Product Delivery Research Articles

Farmers’ perceived effect of the COVID-19 pandemic and its relationship to preparedness and risk perception

The impact of the COVID-19 pandemic across the agri-food sector was significant and pervasive, challenging farmers' resilience through multiple disruptions to the supply chain. To support forward planning in face to future shocks, this research examines the perceived impacts of the COVID-19 pandemic by farmers themselves, providing insights from the UK. Using a nation-wide online survey carried out during two distinct waves of the pandemic in 2021, the study reveals changing perceptions and the relationship between preparedness and perceived impacts. Results indicate that perceptions of both the severity of the COVID-19 impacts and preparedness for such impacts in the future, were scaled down as the pandemic evolved. Findings suggest that a farmer feeling more prepared in the present to withstand shocks is positively influenced by them perceiving the impact of COVID-19's in their business as severe. This effect is reinforced for farmers that felt more prepared to withstand COVID-19's impacts when the pandemic unfolded, as well as for those that perceive the impact of COVID-19 as long-term. Farmers in our sample appear to have adapted to the shocks to their businesses through supply-side interventions, focusing on having higher flexibility in delivery of products and diversifying their supply networks. Doing so requires them to absorb an increase in both fixed and variable costs, which can end-up been transferred to the consumer. Government support moving forward should focus on strengthening and, perhaps, re-imagining the whole supply industry and re-defining the role of farmers as more than food producers, but also as stewards of climate and food resilience.

Modeling a closed-loop inventory routing problem for returnable transport items under horizontal logistics collaborations and dynamic demand structure

ABSTRACT This paper addresses a closed-loop inventory routing problem with multiple suppliers, products, and periods under horizontal collaboration assumptions. Our problem encompasses various decision aspects, including routing, inventory management, product delivery, returnable transport item collection and cleaning. We analyze various logistics collaboration scenarios. The effects of demand dynamicity are also assessed. The problem has been mathematically defined as a Mixed Integer Linear Programming model. A rolling horizon approach and a hybrid heuristic algorithm are proposed for instances that exceed the computational requirements of solving the exact MILP model. The applicability and potential benefits of the MILP model and the proposed solution methodologies are demonstrated through a base case and additional numerical analyses on larger-sized instances and networks. The results show that supplier collaboration significantly reduces routing costs, while customer collaboration reduces inventory costs. Numerical comparisons reveal that the proposed algorithms outperform the MILP model for large-scale problem instances.

Multi-stage incentive contract design of aviation complex product multi-task delivery strategy

The increasing demand for aviation complex products makes complex product manufacturers pay more attention to delivery efficiency, and the efficiency and effect of delivery problems are the key factors that restrict delivery efficiency. A series of delivery strategy combinations of delivery subjects will affect delivery output and delivery efficiency. This study aims to design a long-term effective incentive contract, which stimulates the subjective initiative of the subject and then investigates the effect of the incentive contract on delivery efficiency under different combinations of delivery strategies. We consider the behavioral characteristics of each technical business department to expand the benchmark incentive model, such as the degree of perceived revenue fairness, the variation of the three relationships (neutrality, competition, and cooperation), and the level of technological transformation capability. The multi-stage incentive mechanism which combines explicit incentives and implicit incentives is developed to explore the optimal delivery strategies based on a benchmark model. The results show that incentive contracts can improve delivery efficiency and benefit delivery centers and technical business departments. The delivery decisions of the department are influenced by the closeness of department relationships and the fairness of assignment, and the departments tend to pay more effort in tasks with high relative importance and low effort cost. When the relative importance of tasks is equal to the ratio of marginal cost, a weak incentive zone in the delivery incentive contract exists. The incentive effect of reputation effect is obvious except for the last stage. Based on the designed incentive contracts, the subjective initiative of the participants can be effectively stimulated to empower complex product delivery.

Integrated scheduling of production and material delivery for the intelligent manufacturing system

This paper addresses the issue of integrated scheduling of production and material delivery in an intelligent manufacturing system. The system includes multiple flexible flowlines and a fleet of automated guided vehicles (AGVs), whose capacity is shared among the flowlines. Multiple orders from different customers are processed on the flowlines, with AGVs delivering materials in different batch sizes based on varying product quantities of orders and limited load capacity. The problem is formulated as a mixed-integer linear programming model to minimise total order delays and the number of used AGVs. To handle the complexity caused by the uneven temporal coupling relationship between production and material delivery, a bi-level neighbourhood search solution algorithm is proposed. This algorithm uses variable neighbourhood search for production scheduling and adaptive large neighbourhood search for material delivery scheduling. Extensive numerical experiments using real-case-based instances are conducted to demonstrate the satisfactory performance of our proposed algorithm. Furthermore, we emphasise the advantages of integrated scheduling compared to traditional sequential scheduling across various problem settings.

Empowering DevOps with Infrastructure as Code :Trends, Tools and Techniques

DevOps has arisen as a pillar of modern software engineering, emphasising the integration of development and operations to ensure effective product delivery. Infrastructure as Code (IaC) is an important DevOps technique that involves defining and managing infrastructure requirements using code, enabling for automated provisioning and maintenance. This technique enhances traceability, reuse, and consistency across development and production environments. The introduction of microservices architectures has increased project teams’ infras- tructure responsibilities, making IaC essential for delivering reliable and efficient deployments. IaC enables developers to describe infrastructure in code, simplifying the deployment process. In both large and small businesses, IaC is essen- tial for supporting efficient DevOps processes.The most recent breakthroughs, tools, and techniques in IaC demonstrate a revolutionary impact on software development and deployment workflows. As more businesses adopt cloud-native designs and containerisation technologies, the requirement for automated infrastructure provisioning grows, leading in the growth of IaC tools and methodologies. Organisations that combine IaC with continuous integration and delivery (CI/CD) pipelines can re- duce time-to-market and improve operational efficiency. IaC not only automates infrastructure management, but it also includes software engineering principles like version control and testing into infrastructure provisioning, which improves consistency and reliability. This democratisation of infrastructure management encourages increased collaboration across cross-functional teams, hence improving accountability and innovation. Implementing IaC is therefore crucial for achieving agility, scalability, and resilience in the digital age. Index Terms—Iac, DevOps, CI/CD, Automation, Cloud-native, Infrastructure Provisioning ,Terraform, Ansible, AWS CloudFor- mation

Combination of value stream mapping (VSM) method and kanban system to reduce time waste in the production process of making parts for the four-wheel vehicle industry

Fulfilling orders from customers in the automotive industry is a necessity that inevitably causes problems that arise in product delivery to customers due to production delays and decreased production yields. The production process often experiences wasted time in the production process of four-wheeled vehicle spare parts. This research aims to reduce production process time, provide solutions to reduce waste and increase the number of four-wheeled vehicle spare parts produced. This research method uses the Lean Manufacturing (LM) approach by combining the Value Stream Mapping (VSM) method with Kanban system improvements, the application of Root Cause Analysis (RCA), and the Kaizen method. This research found that four factors cause waste in process time, including environmental factors, methods, humans, and machines. This research produces the right solution in the relay out of the Assembly and Final Inspection production processes, as well as the use of the e-kanban system in material and product inventory. The research results have reduced the production process time from 17.10 days to 10.05 days, meaning a decrease of 41.22%. This affected production results which increased from an average monthly production of 42,917 pcs to 60,157 pcs, meaning an increase of 128.65%.

Operations Management: Roles and Duties of Suppliers and Shippers in Supply Chain Management

In order to enhance competitiveness, companies must prioritize product customization, high quality, cost reduction, and distribution speed. To achieve these goals, it is essential to focus on supply chain management. Supply chain management is defined as the integrated process of obtaining materials and services, transforming them into intermediate and final products, and delivering them to customers. This involves activities such as purchasing and outsourcing, in addition to other functions that are crucial for the relationship between suppliers and distributors. The primary objectives of supply chain management (SCM) are the timely delivery of products to consumers, cost reduction, and the improvement of outcomes across the entire supply chain (as opposed to a single company). Additionally, SCM facilitates the reduction of time, the centralization of planning and distribution activities, and the optimization of resources. Given the current circumstances, the application of SCM is particularly advantageous, as it enables the management of the flow of goods or products in a supply chain.

Financial Effects of Remote Product Delivery: Evidence from Hospitals

Financial Effects of Remote Product Delivery: Evidence from Hospitals

Swirling Capillary Instability of Rivlin–Ericksen Liquid with Heat Transfer and Axial Electric Field

The mutual influences of the electric field, rotation, and heat transmission find applications in controlled drug delivery systems, precise microfluidic manipulation, and advanced materials’ processing techniques due to their ability to tailor fluid behavior and surface morphology with enhanced precision and efficiency. Capillary instability has widespread relevance in various natural and industrial processes, ranging from the breakup of liquid jets and the formation of droplets in inkjet printing to the dynamics of thin liquid films and the behavior of liquid bridges in microgravity environments. This study examines the swirling impact on the instability arising from the capillary effects at the boundary of Rivlin–Ericksen and viscous liquids, influenced by an axial electric field, heat, and mass transmission. Capillary instability arises when the cohesive forces at the interface between two fluids are disrupted by perturbations, leading to the formation of characteristic patterns such as waves or droplets. The influence of gravity and fluid flow velocity is disregarded in the context of capillary instability analyses. The annular region is formed by two cylinders: one containing a viscous fluid and the other a Rivlin–Ericksen viscoelastic fluid. The Rivlin–Ericksen model is pivotal for comprehending the characteristics of viscoelastic fluids, widely utilized in industrial and biological contexts. It precisely characterizes their rheological complexities, encompassing elasticity and viscosity, critical for forecasting flow dynamics in polymer processing, food production, and drug delivery. Moreover, its applications extend to biomedical engineering, offering insights crucial for medical device design and understanding biological phenomena like blood flow. The inside cylinder remains stationary, and the outside cylinder rotates at a steady pace. A numerically analyzed quadratic growth rate is obtained from perturbed equations using potential flow theory and the Rivlin–Ericksen fluid model. The findings demonstrate enhanced stability due to the heat and mass transfer and increased stability from swirling. Notably, the heat transfer stabilizes the interface, while the density ratio and centrifuge number also impact stability. An axial electric field exhibits a dual effect, with certain permittivity and conductivity ratios causing perturbation growth decay or expansion.

Smart Supply Chain Management Using Machine Learning Algorithms

Currently, supply chains are the most widely used in marketing around the world to increase productivity and to add more features to make supply chain management easy to handle. For smart supply chain management, the main focus is on analyzing a product demand and supply chain with fraud suspects of customers and delays in product delivery. RFID can be further extended to authenticate items used to track and manage products. Demand forecasting is a crucial part of any company or supply chain. It aims at predicting and estimating the future demand of products to help in better decision-making and fraud detection is useful because it allows businesses to identify and prevent unauthorized activities. With the integration of machine learning and the IoT, supply chain management can effectively increase the demand for productivity.

Niosomes as a Promising Therapeutic Approach against Colorectal Cancer: A Focus on the Delivery of Chemotherapeutics and Natural Products.

Nanotechnology has emerged as an effective approach to cancer treatment, including Colorectal Cancer (CRC). While conventional treatments, such as chemotherapeutic agents, are used to manage CRC, their efficacy can be improved using drug delivery systems that enhance their bioavailability and reduce side effects. Niosomes, polymeric nanoparticles, have shown promise as biocompatible vehicles that can transport hydrophilic and lipophilic molecules. This can result in reduced drug dosage and increased efficacy. This review examines the use of niosomal formulations as a delivery platform for treating CRC and provides practical insights into their clinical applications.

The viscoelasticity of high concentration monoclonal antibodies using particle tracking microrheology.

The viscoelasticity of monoclonal antibodies (mAbs) is important during their production, formulation, and drug delivery. High concentration mAbs can provide higher efficacy therapeutics (e.g., during immunotherapy) and improved efficiency during their production (economy of scale during processing). Two humanized mAbs were studied (mAb-1 and mAb-2) with differing isoelectric points. Using high speed particle tracking microrheology, we demonstrated that the mAb solutions have significant viscoelasticities above concentrations of 40 mg/ml. Power law viscoelasticity was observed over the range of time scales (-1 s) probed for the high concentration mAb suspensions. The terminal viscosity demonstrated an exponential dependence on mAb concentration (a modified Mooney relationship) as expected for charged stabilized Brownian colloids. Gelation of the mAbs was explored by lowering the pH of the buffer and a power law scaling of the gelation transition was observed, i.e., the exponent of the anomalous diffusion of the probe particles scaled inversely with the gelation time.

Launch Vehicle Assembly Material Delivery Method

To address the current issues of high demand for personnel and low efficiency in the delivery of materials for launch vehicle assembly, and to improve the accuracy of material delivery and the timeliness of material information statistics, this paper proposes a method for the delivery of assembly materials for launch vehicles. This method adopts the "Automated Guided Vehicle (AGV)+Intelligent Management Scheduling System" approach, transforming the traditional manual receipt and delivery into a combination of AGV delivery and receipt terminals. Through the intelligent management scheduling system, it is possible to achieve the collection, management, tracking, and control of full-process material information. This method can effectively enhance production delivery efficiency, reduce the rate of material distribution errors, and improve the timeliness of information statistics.

Evaluation of the effect of remanufacturing diesel particulate filters to minimize environmental impacts

Exhaust gas purification is required for the operation of heavy machinery, e.g., construction machinery which mainly uses diesel engines. Precious metals such as the platinum group are used in catalysts for this purpose, which heavily impacts the environment. In this study, the authors evaluated the potential of remanufacturing diesel particulate filters (DPF) to reduce these impacts. Climate change indicators, i.e., global warming potential (GWP), and resource consumption were evaluated.As a result, the environmental impacts of new product manufacturing, particularly resource production and the manufacturing process, were quantitatively estimated to be significant, while the environmental impacts of the remanufacturing process, product delivery, and disposal of the used products were significantly lower. In addition, 47% of the GWP and 50% of the resource consumption were reduced using remanufactured diesel particulate filters compared with using only new diesel particulate filters.

Assessment of Peanut Protein Powder Quality by Near-Infrared Spectroscopy and Generalized Regression Neural Network-Based Approach.

The global demand for protein is on an upward trajectory, and peanut protein powder has emerged as a significant player, owing to its affordability and high quality, with great future market potential. However, the industry currently lacks efficient methods for rapid quality testing. This research paper addressed this gap by introducing a portable device with employed near-infrared spectroscopy (NIR) to quickly assess the quality of peanut protein powder. The principal component analysis (PCA), partial least squares (PLS), and generalized regression neural network (GRNN) methods were used to construct the model to further enhance the accuracy and efficiency of the device. The results demonstrated that the newly established NIR method with PLS and GRNN analysis simultaneously predicted the fat, protein, and moisture of peanut protein powder. The GRNN model showed better predictive performance than the PLS model, the correlation coefficient in calibration (Rcal) of the fat, the protein, and the moisture of peanut protein powder were 0.995, 0.990, and 0.990, respectively, and the residual prediction deviation (RPD) were 10.82, 10.03, and 8.41, respectively. The findings unveiled that the portable NIR spectroscopic equipment combined with the GRNN method achieved rapid quantitative analysis of peanut protein powder. This advancement holds a significant application of this device for the industry, potentially revolutionizing quality testing procedures and ensuring the consistent delivery of high-quality products to fulfil consumer desires.

Beneficial Effects of Multi-Micronutrient Supplementation with Collagen Peptides on Global Wrinkles, Skin Elasticity and Appearance in Healthy Female Subjects.

With ageing, collagen production slows down, leading to wrinkle appearance and loss of elasticity. Replenishing key structural molecules through oral supplementation is a promising strategy that complements the topical delivery of cosmetic products and creates a holistic skincare regimen. The present study assessed the effectiveness of a food supplement with collagen peptides, vitamins and minerals in improving the quality of the skin and general wellbeing of healthy women. This was an open-label study of 135 women aged between 45 and 65years. A 3-month treatment phase followed a 4-week washout phase, with visits scheduled at baseline and after each month of treatment. Subjects received three tablets of Richelet Skin Renewal daily. The primary outcome was change from baseline to month3 in global wrinkles score by expert grader analysis. Secondary outcomes included changes in skin elasticity and other skin attributes, product assessment via self-perception questionnaires and total antioxidant status. A total of 116 subjects completed the study. The mean global wrinkles score indicated a statistically significant decrease from 5.9 at baseline to 5.0 at month3 (p < 0.0001), with 83.6% of subjects showing an improvement; significant changes were reported at all intermediate visits. The increase in skin elasticity was also statistically significant (R2 score 0.74 at month3; p < 0.0001). All subjects (100%) demonstrated significant improvements in skin texture, skin tone evenness, skin radiance and overall skin quality at the month3 visit. The study product achieved statistically significant, noticeable effects on global wrinkles, skin elasticity and a range of skin attributes after 3months of use in healthy women. These results strengthen the evidence for supplementation of collagen peptides and other micronutrients as an effective component of anti-ageing skincare.

Two-Phase Fuzzy Real-Time Approach for Fuzzy Demand Electric Vehicle Routing Problem with Soft Time Windows

Environmental concerns have called for several measures to be taken within the logistics and transportation fields. Among these measures is the adoption of electric vehicles instead of diesel-operated vehicles for personal and commercial-delivery use. The optimized routing of electric vehicles for the commercial delivery of products is the focus of this paper. We study the effect of several practical challenges that are faced when routing electric vehicles. Electric vehicle routing faces the additional challenge of the potential need for recharging while en route, leading to more travel time, and hence cost. Therefore, in this work, we address the issue of electric vehicle routing problem, allowing for partial recharging while en route. In addition, the practical mandate of the time windows set by customers is also considered, where electric vehicle routing problems with soft time windows are studied. Real-life experience shows that the delivery of customers’ demands might be uncertain. In addition, real-time traffic conditions are usually uncertain due to congestion. Therefore, in this work, uncertainties in customers’ demands and traffic conditions are modeled and solved using fuzzy methods. The problems of fuzzy real-time, fuzzy demand, and electric vehicle routing problems with soft time windows are addressed. A mixed-integer programming mathematical model to represent the problem is developed. A novel two-phase solution approach is proposed to solve the problem. In phase I, the classical genetic algorithm (GA) is utilized to obtain an optimum/near-optimum solution for the fuzzy demand electric vehicle routing problem with soft time windows (FD-EVRPSTW). In phase II, a novel fuzzy real-time-adaptive optimizer (FRTAO) is developed to overcome the challenges of recharging and real-time traffic conditions facing FD-EVRPSTW. The proposed solution approach is tested on several modified benchmark instances, and the results show the significance of recharging and congestion challenges for routing costs. In addition, the results show the efficiency of the proposed two-phase approach in overcoming the challenges and reducing the total costs.

Aerial drone fleet deployment optimization with endogenous battery replacements for direct delivery of time-sensitive products

Aerial drones offer a distinct potential to reduce the delivery time and energy consumption for the delivery of time-sensitive and small products. However, there is still a need in the relevant industry to understand the performance of drone-based delivery under different business needs and drone operating conditions. We studied a drone deployment optimization problem for direct delivery of time-sensitive products with release dates to customers maintaining a specified time window. This paper presents a new mixed-integer programming model, new valid inequalities, a new greedy heuristic algorithm, and a Genetic algorithm to help business owners optimally schedule and route their drone fleet minimizing the required fleet size, the required number of additional batteries, and total energy consumption. A realistic feature of the optimization method is that instead of replacing the drone battery after each return to the depot, it keeps track of the remaining energy in the drone battery and decides on battery replacements accounting for the drone routing and the user-specified minimum required battery energy. Numerical results based on real data from drone flight tests and prepared food delivery industry provide insights into the effect of different practical drone operating parameters on the required fleet size, the required number of battery replacements, and energy consumption. Results demonstrate that the proposed heuristic algorithm substantially outperforms the accelerated CPLEX in runtime while sacrificing the solution quality by a small amount. Additionally, results show that using a mixed fleet of hexacopter and quadcopter drones reduces the total energy consumption by 48.52% compared to using a homogeneous fleet of only hexacopters.

Integration and Implementation of Scaled Agile Framework and V-Model in the Healthcare Sector Organization

The development of medical technology devices leads to the introduction and use of agile methods, which enable the delivery of increasingly complex software with the fastest possible innovations. Delivery of the highest quality software must be considered during development, as medical products are important elements in saving human lives. Their development begins with determining a set of product requirements that exactly correspond to it. The development of specified medical products is finally delivered to the customer, who participates in the development. In this article, we present the use and combination of agile methods in software development, which correct and facilitate timely and continuous delivery of products. They also know how to smooth out a quick reaction to the customer’s changing needs and mainly focus on team management and communication. Specific agile methods make it possible to implement development through gradual improvements by integrating customer requirements towards the product. This article identifies three interconnected approaches to integrating agile methods and principles: SCRUM, SAFe, and Kanban combined with the V-model. The methods are gradually analysed based on the literature review, and the article presents a practical application in Siemens Healthcare Slovakia.

Post‐processing output from ensembles with and without parametrised convection, to create accurate, blended, high‐fidelity rainfall forecasts

AbstractFlash flooding is a significant societal problem, but related precipitation forecasts are often poor. To address, one can try to use output from convection‐parametrising (global) ensembles, post‐processed to forecast at point‐scale, or convection‐resolving limited area ensembles. The new methodology described here combines both. We apply “ecPoint‐rainfall” post‐processing to the ECMWF global ensemble. Alongside we use 2.2 km COSMO LAM ensemble output (centred on Italy), and also post‐process that, using a scale‐selective neighbourhood approach to compensate for insufficient members and to preserve consistently forecast local details. The two resulting scale‐compatible components then undergo lead‐time‐weighted blending, to create the final probabilistic 6 h rainfall forecasts. Product creation for forecasters, in this way, constituted the “Italy Flash Flood use case” within the EU‐funded MISTRAL project; real‐time delivery of open access products is ongoing. One year of verification shows that, of the five components (2 raw, 2 post‐processed and blended), ecPoint is the most skilful. The post‐processed COSMO ensemble adds most value to summer convective events in the evening, when the global model has an underprediction bias. In two typical heavy rainfall case studies we observed underestimation of the largest point totals in the raw ECMWF ensemble, and overestimation in the raw COSMO ensemble. However, ecPoint elevated the ECMWF maxima and highlighted best the most affected areas and merged products seemed to be the most skilful of all. Even though our LAM post‐processing does not include (or arguably need) bias‐correction, this study still provides a unique blueprint for successfully combining ensemble rainfall forecasts from global and LAM systems around the world. It also has important implications for forecast products as global ensembles move ever closer to having convection‐permitting resolution.