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On Paradigm Shifts and Industrial Revolutions: Tracing Prevalent Dressmaking Practices and Apparel Production Systems in the Netherlands and Northwest Europe (1850-2016).

Personalization, production on-demand, and flexible manufacture facilities are growing within the European apparel sector, supported by national and regional public policy. These developments seem to embody a much waited “paradigm shift” in the fashion industry; a shift from global to local scale, from quantity to quality and from standard products to personalized services. Such values, however, are far from new, and scholars have already pointed out the similarities between emerging and pre-industrial systems of production and consumption. This article argues that in order to understand current developments in historical context, we should return to the process of industrialization of the apparel industry during the turn from the 19th to the 20th C, taking into account aspects of production as much as mediation and consumption. With this aim in mind, the article traces the rise of ready-made garments in the Netherlands and northwest Europe, and the associated decline in custom- and home-made garments in the region. Although available statistical data is insufficient to accurately map these phenomena, secondary sources suggest that both processes were not simultaneous and therefore there was not a straightforward substitution of custom- and home-made clothing by ready-mades. While availability and trade of mass-produced ready-mades was escalating since the early 19th C, it was not until mid 20th C that custom- and home-made clothing declined among the middle class. In this study, such a gap is explained by a steady increase in the amount of clothes acquired per person: an expanding culture of consumption during the period under consideration may have enabled these different systems to flourish all together. A parallelism of the findings above with current developments suggests that we should not regard emergent industrial formats as substitutionary of established ones, but as complementary. We may then reevaluate to what extent does the rise of the flexible factory enable a “revolution”, a shift from a problematic present to a contrasting and desirable future. This historical overview indicates that, on the contrary, emerging product-service-systems manufacturing personalized garments on-demand must be considered in relation to – and in coexistence with- traditional industrial models.

Product service systems

Optimizing Product Paths in a Production Grid

Author supplied: In a production environment where different products are being made in parallel, the path planning for every product can be different. The model proposed in this paper is based on a production environment where the production machines are placed in a grid. A software entity, called product agent, is responsible for the manufacturing of a single product. The product agent will plan a path along the production machines needed for that specific product. In this paper, an optimization is proposed that will reduce the amount of transport between the production machines. The effect of two factors that influence the possibilities for reductions is shown in a simulation, using the proposed optimization scheme. These two factors are the redundancy of production steps in the grid and the

A software engineering perspective on building production-ready machine Learning Systems

This chapter discusses how to build production-ready machine learning systems. There are several challenges involved in accomplishing this, each with its specific solutions regarding practices and tool support. The chapter presents those solutions and introduces MLOps (machine learning operations, also called machine learning engineering) as an overarching and integrated approach in which data engineers, data scientists, software engineers, and operations engineers integrate their activities to implement validated machine learning applications managed from initial idea to daily operation in a production environment. This approach combines agile software engineering processes with the machine learning-specific workflow. Following the principles of MLOps is paramount in building high-quality production-ready machine learning systems. The current state of MLOps is discussed in terms of best practices and tool support. The chapter ends by describing future developments that are bound to improve and extend the tool support for implementing an MLOps approach.

A Versatile Simulation Model of Agile Production by Autonomous Systems

Complex situations and systems can be studied by using adequate models in simulation. An important aspect of models and the simulation software is the ability to use a wide range of possible input parameters. The simulation described in this paper is based on agile manufacturing by using transport robots and cheap reconfigurable production platforms, called equiplets. This setup makes agile manufacturing of different products in parallel possible.The simulation showed the maximum load of such a production environment as well as a proof of concept for the distributed approach for transport used. (from IEEE) / paid version: https://doi.org/10.1109/ISADS.2017.54

AI engineering and MLOps: building production-ready machine learning systems

The past two years I have conducted an extensive literature and tool review to answer the question: “What should software engineers learn about building production-ready machine learning systems?”. During my research I noted that because the discipline of building production-ready machine learning systems is so new, it is not so easy to get the terminology straight. People write about it from different perspectives and backgrounds and have not yet found each other to join forces. At the same time the field is moving fast and far from mature. My focus on material that is ready to be used with our bachelor level students (applied software engineers, profession-oriented education), helped me to consolidate everything I have found into a body of knowledge for building production-ready machine learning (ML) systems. In this post I will first define the discipline and introduce the terminology for AI engineering and MLOps.