Michael Lütjen

Quality driven distribution of intelligent containers in cold chain logistics networks

The ‘intelligent container’ represents a novel transport system with the ability to make autonomous decisions regarding the condition of its transported goods. For example, fruit in cold chain logistics networks is very sensitive to mould and tends to perish. This can cause huge losses during transport, because the state-of-the-art reefer containers are able to control the temperature but not in relation to the fruit condition. The ‘intelligent container’ is able to precisely monitor the condition of fruit, as well as track its geographical position. Thus, the transport losses can be reduced due to better climate control and enhanced distribution strategies. This paper focuses on the development of a new scheduling method for distribution by applying principles of quality-driven customer order decoupling corridors (qCODC). Such corridors allow the dynamic change of allocations of container to customer order assignments. These corridors increase the flexibility of the decision-making process. Therefore, a simulation model will be developed and used in order to evaluate the potential of the new scheduling method based on the concept of the ‘intelligent container’ and qCODC.

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.

A framework for the quality-oriented design of micro manufacturing process chains

Purpose – In micro cold forming, the high degree of technological dependencies between manufacturing, quality inspection and handling technologies leads to an extremely complex planning of process chains. In addition, the lack of standardised processes and interfaces further complicates the planning. The paper aims to discuss these issues. Design/methodology/approach – In order to provide consistent and comprehensive planning of micro manufacturing processes, this paper discusses a method, which integrates the planning of process flows, the planning of technological dependencies and capabilities, as well as of the corresponding material flow. Findings – The paper presents the micro-process chain planning and analysis (μ-ProPlAn) framework. It consists of a specific modelling method, a simultaneous engineering procedure model for the model creation, as well as of methods for the analysis of technological dependencies and logistic key values along the modelled process chains. Research limitations/implicatio...

Process Maintenance of Heterogeneous Logistic Systems—A Process Mining Approach

Processes in manufacturing and logistics are characterized by a high frequency of changes and fluctuations, caused by the high number of participants in logistic processes. The heterogeneous landscape of data formats for information storage further complicates efforts to automatically extract process models from this data with the tools from Process Mining. This article introduces a concept for constantly updating process models in logistics, called Process Maintenance, collects requirements for a common view on information in logistics, and shows that Process Mining with logistic data is possible, but still needs improvement to become a regular practice.

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.

Modeling, Planning, and Control of Complex Logistic Processes

1Department of Engineering, Faculty of Engineering and Science, University of Agder, 4898 Grimstad, Norway 2College of Engineering, University of Wisconsin System, Madison, WI, USA 3ArcelorMittal Bremen GmbH, Bremen, Germany 4BIBA-Bremer Institut fur Produktion und Logistik GmbH, Planning and Control of Production Systems (PSPS), University of Bremen, Hochschulring 20, 28359 Bremen, Germany 5University of Picardie Jules Verne, MIS-UPJV, 7 Moulin Neuf, 80000 Amiens, France

Automated Surface Inspection of Micro Parts

This chapter presents a machine vision system for detecting surface imperfections on micro parts. It is part of a quality control concept for micro production. Because of increasing product miniaturization, the mechanical manufacturing of micro components is becoming more and more important. The combination of high manufacturing rates and low tolerances in manufacturing processes enables the economical production of micro components. Due to the small component sizes and the difficulties associated with the handling process, the manual visual inspection retires as testing procedure. A customized surface inspection technology with an efficient image processing and classification system is needed. The objective of our concept is to identify surface imperfections such as raisings, laps and bulges on micro parts. The implementation of the system is explained by reference to a micro deep‐drawn component, which is manufactured within the German Collaborative Research Center (CRC) 747.

Social Media Analytics for Decision Support in Fashion Buying Processes

The Web 2.0 and the emergence of numerous social media services enable individual users to publish and share information on the one hand and to discuss diverse topics online on the other hand. Accordingly, different research streams have emerged in order to tackle the diverse phenomena related to social media. Social media analytics as an interdisciplinary research field has arisen and integrates the different approaches of structural attributes, opinion/sentiment-related as well as topic/trend-related approaches. This research follows topic- and trend-related approaches with the methods content and trend analysis on social media text data. These methods might be applied on different domains including the fashion industry. This research focusses on the fashion industry for three reasons. Firstly, this industry is a highly consumer-oriented industry, and these consumers themselves are the users of social media services. Secondly, the industry faces challenges in meeting the demand of the customer on time. Thirdly, in the last years, fashion blogs have gained increased relevance from the consumers and the industry. Accordingly, the fashion blogs may contain information for supporting decision maker in the industry, to perform their tasks such as meeting the demand with a lower degree of uncertainty. The objective of this chapter is to explore the potential added value of social media analytics for fashion buying processes, not only by presenting an abstract approach, but more by conducting experimental analyses on a fashion blog corpus covering a 5 year time period. Based on the topic detection and tracking research which origins from the intelligent information retrieval, a research approach is presented by integrating a text mining process, on the detecting and tracking of fashion features and topics in the blog corpus. A fashion topic may refer to different features such as a colour, silhouette or style. While for the topic detection task, the feature colour is focussed, the topic tracking includes topics on silhouette, style, colour and decorative applications. The analyses have shown that it is possible to detect single colour and co-occurred colour occurrences. In addition, it was demonstrated that it is possible to track fashion topics over a 5 year time period in a fashion post corpus. The fashion buyer might have an added value for his activities by quantifying the individual perceptions through the application of the presented approach.

Wireless Pick-by-Light: Usability of LPWAN to Achieve a Flexible Warehouse Logistics Infrastructure

Pick-by-light is used for fast localization of items at the picking process in ware-house logistics. In general, pick-by-light systems are installed on racks or shelves which have to be powered by wires. This is very expensive and often involves complex installation procedures, wherefore wireless pick-by-light systems have gained a lot of research interest due to their flexibility, portability and low deployment costs. However, the existing wireless pick-by-light systems have limited range and introduce additional maintenance efforts, which make them inapplicable for bigger warehouses. This paper presents a wireless pick-by-light system based on LoRaWAN, a leading LPWAN standard, which is fully scalable. The proposed system offers long range due to unique LoRa RF modulation technique. In order to extend the battery life and thereby to minimize maintenance costs, the pick-by-light modules are built on power optimized LoRaWAN end devices. The system also suggests RSSI based asset tracking inside warehouses within the same framework for smarter routing of the human picker. In order to verify the proposed system, a prototype is developed and evaluated. The evaluation shows the technical implementation and its results.