Daniel Rippel, Michael Lütjen and Bernd Scholz-Reiter
In micro cold forming, the high degree of technological dependencies between manufacturing, quality inspection and handling technologies leads to an extremely complex planning of…
Abstract
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/implications
As the results presented in this paper originate from an on-going research project, the paper focuses on a detailed presentation of the modelling methodology and the procedure model.
Practical implications
In practice, the μ-ProPlAn framework provides process designers in the field of micro manufacturing with tools and methods to clearly depict the interdependencies between and within a product's different manufacturing stages.
Originality/value
By following a simultaneous engineering approach, μ-ProPlAn aims to reduce the efforts in process design by supporting the design of manufacturing processes in the early stages of the product design and by providing suitable methods for the analysis of these process chains.
Details
Keywords
Michael Hülsmann, Bernd Scholz-Reiter, Philip Cordes, Linda Austerschulte, Christoph de Beer and Christine Wycisk
The intention of this article is to show possible contributions of the concept of autonomous cooperation to enable complex adaptive logistics systems (CALS) to cope with…
Abstract
The intention of this article is to show possible contributions of the concept of autonomous cooperation to enable complex adaptive logistics systems (CALS) to cope with increasing complexity and dynamics and therefore to increase the systems' information-processing capacity by implementing autopoietic characteristics. In order to reach this target, the concepts of CALS and autopoietic systems will be introduced and connected. The underlying aim is to use the concept of self-organization as one of their essential similarities to lead over to the concept of autonomous cooperation as the most narrow view on self-organizing systems, which is discussed as a possible approach to enable systems to handle an increasing quantity of information. This will be analyzed from both a theoretical and an empirical point of view.
Bernd Scholz‐Reiter, Jens Heger, Christian Meinecke and Johann Bergmann
Item classification based on ABC‐XYZ analysis is of high importance for strategic supply and inventory control. It is common to perform the analysis with past consumption data. In…
Abstract
Purpose
Item classification based on ABC‐XYZ analysis is of high importance for strategic supply and inventory control. It is common to perform the analysis with past consumption data. In this context, the purpose of this study is to test the hypothesis that an integration of demand forecasts can improve the performance of item classification, in particular the performance of ABC‐XYZ analysis.
Design/methodology/approach
For the study, real data of an industrial enterprise in the mechanical engineering sector (focal company) were analyzed and evaluated.
Findings
The study shows that a comprehensive data analysis of the focal company can recommend a specific implementation of the ABC‐XYZ classification. In contrast to the classic method of making the ABC‐XYZ analysis based on consumption data only, the approach developed in this paper offers considerable advantages. These are quantifiable in respect to an assumed optimal reference classification.
Originality/value
The evaluation of the results is very promising and applicable to other branches besides mechanical engineering.
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Keywords
Mohammadreza Akbari and Thu Nguyen Anh Do
This paper presents a review of the existing state-of-the-art literature on machine learning (ML) in logistics and supply chain management (LSCM) by analyzing the current…
Abstract
Purpose
This paper presents a review of the existing state-of-the-art literature on machine learning (ML) in logistics and supply chain management (LSCM) by analyzing the current literature, contemporary concepts, data and gaps and suggesting potential topics for future research.
Design/methodology/approach
A systematic/structured literature review in the subject discipline and a bibliometric analysis were organized. Information regarding industry involvement, geographic location, research design and methods, data analysis techniques, university, affiliation, publishers, authors, year of publications is documented. A wide collection of eight databases from 1994 to 2019 were explored using the keywords “Machine Learning” and “Logistics“, “Transportation” and “Supply Chain” in the title and/or abstract. A total of 110 articles were found, and information on a chain of variables was gathered.
Findings
Over the last few decades, the application of emerging technologies has attracted significant interest all around the world. Analysis of the collected data shows that only nine literature reviews have been published in this area. Further, key findings show that 53.8 per cent of publications were closely clustered on transportation and manufacturing industries and 54.7 per cent were centred on mathematical models and simulations. Neural network is applied in 22 papers as their exclusive algorithms. Finally, the main focuses of the current literature are on prediction and optimization, where detection is contributed by only seven articles.
Research limitations/implications
This review is limited to examining only academic sources available from Scopus, Elsevier, Web of Science, Emerald, JSTOR, SAGE, Springer, Taylor and Francis and Wiley which contain the words “Machine Learning” and “Logistics“, “Transportation” and “Supply Chain” in the title and/or abstract.
Originality/value
This paper provides a systematic insight into research trends in ML in both logistics and the supply chain.