliver, which can be quantified by the used methods. The application of penalty costs allows the prioritization of the portfolio for example regarding product profitability or range structure.” In addition, the findings from inventory management help Henkel to evaluate existing safety stocks on the basis of previous demand development.

Another important result of the research project, which was supported by the Bayerische Forschungsstiftung (Bavarian Research Foundation) is the identification of the most important input parameters on today`s supply chains. When modelling and optimizing logistics networks, uncertainties can be described by the following variable input parameters:
- Quantity of Sales: Uncertainty caused e.g. by the introduction of new products to the market or by changes of the economic environment.
- Fluctuations: E.g. seasonal or economical impacts cause uncertainty of workload. Make or Buy?
- Cost of production: Uncertainty caused e.g. by investment costs to enter new markets.
- Raw material costs or raw material mix: Uncertainty e.g. caused by crude oil shortage or failure of crops.

In addition to the variable input parameters, the researchers also determined factors that are necessary for practical modelling and optimization of logistics networks. At producing companies, for example, production volume and complexity costs play an important role. Generally, a production site is more effective if it handles a small number of huge orders instead of a huge amount of small orders. Such efficiency factors have to be considered in cost appraisal and are already required for the optimization. An exact calculation is only possible after the whole product allocation is conducted – in fact, after the optimization. To solve these problems, the project partners developed an iterative approach which has also been integrated in the solution ORion-PI® Network Scale Savings of Axxom. It allows users to consider complexity costs and economy of scale when analyzing and optimizing their logistics and production networks. The result is a more realistic and efficient supply chain optimization.

Axxom Software AG is an international provider of software solutions and services for the comprehensive optimization of value-add processes. With its software ORion-PI® the technology company offers a solution for the design, simulation, planning and optimization of all business processes and systems in all fields of logistics. ORion-PI® is in use on five continents in more than 35 countries. Axxom Software AG was founded in April 2001. Since 2004 Axxom has a development center in Timisoara / Romania.

Axxom Software AG
Jens Verstaen
Corporate Communications
Paul-Gerhardt-Allee 46
81245 Munich / Germany
Tel: +49 (0)89 / 568 23-321
Fax: +49 (0)89 / 568 23-399
E-Mail: jens.verstaen@axxom.com
Internet: www.axxom.com

This release was published on openPR.