Customer Requests Research Articles

Spore-Forming Clostridia in Raw Cow Milk from Northern Italy: A Trend Analysis over the Past 20 Years.

Clostridium species are known for their impact on animal and human health, but also for the spoilage of foodstuffs. Their spores contaminate milk and result in germination and gas production, the latter being particularly evident in the cheeses that suffer severe depreciation. To address this issue, the Primary Production Department of the IZSLER institute in Brescia, Italy conducts the Most Probable Number (MPN) method on bovine milk samples collected from Northern Italian dairies between 2004 and 2023. This approach leverages two semi-quantitative protocols, S2 and S3, to detect Clostridium species spore forms upon customer request. Here, we would like to present an a-posteriori analysis on the results of the S2 and S3 protocols. The goal of this study is to highlight the differences between these two methods and provide evidence of the actual decrease in Clostridium species in raw cow milk over a 20-year period. Our analysis shows that client demand for S2 has progressively decreased, while S3's has remained constant, and both protocols reveal a significant reduction in positives; furthermore, S3's greater sensitivity made it more responsive to environmental changes. This highlights the necessity of choosing the appropriate testing protocol that accounts for both regulatory standards and environmental factors. Overall, our findings underscore the importance of continued monitoring to manage Clostridium species contamination and ensure milk quality.

Dynamic Contract Design in the Presence of Double Moral Hazard

We consider a stylized incentive management problem over an infinite time horizon, where the principal hires an agent to provide services to customers. Customers request service in one of two ways: either via an online or a traditional offline channel. The principal does not observe the offline customers’ arrivals, nor does she observe whether the agent exerts (costly) effort that can increase the arrival rate of customers. This creates an opportunity for the agent (i) to divert cash (that is, to under-report the number of offline customers and pocket respective revenues) and also (ii) to shirk (that is, not to exert effort), thus leading to a novel and thus far unexplored double moral hazard problem. To address this problem, we formulate a constrained, continuous-time, stochastic optimal control problem and derive an optimal contract with a simple intuitive structure that includes a payment scheme and a potential termination time of the agent. We enrich the model to allow the principal to either (i) dynamically adjust the prices for the services in both channels or (ii) monitor the agent. Both tools help the principal to alleviate the double moral hazard problem. We derive respective optimal strategies for using those tools that guarantee the highest profits. We show that the worse the agent’s past performance is, the lower the prices should be set and the more the principal should monitor the agent. This paper was accepted by Ilia Tsetlin, behavioral economics and decision analysis. Funding: The work of F. Tian is financially supported by the Hong Kong RGC Funding [RGC General Research Fund 2023/24 17502023], HKU Seed Fund for Basic Research for New Staff (2022), and the HKU Business School Shenzhen Research Institute [SZRI2023-BRF-04]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.01169 .

A comparison of reinforcement learning policies for dynamic vehicle routing problems with stochastic customer requests

This paper presents directions for using reinforcement learning with neural networks for dynamic vehicle routing problems (DVRPs). DVRPs involve sequential decision-making under uncertainty where the expected future consequences are ideally included in current decision-making. A frequently used framework for these problems is approximate dynamic programming (ADP) or reinforcement learning (RL), often in conjunction with a parametric value function approximation (VFA). A straightforward way to use VFA in DVRP is linear regression (LVFA), but more complex, non-linear predictors, e.g., neural network VFAs (NNVFA), are also widely used. Alternatively, we may represent the policy directly, using a linear policy function approximation (LPFA) or neural network PFA (NNPFA). The abundance of policies and design choices complicate the use of neural networks for DVRPs in research and practice. We provide a structured overview of the similarities and differences between the policy classes. Furthermore, we present an empirical comparison of LVFA, LPFA, NNVFA, and NNPFA policies. The comparison is conducted on several problem variants of the DVRP with stochastic customer requests. To validate our findings, we study realistic extensions of the stylized problem on (i) a same-day parcel pickup and delivery case in the city of Amsterdam, the Netherlands, and (ii) the routing of robots in an automated storage and retrieval system (AS/RS). Based on our empirical evaluation, we provide insights into the advantages and disadvantages of neural network policies compared to linear policies, and value-based approaches compared to policy-based approaches.

A two-phase algorithm for the dynamic time-dependent green vehicle routing problem in decoration waste collection

Transportation activities associated with construction waste generate substantial carbon emissions, an issue that is attracting increasing environmental concern. To raise construction waste transportation efficiency and reduce carbon emission, we address the dynamic time-dependent green vehicle routing problem for decoration waste collection (DTDGVRP-DWC). In the existing literature, many deterministic approaches to the dynamic vehicle routing problem are myopic, as they only react to already arrived requests. In this paper, we propose a stochastic sampling method to tackle with uncertain customer request in the real world. The DTDGVRP-DWC is formulated as an 0-1 programming. In the new model, we consider urban traffic congestion and variable speeds over time, and factors that significantly influence both carbon emissions and fuel costs. To quickly solve the complicated optimization problem, we develop a two-phase algorithm. In the first phase, we embed a competitive simulated annealing algorithm to determine visitation schedules for anticipated and early-request customers. In the second phase, we propose an event-trigger mechanism to decompose the dynamic problem into a series of static sub-problems, and an effective heuristic to solve each sub-problem. Computational results show that as long as the prediction accuracy exceeds a threshold, the forecast routing method consistently performs better than reactive routing. A real-world case demonstrates that an early commitment waiting time strategy benefits timely service requirements, whereas a late or hybrid waiting time strategy takes overall efficiency and customer satisfaction into account.

AI-Driven Resource Provisioning: Enhancing Elasticity and Efficiency with Hybrid RNN-LSTM Models

Automatic resource provisioning techniques that dynamically modify resources in response to service demand are essential to implementing elasticity services. For systems with strict latency or reaction time requirements, such as Enterprise Resource Planning (ERP) systems under high traffic loads, this flexibility is crucial for lowering power consumption and guaranteeing quality of service (QoS). Determining the best point at which to scale resources is still a difficult task. In this research, we provide an AI-integrated system that adjusts resources according to anticipated demand. To predict load requests, the system uses an LSTM model and a Hybrid Recurrent Neural Network (RNN). This strategy seeks to minimize overprovisioning, which will lower the cost of infrastructure and energy usage. The deep learning model aims to estimate the resources required to improve service response time and satisfy customer requests, as well as to anticipate with high accuracy the processing load of distributed servers. Proactive provisioning decisions are made for the servers based on the anticipated load. Our tests on a common server request dataset show that the suggested RNN+LSTM model performs better than traditional deep learning models in terms of efficient resource management and prediction accuracy.

Preprocessing Text Data for the Doc2Vec Model in the Automatic Classification of Tickets in Support Centers of Software Providers

Leading software providers typically implement customer technical support functions, which are crucial for promoting and enhancing the competitiveness of their products and services in global markets. The high volume and heterogeneity of support tickets (functional, temporal, linguistic, etc.) highlight the importance of efficient classification systems. Effective classification optimizes the distribution of these tickets among support center specialists and automates their processing using an established knowledge base. However, classifying these tickets is a loosely formalized task. For companies that have accumulated substantial data on customer requests, automating classification through machine learning methods and natural language processing models, such as Word2Vec, FastText, BERT, and GPT, becomes feasible. It is generally accepted that classification effectiveness primarily depends on the model employed. Nevertheless, the quality of these models is significantly influenced by the nature of the training data. Literature review of the reveals significant research interest in methods for the automatic classification of tickets specifically tailored to the operational conditions of software provider support centers. However, there is a noticeable gap in the literature regarding the impact of data preprocessing on the quality of these models. The article aims to clarify the techniques of data preprocessing and analyze their impact on the effectiveness of text classification, considering the specificity of software provider support centers. This study examines the stages of the automatic classification process for tickets, accounting for the unique characteristics of the data (customer text requests). A relevant set of specified methodological and instrumental tools was developed and tested using open data from a global software provider (DevExpress). The testing involved a database of 165,000 tickets. The study's results indicate that preprocessing can improve classification metrics such as F-measure, Precision, and Recall from 77% to 79%. Additionally, preprocessing significantly reduces the dimensionality of text data (by 48.2%) and increases model training speed (by 26.5%) without loss of accuracy, ensuring cost-efficiency and operational efficiency in the use of computational resources.

Optimizing a Dynamic Vehicle Routing Problem with Deep Reinforcement Learning: Analyzing State-Space Components

Background: The dynamic vehicle routing problem (DVRP) is a complex optimization problem that is crucial for applications such as last-mile delivery. Our goal is to develop an application that can make real-time decisions to maximize total performance while adapting to the dynamic nature of incoming orders. We formulate the DVRP as a vehicle routing problem where new customer requests arrive dynamically, requiring immediate acceptance or rejection decisions. Methods: This study leverages reinforcement learning (RL), a machine learning paradigm that operates via feedback-driven decisions, to tackle the DVRP. We present a detailed RL formulation and systematically investigate the impacts of various state-space components on algorithm performance. Our approach involves incrementally modifying the state space, including analyzing the impacts of individual components, applying data transformation methods, and incorporating derived features. Results: Our findings demonstrate that a carefully designed state space in the formulation of the DVRP significantly improves RL performance. Notably, incorporating derived features and selectively applying feature transformation enhanced the model’s decision-making capabilities. The combination of all enhancements led to a statistically significant improvement in the results compared with the basic state formulation. Conclusions: This research provides insights into RL modeling for DVRPs, highlighting the importance of state-space design. The proposed approach offers a flexible framework that is applicable to various variants of the DVRP, with potential for validation using real-world data.

Scheduling multi-skill technicians and reassignable tasks in a cloud computing company

We investigate a multi-skill technician and reassignable task scheduling problem in a cloud computing company. In the problem, multi-skill technicians are assigned to process a large number of tasks from customer requests in a certain scheduling horizon. The tasks are allowed to be reassigned to another technician multiple times, and one technician can process multiple tasks in parallel. The company not only focuses on processing efficiency, but also expects to improve customers’ experience and technicians’ satisfaction. We characterize the feasible solutions and introduce a weighted objective with three metrics: processing efficiency, response delay, and workload balance. An effective two-stage hierarchical optimization method embedded in a greedy randomized adaptive search procedure framework is proposed. In the first stage, initial solutions are generated by a greedy randomized construction procedure, and then improved by local search with an ejection chain operator to optimize processing efficiency. In the second stage, two local search procedures with five operators for improving response delay or workload balance are designed. Computational experiments are conducted to evaluate the effectiveness of our algorithm. The results show that the proposed algorithm is competent in fast computing a schedule of high quality. It also reveals that reassignments are helpful in reducing response delay and balancing workloads in the scheduling.

Dynamic pickup-and-delivery for collaborative platforms with time-dependent travel and crowdshipping

We study a pickup-and-delivery problem that arises when customers randomly submit requests over the course of a day from a choice of vendors on a collaborative e-commerce portal. Based on the attributes of a customer request, a dispatcher dynamically schedules the delivery service on either a dedicated vehicle or a crowdshipper, both of whom experience time-dependent travel times. While dedicated vehicles are available throughout the day, the availability of crowdshippers is unknown a priori and they appear randomly for only portions of the day. With an objective of minimizing the sum of routing costs, piece-rate crowdshipper payments, and lateness charges, we model the uncertainty in request arrivals and crowdshipper appearances as a Markov decision process. To determine an action at each decision epoch, we employ a heuristic that partially destroys the existing routes and repairs them under the guidance of a parameterized cost function approximation that accounts for the remaining temporal capacity of delivery vehicles. We benchmark our real-time heuristic with an adaptive large neighborhood search and demonstrate the effectiveness of our method with several performance metrics. In addition, we conduct computational experiments to demonstrate the impact of inserting wait time in the route scheduling and the benefit of explicitly modeling time-dependent travel times. Through our computational testing, we also investigate the potential of demand management mechanisms that facilitate many-to-one request bundles or one-to-many request bundles to reduce the cost to service requests.

Survey on the salt usage among Fish Cracker (Keropok lekor) manufacturers in several districts of Kuala Terengganu, Malaysia

Fish crackers (keropok lekor) are known for their tastiness. Although rich in protein and iodine, it contains a high amount of salt or sodium. Therefore, this study aimed to determine the manufacturers’ practice in the preparation of keropok lekor, perception and challenges concerning salt usage, and motivation to reduce salt content. This study also aimed to estimate the sodium content in keropok lekor. Seven manufacturers of keropok lekor in Terengganu were interviewed either face-to-face or online to obtain relevant information. All interviews were recorded, transcribed, and analyzed based on a thematic analysis framework. The recipes of keropok lekor were analyzed using the Nutritionist Pro software to determine the nutritional and sodium content, calculated based on the Malaysian and ASEAN Food Composition Tables. Based on the interviews, most manufacturers (90%) supported the effort to reduce salt in keropok lekor. However, the effort was met with several challenges, such as customer acceptance and negative perception of the taste, decreased sales, and increased material costs. The enabling factors include maintaining the original flavor of keropok lekor, customer requests, marketing opportunities, and the availability of salt substitutes. Support for marketing and financial technology know-how is also necessary. An estimate of 606.43 mg/100g of sodium content in keropok lekor was obtained in this study, which was mainly derived from the usage of sodium chloride and monosodium glutamate (MSG) during the preparation. This study revealed the high salt content in keropok lekor due to the addition of salt and seasonings. There is a potential to reduce salt content in keropok lekor with appropriate support provided to the manufacturers. Nevertheless, further research is needed to produce keropok lekor with reduced salt content, improved sensory, and enhanced microbial and physicochemical characteristics, which can improve customer perception.

Application of artificial intelligence in business management and financial market forecasting

In today's business world, artificial intelligence as a new technology has provided dynamic and flexible capabilities in organizational decision-making processes, considering that organizational decision-making often faces challenges due to the complexity and multiplicity of factors affecting it. The application of artificial intelligence as A powerful tool can be effective in improving the organizational decision-making process and improving the performance of organizational managers. Artificial intelligence is one of the popular and widely used new sciences in almost all aspects of life and can do things faster and more accurately. The numerous advantages of artificial intelligence and its wide applications today are not hidden from anyone. Financial services companies are constantly looking for ways to gain superiority in the capital market and surpass their competitors. To do this, companies are moving away from old systems and models They adopt a more agile business that includes artificial intelligence techniques in capital markets, artificial intelligence in financial markets and machine learning (ML). In capital markets, companies use artificial intelligence and ML projects in coordination with RPA robotic process automation technologies to meet customer requests, speed up financial market analysis, and more accurately identify risk in the business process.

A Profit Maximization Problem of Equipment Rental and Scheduling with Split Services

This study addresses a scheduling problem arising from the “Rent Instead of Buy” business model where rental service platforms rent equipment from suppliers to satisfy customer requests in exchange for a lump-sum payment. Suppliers often implement piecewise linear concave charging policies to encourage longer rental duration. This policy prompts the platform to consolidate service periods into longer rental agreements for lower rental rates, potentially necessitating the splitting of a customer request among multiple facilities. We establish an integer programming model to maximize the platform’s profit, which is equal to the total revenue from serving customers minus the total rental and scheduling costs. To solve the problem, we propose two Dantzig-Wolfe decomposition reformulations and design branch-and-price algorithms accordingly. Furthermore, by exploiting the property of the concave rental cost functions, we derive the optimal structure regarding the start and end times of services, significantly reducing the subproblem’s search space. Numerical experiments based on real-life data validate the efficiency of the designed algorithms compared to commercial solvers and highlight the benefits of split services. Sensitivity analysis reveals that as the rental cost function becomes more segmented, more requests are split-serviced, but the times for splitting a request are seldom more than once.

Product personalization and brand retailer performance: The critical role of brand retailer‐upstream supplier control

AbstractBrand retailers are increasingly offering product personalization to accommodate the requests of individual customers. While prior research has examined product personalization from a customer's or a manufacturer's perspective, this study focuses on product personalization from a brand retailer's perspective. Under product personalization, brand retailers that outsource production to upstream suppliers face two significant challenges: product returns from downstream customers, and personalization costs charged by upstream suppliers. Employing an analysis of transaction data from an online women's wedding dress firm, this study finds that as product requests increase (i.e., more product features must be modified), personalization costs increase, but the likelihood of product returns decreases. Additionally, as time requests increase (i.e., delivery is more urgent), the likelihood of product returns increases. Furthermore, relationship‐specific process control exacerbates (i) the effect of product requests on personalization costs, and (ii) the effect of time requests on the likelihood of product returns. Finally, relationship‐specific outcome control and transaction‐specific control alleviate the impact of time requests on the likelihood of product returns while exacerbating the effect of product requests on personalization costs. The findings suggest that retailers can realize greater benefits from product personalization by selecting control mechanisms tailored to personalization requests.

Modeling routing problems in QUBO with application to ride-hailing

Many emerging commercial services are based on the sharing or pooling of resources for common use with the aim of reducing costs. Businesses such as delivery-, mobility-, or transport-as-a-service have become standard in many parts of the world, fulfilling on-demand requests for customers in live settings. However, it is known that many of these problems are NP-hard, and therefore both modeling and solving them accurately is a challenge. Here we focus on one such routing problem, the Ride Pooling Problem (RPP), where multiple customers can request on-demand pickups and drop-offs from shared vehicles within a fleet. The combinatorial optimization task is to optimally pool customer requests using the limited set of vehicles, akin to a small-scale flexible bus route. In this work, we propose a quadratic unconstrained binary optimization (QUBO) program and introduce efficient formulation methods for the RPP to be solved using metaheuristics, and specifically emerging quantum optimization algorithms.

Analisis Penerapan Standar Operasional Prosedur Terhadap Kualitas Kinerja Karyawan pada Custom Gift Box di Purwokerto Jawa Tengah

To the organization's goals, standard operational procedures (SOPs) require an approach to prevent and reduce errors and deviations in the performance of tasks. SOP is an administrative control model accompanied by supervision by the leadership or staff who acquire duties and functions. Employee performance is influenced by the implementation of Standard Operational Procedures (SOPs), where the work process is based on standards and criteria that have been previously established. Some of the facts still found in the field are: 1) product specifications do not match customer request specifications; 2) delays in delivery times; 3) uneven workloads of employees. Besides, there are activities that haven't run according to the instructions / short and there is still a waste of time on the production process. This research aims to describe the strategy of implementing Standard Operational Procedures in improving the quality of employee performance on Custom Gift Box in Purwokerto Central Java. The research approach used is qualitative where this approach produces findings obtained through data collected in a variety of ways, including interviews, observations, and documentation studies to gain an in-depth understanding of the problem rather than generalization. This research produces a description of the application of Standard Operational Procedures (SOP) production in Custom Gift Box in running the production process of making custom-boxes.In addition, the implementation of Standard Operations procedures in Custom Gift Boxes has an impact on improving the performance of employees in carrying out custom box making process through the awareness of owners and employees.

ANALISIS PENANGANAN KELUHAN PELANGGAN DI PERUSAHAAN DAERAH AIR MINUM (PDAM) TIRTA HIDAYAH KOTA BENGKULU

Responsiveness is one of the dimensions of service quality that is important for assessing the extent to which service providers can respond to customer requests and complaints quickly and appropriately. This research aims to determine the responsiveness of public services in handling customer complaints at PDAM Tirta Hidayah, Bengkulu City. The method used in this research is a qualitative method with a descriptive approach. The theory used in this research is the Responsiveness theory from Zeithml, using 4 theoretical indicators, namely, ability to respond to society, speed of service, accuracy of service, and timeliness of service. The results of the research show that PDAM Tirta Hidayah, Bnegkulu City has attempted to increase responsiveness in handling customer complaints, but based on findings in the field, PDAM Tirta Hidayah still lacks response in terms of timeliness in making service improvements to customers. These findings are expected to provide input for PDAM Tirta Hidayah in improving service quality and customer satisfaction. Keywords: Responsiveness, Public Service, Customer Complaints

Planning the customer service process of a logistics company based on implementing green technologies

The peculiarities of planning environmentally oriented customer service for a logistics company based on implementing "green" technologies are discussed in the article. It has been researched that planning is an integral part of the successful management of any company, especially in the logistics sector, where customer satisfaction and operational efficiency are key factors. Under modern conditions, to respond to customer requests, logistics companies must take into account the impact of their activities on the environment and implement appropriate methods to minimize the negative impact on the environment, and accordingly, the process of planning customer service in a logistics company should include an environmental component. Given this, the importance of integrating environmental factors and business processes at each stage of planning to ensure sustainable development of a logistics company based on forming a cyclic approach to planning environmentally oriented customer service is substantiated. The method of iterative planning that allows a logistics company not only to improve the quality of customer service but also to integrate environmental practices into its activities, which meets the modern requirements of sustainable development and social responsibility of business has been established. The developed model of environmentally-oriented planning of customer service combines business processes of logistics service and "green" technologies, transforms the operational efficiency of a logistics company in the context of sustainable development, substantiates the integration of environmentally sustainable solutions at all stages of the logistics chain - from initial planning to the final delivery of goods to consumers. To support the proposed model, the implementation of Power-to-X technology is recommended, which allows the conversion of excess renewable energy into energy specifically for logistics and transportation purposes. The implementation of this measure enables the logistics company to reduce emissions and increase energy efficiency, to form a comprehensive approach to the development and integration of environmentally friendly technologies in the company's logistics operations to increase efficiency and competitiveness. It is emphasized that the proposed model demonstrates a comprehensive approach to building a highly efficient and at the same time environmentally sustainable logistics system of the company by integrating innovative "green" technologies.

Implementasi Administrasi Project Produksi Transformer Sebagai Penunjang Permintaan Customer di PT Bambang Djaja Ngoro Mojokerto

Project administration is the process of managing various aspects related to a project to achieve predetermined goals. It includes planning, organizing, supervising, and controlling all activities and resources involved in the project. Project administration has an important role in analyzing the transformer production network, including determining the sequence of activities and production activities. Efficiency in project administration can have a significant impact on a company's overall performance, reducing costs, increasing productivity, and ensuring customer satisfaction. Therefore, this paper will describe the administration of transformer production projects to support customer requests at PT Bambang Djaja NgoroMojokerto with a participatory approach. It is hoped that the implementation of transformer production project administration can increase operational effectiveness and efficiency, which is key and can become a strategic advantage for companies in facing a highly competitive business environment. The role of effective project administration is critical to achieving high operational efficiency, which in a competitive business environment can be a strategic advantage for a company.

Digitalization of the national economy as a driver of virtualization of communications among dairy market stakeholders

Theoretical and methodological approaches to the study of key trends in the digitalisation of the national economy, which act as a driver of virtualisation of communications of dairy market stakeholders, have been studied and substantiated. It is established the different speed and slow implementation of digital accounting technologies in the conditions of differentiation of requests of interested organisations and customers, providing time saving, and communication costs are calculated. On the one hand, the involvement of local computer networks in the internal activities of national organisations, the internet – in the environment of virtual external interaction, rather with suppliers than with consumers, and e-mail, websites – in all spheres of their virtual activity is predominantly developed. On the other hand, there is a big lag in the implementation of such key elements of digital technologies as “big data”, “internet of things”, artificial intelligence models, radio-frequency identification, “digital twins”. The feasibility of creating a web showcase site for virtual interactions between innovatively active dairy enterprises and customers is substantiated.

Optimizing Cloud Resource Allocation in Federated Environments through Outsourcing Strategies

Cloud computing enables users to access required resources, with the invention of high-end devices leading to exponential increases in cloud resource requests. This poses significant challenges for resource management due to the scale of the cloud and unpredictable user demands. This paper presents an approach to managing resources during peak request periods for virtual machines (VMs) by leveraging cloud federation, outsourcing requests to other federation members. An algorithm is proposed to initiate cloud federation and allocate customer requests within it. The primary objectives are to increase the profit of cloud providers and improve resource utilization. An ensemble algorithm maximizes profit using both the proposed algorithm and three established ones. Experimental results demonstrate that our method outperforms existing approaches in profit, resource utilization, and rejected requests in most scenarios.