Digital Twin Based Optimization of a Manufacturing Execution System to Handle High Degrees of Customer Specifications

Abstract

Lean production principles have greatly contributed to the efficient and customer-oriented mass production of goods and services. A core element of lean production is the focus on cycle times and designing production controls and buffers around any bottlenecks in the system. Hence, a production line organized by lean principles will operate in a static or at least quasi-static way. While the individualization of products is an interesting business approach, it can influence cycle times and in-time production. This work demonstrates how performance losses induced by highly variable cycle times can be recovered using a digital twin. The unit under analysis is an industrial joiner’s workshop. Due to the high variance in cycle time, the joinery fails its production target, even if all machines are below 80% usage. Using a discrete event simulation of the production line, different production strategies can be evaluated efficiently and systematically. It is successfully shown that the performance losses due to the highly variable cycle times can be compensated using a digital twin in combination with optimization strategies. This is achieved by operating the system in a non-static mode, exploiting the flexibilities within the systems.

Towards the Development of a Digital Twin for a Sustainable Mass Customization 4.0 Environment: A Literature Review of Relevant Concepts

“…A Digital Twin (DT) is a structure of inter-connected digital replicas of physical entities [52,53] plus their related meta-information and semantics [54], that enables real-time interaction and integration [55] between the physical and digital worlds [56], i.e., physical manufacturing system and the digital cyberspace [2,57,58]. This translates in an intensive bidirectional [59,60], standardized and/or automatized [61], real-time information flow [35] related to the current products, processes and resources [62] between the DT and the real manufacturing system [63,64], via the internet [65]. A more formal definition of DT manufacturing is provided by [66]: " .…”

“…Mass customization: the demand for highly individualized products with shorter lifestyles drives modern manufacturing systems to focus on the use of information technology-based manufacturing systems [71], such as the so-called data-driven Digital Twins [58]. A DT of a manufacturing system in the form of a simulation and data model [61] that synchronizes both the physical and digital worlds [72] can be used to address the issue of manufacturing customized products [2], as it makes the deployment of the required flexible and reconfigurable manufacturing system possible [73];…”

Considerations on Present Production Science Theory and Didactics: The Evident Lack of a Rational Manufacturing Theory

“…Such production-control integrated DT can propose and implement for the decision maker (production manager) a highly valuable optimal scheduling for OTD, e.g. (Barni, Pietraroia, Züst, West, & Stoll, 2020). Advanced application may not only lie in twinning the shopfloor status for simulation, but finally oriented for interacting dynamically and directly (autonomously and automatic actuated) or better indirectly (still human controlled activation) with the physical entities, such as workstations, AGV, and products.…”

Digital Twin Based SUDIHA Architecture to Smart Shopfloor Scheduling

“…In today's competitive markets, where mass customization is a new challenge, digitalization in manufacturing is seen as an opportunity to achieve higher levels of productivity [1]. Digitalization, also known as Industry 4.0, consists of using new technologies, including the Internet of Things (IoT), Augmented reality, Cyber Physical system (CPS), Cloud computing and analytics to revolutionize the way the products are manufactured and distributed [2].…”