Order Processing Time Research Articles

How to improve efficiency for prefabricated component production scheduling with worker allocation and order outsourcing?

Prefabricated building is an advanced building mode that is actively promoted in China owing to its high-efficiency, energy-saving and labour-saving characteristics. However, because of the limited production capacity and production resources of the final assembly parts production plant, it is usually impossible for a factory to accept all orders. Therefore, the factory will consider outsourcing part of the orders to reduce delay penalties. In addition, the allocation of workers in the production process will significantly affect the processing time of orders. Regarding those two issues, a production scheduling integrated optimization model, considering worker allocation and order outsourcing, is established to maximize net profit. To address the complexity of problem solving caused by the NP-hard nature of this problem, a hybrid improved genetic–accelerated iterative greedy search algorithm is developed. Finally, a numerical example is delivered to verify that the proposed algorithm has improved solution quality and convergence.

MEDOIDS AS A PACKING OF ORB IMAGE DESCRIPTORS

The aim of the research. The paper presents the research about the feasibility to use matching medoids obtained from the set of ORB descriptors instead matching the full set of binary descriptors for image classification problem. Research results. Different methods that include direct brute force medoids matching, grouping of medoids for separate classes, and grouping of descriptors followed by calculation of medoids amongst them were proposed. Numerical experiments were performed for all these methods in order to compare the classification accuracy and inference time. It has been shown that using of medoids allowed us to redistribute processing time in order to perform more calculations during preprocessing rather than during classification. According to modelling performed on the Leeds Butterly dataset matching images based on medoids could have the same accuracy as matching of descriptors (0.69–0.88 for different number of features). Medoids require additional time for the calculation during preprocessing stage but classification time becomes faster: in our experiments we have obtained about 9–10 times faster classification and same 9–10 times increasing preprocessing time for the models that have comparable accuracies. Finally, the efficiency of the proposed ideas was compared to the CNN trained and evaluated on the same data. As expected, CNN required much more preprocessing (training) time but the result is worth it: this approach provides the best classification accuracy and inference time. Conclusion. Medoids matching could have the same accuracy as direct descriptors matching, but the usage of medoids allows us to redistribute the overall modeling time with the increasing preprocessing time and making inference faster.

Artificial Intelligence for Decision Making in the Supply Chain

Artificial intelligence (AI) is a technology that has the potential to significantly impact organizational decision-making. In supply chain management (SCM), data analysis and organizational decision-mak- ing play a crucial role in providing insights into various aspects of the supply chain, such as relevant goods, inventory levels, order processing times, delivery times, and transportation costs. By analyzing data, companies can identify inefficiencies in their supply chain and take corrective actions to improve performance. The SC system is a customer-oriented and integrated system that determines planning, administration, and management processes for internal and external material and related flows, for- mulating the optimal factor of added value. In this study, we aim to identify hazardous products by analyzing product identification information at customs checkpoints and locations where vendible items are delivered to customers. We have identified several commodity groups and substances that are potentially harmful and dangerous to human health.

Optimization of Emergency Epidemic Prevention Material Reserve and Supply System of Shanxi Province in China: Based on Anylogic Simulation

Due to the randomness and uncertainty of epidemic occurrence, there are insufficient emergency supplies and low efficiency of transportation equipment in Shanxi Province. In order to meet the needs of materials in Shanxi Province, it is urgently necessary to establish a reserve and supply system with perfect facilities and equipment. According to the geographical location of 11 prefecture-level cities in Shanxi Province and the actual situation of transportation equipment, this paper simulates the location of warehouse construction in each city. Through simulation modeling, the emergency epidemic prevention materials reserve and supply system of prefecture-level cities in Shanxi Province was simulated under the epidemic situation. The research idea of "simulation-analysis-optimization-summary" was defined, and the AnyLogic software was used to simulate and analyze the emergency epidemic prevention materials reserve and supply system of the whole Shanxi Province. Through the simulation of different scenarios, the optimization direction of transportation equipment is obtained. Based on the optimization effect of each plan of transportation equipment loading on the emergency epidemic prevention materials reserve and supply system in Shanxi Province, the best optimization plan was obtained. The results show that the efficiency of transportation equipment allocation is improved and the loading capacity of transportation equipment is reasonable and effective. After optimization, the order backlog of each prefecture-level city storage warehouse is reduced, and the order processing time is reduced. The optimization ideas proposed in this paper provide the optimization direction for the emergency epidemic prevention materials reserve and supply system in Shanxi Province, and also have certain guiding significance for how to deal with major public health emergencies.

Multi-objective optimization of production system with staggered production

In the manufacturing industry, timely order fulfilment from diverse customers is important for operational profitability, given the constraints of limited production capacity. This paper proposes a novel queueing system for a production system, in which order processing times are categorised as regular or rush based on customer preference. The proposed queueing system employs variable service rates and service strategies in conjunction with staggered production to deal with heterogeneous arrivals. We compute the steady-state probability distribution and develop system performance indicators. The lower and upper bounds of the expected sojourn time for rush and regular orders are derived. Numerical results show that an appropriate switching policy can reduce rush orders by approximately 35% while regular orders increase by less than 10%, demonstrating significant improvement in order fulfilment management. We formulate a multi-objective optimisation problem and implement the NSGA-II algorithm to obtain Pareto optimal solutions. Finally, three regression models are proposed to determine the minimum cost and the associated optimal service rates, given the maximum acceptable value of the expected number of rush orders. The established models can explain at least 80% of the dataset of the optimal solutions and thus our numerical findings provide useful guidance for decision-makers and production managers.

Enhancing Order to Cash Processes in SAP Sales and Distribution

In the dynamic landscape of global business, optimizing Order to Cash (O2C) processes has become imperative for organizations striving to achieve operational excellence and customer satisfaction. This paper explores strategies for enhancing O2C processes within the SAP Sales and Distribution (SD) module, focusing on improving efficiency, accuracy, and overall performance. The SAP SD module plays a pivotal role in managing sales transactions, from order processing to invoice generation and payment collection. However, many organizations face challenges such as manual data entry, delayed order fulfillment, and inconsistent customer interactions, which can hinder their financial performance and customer relationships. This study presents a comprehensive analysis of best practices for streamlining O2C processes in SAP SD. Key areas of focus include automating routine tasks to reduce manual errors, integrating data across different systems to enhance visibility and control, and leveraging advanced analytics to gain insights into process inefficiencies. Additionally, the paper examines the implementation of innovative technologies such as artificial intelligence and machine learning to further refine order management and forecasting capabilities. By adopting these enhancements, organizations can achieve faster order processing times, improved cash flow management, and higher customer satisfaction levels. The paper concludes with practical recommendations for businesses seeking to optimize their O2C processes, emphasizing the importance of a holistic approach that integrates technology, process reengineering, and continuous improvement. This research provides valuable insights for both practitioners and scholars interested in advancing the efficiency and effectiveness of O2C processes within the SAP SD framework, ultimately contributing to enhanced organizational performance and competitive advantage.

Design and development of an automated product-fetching platform for electronic orders

We present "E-shopaholic," an innovative e-commerce platform that integrates advanced automation techniques to optimize the online shopping experience. "E-shopaholic" exploits automation to address challenges of traditional e-commerce systems, particularly in order-picking. Leveraging the Internet of Things (IoT), "E-shopaholic" employs a NodeMCU ESP8266 module and a servo motor to simulate the picking and placing of products, aiming to contribute to the evolution of efficient e-commerce operations through automation. The platform, with its real-time automated order processing and updates, aims to enhance operational efficiency by lowering order processing times, increasing accuracy, and improving the overall order fetching process. The system design and implementation are discussed in detail, highlighting the potential of IoT and automation to transform e-commerce practices. "E-shopaholic" serves as a proof of concept for scalable and cost-effective solutions in the e-commerce sector, suggesting a path forward for research and development in automated systems.

Investigating the Impact of Perceived Value and Customer Satisfaction on Repurchase Intentions Among Female E-Commerce Customers

This study investigates the impact of perceived value and customer satisfaction on repurchase intentions, focusing specifically on Shopee's customer base. Employing a quantitative approach with a descriptive methodology, primary data was collected through online questionnaires distributed to female Shopee customers. A total of 100 questionnaires were gathered using purposive sampling, targeting females with previous Shopee purchases. Data analysis utilized SPSS and included validity and reliability testing, coefficient of determination, hypothesis testing, as well as partial and simultaneous testing. Additionally, multiple linear regression analysis was conducted. Results indicate a significant positive influence of perceived value and customer satisfaction on repurchase intentions. Recommendations for Shopee include prioritizing aspects such as shopping convenience, pricing, product quality, and order processing time to enhance perceived value and encourage repurchase intention.

Factors Affecting Customer Satisfaction in Online Shopping in Sri Lanka

The purpose of this study is to find the key factors affecting customer satisfaction in online shopping in Sri Lanka. The main objective of this study is to find the Factors affecting customer satisfaction in online shopping in Sri Lanka. Examining the impact of Product quality, product price, convenience, Delivery service, Payment methods, After-sale service, and Data security on customer satisfaction in online shopping in Sri Lanka is the specific objective. The methodology is carried out by using purposeful sampling strategies to collect data from 365 online customers of nine provinces in Sri Lanka through the use of an online questionnaire. Seven hypotheses were tested using a variety of statistical approaches, such as independent sample t-tests, one-sample tests, and descriptive statistics, as part of a deductive research methodology. After examining the data, it became clear that demographic variables including income, gender, age, and education had no bearing on consumer satisfaction with online purchases. Nevertheless, product quality, convenience, payment methods, after-sale service, and data security were highly significant indicators of online customer satisfaction in Sri Lanka. The study found implications and suggested that online retailers prioritize easy-to-use platforms, premium goods, and practical payment options. It emphasizes how crucial data security and after-sale support are to ensuring client happiness. Improving online customer satisfaction requires addressing difficulties with website accessibility, order fulfillment inaccuracies, inconsistent product availability, order processing times, and return policy simplification. Future Works have been discovered, but further research is necessary to generalize and expand understanding. Specifically, qualitative investigations are needed to explore changing trends in the dynamic online retail ecosystem of Sri Lanka and to find underlying reasons behind the elements that have been found.

ЭКОНОМИЧЕСКИЙ АНАЛИЗ ИСПОЛЬЗОВАНИЯ МЕХАТРОНИКИ В СИСТЕМАХ АВТОМАТИЗАЦИИ СКЛАДОВ И ЛОГИСТИКИ

The article discusses the importance of mechatronics in the automation of warehouses and logistics systems. The role of economic analysis in assessing the feasibility of introducing mechatronics is emphasized. The potential benefits of using mechatronics, such as improved productivity and reduced order processing time, are discussed. The prospects for the development of this technology in the future, including its impact on other industries, are highlighted.

ПРИНЦИПИ РОЗВИТКУ ІНФОРМАЦІЙНОГО ЗАБЕЗПЕЧЕННЯ ЛОГІСТИЧНИХ ПРОЦЕСІВ У ТОРГІВЛІ

he development of information support for logistical processes in trade encompasses various aspects aimed at optimizing and improving their efficiency. However, issues such as system instability or obsolescence, software deficiencies, and limited compatibility among them can complicate the implementation of new technologies. The increasing volume of digital information requires security measures to preserve data confidentiality and integrity. Based on a thorough analysis of the key principles of developing information support for logistical processes in trade, it has been demonstrated that the integration of technologies, digital transformation, openness, standardization, analytics, forecasting, flexibility, scalability, security provision, mobility, as well as collaboration and data exchange are crucial principles for successful supply chain management. These principles are interrelated and complementary creating a solid foundation for enhancing efficiency, optimizing costs, and improving customer satisfaction. It is argued that implementing these principles enables companies to quickly adapt to changes in market conditions, effectively respond to challenges in the modern business environment, and ensure a high level of competitiveness. This is made possible by optimizing logistical processes, reducing order processing time, improving demand forecasting accuracy, and ensuring supply continuity. It is argued that the successful implementation of these principles requires companies not only to adopt advanced technologies but also to be prepared for cultural changes, including openness to innovation, willingness to collaborate, and data exchange among all participants in the logistics chain. Through this, companies can create an efficient, flexible, and secure logistics system capable of meeting the needs of the modern market and ensuring high customer satisfaction. The development of information support for logistical processes in trade, based on the principles of technology integration, digital transformation, flexibility, and other key aspects, forms the basis for increasing efficiency, reducing costs, and improving service. Collaboration and data exchange provide companies with the ability to quickly adapt to market changes, optimize supply chains, and ensure competitiveness. The use of modern technologies and approaches, including mobility and data security provision, plays a crucial role in creating an effective and reliable logistics system.

Disruptions in the COVID-19 Pandemic in the Supply Chains of the Automotive Industry as Crucial for the Polish Economy

The purpose of this article is to identify disruptions occurring in the supply chain of automotive enterprises operating in Poland during the COVID-19 pandemic and ways to counteract them, taking into account the type of business conducted. The specific objectives are to identify and characterise the types of enterprises operating in automotive supply chains, to identify changes during the pandemic in automotive enterprises, depending on the type of activity, and to show disruptions in automotive supply chains and ways to counteract these disadvantages depending on the type of activity. Automotive enterprises operating in Poland were selected for the study purposefully. A random selection method was used to select 500 automotive companies for the study. The data sources were surveys conducted in June 2023 among 500 enterprises in the form of a direct telephone interview. The Pearson χ2 test of independence was used to check the existing relationships between the type of business activity and the results and responses obtained in the surveys. Automotive companies experienced supply chain disruptions, regardless of the type of business. Most often, these were caused by lockdowns and official closures, low stocks of materials and products, and problems with employees. The type of business conducted was not a factor determining the type of disruptions in the supply chain. All types of enterprises reported similar problems in the supply chain, such as longer order processing times, increased transport, storage, and labour costs, and decreased on-time delivery. Disruptions were most often short-lived, up to 1 year. Car producers indicated that disruptions were longer and even longer lasting compared to sales, maintenance, and repairs. Companies countered disruptions by using mostly operational measures, which were short-lived. Only a small percentage of operators made strategic decisions and countered disruptions over a longer period. The scale of these actions was greater among manufacturers than among dealers and car service companies. The theoretical contribution to science was the identification of disruptions and ways of responding to them in supply chains in the automotive industry, depending on the main types of activities conducted. A practical contribution may be the presentation of enterprises’ reactions to the uncertainty and the recommendation that enterprises have ready scenarios for responding to occurring disruptions.

Optimizing Production Schedules: Balancing Worker Cooperation and Learning Dynamics in Seru Systems

This paper aims to investigate the seru scheduling problem while considering the dual effects of worker cooperation and learning behavior to minimize the makespan and order processing time. Given the complexity of this research problem, an improved shuffled frog leaping algorithm based on a genetic algorithm is proposed. We design a double-layer encoding based on the problem, introduce a single point and uniform crossover operator, and select the crossover method in probability form to complete the evolution of the meme group. To avoid damaging grouping information, the individual encoding structure is transformed into unit form. Finally, numerical experiments were conducted using numerical examples of large and small sizes for verification. The experimental results demonstrate the feasibility of the proposed model and algorithm, as well as the necessity of considering worker dual behavior in the seru scheduling problem.

Clearing the path: Leveraging operational optimization strategies to enhance patient access.

450 Background: The complexities of navigating the healthcare system for an oncology patient can be extensive and overwhelming. Prioritizing ease of access to care is essential. The time and effort required to coordinate visits, treatments, and other appointments can result in time toxicity, limiting the patient’s ability to engage in activities that enhance their quality of life. Minnesota Oncology, a practice within The US Oncology Network, is a large community oncology practice in Minneapolis, MN with 90 providers and 14 sites of care. The leadership team at Minnesota Oncology recognized challenges with patient scheduling as a priority. This was based on patient and staff feedback which included patient satisfaction surveys, significant telephone hold times for scheduling, and missed appointments due to a delay in order processing time. Methods: The quality improvement initiative followed a methodological framework that includes identifying the root causes of challenges patients face related to access to care and the subsequent development and execution of effective improvement solutions. The primary objective of the improvement initiative was to address telephone wait times for scheduling, overall patient satisfaction with scheduling and access, and order processing time. To identify the necessary focus for interventions, a ‘Kaizen’ event was hosted involving front line scheduling and clinical staff. The team aimed to identify workflow redundancies, bottlenecks, and inefficiencies via workflow mapping and root cause analysis methodologies. Leveraging the insights gained from these approaches, the following strategies for improvement were executed: Regionalized resource utilization to maximize productivity; strategic redistribution of workload to improve efficiency; workflow redesign to streamline processes and eliminate waste; and redefinition of key performance indicators for business unit performance management. Results: Through the methodological identification of root cause analyses and a tailored execution approach, results were achieved (table.). Conclusions: The improvements in the identified categories were achieved through strategic operational optimization, without the need for additional resources. This underscores the importance of adopting a culture of methodological continuous improvement in the management of clinic operations and highlights the importance of ongoing improvement efforts in delivering efficient care and achieving favorable business and patient experience outcomes.[Table: see text]

Development of an Order Processing System using Google Sheets and Appsheet for a Malaysian Automotive SME Factory Warehouse

The automotive industries in Malaysia have grown well and developed many suppliers in their supply chain including Small Medium Enterprise (SME). The order handling process at a supplier's factory has become one of the most critical areas in the supply chain. Adaptation to technology such as IoT enables the automotive supplier to better manage their customer orders and avoid mistakes that affect the supply chain. In order to improve the order processing activities, a study has focused on developing a mobile device application using Google Appsheet and Google Sheets as a cost-effective system for managing supply orders. A study was conducted in one of Malaysian SME automotive companies, which manages the orders manually by using a log book with a lot of recording and redundant work. By using Google Sheets, all the information and data involved in order processing activities is imported and digitized. Then, a mobile application is created using Appsheet so that the ordering activities and processing can be completed on a mobile device. All information gathered by the mobile app (Google Appsheet) is immediately saved in Google Sheets on an Excel-based database, allowing for further data analysis. The research conducted has managed to integrate these two applications into a system for Malaysia's SME factory to manage the ordering activities in the automotive supply chain. This system enables the user to shorten their order processing time since data is captured in real time and mistakes due to manual error can be avoided.

Use of Technology in Warehouse Operations

The primary aim of this research is to examine how information technology impacts the efficiency of warehouse operations at a pharmaceutical supply agency. The study used a descriptive method and a cross-sectional study design. The participants were all professional staff members working in the stock and distribution directorate, and all of them were included in the study. The information technology tool employed in the study was found to enhance the performance of various warehouse operations, including inventory accuracy, picking accuracy, put-away accuracy, receiving time, order processing time, and warehouse space utilization. However, the study also revealed that the information technology implementation did not prevent theft and product leakage. Several challenges were identified, including frequent electric power interruptions, slow or unreliable internet connectivity, insufficient training and support for staff, and a lack of confidence among employees in using the information technology for effective warehouse operations management.

Research on Impact of IoT on Warehouse Management.

Automation and digitisation are the driving force of the Industrial Revolution 4.0. Industrial revolutions led to the mass production of goods, which increased the need for modern warehouses. Every year, the operation of warehouses becomes increasingly more complicated due to the increasing abundance of goods, thus the usual warehouse management strategies are no longer suitable. In order to cope with huge product flows, modern innovations should be used more extensively to manage these processes. Successful management will help provide quality service to rapidly changing business sectors. The Internet of Things (IoT) is a technology designed to process large amounts of data with maximum efficiency in real time. This technology can facilitate the implementation of smart identification, tracking, tracing, and management using radio frequency identification (RFID), infrared sensors, global positioning systems (GPS), laser scanners, and other detection tools. Such innovations as IoT have made a significant impact on warehousing operations. The aim of IoT is to perform administrative work, i.e., to efficiently manage warehouse data. IoT can be used to monitor and track goods, forecast demand trends, manage inventory, and perform other warehouse operations in real time. The key elements of a warehouse are sales and customer satisfaction. Implementing IoT improves financial performance, work productivity, and customer satisfaction. However, innovation requires additional investment in, for instance, implementation and maintenance. It is necessary to investigate how warehouse elements such as inventory accuracy or order processing time are affected by the internet of things in companies of different sizes. Research on the impact of IoT on warehouse management focuses on IoT advantages, disadvantages, mitigation risks, and the use of IoT in warehouses. The aim of this work is to research the impact of IoT on warehouse management in companies of different sizes and to determine whether the costs and benefits of IoT differ in the same scenario. As a result, the conceptual model for the adoption of IoT measures in warehouse companies was created, and its suitability was assessed by experts.

THE INFLUENCE OF PERCEIVED PRICE AND QUALITY OF DELIVERY ON ONLINE REPEAT PURCHASE INTENTION: THE EVIDENCE FROM VIETNAMESE PURCHASERS

The purpose of this study is to explore the role of perceived price and delivery quality in influencing the repeat purchase intention through perceived value and consumer satisfaction. A total of 791 survey questionnaires were collected by the convenience sampling method from consumers who have previously purchased from the same online store. This study uses partial least squares to analyze data through SmartPLS 3.0 software. The data analysis procedure consists of assessing the reliability and validity of the measurement model and the evaluation of the structural model. The research results show that delivery quality and perceived price affect the perceived value and do not affect satisfaction. In contrast, perceived value directly affects repeat purchase intention and indirectly through satisfaction. Online stores should pay attention to order processing time, packaging time and cooperate with shipping companies to improve delivery quality. In addition, online stores should also have appropriate pricing policies to help increase perceived value and form repeat purchase intentions.

A Pareto-based discrete particle swarm optimization for parallel casting workshop scheduling problem with fuzzy processing time

The parallel casting workshop scheduling problem is essentially a type of unrelated parallel machine scheduling (UPMS) problem with fuzzy order processing time. In this paper, a tri-objective parallel casting workshop optimization model is investigated to minimize the order advance/delay penalty, makespan, and workload imbalance, and a formula of the solution scale with the number of orders and workshops is derived. In line with the multi-objective integer programming model, a new Pareto-based discrete particle swarm optimization (PDPSO) is proposed. In this algorithm, the particles are encoded with integers. A novel two-phase heuristic is developed for integer-encoded particle updates. The first phase determines the order assignment through a weighting vote, and the second phase sorts the orders according to their dominance in each workshop. Every particle’s update is simultaneously guided by the best compromise solution selected from the personal and global Pareto solution. Meanwhile, the Pareto simulated annealing strategy is introduced in PDPSO to enhance its exploration ability. Multiple simulations under different scales are tested to validate the effectiveness of our proposed model and algorithm. The results show that the proposed PDPSO outperforms the other two multi-objective algorithms and has large advantages in finding the Pareto frontier (PF) with an acceptable solution number and solving speed, especially for small and medium-sized instances.

Estimating impacts of logistics processes on online customer ratings: Consequences of providing technology‐enabled order tracking data to customers

AbstractRecent advances in logistics tracking technologies have enabled e‐commerce firms to both accurately track the shipments and obtain accurate estimates of delivery times. With customers constantly tracking the status of their orders, the effects of logistics processes on customers' evaluation of their online shopping experience are relatively unknown as academic literature provides little guidance. Drawing upon the expectation‐disconfirmation theory and attribution theory, and using a unique dataset assembled in collaboration with an e‐commerce firm, we empirically investigate the effects of order processing and delivery times on online customer ratings. We also estimate the impact of positive deviation (i.e., early delivery) and negative deviation (i.e., late delivery) from the promised delivery date on online customer ratings and whether the impact of delivery status (early or late) depends on order cost and freight cost. Our empirical findings demonstrate that longer order processing and delivery times are associated with lower ratings. Our results also highlight that the late delivery of an order is negatively associated with ratings and that the order cost amplifies this effect. Furthermore, freight cost reduces both the negative effect of late delivery and the positive effect of early delivery on online ratings. Our results also indicate that there exist curvilinear relationships between online ratings and the number of days an online order is late or early. Specifically, the negative effect of a late delivery follows a convex‐shape curve such that its impact decreases as the number of days an online order is late increases. Likewise, the positive effect of an early delivery increases at a decreasing rate as the number of days an online order is early increases. Our supplementary analyses further account for potential endogeneity issues and corroborate our main results with additional empirical evidence obtained through alternative model specifications and estimation strategies. We present important theoretical implications and managerial takeaways from our findings.