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Ameliorating and deteriorating products, or, more generally, items that change value over time, present a high sensitiveness to the surrounding environment (e.g., temperature, humidity, and light intensity). For this reason, they should be properly stored along the supply chain to guarantee the desired quality to the consumers. Specifically, ameliorating items face an increase in value if there are stored for longer periods, which can lead to higher selling price. At the same time, the costumers’ demand is sensitive to the price (i.e., the higher the selling price the lower the final demand), sensitiveness that is related to the quality of the products (i.e., lower sensitiveness for high-quality products). On the contrary, deteriorating items lose quality and value over time which result in revenue losses due to lost sales or reduced selling price. Since these products need to be properly stored (i.e., usually in temperature- and humidity-controlled warehouses) the holding costs, which comprise also the energy costs, may be particularly relevant impacting on the economic, environmental, and social sustainability of the supply chain. Furthermore, due to the recent economic crisis, companies (especially, small and medium enterprises) face payment difficulties of customers and high volatility of resources prices. This increases the risk of insolvency and on the other hand the financing needs. In this context, supply chain finance emerged as a mean for efficiency by coordinating the financial flow and providing a set of financial schemes aiming at optimizing accounts payable and receivable along the supply chain. The aim of the present study is thus to investigate through a systematic literature review the two main themes presented (i.e., inventory management models for products that change value over time, and financial techniques and strategies to support companies in inventory management) to understand if any financial technique has been studied for supporting the management of this class of products and to verify the existing literature gap.

The diversity of the diprotodontids provides an excellent opportunity to study how a basic marsupial cortical plan has been modified for the needs of the mammals living in different habitats. Very little is known about the connections of the cerebral cortex with the deep brain structures (basal ganglia and thalamus) in this evolutionarily significant group of mammals. In this study, we performed mapping of brain regions and connections in a diprotodontid marsupial from data obtained from an excised brain scanned in high-field (9.4 T) microstructural magnetic resonance imaging (MRI) instrument. The analysis was based on two MRI methodologies. First, high-resolution structural scans were used to map MRI visible brain regions from T1w and T2w images. Second, extensive diffusion tensor imaging (DTI) data were obtained to elucidate connectivity between brain areas using deterministic diffusion tracking of neuronal brain fibers. From the data, we were able to identify corticostriate connections between the frontal association and dorsomedial isocortex and the head of the caudate, and between the lateral somatosensory cortex and the putamen. We were also able to follow the olfactory and limbic connections by tracing fibers in the fornix, cingulum, intrabulbar part of the anterior commissure, and lateral olfactory tract. There was segregation of fibers in the anterior commissure such that olfactory connections passed through the rostroventral part and successively more dorsal cortical areas connected through more dorsal parts of the commissure. Our findings confirm a common pattern of cortical connectivity in therian mammals, even where brain expansion has occurred independently in diverse groups.

A consignment stock (CS) policy is a promising supply chain coordination mechanism. Delay-in-payments is a financing arrangement that facilitates purchases and increases sales by postposing a payment to some future time. It lowers costs and increases profitability, somewhat like what CS does. A study in the literature shows that combing the two reaps more benefits by considering different lot sizing and payment scenarios. Buy now, pay later, technically delay-in-payments, is a business practice for increasing sales. This paper revisits that work by assuming demand increases with the length of the delay period. This increases sales and, subsequently, profits beyond what that study reports.

We successfully radiolabelled a novel prospective cannabinoid type 2 receptor ligand with 18F and tested its biodistribution in animal models by positron emission tomography (PET)/computed tomography (CT) imaging. The radiolabelling process was conducted on an alkyl mesylate fragment of the main naphthyridine core, using highly efficient microfluidic technology. No preliminary protection was needed, and the product was purified by semi-prep HPLC and SPE formulation, allowing the desired diastereomeric mixture to be obtained in 29 % radiochemical yield and > 95 % radiochemically pure. SOD1G93A mice were used as model of overexpression of CB2 receptors; PET imaging revealed a significant increase of the tracer distribution volume in the brain of symptomatic subjects compared with the asymptomatic ones.

Perishable goods, such as chilled and frozen foods, have a short shelf life and high sensitivity to their surrounding environment (e.g., temperature, humidity, and light intensity). For this reason, they must be distributed within a specific time and require special equipment and facilities (e.g., refrigeration and dehumidification systems) throughout the entire chain from farm to fork to ensure slow deterioration and to deliver safe and high-quality products to consumers. Cold chains can last for short periods, such as a few hours, or for several months or even years (e.g., frozen food products) depending on the product and the target market. A huge amount of energy is required to preserve quality by maintaining the desired temperature level over time. The required energy is also affected by inventory management policies (e.g., warehouse filling levels affect the cooling demand per unit of a product) and the behavior of the operators (e.g., number and duration of door openings). Furthermore, waste entails the loss of energy and other resources consumed for processing and storing these products. The aim of the present study is to propose a quantitative approach in order to map the energy flows throughout the cold chain in the food and beverage sector and to evaluate the overall energy performance. The results of the energy flow mapping give decisionmakers insights into the minimum energy required by the cold chain and allow them to prioritize energy efficiency measures by detecting the most energy consuming stages of the cold chain. The implementation of a holistic approach, shifting from a single-company perspective to chain assessment, leads to a global optimum and to an increased implementation rate of energy efficiency measures due to the reduced barriers perceived by different actors of the cold chain.

To carry out a comprehensive analysis of additive manufacturing (AM) technologies, two major aspects need to be considered. Firstly, identification of AM’s capabilities in manufacturing final products in industry requires an accurate evaluation of their impacts across all supply chain stages. Secondly, making products additively entails potential consequences for different product lifecycle phases, e.g., design, preparation, production, usage, etc. that need to be managed accordingly. The current study tries to jointly address these two aspects by focusing on an extensive review of qualitative and quantitative cases taken from literature, as well as empirical observations made in industry. The study shows that impacts of AM technologies are not partial or isolated disruptions, but rather encompass a range of changes whose overall consequences spread across different phases of supply chain. Another result of the study is that a benchmarking of AM and conventional technologies on their overall performance is exhaustive only when all features of productions and logistics networks are evaluated simultaneously, rather than in their isolated states.

Perishable foodstuffs present a limited shelf life and a high sensitivity to the ambient temperature. To slow deterioration and to deliver safe and high-quality products to consumers, their processing and distribution from farm to fork should occur within a specified time in a chilled or frozen state. Food refrigeration has a significant impact on the environment, first of all, through direct emissions related to the eventual refrigerant leakages of refrigerants characterized by huge global warming potential and, second, indirectly through the emissions generated by nonrenewable energy consumption generated in power plants where fossil fuels are still primarily used. In recent years, significant progress has been made, for example, better system design and leakage sensing have been developed to reduce direct emissions. However, emissions are still relevant. Energy consumption is related to the temperature set during logistic activities, the storage time, and the behavior of the operators. Furthermore, the energy consumed for processing and storing foods then wasted throughout the supply chain is lost. The present chapter investigates how cold chains in the food sector contribute to environmental issues and to identify best practices that allow decreasing the environmental impact by lowering energy consumptions and quality losses. These best practices can belong to investments in more efficient technologies or the implementation of maintenance and operational practices as well as better coordination across actors of the chain.

Despite the various technical solutions for making lighting ‘smart,’ today’s lighting systems are often kept simple, and they are frequently not adjusted to user behaviours. This is especially the ...

Cold supply chains (CSCs) are responsible for preserving the quality of perishable goods in storage and transport. They consume significant amounts of energy to maintain cooling temperatures constant over time continuously and ubiquitously, which is affected by the surrounding environment and the users&rsquo, behavior. The storage filling level is one specific feature of refrigerated warehouses observed in practice: they are more energy efficient when kept full of items, reducing the space that air occupies. Inventory management models that consider energy consumption have received increasing attention recently due to an increase in stakeholders&rsquo, awareness of sustainability. Despite this interest, there is no work that jointly considers the effects of the filling level and the temperature inside the warehouse. This study, therefore, integrates those aspects into the economic order quantity model and finds the optimal lot size quantity that minimizes the total system cost, which is the performance measure used herein. It provides numerical results and brings some insights into the behavior of the model proposed.

The rapid expansion of emerging markets, combined with the influence of hedge funds and market speculators, highly increased commodity prices. At the same time, the global economic recession and an adverse credit environment, caused the price of commodities to drop with alarming speed. This huge volatility in prices generates a significant commodity risk, which creates issues of concern for companies while determining production, procurement, and risk-mitigation strategies. The context described is particularly challenging for those companies that have limited market power, since they may hardly transfer price increases to the end customers. In this regard, we extend the inventory theory by combining the operation management with two specific financial techniques, that may allow the commodity risk mitigation. Specifically, the warehouse financing practice and the use of futures contracts are included and investigated in the traditional joint economic lot size model, under two different coordination policies, as means for hedging stocks. The decision variables are the order quantity, the number of shipments between the vendor and the buyer, and the production rate. A numerical study is also presented in order to provide managerial insights on the economic performance of the different scenarios. The paper compares also the numerical results of the two financing practices with a base model, i.e. without any kind of supply chain finance. The results highlight that both the financing practices allow to reduce the total cost of the supply chain, and that the convenience of one practice, over the other, strictly depends on the relevant parameters distinguishing the case study.

Supply chain finance has been gaining attention in theory and practice. A company&rsquo, s financial position affects its performance and survivability in dynamic and volatile markets. Those that have weak financial performance are vulnerable when operating in environments that are uncertain and financially unstable. Companies adopt various solutions and techniques to manage, effectively and efficiently, the flow of money to and from its suppliers and buyers. Reverse factoring is an innovative technique in supply chain financing. This paper develops a joint economic lot size model where a vendor coordinates operational and financial decisions with its multiple suppliers through the establishment of a reverse factoring arrangement. The creditworthy vendor systematically informs a financial institution (e.g., bank) of payment obligations to selected suppliers, enabling the latter to borrow against the value of the relevant accounts receivable at low interest (borrowing) rates. The paper also presents a numerical example and a sensitivity analysis to illustrate the behavior of the model and to compare the economic and operational performance of a supply chain with and without a reverse factoring agreement. The results show that the establishment of a reverse factoring agreement within the supply chain improves the economic performance and impacts on the operational decisions.

Carbon dioxide (CO 2 ) enrichment in controlled environmental conditions has proved to enhance the growth and production of a wide variety of crops. Previous studies suggested that the use of this horticulture practice in greenhouses may represent a useful opportunity for the capture and utilization of industrial CO 2 emissions. The symbiosis among industrial installations and horticulture facilities may in fact allow to reduce the overall amounts of CO 2 released in the atmosphere, by reusing the direct production of carbon dioxide into crop enrichment processes. The present study provides a quantitative analysis of the economic benefits related to the use of CO 2 emissions generated by an industrial activity into a nearby greenhouse facility. The analysis takes into account the savings related to the reuse of carbon emissions from the industrial process, and the additional revenues due to the increase of the yield of products from the horticulture activity. Since the results of the enrichment process strictly depend on the specific crop, the analysis was conducted by comparing three different cultivations, viz. tomatoes, cucumbers and strawberries. The outcomes showed that, for the considered case study, the total benefits related to the implementation of the symbiosis network could reach up to 8.1 € per square meter of cultivated soil per each production cycle, and would also allow to capture up to the 21% of the overall carbon dioxide emissions produced by the industrial process.

The design of the propulsion system for Unmanned Aerial Vehicles (UAVs) demands an inclusive multidisciplinary approach from the earliest design phases, since every design choice strictly affects and is affected by the overall working conditions. This paper presents a review of the scientific literature focused on the design methods applied in defining and sizing the propulsion system of drones. The analysis, performed with a systematic approach, evaluated 123 papers according to two custom classification taxonomies, which investigated respectively the primary aim and specific content of the works. Finally, literature indications and hints were combined into an integrated framework for the functional design of the propulsion system of UAVs. The procedure aimed to support the designer in the preliminary selection of the propulsion candidates and the quick sizing of the supply system, during the first phases of the design process. According to the literature, design methods dramatically change depending on the expected applications and working conditions of UAVs, so that the detailed design of specific drone elements and propulsion components represents the focus of most of the papers in this field.

This paper presents an economic order quantity (EOQ) inventory model with partial backordering, where a buyer purchases its required products from a supplier. Each received batch from the supplier includes a random proportion of defective items. In other words, supplier’s production process works to a random yield. A recursive solution method to find the optimal values for main decision variables is examined through a series of numerical examples. Moreover, it is considered that the yield can be improved at a given cost. To this extent, two ways to achieve the yield improvement have been considered: the first way is investment in order to improve the mean of the yield to an ideal value, and the second way is investment to reduce the yield variability to an acceptable value. The impacts of the proposed strategies are investigated through some numerical analysis and managerial insights are proposed.

The increase of renewable energy sources (RES) installations all over the world during the past decades leads to a more sustainable energy scenario, however some drawbacks still persists and hinder the complete replacement of traditional fossil fuels. One of the main challenging issue is related to the intermittent production that generates serious problem in the power grids operations. Battery energy storage systems (BESS) represent one of the most promising technology which can help to overcome this issue, revolutionizing the way in which electrical power grids are designed and operate. The main aspects of these devices are related to the grid energy management, increasing the host capacity of RES, the system reliability and the users’ self-consumption. However, even energy storage systems present several pitfalls. They should be correctly maintained over the lifetime and they cannot be completely recycled at the end of their life, in addition, maintenance, decommissioning and disposal costs represent a significant share of the total cost. So as to consider these aspects in a holistic model the present work proposes a life cycle cost (LCC) model, which take into account all the most relevant cost components during the entire operation of the system.

This paper investigates impacts of merging components and reduction of sub-assemblies by exploiting additive manufacturing (AM) capabilities to allow for production of various components forming the final part composition as just one single part. The analysis is performed by considering a base structure for part composition, and inclusion of final components currently produced with conventional methods, and then comparing this with a list of alternatives that consist of merged components realized through AM. The comparative analysis among three main factors of reliability, logistics, and production cost and their impacts over average expected cost are the main aims of this study.

Electrical energy storage systems gathered a large interest among the energy market, since they have a key role in increasing the hosting capacity of renewables overcoming their main drawbacks (i.e. intermittency and uncertainty of the power generation). They also lead to increased share of self-consumption and improved reliability of the electric power distribution. Nevertheless, the economic feasibility of energy storage devices for distributed generation is still questionable: revenues and savings coming from the increase of self-consumption are usually not sufficient to cover the investment costs. The economic benefits are strictly dependent on the electricity tariff, which usually can be defined through a trinomial structure made up of a fixed part and two variable components depending on power and energy components. Electrical energy storage devices can be used for different purpose (e.g., load shifting and peak shaving), and the selection of the most performant is affected by the ratio between the two variable components of the tariff. The main purpose of this contribution is to investigate how the electricity tariffs could affect the diffusion of electrical energy storage systems in support of distributed generation based on renewable energy sources for different loads of end-users.

Environmental issues, mainly greenhouse gas (GHG) emissions, are in large part the result of the excessive use of energy in production systems. The scarcity of resources, governmental regulations, and public awareness about sustainability make them expensive for companies. The speed of producing items (production rate) impacts GHG emissions generation in manufacturing, i.e., usually faster production results in more emissions, which is controllable in many cases. The production rate also affects the process quality and reliability; i.e., fast production speeds deteriorate the system fast, resulting in reworking defective items and machine failure. Such quality and reliability issues increase energy consumption and subsequently costs. This paper develops two-level (vendor-buyer) supply chain models that tackle these issues. It considers two coordinated policies: classical and vendor-managed inventory with consignment stock (VMI-CS), where the objective is to find the values of the decision variables that yield the minimum total supply chain cost. It includes the costs of holding inventory, GHG emissions and tax, energy usage, product and process quality, and transportation operations. The decision variables are the order quantity, the number of shipments, and the production rate. The paper compares the numerical results of the two coordination policies. It also provides managerial insights on the economic and environmental performance of the supply chains.

The economic order quantity model and its variants are the oldest reported scientific inventory models in the literature. They are mathematically simple to use and understand. This characteristic contributed to their popularity. However, they have limitations that restrict their application in practice. Researchers have extended those models by relaxing some of their restrictive assumptions. One of the main limitations of the EOQ model is the estimation of its cost parameters, such as setup and holding costs, which include some hidden (or difficult to estimate) components. Emerging research proposes using the second law of thermodynamics when to measure the hidden (entropy) costs of inventory systems. Another research stream focuses on learning-by-doing, which reduces the unit cost of a product, thus reducing its price and enhancing a firm's competitiveness. This paper assumes that a buyer receives a shipment from its vendor following the EOQ model. The buyer invests in the vendor's process to accelerate learning (mainly workers training, among other things). The buyer's unit purchase cost reduces because of learning at the vendor's side. The buyer's competitors also learn, increasing competitiveness. For this purpose, we develop an EOQ model where price-dependent demand is analogous to a heat transfer equation, which is used to determine the entropy cost. It accounts for improvement by learning as a result of the buyer's investment to compete in a market. The implementation and management of improvement programs are not usually smooth, resulting in disorder quantified by an entropy cost function. The developed EOQ model is investigated with and without entropy costs. Numerical results representing different scenarios are provided to illustrate the behavior of the models and identify the factors affecting the decision variables and, subsequently, the buyer's profitability. The results showed that in a competitive market, the buyer has to maintain a learning rate faster than those of its competitors to ensure a prolonged, but indefinite, profitability. The results also showed that investment in learning is meaningful when the competition is fierce.