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Nowadays, climate change requires companies to reduce their energy consumption and make their production systems more efficient. However, the complexity of the methodologies, the lack of transparency or high efforts (personnel/time) make this challenge especially difficult for SMEs. In this context, the present paper proposes a workflow to supports SMEs in a lean energy analysis. Through the implementation of several methodologies, a comprehensive assessment of energy consumption was carried out. The application to a real case study allowed to identify energy inefficiencies and to evaluate the energy saving and performance improvement actions.

The assessment and monitoring of energy and resource efficiency is an essential activity toward the implementation of sustainable manufacturing practices. Existing energy/resource assessment methods and tools are not based on a comprehensive approach, lack on the use of specific key performance indicators, are dedicated to expert stakeholders and do not provide useful suggestions for improving production systems. This paper proposes an innovative method, called Resources Value Mapping that aims to map and classify activities and related energy/resource consumptions according to lean philosophy principles (value-added, non value-added, waste). A user-friendly map and two efficiency indicators (Cost Index and Muda Index) are proposed to quantitatively support the identification of criticalities related to activities, processes, lines, plants, etc., and to successively guide the decision-making process during the improvement strategies implementation. The method has been used to analyze a manufacturing plant that produces cooking appliances. The case study demonstrated the applicability of the method in real industrial contexts and its effectiveness in identifying the energy/resource flows (electricity and compressed air), departments (sheet department) and lines (mechanical and hydraulic presses) for which the waste and non value-added consumptions are prominent. The analysis highlighted that less of 20% of the resources consumed during the process creates value, offering wide margins for improvement. Finally, it aided the definition of an action plan leading to relevant reduction of resource consumptions, economic savings and environmental benefits.

Manufacturing systems are among the main consumers of energy. Several methods and tools have been developed to support companies toward energy efficiency. However, they generally require high computational effort, onerous measurement campaigns or complex models. In this paper, a method to perform a lean energy assessment starting from the asset characteristics is proposed. It is based on a set of corrective factors that consider both technical and productive aspects. The final goal is to overcome the lack of precise data needed to identify hotspots from the energy point of view. A real industrial case study allows implementing the method and demonstrating its usefulness.

Product customization is largely considered one of the most important competitive factors in several industrial sectors, including high-end footwear. However, in this sector, products are generally manufactured through manual and artisanal operations, processes are not standardized and repeatable, and the personal skills of expert operators play a leading role. The objective of the present paper is to propose a workflow enabled by a framework including several technologies (computer aided design tool, augmented reality systems, traceability infrastructure), to support industrial companies of the high-end footwear sector during the different phases of shoes customization (from configuration to delivery). All these technologies jointly contribute to innovate the shoes customization process by increasing the flexibility of internal processes, improving the ability of companies to answer to specific requirements thanks to the direct involvement of customers, maximizing the efficiency of data sharing, making the organizational, design, production and management processes more efficient and repeatable, and reducing the customer response time.