Since the introduction of the concept of learning curves in manufacturing, many articles have been applying the model to study learning phenomena. In assembly, several studies present a learning curve when an operator is trained over a new assembly task; however, when comparisons are made between learning curves corresponding to different training methods, unaware researchers can show misleading results. Often, these studies neglect either or both the stochastic nature of the learning curves produced by several operators under experimental conditions, and the high correlation of the experimental samples collected from each operator that constitute one learning curve. Furthermore, recent studies are testing newer technologies, such as assembly animations or augmented reality, to provide assembly aid, but they fail to observe deeper implications on how these digital training methods truly influence the learning curves of the operators. This article proposes a novel statistical study of the influence of expert video aid on the learning curves in terms of assembly time by means of functional analysis of variance (FANOVA). This method is better suited to compare learning curves than common analysis of variance (ANOVA), due to correlated data, or graphical comparisons, due to the stochastic nature of the aggregated learning curves. The results show that two main effects of the expert video aid influence the learning curves: one in the transient and another in the steady state of the learning curve. The transient effect of the expert video aid, where the statistical tests suffer from a high variance in the data, appears to be a reduction in terms of assembly time for the first assemblies: the operators seem to benefit from the expert video aid. As soon as the steady state is reached, a slower and statistically significant effect appears to favor the learning processes of the operators who do not receive any training aid. Since the steady state of the learning curves represents the long term production efficiency of the operators, the latter effect might require more attention from industry and researchers.

Since the introduction of the concept of learning curves in manufacturing, many articles have been applying the model to study learning phenomena. In assembly, several studies present a learning curve when an operator is trained over a new assembly task; however, when comparisons are made between learning curves corresponding to different training methods, unaware researchers can show misleading results. Often, these studies neglect either or both the stochastic nature of the learning curves produced by several operators under experimental conditions, and the high correlation of the experimental samples collected from each operator that constitute one learning curve. Furthermore, recent studies are testing newer technologies, such as assembly animations or augmented reality, to provide assembly aid, but they fail to observe deeper implications on how these digital training methods truly influence the learning curves of the operators. This article proposes a novel statistical study of the influence of expert video aid on the learning curves in terms of assembly time by means of functional analysis of variance (FANOVA). This method is better suited to compare learning curves than common analysis of variance (ANOVA), due to correlated data, or graphical comparisons, due to the stochastic nature of the aggregated learning curves. The results show that two main effects of the expert video aid influence the learning curves: one in the transient and another in the steady state of the learning curve. The transient effect of the expert video aid, where the statistical tests suffer from a high variance in the data, appears to be a reduction in terms of assembly time for the first assemblies: the operators seem to benefit from the expert video aid. As soon as the steady state is reached, a slower and statistically significant effect appears to favor the learning processes of the operators who do not receive any training aid. Since the steady state of the learning curves represents the long term production efficiency of the operators, the latter effect might require more attention from industry and researchers.

Technological advancements in the information technology domain such as cloud computing, industrial internet of things (IIoT), machine to machine (M2M) communication, artificial intelligence (AI), etc. have started to profoundly impact and challenge not only the ISA95 compliant traditional (ISA95-CTS) but also the smart manufacturing systems (SMMS). Our literature survey pinpoints that systems scalability, interoperability, information security, and data quality domains are among those where many challenges occur. Blockchain technology (BCT) is a new breed of technology characterized by decentralized verifiability, transparency, data privacy, integrity, high availability, and data protection properties. Although many researchers leveraged BCT to empower various aspects of industrial manufacturing systems, there is no study dedicated to addressing the challenges impacting the manufacturing systems compliant with the ISA95 standard. Thereby, our study aims to fill the identified research gap systematically. This paper thoroughly analyzes the challenges hampering the ISA95-CTS and SMMS and methodically addresses them with corresponding BCT capabilities. Furthermore, this paper also discusses various aspects, including the weaknesses, of BCT convergence to ISA95-CTS and SMMS.

Can automatically-authored videos of industrial operators help other operators to learn procedural tasks? This question is relevant to the advent of the industrial internet of things (IIoT) and Industry 4.0, where smart machines to help human operators rather than replacing them, in order to benefit from the best of humans and machines. The study considers an industrial ecosystem where procedural knowledge (PK) is quickly and effectively transferred from one operator to another. Assembly tasks are procedural in nature and present a certain complexity that still does not allow machines and their sensors to capture all the details of the operations. Especially if the assembly operation is adaptive and not fixed in terms of assembly sequence plan. In order to help the operators, videos of other operators executing the complex procedural tasks can be automatically recorded and authored from machines. This study shows by means of statistical design and analysis of experiments that expert aid, provided before each subassembly, can reduce the assembly time of an untrained operator, whereas automatically authored video aids can transfer PK but producing an opposite effect on the assembly time. Therefore, hybrid training methods are still necessary and trade-offs have to be considered. Managerial insights from the results suggest an unneglectable impact of the choice to digitize industrial operations too early. The experimental studies presented can act as guidelines for the correct statistical testing of innovative solutions in industry.

Trends like IoT, digitalization and Industry 4.0 are bringing several opportunities as well as lot of challenges for the manufacturing industry particularly SMEs (Small and Medium Enterprises). One of the possible ways to deal with some of the challenges, such as reducing time-to-market and dealing with market uncertainties, is to improve the New Product Development Process (NPDP). This in turn needs a holistic approach to address both technological and non-technological aspects simultaneously. The research presented in this article investigates the factors and their dependencies which can accelerate or decelerate NPDP. The findings are based on empirical studies from two Swedish manufacturing companies. The data is obtained for the projects related to digitalization and connectivity as well as traditional product offerings by the case companies during the last 10 years. From managerial perspective the work provides guidelines as a basis for improving NPDP in an organization for attaining sustainable competitive advantage in the adoption of new trends.

Literature shows that reinforcement learning (RL) and the well-known optimization algorithms derived from it have been applied to assembly sequence planning (ASP); however, the way this is done, as an offline process, ends up generating optimization methods that are not exploiting the full potential of RL. Today’s assembly lines need to be adaptive to changes, resilient to errors and attentive to the operators’ skills and needs. If all of these aspects need to evolve towards a new paradigm, called Industry 4.0, the way RL is applied to ASP needs to change as well: the RL phase has to be part of the assembly execution phase and be optimized with time and several repetitions of the process. This article presents an agile exploratory experiment in ASP to prove the effectiveness of RL techniques to execute ASP as an adaptive, online and experience-driven optimization process, directly at assembly time. The human-assembly interaction is modelled through the input-outputs of an assembly guidance system built as an assembly digital twin. Experimental assemblies are executed without pre-established assembly sequence plans and adapted to the operators’ needs. The experiments show that precedence and transition matrices for an assembly can be generated from the statistical knowledge of several different assembly executions. When the frequency of a given subassembly reinforces its importance, statistical results obtained from the experiments prove that online RL applications are not only possible but also effective for learning, teaching, executing and improving assembly tasks at the same time. This article paves the way towards the application of online RL algorithms to ASP.

The next-generation technologies enabled by the industry 4.0 revolution put immense pressure on traditional ISA95 compliant manufacturing systems to evolve into smart manufacturing systems. Unfortunately, the transformation of old to new manufacturing technologies is a slow process. Therefore, the manufacturing industry is currently in a situation that the legacy and modern manufacturing systems share the same factory environment. This heterogeneous ecosystem leads to challenges in systems scalability, interoperability, information security, and data quality domains. Our former research effort concluded that blockchain technology has promising features to address these challenges. Moreover, our systematic assessment revealed that most of the ISA95 enterprise functions are suitable for applying blockchain technology. However, no blockchain reference architecture explicitly focuses on the ISA95 compliant traditional and smart manufacturing systems available in the literature. This research aims to fill the gap by first methodically specifying the design requirements and then meticulously elaborating on how the reference architecture components fulfill the design requirements.

Virtual Reality, or VR, is a family of technologies defined in the 60s that aims at mimicking reality through computers with different purposes. VR is usually classified according to the level of user immersion achieved. VR has attracted increasing attention between academic and practitioners during the last decade due to technological progresses that achieved high reliability and relatively low cost. VR potential to visualize information, replace physical presence and stimulate interaction make of this technology an essential part in the toolbox of future engineers. Higher Education Institution must align their curricula to fulfill this purpose. At the same time VR can be a support to enhance the learning process itself. This work analyses the impact of fully-immersive VR technology on an average production engineering curricula based on the educational offer at KTH Royal Institute of Technology in Stockholm, Sweden. The results show that VR has a great potential to shape the production engineering curricula in the coming years both in term of content and design of the learning experiences. 

Mass customization and shortening product life cycles pose a heightened set of requirement on modern production systems. Fast response to changing conditions has been found to be a key to competitive advantage for manufacturing firms. In order to cope with this, modern firms are entering in the fourth industrial revolution. Smart pieces of equipment, based on ubiquitous computation and reliable communication, are being deployed in the shop-floors and supply information to the whole enterprise. The work presented in this paper belong to a pilot project to explore promising technologies in this domain and deploying them in a test-line at a large vehicle manufacturer in Sweden. Traceability and visibility of assets plays a critical role in the process of improving shop-floor performance, contributing to better control, planning and scheduling decisions. In detail this article focuses on the collection of requirement and design of a real-time positioning system for asset tracking including its implementation with other technologies in the company demonstrator.