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This paper proposes a new metric, named Operational Space Efficiency (OpSE), intended to diagnose and quantify the inefficient use of floor space for stocking materials in industrial workstations. OpSE presents a formulation analogous to the well-known Overall Equipment Effectiveness and can be obtained as the product of three distinct indicators: Standard Compliance Effectiveness, Standards Selection Effectiveness and Design Space-usage Effectiveness.

This indicator scrutinizes how usefully floor space in workstations is used to temporarily stock materials in the form of raw materials, semi-finished products, parts and components. It is suited for analyzing fixed-position layouts as well as product layouts typical of repetitive manufacturing settings, such as assembly lines in the automotive sector. The proposed indicator leverages an appropriate loss structure that features those factors affecting floor space utilization in workstations with regard to supplying and stocking materials.

An Italian manufacturer in the field of electro-technology was used as an industrial case study for the application of the methodology. The application shows how the three indicators work in practice, the effectiveness of OpSE and the methodology as a whole, in diagnosing floor space usage inefficiencies and in properly addressing improvement actions of the internal logistics in industrial settings.

The paper scrutinizes some important Key Performance Indicators (KPIs) dealing with space usage efficiency and identifies some significant drawbacks. Then it suggests a new, inclusive structure of losses and a KPI that not only measures efficiency but also allows to identify viable countermeasures.

This paper presents a new and well-structured framework that aims to assess the current environmental impact from a Greenhouse Gas (GHG) emissions perspective. This tool includes a new set of Lean Key Performance Indicators (KPIs), which translates the well-known logic of Overall Equipment Effectiveness in the field of GHG emissions, that can progressively detect industrial losses that cause GHG emissions and support decision-making for implementing improvements.

The new metrics are presented with reference to two different perspectives: (1) to highlight the deviation of the current value of emissions from the target; (2) to adopt a diagnostic orientation not only to provide an assessment of current performance but also to search for the main causes of inefficiencies and to direct improvement implementations.

The proposed framework was applied to a major company operating in the plywood production sector. It identified emission-related losses at each stage of the production process, providing an overall performance evaluation of 53.1%. The industrial application shows how the indicators work in practice, and the framework as a whole, to assess GHG emissions related to industrial losses and to proper address improvement actions.

This paper scrutinizes a new set of Lean KPIs to assess the industrial losses causing GHG emissions and identifies some significant drawbacks. Then it proposes a new structure of losses and KPIs that not only quantify efficiency but also allow to identify viable countermeasures.

The paper proposes a new workforce effectiveness metric that is a sophisticated evolution of a pre-existing overall labour effectiveness (OLE) indicator. The KPI, named revised OLE (ROLE), provides a structured methodology to measure in a holistic way the losses relating to labour, maintaining some formal similitude to the overall equipment effectiveness (OEE)

A new structure of losses is proposed to overcome the drawbacks and the difficulties that usually affect the data collection stage, referring to directly measurable quantities or, when this is not the case, suggesting a viable method to quantify the loss. Besides, this approach facilitates the comprehension of labour-related issues, suggesting potential countermeasures. The novel ROLE indicator has been defined, based on this new structure, to evaluate the labour effectiveness in batch process industries. A real case study is provided, which explains the methodology and illustrates the capability of the corresponding KPI.

The present work analyses the labour performance indexes available in literature, with the aim of evidencing those aspects that can be properly observed and quantified and, at the same time, categorizing them to identify their possible drawbacks. A new structure of losses is derived, with respect to four different categories, which may help to measure the losses themselves more effectively

The paper investigates some important KPIs dealing with labour performance and individuates some significant drawbacks. Then it suggests a new, inclusive structure of losses and a modified KPI that not only measures effectiveness but also allows to identify viable countermeasures.

This paper presents Quick Changeover Design (QCD), which is a structured methodological approach for Original Equipment Manufacturers to drive and support the design of machines in terms of rapid changeover capability.

To improve the performance in terms of set up time, QCD addresses machine design from a single-minute digit exchange of die (SMED). Although conceived to aid the design of completely new machines, QCD can be adapted to support for simple design upgrades on pre-existing machines. The QCD is structured in three consecutive steps, each supported by specific tools and analysis forms to facilitate and better structure the designers' activities.

QCD helps equipment manufacturers to understand the current and future needs of the manufacturers' customers to: (1) anticipate the requirements for new and different set-up process; (2) prioritize the possible technical solutions; (3) build machines and equipment that are easy and fast to set-up under variable contexts. When applied to a production system consisting of machines subject to frequent or time-consuming set-up processes, QCD enhances both responsiveness to external market demands and internal control of factory operations.

The QCD approach is a support system for the development of completely new machines and is also particularly effective in upgrading existing ones. QCD's practical application is demonstrated using a case study concerning a vertical spindle machine.

The purpose of this paper is to present a structured framework whose objectives are to identify, analyse and eliminate fashion-luxury supply chains inefficiencies.

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.

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.

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.

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 purpose of this paper is to propose a new Lean metric named Overall Task Effectiveness (OTE), which can help analyst to define target task times and to identify the hidden losses that account for most of the recorded time of manual assembly activities.

An alternative classification structure of the losses is developed to divide them in two classes. In the first one the losses that are external to the project order are included, and in the other one those due to inefficiencies directly ascribable to the project order are considered. Starting from this classification structure of the losses, a novel Lean metric, inspired from the well-known Overall Equipment Effectiveness (OEE), is developed to evaluate the effectiveness of a manual assembly task. A case study, which briefly explains the methodology and illustrates the capability of the corresponding metric, is provided.

This tool can be considered a suitable method to achieve simultaneously a dual purpose to establish time standards and to identify the hidden losses that account for most of the recorded time of manual assembly activities, estimating the impacts of potential corrective actions in terms of both efficiency and effectiveness.

OTE provides practitioners with an operative tool useful to highlight the points where the major inefficiencies take place in industries producing large complex items via manual assembly lines. Its practical application is demonstrated using a case study concerning a manufacturer of train wagons.

One distinctive, and contemporarily appealing, feature of OTE with respect to other analogous KPIs is that it provides a breakdown structure for process losses that simplifies the task of evaluating the current performances and, at the same time, individuates both the source of losses and the corresponding corrective actions.

The purpose of this paper is to present a new operational approach to equipment maintenance designed to exploit the complementary nature of total productive maintenance (TPM) and reliability-centered maintenance (RCM).

This paper focuses on the operational aspect of the maintenance practice, proposing an equipment maintenance methodology that can be suitably adopted to define a new maintenance plan or check and improve an existing one. It integrates principles and methods of TPM and RCM and uses different tools to support decision-makers and operators. It proposes several worksheets which facilitate planning and implementing maintenance activities, that range from autonomous maintenance tasks to maintainability improvement actions.

This paper exploits the complementary nature of TPM and RCM to design or improve maintenance plans for a generic system. The tools and worksheets presented can be helpful to practitioners and operators.

While the integration of TPM and RCM is evidently a central issue in the maintenance management context, it has received limited attention in literature. To the best of the authors’ knowledge, it lacks a structured practical equipment maintenance methodology that exploits the complementary nature of TPM and RCM. Therefore, the approach presented in this paper would fill the current gap.

The purpose of this paper is to provide a structured methodology for performing build‐in reliability (BIR) investigation during a new product development cycle.

The methodology in this paper represents an extension of the Quality Functional Deployment/House of Quality (QFD/HoQ) concepts to reliability studies. It is able to translate the reliability requisites of customers into functional requirements for the product in a structured manner based on a Failure Mode And Effect Analysis (FMEA). Besides, it then allows it to build a completely new operative tool, named House of Reliability (HoR), that enhances standard analyses, introducing the most significant correlations among failure modes. Using the results from HoR, a cost‐worth analysis can be easily performed, making it possible to analyse and to evaluate the economical consequences of a failure.

The paper finds that the application of the proposed approach allows users to identify and control the design requisites affecting reliability. The methodology enhances the reliability analysis introducing and managing the correlations among failure modes, splitting the severity into a detailed series of basic severity aspects, performing also cost/worth assessments.

It is shown that the methodology enables users to finely analyse failure modes by splitting severity according to the product typology and the importance of each Severity criterion according to laws or international standards. Moreover the methodology is able to consider the “domino effects” and so to estimate the impact of the correlation between the causes of failure. Finally a cost/worth analysis evaluates the economical consequences of a failure with respect to the incurred costs to improve the final reliability level of the product.

The paper proposes a completely new approach, robust, structured and useful in practice, for reliability analysis. The methodology, within an integrated approach, overcomes some of the largely known limits of standard FMECA: it takes into account multiple criteria, differently weighted, it analyses the product considering not only the direct consequence of a failure, but also the reaction chain originated by a starting failure.

The purpose of this paper is to select the best maintenance policy for different types of equipment of a manufacturer integrating the fuzzy analytic hierarchy process (FAHP) and the technique for order of preference by similarity to ideal solution (TOPSIS) models.

The decision hierarchy of this research includes three levels. The first level aims to choose the best maintenance policy for different types of equipment of an acid manufacturer. These equipment pieces include molten sulfur ponds, boiler, absorption tower, cooling towers, converter, heat exchanger and sulfur fuel furnace. The second level includes decision criteria of added-value, risk level and the cost. Lastly, the third level comprises time-based maintenance (TBM), corrective maintenance (CM), shutdown maintenance and condition-based maintenance (CBM) as four maintenance policies.

The best maintenance policy for different types of equipment of a manufacturer is the main finding of this research. Based on the obtained results, CBM policy is suggested for absorption tower, boiler, cooling tower and molten sulfur ponds, TBM policy is suggested for converters and heat exchanger and CM policy is suggested for a sulfur fuel furnace.

This research develops a novel model by integrating FAHP and an interval TOPSIS with concurrent consideration of added-value, risk level and cost to select the best maintenance policy. According to the highlights of the previous studies conducted on maintenance policy selection and related tools and techniques, an operative integrated approach to combine risk, added-value and cost with integrated fuzzy models is not developed yet. The majority of the previous studies have considered classic fuzzy approaches such as FAHP, FANP, Fuzzy TOPSIS, etc., which are not completely capable to reflect the decision makers’ viewpoints.

The purpose of this paper is to present a reliability centered maintenance (RCM) embedded integer linear programming approach (suited to the budget monetary resources allocation task) to the maintenance strategies mix selection for an industrial plant equipment.

The developed approach allows to determine the optimal maintenance strategies mix for a set of equipment in a more quantitative way than the classic RCM approach. The proposed model takes into account, for each potential failure determined using the FMECA and for each admissible strategy, the costs and the potential risk priority number (RPN) reduction. Finally, an industrial case concerning an Italian paper-mill plant is reported to demonstrate the effectiveness of the approach presented.

The paper finds that the application of the proposed approach allows to optimally allocate the budget monetary resources, determining which suitable maintenance practice apply to each failure, taking into account the costs of each strategy and the potential reduction of the RPN.

The proposed model permits to assign (during the budget monetary resources allocation task) to each failure the optimal strategy, among a set of suitable maintenance practices, considering the costs and the estimated RPN reduction.

The paper proposes a completely new RCM embedded approach to the maintenance strategies selection, in order to optimally allocate the budget monetary resources. This model overcomes the limits of the traditional RCM approach, taking into account quantitative aspects, i.e. the compatibility constraint between failures and policies, the maintenance strategies costs, and the RPN estimated reduction.