This paper presents a tool for reliability and failure mode analysis based on an advanced version of the popular failure mode, effects and criticality analysis (FMECA) procedure. To help the analyst formulating efficiently effective criticality assessments of the possible causes of failure, the fuzzy logic technique is adopted. Particular attention has been devoted to support the maintenance staff with a fuzzy criticality assessment model easy to implement and design. To test the proposed methodology, an actual application concerning a process plant in milling field for human consumption flour is showed in the paper.

Inventory constraints, costs of lost production, safety and environmental objectives, strategies of maintenance adopted, logistics aspects of spare parts are some of the criteria taken into account, and spare parts classification is thus defined with respect to multiple attributes. In virtue of the large number of the potential operational characteristics to be considered, the decision diagram is integrated with a set of analytic hierarchy process models used to solve the various multi‐attribute decision sub‐problems at the different levels/nodes of the decision tree. An inventory policy matrix is defined to link the different classes of spare parts with the possible inventory management policies so as to identify the “best” control strategy for the spare stocks. The principles of the theory and an actual application in a company operating in the paper industry are reported in the paper.

This paper is grounded on a discrete-event simulation model, reproducing a fast moving consumer goods (FMCG) supply chain, and aims at quantitatively assessing the effects of different supply configurations on the resulting total supply chain costs and bullwhip effect. Specifically, 30 supply chain configurations are examined, stemming from the combination of several supply chain design parameters, namely number of echelons (from 3 to 5), re-order and inventory management policies (EOQ vs. EOI), demand information sharing (absence vs. presence of information sharing mechanisms), demand value (absence vs. presence of demand ‘peak’), responsiveness of supply chain players. For each configuration, the total logistics costs and the resulting demand variance amplification are computed. A subsequent statistical analysis is performed on 20 representative supply chain configurations, with the aim to identify significant single and combined effects of the above parameters on the results observed. From effects analysis, bullwhip effect and costs outcomes, 11 key results are derived, which provide useful insights and suggestions to optimise supply chain design.