Sviluppo di modelli decisionali per la supply chain di prodotti deperibili mediante l’applicazione di tecnologie innovative

The supply chain of perishable products, as fruits and vegetables is affected by environmental abuses from harvest to the final destination which are responsible the uncontrolled deterioration of food. In order to reduce such phenomena the supply chain members should control and monitor the conditions of goods in order to ensure their quality for consumers and to comply with all legal requirements (Garcia Ruiz, 2008). The most important factor influencing the food quality is the temperature able to prolonging the shelf life of the products. Since the temperature can inhibit or promote the maturation and deterioration process, this parameter is involved both in the growing process of fruits and vegetables and in the transport and storage stages. Given this the aim of the present thesis is to show that the monitoring of such parameter during the pre and post harvest stages allows to improve the decision making process. In the context of temperature monitoring the introduction of emerging information technologies such as the Wireless Sensors Networks and the Radio Frequency Identification can now provide real-time status knowing of product managed. The real time monitoring can be of great help in the definition of the actual maturation level of products both in the field and during the cold chain. The suitability of such an approach is evaluated by means of case studies. The first case study concerns the monitoring of grapes growth directly in the vineyard. The suitability of Wireless Sensors Networks in the monitoring of the grapes growth process is evaluated in terms of the possibility to determine the date of starting or ending of phenological phases. This information allows to make faster decisions about the vineyard operations which must be performed during the grape growth and finally allows to predict the maturation date in order to optimize the harvest operations. In the next case study the possibility to apply the Radio Frequency Identification technology to the monitoring of the fresh fruits along the cold chain has been faced and the quality of the products at any stage of the supply chain has been determined through a mathematical model. The knowing of the current quality level allows to make decisions about the destination of products. In this case those products having a shorter shelf life can be distributed to a local market while those with longer shelf life can be distributed to more distant location. In the next case study the information about the current deterioration state of perishable products has been translated into a warehouse management system in order to determine the operational parameters able to optimize the quality of products stored. Even in this case the goal of the study was to provide a decision making tool for the proper management of the perishable products stored. However besides the advantages achievable by the real time evaluation of environmental conditions the costs involved with the implementation of innovative technologies must be determined in order to establish the suitability of the investment in such innovative technologies. The present thesis also faces this question by determining the optimal number of devices to apply to the stock keeping unit in order to minimize the total cost associated to the transferring batch from the producer to the distributor. In this case the methodology employed is that of a mathematical model including all costs associated to the product management. Finally the study conducted through the present thesis shows that in all of the cases treated the use of the innovative technologies allows to support the decision making process in the pre and post harvest phases thus improving the perishables management

ANALYSIS OF A WAREHOUSE MANAGEMENT SYSTEM BY MEANS OF SIMULATION EXPERIMENTS

The supply chains for perishable products are nowadays affected by significant wastes and losses. Their management hence requires optimized approaches in order to remove such inefficiencies. In particular optimized warehouse management policies is a well established research topic which has been recently enriched with specific formulations for deteriorating stocks and shelf life based picking rules. In such context the proposed research aims at investigating the optimal warehouse management policy, taking into account the effects of uncertainty by means of simulation and approaching the effect of optimal picking rules. In the proposed approach, on the basis of a Weibull deterioration process, the optimal order quantity is calculated taking into account the deterioration cost, and the performance of the system is analyzed taking into account the inherent variability in the quality of the products entering the cold chain. The numerical application developed confirms the effectiveness of the model proposed

An expert system for vineyard management based upon pervasive computer technologies

Determining the optimal maturity level for performing viticulture operations and harvesting activities is a difficult task, because, depending on the variety, the climatic conditions and cultural practices, the phenologic maturation process occurs at different times. Recently, ubiquitous computing technologies allow an extremely precise and cost effective monitoring of environmental conditions by means of an RFID based sensor networks. The implementation of such technologies in vineyard management is nowadays under development, however, besides the possibility of gathering data, the need is perceived of developing decision support tools to fully exploit the potential opportunities of these new technologies. The present research aims at establishing a suitable method to support the decision process with the environmental data gathered automatically by a sensor network. The paper reports the results of an experimental study on a Sicilian vineyard showing that by means of the data collected by an RFID infrastructure it is possible to forecast the occurrence of phenologic maturity stage

CROSS-DOCKING TRANSSHIPMENT PROBLEM APPROACHED BY NON LINEAR PROGRAMMING AND SIMULATION ANALYSIS

The need for fast product delivery causes the attention of supply chain is addressed to strategies able to optimize the distribution process. In this field the crossdocking seems to be an efficient strategy which makes possible to reduce or eliminate the storage phase by meeting customer demand. In this paper a transshipment problem for cross-docking strategy is considered by means of a deterministic model studied through the non linear programming technique. The solution found allows to determine the optimal quantities to ship, the number of routes activated and the optimal truck number when the constraint on truck capacity is enforced. The influence of the demand fluctuation is also addressed through a simulation tool representing the cross-docking system. Finally the comparison between the cross-docking strategy and the direct delivery one is considered in terms of cost efficiency and trucks utilization

The expected value of the traceability information

Recent regulations on agri-food traceability prescribe traceability throughout the entire supply chain, in order to ensure consumers’ safety and product quality. This has led producers and retailers to consider the opportunity to improve the firm’s reputation and consumer confidence through the implementation of traceability systems designed not only to satisfy the legal requirements, but also to track the quality of the products through the supply chain for optimization purposes. However the actual implementation of such systems depends on the possibility of gathering specific information related to the product quality. Nowadays, innovative and non invasive technologies such as the Radio Frequency Identification (RFID) allow the automatic real time collection of data, thus enabling the development of effective traceability systems. In such context the expected value of traceability is a fundamental issue concerning the economic analysis of costs involved in such an investment and the optimal granularity level of implementation. This paper aims at evaluating the expected value of the implementation of traceability systems for perishable products like fruits and vegetables, and its profit. The study presents a mathematical stochastic approach for optimizing the supply chain profit and establishing the optimal granularity level (namely the Economic Traceability Lot) when a RFID solution is adopted. In particular, the supply chain profit in the presence of RFID traceability system has been calculated and compared with the expected profit in absence of such a system, and the results confirm the importance of the specific characteristics of the supply chain in determining the optimal configuration of the traceability syste

An expert system for financial performance assessment of health care structures based on fuzzy sets and KPIs

Interest in the field of performance assessment of health care structures has grown in recent decades. In fact, the possibility of determining overall performances of health care structures plays a key role in the optimization of resource allocation and investment planning, as it contributes to reducing the uncertainty of future performance. In this context, key performance indicator (KPI) tools have been developed to assess the performance of health care structures from process, organizational, cost, financial, and output points of view. In practice, they are periodically calculated, and the effect of several KPIs on the overall performance of health care structures is determined by management through human judgment or software that provides synthetic dashboards. Given their non-stationary nature, performance assessment and forecasting are generally tackled by employing adaptive models, but these approaches cannot reflect the holistic nature of performance itself, nor take into account the impact of KPIs on the overall performances. In order to overcome these shortcomings, this study presents an expert system whose engine relies on fuzzy sets, in which the input-output relations and correlations have been modeled through inference rules based on time-series trends. The focus is on the financial performance assessment of a health care structure, such as a hospital. The approach is of an interdisciplinary kind, as several indicators were taken as inputs that relate to output, process, and cost KPIs, and their impact on the output measure, which is of a financial kind (namely the total reimbursement). The output measure calculated by the expert system was then compared with that predicted using only adaptive forecasting models, and the error with respect to the actual value was determined. Results showed that measures determined by fuzzy inference, able to effectively model actual input-output relations, outperform those of adaptive models.Interest in the field of performance assessment of health care structures has grown in recent decades. In fact, the possibility of determining overall performances of health care structures plays a key role in the optimization of resource allocation and investment planning, as it contributes to reducing the uncertainty of future performance. In this context, key performance indicator (KPI) tools have been developed to assess the performance of health care structures from process, organizational, cost, financial, and output points of view. In practice, they are periodically calculated, and the effect of several KPIs on the overall performance of health care structures is determined by management through human judgment or software that provides synthetic dashboards. Given their non-stationary nature, performance assessment and forecasting are generally tackled by employing adaptive models, but these approaches cannot reflect the holistic nature of performance itself, nor take into account the impact of KPIs on the overall performances. In order to overcome these shortcomings, this study presents an expert system whose engine relies on fuzzy sets, in which the input-output relations and correlations have been modeled through inference rules based on time-series trends. The focus is on the financial performance assessment of a health care structure, such as a hospital. The approach is of an interdisciplinary kind, as several indicators were taken as inputs that relate to output, process, and cost KPIs, and their impact on the output measure, which is of a financial kind (namely the total reimbursement). The output measure calculated by the expert system was then compared with that predicted using only adaptive forecasting models, and the error with respect to the actual value was determined. Results showed that measures determined by fuzzy inference, able to effectively model actual input-output relations, outperform those of adaptive models. (C) 2016 Elsevier B.V. All rights reserved

An expert system for vineyard management based upon ubiquitous network technologies

Vineyard operations for quality wines production are currently based upon costly and time-consuming manual sampling operations required to assess the maturity phases of grapevines. The ripening process however is significantly influenced by the environmental parameters which nowadays can be effectively monitored by means of ubiquitous computing technologies. Besides the possibility of gathering data, hence, suitable tools are required to support the vineyard management process. The present research concerns the development of an expert system to effectively manage the vineyard operations. The methodology is based on the analysis of the time series of indices related to the maturation phases by means of referenced growth models, and on the prediction of the achievement of maturation thresholds. The paper also reports the results of an experimental study on a Sicilian vineyard