Michael Teucke

Robust production planning in fashion apparel industry under demand uncertainty via conditional value at risk

This paper presents a mathematical model for robust production planning. The model helps fashion apparel suppliers in making decisions concerning allocation of production orders to different production plants characterized by different lead times and production costs, and in proper time scheduling and sequencing of these production orders. The model aims at optimizing these decisions concerning objectives of minimal production costs and minimal tardiness. It considers several factors such as the stochastic nature of customer demand, differences in production and transport costs and transport times between production plants in different regions. Finally, the model is applied to a case study. The results of numerical computations are presented. The implications of the model results on different fashion related product types and delivery strategies, as well as the model’s limitations and potentials for expansion, are discussed. Results indicate that the production planning model using conditional value at risk (CVaR) as the risk measure performs robustly and provides flexibility in decision analysis between different scenarios.

A survey on retail sales forecasting and prediction in fashion markets

Sales forecasting is an essential task in retailing. In particular, consumer-oriented markets such as fashion and electronics face uncertain demands, short life cycles and a lack of historical sales data which strengthen the challenges of producing accurate forecasts. This survey paper presents state-of-the-art methods in the sales forecasting research with a focus on fashion and new product forecasting. This study also reviews different strategies to the predictive value of user-generated content and search queries.

Forecasting of Seasonal Apparel Products

Demand forecasting of fashion apparel products has to cope with serious difficulties in order to get more accurate forecasts early enough to influence production decisions. Demand has to be anticipated at an early date due to long production lead times. Due to the absence of historical sales data for new products, standard statistical forecasting methods, like, e.g., regression, cannot easily be applied. This contribution applies selected methods into improve forecasting customer demand of fashion or seasonal apparel products. We propose a model which uses retailer pre-orders of seasonal apparel articles before the start of their production to estimate later, additional post-orders of the same articles during the actual sales periods. This allows forecasting of total customer demand based on the pre-orders. The results show that under certain circumstances it is possible to find correlations between the pre-orders and post-orders of those articles, and thus better estimate total demand. The model contributes to the improvement of production volumes of apparel articles, and thus can help reduce article stock-outs or unwanted surpluses.

Considerations on a Lifecycle Model for Cyber-Physical System Platforms

Cyber-physical system platforms are information infrastructures connecting different cyber-physical systems and other information systems. This infrastructure is the base for realizing the “Industrie 4.0” paradigm aiming for collaborative industrial processes involving smart objects and smart factories. In inter-organizational value networks, a cyber-physical system platform becomes a shared resource that has to be managed cooperatively along its lifecycle. This paper looks at cyber-physical system platforms from a lifecycle perspective. It describes the complexity of networks of cyber-physical systems and cyber-physical system platforms within value networks and the resulting restrictions influencing their various lifecycles. A selection of different lifecycle models from literature is reviewed to extract aspects that provide a promising basis for the development of a specific lifecycle model of cyber-physical system platforms.

Limitations of Autonomous Control in Practical Applications: Report on Lessons Learned from Vehicle and Apparel Logistics

Autonomous control is the research topic of the Collaborative Research Centre 637 (CRC 637). The CRC 637 defines autonomous control as processes of decentralized decision making in non-deterministic systems. An autonomous controlled system consists of interacting elements, which are characterized by their ability to process information, and to render and to execute decisions independently and on their own [4]. The application of autonomous control aims at increasing a system’s logistic performance and, in particular, its robustness, i.e. to improve a logistic system’s response to dynamic instability [9].

Effects of Sensor-Based Quality Data in Automotive Supply Chains – A Simulation Study

Supply chain risk management (SCRM) is becoming increasingly attractive as it opens up various control opportunities in case of rising volatility in value-added networks. Sensor-based, real-time quality data will be the founding an event-driven organization of supply chains with regard to more transparency. The following article presents the opportunities of using real-time, sensor-based quality data in automotive supply chain (SC) analyzed within a simulation study. Therefore, a discrete-event simulation of an automotive SC evaluates the usage of quality data. Different scenarios of control mechanisms are developed in three test cases characterized by different quality failure probabilities. For each of the test cases, the effect on stocks is described. The investigations show the positive effect of using real-time quality data to reduce stocks. The most positive effect is related to methods like special transports, but their cost-intensive structure has to be optimized. In conclusion, sensor-based quality data can face the rising volatility. Further research should focus on innovative controlling methods.

Einsatz mobiler Computersysteme im Rahmen von Industrie 4.0 zur Bewältigung des demografischen Wandels

Der Beitrag beschaftigt sich mit dem Einfluss cyber-physischer Systeme (CPS) auf die zunehmend durch Industrie 4.0 gepragte Arbeitswelt. Hierbei werden Herausforderungen und Potenziale, die durch den demografischen Wandel sowie die Einbindung von Geringqualifizierten entstehen, illustriert und diskutiert. Der Fokus des Beitrags liegt auf den Einsatzmoglichkeiten von mobilen Systemen, mit dem Schwerpunkt Wearable Computing-Losungen, in den Bereichen Produktion und Logistik, welche die Beschaftigten im Rahmen eines Ambient Assisted Working-Ansatzes unterstutzen konnen.

Managing the Life Cycle of IT-Based Inter-firm Resources in Production and Logistics Networks

This paper applies the concept of inter-firm resources to inter-organizational information systems that are shared between enterprises within production and logistics networks. The paper focuses on challenges during collaborative initiation, development, implementation, operation, and maintenance as well as termination of such shared information systems. The availability of instruments for improving all these life cycle phases of shared information systems is discussed based on the implementation of an EPCIS-based data exchange infrastructure within an automotive production network.

Design of SmartGate Technologies for Enhanced Material Handling

The use of information technologies in logistic processes leads to higher automation and efficiency. Nevertheless, information of cargo is often incomplete or incorrect. This affects the material handling processes in planning as well as in operation. This paper presents a SmartGate approach, which contains multiple technologies for identification and exploration of goods for enhanced material handling. This includes identification as well as optic and haptic exploration technologies. The idea is to have a system, which gathers all available information of a good by use of non-destructive testing methods. Besides the optic and haptic exploration technologies, a feasible material handling system and the utilization of the gained information for load planning is shown.