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

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Given the increasingly significant impact of an efficient product-location strategy on warehouses’ performance from a service level and operational costs perspective, this paper presents a possible operations research-oriented solution to provide a tangible reduction of the overall required warehousing space, thereby translating the storage location assignment problem (SLAP) into a vertex colouring problem (VCP). Developing the topic of their previous work on the development of an effective multi-product slotcode optimization heuristic, the authors focused on finding a cost-effective way to solve the SLAP through a mathematical-optimization approach. The formulation validation on a real industrial case showed its high optimization potential, and benchmarking simulations displayed performances significantly close to the best theoretical case. Indeed, the optimized value results were definitively close to the SLAP lower bound calculated assuming a randomized storage policy which, distinct from the developed solution, must inevitably be supported by warehouse management system software. On the contrary, the proposed methodology relies upon a dedicated storage policy, which is easily implementable by companies of all sizes without the need for investing in expensive IT tools

PurposeThis paper discusses and integrates the concept of complexity in the performance measurement and management (PMM) theory by providing a comprehensive framework to design and evaluate the overall coherence alignment of an indicators hierarchy in unstable and changing environment.Design/methodology/approachAn original, comprehensive and dynamic framework has been proposed and then applied on a sample case of a large-scale retail trade (LSRT) company, starting from relevant frameworks and criteria in the scientific literature.FindingsThis research shows that organizational changes may significantly impact the coherence alignment of an organization's indicators hierarchy. In addition, it finds that even though the alignment at the operational level is obtained, its effectiveness should be evaluated in relation to the organization's strategic orientation. Indeed, without assessing the strategic alignment of an indicators system, an aligned hierarchy at the operational level could lead to ineffective results.Research limitations/implicationsThis paper focuses on the topic of measuring the coherence inside an indicators hierarchy, which seems not to be addressed in the literature. Thus, it opens a new research stream, integrating the studies on performance indicators with an essential element that often causes flawed performance measures in organizations.Practical implicationsOrganizations could adopt this framework to design effective PMM systems and maintain them in light of the organizational changes.Originality/valueThis study introduces different metrics to evaluate the coherence and alignment of an indicators system, being one of the few research studies to address this topic in the context of complex and changing environments.

Each productive system manager knows that finding the optimal trade‐off between reducing inventory\ud and decreasing the frequency of production/replenishment orders allows a great cut‐back in operations costs. Several authors have focused their\ud contributions, trying to demonstrate that among the various dynamic lot sizing rules there are big differences\ud in terms of performance, and that these differences are not negligible. In this work, eight of the best known lot sizing algorithms have been described with a unique modelling approach and have then been exhaustively tested on several different scenarios, benchmarking\ud versus Wagner and Whitin’s optimal solution. As distinct from the contributions in the literature, the operational behaviour has been evaluated in order to determine\ud which one is more suitable to the characteristics of each scenario

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