Saaty's AHP is helpful in evaluating alternatives thanks to its effective procedure to determine the relative weights of several comparison criteria. Combining the results of expert interviews, AHP can be very useful for a company in choosing a third party logistics service provider (3PL). However, in the traditional AHP procedure, several results may be rejected when the consistency ratio (CR) of the respondent exceeds a certain threshold. As a consequence, AHP interviews may be repeated several times with a consequent waste of time. In many industrial domains, a faster way to choose a supplier would thus be appreciated. In this paper we propose a mathematical method that combines AHP, DEA and linear programming in order to support the multi-criteria evaluation of third party logistics service providers. The proposed model aims to overcome the limitation of the AHP method, merging experts' indications with objective judgments which originate from historical data analysis. Suppliers' past performance is thus used to correct eventual errors resulting from the acceptance of interviews where the consistency ratio is high. The proposed model has been validated on the real case of an international logistics service provider.

This paper describes the structure of the logistic maturity model (LMM) in detail and shows the possible \ud improvements that can be achieved by using this model in terms of the identification of the most appropriate \ud actions to be taken in order to increase the performance of \ud the logistics processes in industrial companies. The paper also gives an example of the LMM’s application to a \ud famous Italian female fashion firm, which decided to use the model as a guideline for the optimization of its supply chain. Relying on a 5-level maturity staircase, specific \ud achievement indicators as well as key performance indicators and best practices are defined and related to \ud each logistics area/process/sub-process, allowing any user \ud to easily and rapidly understand the more critical logistical issues in terms of process immaturity

In the fashion industry, demand forecasting is particularly complex: companies operate with a large \ud variety of short lifecycle products, deeply influenced by seasonal sales, promotional events, weather conditions, advertising and marketing campaigns, on top of festivities and socio-economic factors. At the same time, shelf-out-of-stock phenomena must be avoided at all costs. Given the strong seasonal nature of the products that characterize the fashion sector, this paper aims to highlight how the Fourier method can represent an easy and more effective forecasting method compared to other widespread heuristics normally used. For this purpose, a comparison between the fast Fourier transform algorithm and another two techniques based on moving average and exponential smoothing was carried out on a set of 4-year historical sales data of a €60+ million turnover medium- to large-sized Italian fashion company, which \ud operates in the women’s textiles apparel and clothing sectors. The entire analysis was performed on a common spreadsheet, in order to demonstrate that accurate results \ud exploiting advanced numerical computation techniques can be carried out without necessarily using expensive software