Using AI and ML to predict shipment times of therapeutics, diagnostics and vaccines in e-pharmacy supply chains during COVID-19 pandemic

PurposeThis paper aims to address the pressing problem of prediction concerning shipment times of therapeutics, diagnostics and vaccines during the ongoing COVID-19 pandemic using a novel artificial intelligence (AI) and machine learning (ML) approach.Design/methodology/approachThe present study used organic real-world therapeutic supplies data of over 3 million shipments collected during the COVID-19 pandemic through a large real-world e-pharmacy. The researchers built various ML multiclass classification models, namely, random forest (RF), extra trees (XRT), decision tree (DT), multilayer perceptron (MLP), XGBoost (XGB), CatBoost (CB), linear stochastic gradient descent (SGD) and the linear Naïve Bayes (NB) and trained them on striped datasets of (source, destination, shipper) triplets. The study stacked the base models and built stacked meta-models. Subsequently, the researchers built a model zoo with a combination of the base models and stacked meta-models trained on these striped datasets. The study used 10-fold cross-validation (CV) for performance evaluation.FindingsThe findings reveal that the turn-around-time provided by therapeutic supply logistics providers is only 62.91% accurate when compared to reality. In contrast, the solution provided in this study is up to 93.5% accurate compared to reality, resulting in up to 48.62% improvement, with a clear trend of more historic data and better performance growing each week.Research limitations/implicationsThe implication of the study has shown the efficacy of ML model zoo with a combination of base models and stacked meta-models trained on striped datasets of (source, destination and shipper) triplets for predicting the shipment times of therapeutics, diagnostics and vaccines in the e-pharmacy supply chain.Originality/valueThe novelty of the study is on the real-world e-pharmacy supply chain under post-COVID-19 lockdown conditions and has come up with a novel ML ensemble stacking based model zoo to make predictions on the shipment times of therapeutics. Through this work, it is assumed that there will be greater adoption of AI and ML techniques in shipment time prediction of therapeutics in the logistics industry in the pandemic situations.

Intelligent Contracts in Engineering Enterprise Supply Management

Intellectual contracts based on blockchain technology improve the efficiency of supply management of an automobile enterprise by optimizing the transactional costs of supply logistics. The present research featured KAMAZ PTC. The goal was to develop an interaction mechanism for all participants of an intellectual contract in supply activities. The article includes a review of Russian and foreign publications about intellectual contracts in various business spheres, supply management efficiency, optimization of transactional costs, and blockchain technology. The study made it possible to build an interaction mechanism of the parties involved in a blockchain intellectual contract. It also revealed a pattern of changes introduced to the intellectual contract at different stages of interaction between the initiator and suppliers. The authors also highlighted the difference between smart contract and intellectual contract. An intellectual contract appears as a logical development of a smart contract and allows the sides to change the terms. The party interaction mechanism can improve the supply efficiency as it optimizes the magnitude of transactional costs.

Characterization of the Composition, Structure, and Mechanical Properties of Endocarp Biomass

HighlightsEndocarps have higher lignin content, cellular and bulk density, and hardness than typical biomass feedstocks.The impacts of lignin content, bulk density, and mechanical properties on energy consumption are discussed.Endocarps can be a potential feedstock for a biorefinery coproducing biofuel and bioproducts.Abstract. Lignin is an abundant biopolymer and a promising source of feedstock for high-value chemicals and materials. This study aims to characterize the lignin-rich endocarp biomass and identify features of this unique feedstock that are relevant to feedstock preprocessing and logistics. The chemical composition and cellular structure of walnut and peach endocarps were characterized using HPLC and scanning electron microscopy (SEM) imaging. Mechanical properties of the endocarps were investigated using nanoindentation. Mechanical tests revealed hardness values of up to 0.48 and 0.40 GPa for walnut and peach endocarps, respectively. With screen sizes of 1 and 2 mm, the specific energy consumption was 9.21 and 1.86 MJ kg-1 for walnut and 12.6 and 2.72 MJ kg-1 for peach, respectively, as determined using a knife mill. Milling energy consumption was correlated to screen size, lignin content, bulk density, and mechanical properties. This study provides critical information on feedstock supply logistics necessary to implement a novel feedstock in biorefineries and evaluate the economic feasibility for coproduction of biofuels and lignin-derived products. Keywords: Biomass feedstock, Lignin, Mechanical properties, Nanoindentation, Size reduction.

Global Contraction and Local Strengthening of Firms’ Supply and Sales Logistics Networks in the Context of COVID-19: Evidence from the Development Zones in Weifang, China

The stagnation of multinational and cross-regional goods circulation has created significant disruptions to manufacturing supply chains due to the outbreak of the COVID-19 pandemic. To explore the impact of COVID-19 on the circulation of manufacturing industry products at different geographical scales, we drew upon a case study of development zones in the city of Weifang in China to analyze the characteristics of firms’ logistics networks in these development zones, and how these characteristics have changed since the outbreak of the COVID-19 pandemic. The data used in this study were collected from fieldwork conducted between 26 August 2020 and 15 October 2020, and included the supply originations of firms’ manufacturing sources and the sales destinations of their goods. We chose the two-mode network analysis method as our study methodology, which separates the logistics networks into supply networks and sales networks. The results show the following: First, the overall structure of firms’ logistics networks in Weifang’s development zones is characterized by localization. In the context of the COVID-19 pandemic, the local network links have further strengthened, whereas the global links have seriously declined. Moreover, the average path length of both the supply and sales logistics networks has slightly decreased, indicating the increased connectivity of the logistics networks. Second, in terms of the network node centrality, the core nodes of the supply logistics networks are the development zones and the city in which the firms are located, whereas the core nodes of the sales logistics networks are the core companies in the development zones. However, since the outbreak of the COVID-19 pandemic, the centrality of supply originations and sales destinations at the local scale has increased, whereas the centrality of supply originations and sales destinations at the global scale has decreased significantly. Third, the influencing factors of such changes include controlling personnel and goods circulation based on national boundaries and administrative boundaries, forcing the logistics networks in the development zones to shrink to the local scale. Moreover, there are differences in the scope of spatial contraction between supply logistics networks and the sales logistics networks.

AUTOMATIC OBJECT SEGMENTATION TO SUPPORT CRISIS MANAGEMENT OF LARGE-SCALE EVENTS

The management of large-scale events with a widely distributed camping area is a special challenge for organisers and security forces and requires both comprehensive preparation and attentive monitoring to ensure the safety of the participants. Crucial to this is the availability of up-to-date situational information, e.g. from remote sensing data. In particular, information on the number and distribution of people is important in the event of a crisis in order to be able to react quickly and effectively manage the corresponding rescue and supply logistics. One way to estimate the number of persons especially at night is to classify the type and size of objects such as tents and vehicles on site and to distinguish between objects with and without a sleeping function. In order to make this information available in a timely manner, an automated situation assessment is required. In this work, we have prepared the first high-quality dataset in order to address the aforementioned challenge which contains aerial images over a large-scale festival of different dates. We investigate the feasibility of this task using Convolutional Neural Networks for instance-wise semantic segmentation and carry out several experiments using the Mask-RCNN algorithm and evaluate the results. Results are promising and indicate the possibility of function-based tent classification as a proof-of-concept. The results and thereof discussions can pave the way for future developments and investigations.

Material resource management. Material and technical support (railway transport)

The textbook presents the theoretical and practical issues of the methodology of material and technical support, as well as the developing provisions of the academic disciplines "Material Resource Management" and "Logistics of supply" of railway transport. Such issues as the concept of strategic management, breakthrough transformations in the supply system, quality management, lean manufacturing, process approach, logistics analysis and cybernetics of business technologies, development strategies, management innovations based on the ARIS modeling environment, production inventory management in railway transport, and many others are considered in detail. It is intended for students studying in the areas of "Management", "Economics", as well as for all those interested in the economics of railway transport and supply logistics. It will be useful for managers and specialists of JSC "Russian Railways".

A Personal Respirator to Improve Protection for Healthcare Workers Treating COVID-19 (PeRSo)

Introduction: SARS-CoV-2 infection is a global pandemic. Personal Protective Equipment (PPE) to protect healthcare workers has been a recurrent challenge in terms of global stocks, supply logistics and suitability. In some settings, around 20% of healthcare workers treating COVID-19 cases have become infected, which leads to staff absence at peaks of the pandemic, and in some cases mortality.Methods: To address shortcomings in PPE, we developed a simple powered air purifying respirator, made from inexpensive and widely available components. The prototype was designed to minimize manufacturing complexity so that derivative versions could be developed in low resource settings with minor modification.Results: The “Personal Respirator – Southampton” (PeRSo) delivers High-Efficiency Particulate Air (HEPA) filtered air from a battery powered fan-filter assembly into a lightweight hood with a clear visor that can be comfortably worn for several hours. Validation testing demonstrates that the prototype removes microbes, avoids excessive CO2 build-up in normal use, and passes fit test protocols widely used to evaluate standard N95/FFP2 and N99/FFP3 face masks. Feedback from doctors and nurses indicate the PeRSo prototype was preferred to standard FFP2 and FFP3 masks, being more comfortable and reducing the time and risk of recurrently changing PPE. Patients report better communication and reassurance as the entire face is visible.Conclusion: Rapid upscale of production of cheaply produced powered air purifying respirators, designed to achieve regulatory approval in the country of production, could protect healthcare workers from infection and improve healthcare delivery during the COVID-19 pandemic.

Peracetate/singlet oxygen chemistry used in post-bleaching of kraft pulp as a practical oxidant for paper machines

The use of a novel sodium peracetate/singlet oxygen chemistry for brightening bleached kraft pulp shows exciting potential for technical performance, supply logistics, safety, and cost reduction. Potential chemical carryover to the paper machine raises questions about whether peracetate will impact paper machine performance, such as metal corrosion, useful press felt life, and interference with existing biocide programs or paper machine chemistry. Sodium peracetate/singlet oxygen chemistry can be used in high-density storage chests for brightening/whitening and to increase color stability. Any oxidant used directly before the paper machine has the possibility of impacting paper machine operations. Traditional oxidants used in bleaching, such as chlorine dioxide and hydrogen peroxide, are known to cause corrosion on machinery metals and press felts. Hydrogen peroxide residuals can interfere with common biocide programs. Traditional oxidants used in biocide treatments themselves significantly degrade press felt life when the rule-of-thumb concentration thresholds are exceeded. Sodium peracetate is evaluated in this paper for its impact on nylon press felt fiber degradation, metal corrosion, and interference with typical biocide programs. Laboratory results indicate that sodium peracetate/singlet oxygen chemistry is less corrosive than chlorine, bro-mine, and hydrogen peroxide on press felt nylon fiber and can therefore be used at higher levels than those chemistries to increase brightness without increasing negative downstream impact. Sodium peracetate can also be used with current biocide programs without negative impacts such as consumptive degradation. Higher residuals of per-acetate going to the paper machine may be useful as a biocide itself and can complement existing programs, allowing those programs to stay within their safe operating levels and thereby extend press felt useful life.

Risk-oriented simulation of the long logistics supply chains in developing enterprises

The paper defines and solves the urgent problem of research of long logistics supply chains in developing enterprises. Due to the distribution of the production system as well as to the presence of a large number of remote suppliers of materials, raw materials and components supply plans are threatened. Supply disruptions in their turn may affect the plans of the main production resulting in possible fines, economic losses, and disruptions in supply of manufactured articles to the markets of high-tech and science-intensive products. To study threats and vulnerabilities in supply logistics the risk-oriented approach that considers potential threats using past statistics and expert assessments has been proposed. The objective of the paper is to develop a risk-oriented method to study the existing threats and assess their impact on the vulnerabilities of the logistics chains of the distributed production system. Due to the complexity of the problematic logistics task, the study is conducted in three different stages: development of the method to simulate the risks in long supply chains; identification of possible bottlenecks in the transport system of supply logistics; threat simulation and vulnerability analysis in supply logistics. To model the risks, an agent model is used, in which the accumulation of risks is carried out by passing orders in the transport system. To simulate bottlenecks, a simulation event model is used, in which large queues that occur in the transport system are analyzed. A stochastic simulation model is used to model threats and vulnerabilities. The new scientific results are risk-oriented method of long logistics supply chains simulation; simulation of supply logistics threats and vulnerabilities. Mathematical methods used: risk theory; simulation event modeling; agent modeling; queuing theory. The proposed approach as a set of developed simulation models should be used to plan the supply of developing production.