Publikationen

Sustainability is a contributor to competitive advantage and a potential key to increase market shares in the saturated apparel sector. Managing supply chains environmentally and socially sustainable is a way to prevent from being associated with the many negative examples in the industry that are presented on a regular basis by NGOs and media. In order to avoid negative consequences from unsustainable behavior of suppliers, sustainability needs to be propagated to upstream partners in the supply chain. The paper at hand illustrates opportunities to strengthen sustainability in apparel supply chains. Based on a systematic review of related literature, a taxonomy of apparel supply chains is derived and sustainability aspects are identified. A particular focus is put on finding alternative sources such as organic cotton from Africa. The presented study represents a basis to stimulate future research on sustainable apparel supply chains. Practitioners may find innovative approaches to improve supply chain sustainability.

Sustainability has become a highly relevant factor for companies, economies and societies. As a consequence, the consideration of environmental and social factors is crucial to manage supply chains sustainably. The study at hand analyses and compares conceptual frameworks and empirical studies on sustainable supply chain management. The gained insights are transferred to case studies of two German firms.

Formal models that support multi-criteria decision making represent a strongly growing area in sustainable supply chain management research. However, uncertainties and risks are seldom considered in quantitative models for green supply chain (SC) design. The paper at hand suggests a hybrid approach to configure an eco-efficient SC for a new product under consideration of economic and environmental risks. Discrete-event simulation is applied to assess the financial, operational and environmental performance of different SC configuration options while the value-at-risk concept is adapted to evaluate related SC risks. The analytic hierarchy process is employed to solve the resulting multi-criteria decision problem of choosing the best option. The approach is illustrated at a case example of a fast moving consumer goods manufacturer.

Sustainability, the consideration of environmental factors and social aspects, in supply chain management (SCM) has become a highly relevant topic for researchers and practitioners. The application of operations research methods and related models, i.e. formal modeling, for closed-loop SCM and reverse logistics has been effectively reviewed in previously published research. This situation is in contrast to the understanding and review of mathematical models that focus on environmental or social factors in forward supply chains (SC), which has seen less investigation. To evaluate developments and directions of this research area, this paper provides a content analysis of 134 carefully identified papers on quantitative, formal models that address sustainability aspects in the forward SC. It was found that a preponderance of the publications and models appeared in a limited set of six journals, and most were analytically based with a focus on multiple criteria decision making. The tools most often used comprise the analytical hierarchy process or its close relative, the analytical network process, as well as life cycle analysis. Conclusions are drawn showing that numerous possibilities and insights can be gained from expanding the types of tools and factors considered in formal modeling efforts.

Supply chain management (SCM) strives for creating competitive advantage and value for customers by integrating business processes from end users through original suppliers. However, the question of how SCM influences the value of a firm is not fully answered. Various conceptual frameworks that explain the coherence of SCM and company value, comprehended as value-based SCM, are well accepted in scientific research, but quantitative approaches to value-based SCM are found rather seldom. The book contributes to this research gap by proposing quantitative models that allow for assessing influences of SCM on the value of a firm. Opposed to existing models that limit the observation to chosen facets of SCM or selected value drivers, this holistic approach is adequate to • reflect configurational and operational aspects of SCM, • cover all phases of the product life cycle, • financially compare value impacts of profitability-related and asset-related value drivers, and • assess influences of dynamics and uncertainties on company value.

The research question addressed is to which extent supply chain management (SCM) creates value from cost and working capital. The paper provides an empirical evaluation including insights on important criteria for value creation. In a secondary data analysis, 10 leading fast moving consumer goods (FMCG) companies are benchmarked regarding the value created from cost of goods sold (COGS) and working capital within the time horizon 2003–2008. The study applies benchmarking methodology and a discounted cash flow (DCF) based model for quantifying value contributions. It is shown that SCM is realized in a value-adding way with different emphasis on COGS or working capital. Monetarily working capital components (trade payables, trade receivables) have a high relevance for value creation. Continuous improvements and long lasting developments of value drivers are more appropriate for value creation than alternating improvements and deteriorations. Timing aspects of value driver developments have to be considered for value creation. The value of the paper stems from empirical comparison of value created by working capital and COGS and from evidence of important criteria for value creation. Further analysis based on cost components as well as benchmarking with different or extended content, such as fixed asset performance or cross-industry benchmarking, leave room for future research.

Supply Chain Management (SCM) is identified and accepted as a competitive advantage. Nevertheless holistic approaches for value-based SCM to leverage this advantage in a value adding way are missing so far. Efficient approaches to quantify and compare value contributions from Supply Chain (SC) value drivers are needed. This paper contributes to this need by proposing a model to efficiently quantify and compare value contributions from SC cost and working capital, that affect the profitability and asset performance. Properties and characteristics of the model, which is based on the Discounted Cash Flow concept, are illustrated by an industrial example of a single company. In this example, the relevance of timing and continuity of developments of SC cost and working capital for value creation is pointed out.

Supply chain management in chemical process industry focuses onproduction planning and scheduling to reduce production cost and inventories andsimultaneously increase the utilization of production capacities and the service level.These objectives and the specific characteristics of chemical production processesresult in complex planning problems. To handle this complexity, advanced planningsystems (APS) are implemented and often enhanced by tailor-made optimization algo-rithms. In this article, we focus on a real-world problem of production planning arisingfrom a specialty chemicals plant. Formulations for finished products comprise severalproduction and refinement processes which result in all types of material flows. Mostprocesses cannot be operated on only one multi-purpose facility, but on a choice ofdifferent facilities. Due to sequence dependencies, several batches of identical pro-cesses are grouped together to form production campaigns. We describe a methodfor multicriteria optimization of short- and mid-term production campaign schedulingwhich is based on a time-continuous MILP formulation. In a preparatory step, deter-ministic algorithms calculate the structures of the formulations and solve the bills ofmaterial for each primary demand. The facility selection for each production campaignis done in a first MILP step. Optimized campaign scheduling is performed in a secondstep, which again is based on MILP. We show how this method can be successfullyadapted to compute optimized schedules even for problem instances of real-worldsize, and we furthermore outline implementation issues including integration with anAPS.

We consider a complex scheduling problem in the chemical process industry involving batch production. The application described comprises a network of production plants with interdependent production schedules, multi-stage production at multi-purpose facilities, and chain production. The paper addresses three distinct aspects: (i) a scheduling solution obtained from a genetic algorithm (GA) based optimizer, (ii) a mechanism for collaborative planning among the involved plants, and (iii) a tool for manual updates and schedule changes. The tailor made optimization algorithm simultaneously considers alternative production paths and facility selection as well as product and resource specific parameters such as batch sizes, and setup and cleanup times. The collaborative planning concept allows all the plants to work simultaneously as partners in a supply chain resulting in higher transparency, greater flexibility, and reduced response time as a whole. The user interface supports monitoring production schedules graphically and provides custom-built utilities for manual changes to the production schedule, investigation of various what-if scenarios, and marketing queries.