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The capability to overcome tradeoffs among costs, quality and time has become a must in high-margin businesses too. Lean thinking may be a solution, but applications in the luxury-fashion market are still rare. In order to shed light on this apparent contradiction, the purpose of this paper is to identify the key features of the luxury-fashion market that may act as barriers for the adoption of lean principles. Next, based on the results of this preliminary analysis, the paper tries to verify, if and how, lean principles can be properly reinterpreted, so as to properly fit the requirements of this market.

Due to the operating nature of lean, an empiric approach was followed. From the evidences gathered during a lean project of a world-wide company, critical elements of the luxury-fashion market were identified and used as criteria to select, among lean tools, the most appropriate ones. Lastly, selected tools were integrated in a structured framework (for lean implementation) that was used to analyze and to improve many logistics and manufacturing processes.

Developed solutions were implemented as pilot projects, with outstanding preliminary result. Results are case specific and trying to infer general considerations may be hazardous. Nonetheless, due to the relevant dimension of the project, they can be considered more than a clue concerning the robustness of the framework and, most of all, concerning the real potentialities of lean in the luxury-fashion market.

The framework is extremely operational and, together with the proposed industrial cases, can be used as a guideline to support practitioners during the implementation of similar projects.

Lean thinking is relatively new in the luxury-fashion market, where the focus on operational costs has been traditionally considered as a marginal issue. Thus, the application of lean principles in this market is the innovative element of the paper.

The purpose of this paper is to define the standard structure of a vendor managed inventory (VMI) agreement, which can be used as a guideline for the early definition of the agreement.

Starting from an industrial application of relevance, the information flow and the technical details, which are to be defined before the operation startup, are identified and discussed. These data are used as the key points for the definition of the basic frame of the agreement. A particular emphasis is given to the “Technical Specification” and the “Service Level Agreement” sections.

It is shown that a VMI agreement should be arranged into parts dealing with the generic and legal sides of the agreement, whereas the technical aspects and the relation‐specific topics should be addressed in the annexes. This increases the flexibility of the agreement in that, as the VMI relationship evolves over time, changes will affect only the annexes leaving the main body of the agreement unaltered.

The proposed agreement has a flexible structure and can be easily adopted by the personnel involved to correctly define and implement VMI in several industrial fields.

By approaching VMI from a practical point of view, this paper identifies the main issues that must be covered in the agreement to fit the needs of both parties and to assure benefits on both sides.

Overall equipment effectiveness (OEE) is the key metric to measure the performance of individual equipment. However, when machines operate jointly in a manufacturing line, OEE alone is not sufficient to improve the performance of the system as a whole. The purpose of this paper is to show how to overcome this limitation, by presenting a new metric (overall equipment effectiveness of a manufacturing line – OEEML) and an integrated approach to assess the performance of a line.

An alternative losses classification structure is developed to divide the losses that can be directly ascribed to equipment, from the ones that are spread in the line. Starting from this losses classification structure, an approach based on OEE is developed to evaluate the criticalities and the effectiveness of the line.

This method has been applied to an automated line for engine basements production. Results show that OEEML successfully highlights the progressive degradation of the ideal cycle time, explaining it in terms of: bottleneck inefficiency, quality rate, and synchronisation‐transportation problems.

OEEML alone fails to explain to which extent effectiveness is supported by in process‐inventories and should be integrated with additional metrics to estimate the inventories‐related costs.

OEEML provides practitioners with an operative tool useful to highlight the points where the major inefficiencies take place and to foresee the potential benefits of corrective actions.

In relation to other methodologies, OEEML presents two main advantages: it detects and quantifies the line's critical points and it can be applied even in presence of buffers, without underestimating the efficiency of the system.

Automated flow line manufacturing systems are becoming more and more relevant in industry, especially in the food and beverage sector. Improving the efficiency of automated flow line manufacturing systems is the core objectives of all companies as measured by the overall equipment effectiveness (OEE) index. The purpose of this paper is to carry out an innovative micro downtime data collection and statistical analysis in the food and beverage sector; it introduces a numerical indicator called “Cost Performance Indicator-CPI” to estimate the performance improvement of investment activities. Moreover this analysis will be used as a basis to carry out a new simulative model to study micro downtime of automatic production lines. In addition, the presented micro downtime data collection and statistical analysis will be used to construct a new simulative model to support improvement activities.

Descriptive and statistical analyses are carried out about OEE, time to repair (TTR) and time to failure (TTF) data. The least efficient production line is identified and principal causes of inefficiency are investigated. Micro downtime (downtime lower than 15 minutes) covers 57 percent of inefficiency. Investigations are carried out into the three principal machines affected by this inefficiency. The study then investigates the causes of micro downtime of these machines using ad hoc data collection and analysis. The probability distributions of TTF and TTR are evaluated and an analysis of micro downtime causes and a cause-effect is carried out. The most attractive investment in terms of recoverable OEE (1.44 percent) and costs is analyzed through the calculation of a CPI. One of the conclusions is to recommend the introduction of a payback period with a variable contribution margin.

This study get the basis for the construction of a new simulative model based on ad hoc micro downtime probability distributions, applied in automated flow line manufacturing systems. It gives an effort to downtime analysis in automated production lines and a guideline for future analysis. Results of this study can be generalized and extended to other similar cases, in order to study similar micro downtime inefficiency of other production lines. The statistical analysis developed could also potentially be used to further investigate the relationship between the reliability of specific machines and that of the entire line.

The case study presents a new detailed micro downtime data collection and statistical analysis in the beverage sector with the application of a numerical indicator, the CPI, in order to drive future actions. In addition, the presented micro downtime data collection and statistical analysis will be used to construct a new simulative model to support improvement activities. Moreover, results can be generalized and used as a basis for other micro downtime analyses involving the main causes of inefficiency in automated production lines.

This paper aims to overcome the inability of both comparing loss costs and accounting for production resource losses of Overall Equipment Effectiveness (OEE)-related approaches.

The authors conducted a literature review about the studies focusing on approaches combining OEE with monetary units and/or resource issues. The authors developed an approach based on Overall Equipment Cost Loss (OECL), introducing a component for the production resource consumption of a machine. A real case study about a smart multicenter three-spindle machine is used to test the applicability of the approach.

The paper proposes Resource Overall Equipment Cost Loss (ROECL), i.e. a new KPI expressed in monetary units that represents the total cost of losses (including production resource ones) caused by inefficiencies and deviations of the machine or equipment from its optimal operating status occurring over a specific time period. ROECL enables to quantify the variation of the product cost occurring when a machine or equipment changes its health status and to determine the actual product cost for a given production order. In the analysed case study, the most critical production orders showed an actual production cost about 60% higher than the minimal cost possible under the most efficient operating conditions.

The proposed approach may support both production and cost accounting managers during the identification of areas requiring attention and representing opportunities for improvement in terms of availability, performance, quality, and resource losses.