An Industry 4.0 framework, showing every possible action towards the complete digitalization of a company both considering strategic and technological aspects, is proposed. The state of the art analysis and re-elaboration allows to coherently integrating many valuable but scattered and narrow contributions into a holistic view covering the following dimensions: value drivers, levers of action, processes and enabling technologies. Based on this framework the consequences of digitalization in shaping a new working environment are analyzed focusing on workforce impact, looking at the modification of both performed tasks and required competences as well as to more generic social consequences (job satisfaction, work-life balance, new forms of employment). To support the application of the proposed roadmap a Digitalization Maturity Model is developed to assess the state of a company journey towards Industry 4.0 considering the following dimensions: Strategy, Processes, Technologies, Products & Services and People.

Even if the economy nowadays is still locked into a linear model of production, tighter environmental standards, resource scarcity and changing consumer expectations are forcing organizations to find alternatives to lighten their impacts. The concept of Circular Economy (CE) is to an increasing extent treated as a solution to this series of challenges. That said, the multitude of approaches and definitions around CE and Life Cycle Extension Strategies (LCES) makes it difficult to provide (Small and Medium Enterprise) SMEs with a consistent understanding of the topic. This paper aims at bridging this gap by providing a systematic literature review of the most prominent papers related to the CE and lifetime extension, with a particular focus on the equipment and machinery sector. A taxonomy was used to define and cluster a subset of selected papers to build a homogeneous approach for understanding the multiple strategies used in the industry, and the standards in maintenance and remanufacturing strategies. As a final research step, we also propose a Strategy Characterization Framework (SCF) to build the ground for the selection of the best strategy to be applied for production equipment life cycle extension on several industrial use cases.

Digitalization has shown the potential to disrupt industrial value chains by supporting real-time, risk-free and inexpensive inputs to decision making towards enhanced companies' productivity and value networks flexibility. Developing a reliable and robust digital replica of the physical systems of the value chain is one of the most advanced (and challenging) approaches to digitalization, condensed in the concept of Digital Twin (DT). DT plays a fundamental role in creating a data-rich environment where simulation and optimization procedures can be run. With DT expected to become a commodity in the coming years, simulation and optimization become therefore a more accessible instrument for the improvement of manufacturing and business processes also in small enterprises with limited investment capacity. While scientific literature has analysed the adoption of DT in the optimization of products lifecycle, no contributions have yet focused on the exploitation of DT to improve the sustainability performances of whole value chains. In this paper we propose a reference framework where DTs built upon process and system data gathered from the field, allow to quickly assess the sustainability performances of both existing and planned production mixes and to compare achievable impacts with changing processes and technologies, thus enabling advisory features for sustainability-aware decision making in structured, multi-entity value networks. Internal validation will be deployed referring to real case studies.

As a result of the worldwide depletion of natural resources, increased energy use, and environmental, economic, and social imbalance, organizations are working to identify the proper strategies supporting the continuous reduction of their impacts. While this trend is fundamentally agreed upon in the literature, several manufacturing industries still fail to identify which elements most influence their contributions to the impact of sustainability and how to easily manage the calculation of these effects within a manufacturing system. The purpose of this article is to incorporate sustainability practices into manufacturing by developing a set of key performance indicators (KPIs) for assessing and improving environmental and economic management practices at the corporate and production level. The definition of the framework began with in-depth research of the leading indicators and framework types in the literature, integrating the most exploited industrial standards to make them easily acceptable in the industrial domain. Then, to provide a broad view of company behavior, the framework has been designed to take either an inventory and impact point of view, thus providing indicators for the online monitoring of the company operations, or assessing their impacts in an LCA-LCC perspective. In selecting the indicators and the definition of the framework structure, five industrial cases covering different business sectors were involved in identifying the most critical indicators in terms of calculability and defining a structure that would allow for their application in various business situations. Therefore, the defined framework has been validated at a conceptual level, thus laying the basis for future quantitative validation. Twenty key performance indicators (KPIs) for assessing the sustainability of manufacturing firms have been created based on the 163 indicators studied.

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.