Eco-Management and Audit Scheme (EMAS): An evaluation of success factors in the agricultural sector. The case of Italy's Emilia Romagna region.

Different tools have been used to set up and adopt the model for the fulfillment of the objective of this research. 1. The Model The base model that has been used is the Analytical Hierarchy Process (AHP) adapted with the aim to perform a Benefit Cost Analysis. The AHP developed by Thomas Saaty is a multicriteria decision - making technique which decomposes a complex problem into a hierarchy. It is used to derive ratio scales from both discreet and continuous paired comparisons in multilevel hierarchic structures. These comparisons may be taken from actual measurements or from a fundamental scale that reflects the relative strength of preferences and feelings. 2. Tools and methods 2.1. The Expert Choice Software The software Expert Choice is a tool that allows each operator to easily implement the AHP model in every stage of the problem. 2.2. Personal Interviews to the farms For this research, the farms of the region Emilia Romagna certified EMAS have been detected. Information has been given by EMAS center in Wien. Personal interviews have been carried out to each farm in order to have a complete and realistic judgment of each criteria of the hierarchy. 2.3. Questionnaire A supporting questionnaire has also been delivered and used for the interviews . 3. Elaboration of the data After data collection, the data elaboration has taken place. The software support Expert Choice has been used . 4. Results of the Analysis The result of the figures above (vedere altro documento) gives a series of numbers which are fractions of the unit. This has to be interpreted as the relative contribution of each element to the fulfillment of the relative objective. So calculating the Benefits/costs ratio for each alternative the following will be obtained: Alternative One: Implement EMAS Benefits ratio: 0, 877 Costs ratio: 0, 815 Benfit/Cost ratio: 0,877/0,815=1,08 Alternative Two: Not Implement EMAS Benefits ratio: 0,123 Costs ration: 0,185 Benefit/Cost ratio: 0,123/0,185=0,66 As stated above, the alternative with the highest ratio will be the best solution for the organization. This means that the research carried out and the model implemented suggests that EMAS adoption in the agricultural sector is the best alternative. It has to be noted that the ratio is 1,08 which is a relatively low positive value. This shows the fragility of this conclusion and suggests a careful exam of the benefits and costs for each farm before adopting the scheme. On the other part, the result needs to be taken in consideration by the policy makers in order to enhance their intervention regarding the scheme adoption on the agricultural sector. According to the AHP elaboration of judgments we have the following main considerations on Benefits: - Legal compliance seems to be the most important benefit for the agricultural sector since its rank is 0,471 - The next two most important benefits are Improved internal organization (ranking 0,230) followed by Competitive advantage (ranking 0, 221) mostly due to the sub-element Improved image (ranking 0,743) Finally, even though Incentives are not ranked among the most important elements, the financial ones seem to have been decisive on the decision making process. According to the AHP elaboration of judgments we have the following main considerations on Costs: - External costs seem to be largely more important than the internal ones (ranking 0, 857 over 0,143) suggesting that Emas costs over consultancy and verification remain the biggest obstacle. - The implementation of the EMS is the most challenging element regarding the internal costs (ranking 0,750).

Analytics and Methods for Logistic System Design and Operations Management

Analytics is the technology working with the manipulation of data to produce information able to change the world we live every day. Analytics have been largely used within the last decade to cluster people’s behaviour to predict their preferences of items to buy, music to listen, movies to watch and even electoral preference. The most advanced companies succeded in controlling people’s behaviour using analytics. Despite the evidence of the super-power of analytics, they are rarely applied to the big data collected within supply chain systems (i.e. distribution network, storage systems and production plants). This PhD thesis explores the fourth research paradigm (i.e. the generation of knowledge from data) applied to supply chain system design and operations management. An ontology defining the entities and the metrics of supply chain systems is used to design data structures for data collection in supply chain systems. The consistency of this data is provided by mathematical demonstrations inspired by the factory physics theory. The availability, quantity and quality of the data within these data structures define different decision patterns. Ten decision patterns are identified, and validated on-field, to address ten different class of design and control problems in the field of supply chain systems research.

Characterization and management of particular classes of waste at a regional level. case study: Marche region

There are various methods to analyse waste, which differ from each other according to the level of detail of the compositio. Waste composed by plastic and used for packaging, for example, can be classified by chemical composition of the polymer used for the specific product. At a more basal level, before dividing a waste according to the specific chemical material of which it is composed it is possible and also important to classify it according to the material category. So, if the secondary aim is to consider the particular polymer that constitutes a plastic waste, or what kind of natural polymer composes a specific waste made of wood, the first aim is to classify the product category of the material that makes up the waste, so, if it is wood made, or plastic, or glass made or metal, or organic. There are not specific instruments to make this subdivision, not specific chemical tests, but only a manual recognition of the material that makes up the product or waste. The first steps of this study is a recognition of the materials of which the waste is composed, the second is a the quantification of differentiated and unsorted waste produced in the area under study, the third is a mass balance of the portions of waste sent for recovery in order to obtain information on quantities that can be effectively recovered and ready for new life cycle as raw material; the fourth and last step is an environmental assessment that provides information on the environmental cost of the recovery process. This process scheme is applied to various specific kinds of waste from separate collection generated in a specific area with the aim to find a model analysis appliable to other portions of territory in order to improve knowledge of recovery technologies.