This paper examines the design of low-cost digital solutions for manufacturing. A set of criteria are established that take into account the limited designer experience and limited time budget that accompany a low-cost project. Alternatives are assessed and a design approach is proposed that addresses these criteria using a set of identified features. Development of the proposed approach is not yet complete, however it already provides a simple, accessible, and streamlined method for implementing low-cost digital solutions.
Machine learning (ML) is increasingly used to enhance production systems and meet the requirements of a rapidly evolving manufacturing environment. Compared to larger companies, however, small- and medium-sized enterprises (SMEs) lack in terms of resources, available data and skills, which impedes the potential adoption of analytics solutions. This paper proposes a preliminary yet general approach to identify low-cost analytics solutions for manufacturing SMEs, with particular emphasis on ML. The initial studies seem to suggest that, contrarily to what is usually thought at first glance, SMEs seldom need digital solutions that use advanced ML algorithms which require extensive data preparation, laborious parameter tuning and a comprehensive understanding of the underlying problem. If an analytics solution does require learning capabilities, a ‘simple solution’, which we will characterise in this paper, should be sufficient.
The creation of digital manufacturing solutions at low cost is characterised by a successive development and a combination of disparate hardware and software elements. Modular building blocks address these limitations. This paper proposes and evaluates an explicit two-stage approach to develop configurable digital manufacturing solutions from bespoke solutions by using modular hardware and software building blocks. In the first stage, the solution is being made configurable through decomposition, while the second stage creates building blocks from resulting solution elements. For demonstration, the approach is applied to two tailor-made solutions. Resulting building blocks are then reused to create a new solution. We provide further insights by discussing challenges when applying our approach, and justifying its usage by qualitatively evaluating to what extent solution configurability can be achieved at low cost, which characterises the main objective of this study.
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