Purpose: The purpose of this paper is to present a structured framework whose objectives are to identify, analyse and eliminate fashion-luxury supply chains inefficiencies.Design/methodology/approach: A Lean Manufacturing tool, the 5-Whys Analysis, has been used to find out the root causes associated with the problem identified from a data analysis of production orders of a fashion-luxury company. A case study, which explains the methodology and illustrates the capability of the tool, is provided.Findings: This tool can be considered a suitable instrument to identify the causal factors of inefficiencies within luxury supply chains, suggesting potential countermeasures able to eliminate the problems previously highlighted. In addition, enabling technologies that deal with Industry 4.0 are associated with the root causes to enable further improvement of the supply chain.Practical implications: The effectiveness and practicality of the tool are illustrated using an industrial case study concerning an international Italian signature in the world of fashion-luxury footwear sector.Originality/value: This framework provides practitioners with an operative tool useful to highlight where the major inefficiencies of fashion-luxury supply chains take place and, at the same time, individuates both the root causes of inefficiencies and the corresponding corrective actions, even considering Industry 4.0 enabling technologies.

The present work presents a modular, object-oriented and agent-based simulation model of a large warehouse for paper products. It integrates the well-known Ultra High Frequency Radio Frequency Identification (UHF RFID) technology to transform a simulation model into an interactive digital twin that receives data from the sensors and optimises routes and space allocation while trying to reduce the travelled distance, the handling and the potential congestion within the aisles. The system performs experiments and provides the operators with the optimised routes for the next 30 minutes, allowing to improve the management of the system.

COntrol of BAlance by CArd-BAsed NAvigation (COBACABANA) is a card-based system for job shop control. This paper presents a real industrial application of COBACABANA aimed at solving production control problems in an Italian manufacturer of valves and flanges, configured as a job shop system. To the best of our knowledge, this is the first industrial implementation of COBACABANA. Although COBACABANA can be extended to estimating delivery time allowances, this research focuses only on its shop floor control mechanism. In the case study, the COBACABANA boards and cards are carefully reviewed. The criticalities and limitations of traditional COBACABANA are fully explored and, subsequently, two new boards and a new form are introduced to facilitate its practicality and ease of implementation. The new tools enable planners to better monitor the pre-shop pool status and the progress of orders.

This paper aims to provide a case study related to two small and medium-sized enterprises (SMEs) of the Italian footwear supply chain, comparing sales and production data from pre-pandemic years with those affected by the COVID-19 (SARS-CoV-2) pandemic. Specifically, two Tuscany companies in the world of fashion footwear sector have been assisted in the analysis of their supply chains. In particular, the case research method has been employed for theory building to evaluate how companies reacted to the disruption caused by the COVID-19 pandemic to focus on potential resilience strategies that could be adopted to deal with a disruption, such as that caused by the COVID-19 pandemic. Specifically, in order to understand the dynamics of the supply chains, the standard production processes were initially studied and mapped, analyzing in detail the planning, programming, and control phase. After conducting a descriptive analysis of the data, possible resilience factors of SMEs’ fashion supply chains have been extracted, and strategies that SMEs could adopt to better cope with the disruption caused by the pandemic have been suggested. The outcomes of this study can be used by decision-makers to predict the operative and long-term impacts of epidemic outbreaks on the supply chains with some suggestions of potential resilience improvement strategies. The paper is concluded by summarizing the most important insights and outlining future research agenda.

The use of simulation modelling in science and engineering is today well established. Three methods exist that allow to build complex simulation models in many different contexts: Discrete Event Simulation, System Dynamics, and Agent Based Simulation. Despite the interesting results obtained, what has really limited the parallel and combined application of the three currently used paradigms was the lack of integrated simulation tools. This paper presents the outcomes of a complex plant simulation that has been carried out by means of the parallel and combined application of Discrete Event Simulation and Agent Based Simulation, while using a well-structured modular approach. This presents the advantage of simplifying the building, the debugging and the validation of the whole simulation model, while, at the same time, it offers the unquestionable benefit of allowing a widespread reusability of the simulation code.