اطلاعات مورد نیاز برای برنامه ریزی استراتژیک تعمیر و نگهداری الکترونیکی : مطالعه معیاری در سیستم های تولیدی پیچیده

The selection of the maintenance policy is one of the most important decisions of strategic maintenance management. It guarantees not only adequate control of the maintenance costs but also competitive performances of the production system in terms of safety, availability and quality. An optimised selection of the maintenance policy also serves the competitiveness of an e-maintenance service. This calls for fine tuning with the conditions of one plant operations, henceforth integration is required between the plant management and the e-maintenance service provider. Integration may be organised at different levels, i.e. at the strategic or operational level. The paper is concerned with the strategic planning of an e-maintenance service, to properly size the maintenance logistics support. It will present a discussion on the information required to select the policy in the case of an age based component replacement. A test will also prove the information requirements in the context of a simulated machining line wherein the most important loss is system unavailability. A benchmark study is performed therein, in order to analyse how the selection of the maintenance policy may change when information is collected with regard to the production system as a whole instead of its separate equipments. The study will demonstrate that the maintenance policy may change depending on the analysis level, i.e. from equipment level to production system level. The analysis at equipment level will reveal itself to be not so accurate, if compared with the analysis at the system level, as far as the line layout is endowed with some flexibility features and it is operated in a range of medium to high saturation rates. A more comprehensive analysis at the production system level will conversely achieve better performances though a reduced number of preventive maintenance stops during the e-maintenance service operation.

E-maintenance of an industrial process offers solutions for innovative customer–supplier relationships wherein the customer not only expects high quality products from a manufacturer but also an effective service during product use. This calls for a proper integration of information and processes during the product use phase. In tele-service maintenance, the integration is reached by sharing information of a product and its actual operational conditions between different locations and partners, in order to enable the remote product diagnostics and repairing services [11]. The new working context envisioned by e-maintenance extends the tele-service maintenance to a knowledge-driven organisation where the information flows integrate diverse processes (monitoring of equipments, detection and diagnostics of their faults and troubleshooting, prognosis of their residual life), knowledge providers (technicians of the service provider, machinery builder/engineers and technicians and operators on field) and systems supporting analysis and decisions (intelligent systems based on a variety of technologies such as for degradation evaluation and prediction, knowledge learning on system failures and collaborative diagnostics and troubleshooting) [8] and [1]. Eventually, the integration may also regard the level wherein the strategy of the e-maintenance service is decided. This paper aims to focus at this level. It will point out the integration requirements, by means of focusing on the information sharing needed for analysis and definition of maintenance policies for the next period of service operation. An experimental proof will be provided that not only the information at the equipment level should be collected and analysed, but also a systemic approach should be adopted, for addressing the level of the overall production system. In the present paper, indeed, the selection of the most convenient maintenance policy for each equipment is a decision resulting from a model of the production system, where the equipment plays its role as a component. Thereafter, the objective of the paper is to prove that the decisions resulting from the analysis at the production system level may significantly differ from decisions deriving from the analysis at the equipment level. Thus, a simulation model based analysis is proposed for this demonstration, enabling a benchmark study in order to: (i) determine if decisions at production system level differ significantly from the ones based on a model in which any equipment is modelled as a stand-alone component; (ii) identify the main features of the production affecting this shift. The simulation technique is briefly reviewed in Section 2. Focus is provided, at first, on its basic capabilities, referring to applications to maintenance planning problems in general; afterwards, a closer look is given to the selection of maintenance policies through a simulation based analysis. The two simulation models are introduced afterwards (Section 3). Then the experimentation plan is presented (Section 4) and its results are analysed (Section 5). As a result the information required to enable the selection of the maintenance policies is validated and a strategic planning practice is derived to support the selection of the maintenance policies during the use phase of a plant (Section 6). This practice is presented as a model for application at the strategic level of an e-maintenance service. Further research work is finally envisioned for future activities (Section 7).

The present paper demonstrated that an analysis carried out at the equipment level may be non accurate to support decisions of maintenance planning if some specific features of the production system holds. The results of the test-bench adopted for the benchmark study showed, in fact, that, if one adopted an analysis at the equipment level, the requirements of the optimal frequency of preventive maintenance control were estimated higher than needed in some operating condition of the production system (medium to high saturation, flexible routing). In these operating conditions, an analysis at the production system level was advisable in order to avoid over-sizing of requirements of the maintenance policy and their subsequent implementation costs. This benchmark study was however fixed in its scope and might be extended in future research activities in order to specify better the conclusion of this work. 1. The benchmark scope was limited to a clearly defined decision: the selection of the optimal period for an age based component replacement, other decisions pertaining to the selection of maintenance policies might be studied, such as, e.g., the identification of the optimal degradation threshold for a condition based maintenance policy. 2. The benchmark scope was also limited for the cost function and the performance losses considered therein. Logistic performance losses were in fact measured and accounted in the cost model in terms of WIP and output rate losses. Other performance losses might be integrated, depending on the case study, in the cost function. Integration may regard either other measures for the logistic performance losses (e.g., penalties related to delivery times, …) or losses in other performance categories (e.g., regarding production quality). 3. The benchmark scope was eventually limited in the experimentation plan. The experimentation plan might be extended in different directions. This could be achieved either by enlarging the range of the changing conditions actually planned – e.g., a sensitivity analysis upon the failure behaviour may be carried out over a range from the quasi exponential behaviour to the Weibull model of machine wear out now tested in the present study … – or by studying the effects of variations of the parameters that were blocked in the actual analysis—a new test may serve to evaluate the effects of the changing characteristics of production cycles, from rigid operations’ sequences, as it was tested in this case study, to some more flexible sequences; this would help to evaluate a combined effect of flexible production cycles and flexible plant configurations, …. The main future intents of the authors of this paper are: (i) to extend the experimentation plan in order to provide empirical proofs of the relationships between the flexibility features of a production system (flexibility of production cycles, plant layout and operations management) and the selection of the maintenance policies; (ii) to extend the scope of decisional problems, in order to integrate condition based maintenance strategies (and related planning decisions); (iii) to extend the scope of the cost based optimisation, in order to integrate penalty costs for late deliveries and manufacturing order backlog with respect to fixed due dates. Correspondingly, the information requirements of the e-maintenance strategic planning are expected to enlarge, in order to include other costs of logistic inefficiencies and, in the life data, the information available from condition monitoring.