Showing papers on "Service level published in 2022"

Decision support system for adaptive sourcing and inventory management in small- and medium-sized enterprises

Abstract: Elevated business uncertainties and competition over recent years have caused changes to the data-driven supply chain management of sourcing and inventories across industries. However, only large-sized enterprises have the resources to harness data for aiding their decision-making and planning. By contrast, small- and medium-sized enterprises (SMEs) commonly have limited resources and knowledge, which affects their ability to collect and utilize data. Thus, it is a challenge for them to implement advanced decision support tools to mitigate the effects of market uncertainties. This paper proposes a decision support system (DSS) for sourcing and inventory management, with the aims of helping SMEs compile and exploit data, and supporting their decisions under business ambiguities. The DSS was developed using a simulation-optimization approach by incorporating an artificial neural network and a genetic algorithm for problem representation and optimizing decision support solutions. The exploitation of observational and empirical data reduces the burden of data compilation obtained from unorganized data sources across SME operations. Further, uncertainty factors such as raw material demand, price, and supply lead time were considered. When implemented in a medium-sized food industry company, the DSS can provide decision support solutions that integrate the selection of recommended suppliers and optimal order quantities. It can also help decision-makers to shape their inventory management policies under uncertain raw material demands, while also considering service levels, sales promotions, lead times, and material availability from multiple suppliers. Consequently, implementation of the DSS helped to reduce the total purchased raw material costs by an average of 51.62% and reduced the order interval and on-hand inventory costs by an average of 54.24%.

A robust optimization approach for multi-product inventory management in a dual-channel warehouse under demand uncertainties

Abstract: This study explores the multi-product inventory problem of a dual-channel warehouse to determine the optimal continuous review inventory policies. The warehouse is divided into two areas, one for fulfilling online orders and the other for storing products and fulfilling offline orders. Uncertainties are involved in the demands of the products during lead time in the two areas with their means and variances as the only known demand information. Using a distributionally robust optimization approach, the problem is formulated as a multi-product inventory model with an individual chance constraint and a multi-product inventory model with a joint chance constraint for the warehouse capacity to minimize the annual total expected cost. Two types of service levels are considered to ensure an adequate customer satisfaction. Through mathematical manipulations, the developed distributionally robust multi-product inventory models are transformed into convex programming models which can be solved efficiently, and the corresponding solution algorithms are developed. In particular, the closed-form solution of the order quantities is derived for the model with the individual chance constraint. Numerical experiments are performed to verify the effectiveness and practicality of the proposed models and the solution approaches in dealing with demand uncertainties and to draw specific managerial insights. Effects of important problem parameters on inventory policies and cost performance are also analyzed through numerical studies. Recommendations on the warehouse structure are given for business firms engaged in both online and offline sales. The continuous review inventory policies obtained by the proposed approach are robust and are flexible in making decisions for the operations of dual-channel warehouses and supply chains with only limited demand information. The proposed algorithms are proved to be very efficient through computational experiments.

Shared autonomous vehicle management considering competition with human-driven private vehicles

Abstract: Shared autonomous vehicles (SAVs) are expected to be an essential component in developing efficient and sustainable transportation systems. This paper focuses on optimizing the operational decisions of SAV systems when the mode choices between SAVs and human-driven private vehicles are considered. A time–space network flow model is formulated to optimize the vehicle assignment and relocation decisions, while a binary logit model is used to describe the nonlinear relationship between the elastic demand and its various important attributes, including the trip price and the level of service. An enhanced outer–inner approximation approach is proposed for solving the mixed-integer nonlinear programming (MINLP). For reducing the approximation error, a dynamic programming algorithm is developed to select the optimal breakpoints. The proposed modeling and solution approaches are tested based on a case study in Singapore. Our computational experiments show that the proposed solution approaches can efficiently obtain the optimal solutions. Numerical results reveal that a SAV system can achieve higher performance by the active relocation activities under the elastic demand. And the SAV system with a high level of service could produce a higher operating profit if users are more sensitive to the level of service.

Intelligent Model for Predicting the Quality of Services Violation

Abstract: Cloud computing is providing IT services to its customer based on Service level agreements (SLAs). It is important for cloud service providers to provide reliable Quality of service (QoS) and to maintain SLAs accountability. Cloud service providers need to predict possible service violations before the emergence of an issue to perform remedial actions for it. Cloud users’ major concerns; the factors for service reliability are based on response time, accessibility, availability, and speed. In this paper, we, therefore, experiment with the parallel mutant-Particle swarm optimization (PSO) for the detection and predictions of QoS violations in terms of response time, speed, accessibility, and availability. This paper also compares Simple-PSO and Parallel Mutant-PSO. In simulation results, it is observed that the proposed Parallel Mutant-PSO solution for cloud QoS violation prediction achieves 94% accuracy which is many accurate results and is computationally the fastest technique in comparison of conventional PSO technique.

Merging make-to-stock/make-to-order decisions into sales and operations planning: A multi-objective approach

Abstract: With the advent of mass customization and product proliferation, the appearance of hybrid Make-to-Stock(MTS)/Make-to-Order(MTO) policies arise as a strategy to cope with high product variety maintaining satisfactory lead times. In companies operating under this reality, Sales and Operations Planning (S&OP) practices must be adapted accordingly during the coordinated planning of procurement, production, logistics, and sales activities. This paper proposes a novel S&OP decision-making framework for a flow shop/batch company that produces standard products under an MTS strategy and customized products under an MTO strategy. First, a multi-objective mixed-integer programming model is formulated to characterize the problem. Then, a matrix containing the different strategies a firm in this context may adopt is proposed. This rationale provides a business-oriented approach towards the analysis of different plans and helps to frame the different Pareto-optimal solutions given the priority on MTS or MTO segments and the management positioning regarding cost minimization or service level orientation. The research is based on a real case faced by an electric cable manufacturer. The computational experiments demonstrate the applicability of the proposed methodology. Our approach brings a practical, supply chain-oriented, and mid-term perspective on the study of operations planning policies in MTS/MTO contexts.

Intelligent inventory management with autonomation and service strategy

Abstract: The manufacturer’s service to the customer is one of the critical factors in maximizing profit. This study proposes the innovative (Q, r) inventory policy integrated with autonomated inspection and service strategy for service-dependent demand. First, an advanced autonomated inspection makes the product error-free. Therefore, this makes customers more satisfied and increases profit. The proposed model decides the optimal investment for such autonomated inspection. Second, three types of services are considered in the study: unpaid, partially paid, and fully paid services. Each type of service has a different service level and the amount of the customer’s payment. Our model finds the optimal service strategy based on the variable conditions along with the optimal quantity and reorder level of inventory policy. Numerical analyses are made for different service strategies, along with a sensitivity analyses for various critical parameters. Results show that the full paid service is 84.88% beneficial compared to the unpaid service, and the autonomated inspection policy is 5.02% beneficial compared to the traditional ones. The increase in unit servicing costs always increases the profit of the company.

Centralised Quality of Experience and Service Framework Using PROMETHEE-II for Cloud Provider Selection

Abstract: The extensive diffusion of cloud services has fostered a business growth culture and innovation that propagate to many consumers and providers. For enabling a sustainable trusted relationship and for forming practicable successful service level agreements (SLAs), all stakeholders need a centralised Quality of Experience (QoE) and Quality of Service (QoS) repository that assists them in forming such an agreement. A cloud consumer needs a centralised QoE repository that supports them in selecting the right service provider that satisfies consumer’s requirements in terms of cost, reliability, efficiency and other QoS parameters. On the other end, a cloud provider needs a reliable QoS repository that provides consumers with up-to-date information about services and enables a provider to take an optimal decision to allocate the amount of marginal resources while forming an SLA. Due to the elastic nature of a cloud and lack of proper resource management, the service provider usually caught in service violation, leading to violation penalties both in terms of trust and money. Existing literature lacks studies on a centralised repository to assist cloud providers in resource management and cloud consumer service selection. To address the issue, we discuss the idea of a Centralised Quality of Experience and Service (CQoES) repository framework. The approach uses PROMETHEE-II method where each alternatives are assessed based on consumer’s custom weighted QoS attributes. The framework ensures the cloud marketplace’s economic growth and helps the interacting parties build a durable and long-term trusted relationship.

Optimizing service level, price, and inventory decisions for a supply chain with retailers' competition and cooperation under VMI strategy

Abstract: In a vendor-managed inventory (VMI) system, a manufacturing vendor manages their retailer inventories. Studies on VMI-type supply chains mostly have not considered competition between retailers. There are few works on the price competition; however, to the best of the authors' knowledge, none of the papers formulated a service competition strategy. The service level is one of the competitive factors among competing retailers. Sometimes retailers choose to compete cooperatively instead of competing independently with the manufacturer. The present work investigates service, price, and inventory decisions under retailers’ competition and cooperation. Considering the manufacturer and retailers as the leader and followers, respectively, a Stackelberg game model of the problem is developed. The present study proposes a solution algorithm to search the Stackelberg-Nash equilibrium in the retailer cooperation and retailer independence models. The algorithm is numerically demonstrated to explore the impacts of decision parameters. To validate the model, a number of parameters are subjected to sensitivity analyses. It was found that a higher self-service (cross-service) level parameter would lead to higher (lower) profits of the retailer and manufacturer and the total profit in the two models. Retailer cooperation enhances retailer performance; however, manufacturer and system profits decline. Furthermore, when retailers cooperate, they are motivated to offer lower service levels.

SLA-Based Group Tasks Max-Min (GTMax-Min) Algorithm for Task Scheduling in Multi-Cloud Environments

Abstract: Cloud is a distributed heterogeneous computing paradigm that facilitates on-demand delivery of IT heterogeneous resources to the customer based on their needs over the Internet with a pay-as-per service they use. Service level agreement (SLA) specifies the customer’s expected service levels through cloud service provider (CSP) and the remedies or penalties if any of the CSP does not meet agreed-on service levels. Before providing the requested services to the customer, CSP and customer negotiate and sign on an SLA. CSP earns money for the service provided to the customer on satisfying the agreed-on service levels. Otherwise, CSP pays the penalty cost to the customer for the violation of SLA. Task scheduling minimizes task execution time and maximizes resource usage rate. Scheduling objective tends to improve quality of service (QoS) parameters like resource usage, with a minimum execution time and cost (without violating SLA). The proposed algorithm SLA-GTMax-Min schedules the tasks efficiently to the heterogeneous multi-cloud environment satisfying SLA and balances makespan, gain, and penalty/violation cost. Proposed SLA-GTMax-Min represents three levels of SLA provided with three types of services expected by the customers. The services are namely tasks minimum execution time, tasks minimum gain cost, and tasks both minimum execution time and gain cost in percentage, respectively. Makespan is termed as tasks minimum execution time. Gain cost represents minimum execution cost for completing tasks execution. The proposed algorithm SLA-GTMax-Min incorporates the SLA gain cost for providing service successfully and SLA violation cost for providing service unsuccessfully. Performance analysis of algorithm SLA-GTMax-Min and existing algorithm is measured based on the benchmark dataset values. The experimental results of SLA-GTMax-Min algorithm and the existing scheduling algorithms, namely, SLA-MCT, Execution-MCT, Profit-MCT, SLA-Min-Min, Execution-Min-Min, and Profit-Min-Min, are compared by evaluation metrics. Evaluation measure considered for evaluating the performance of the proposed SLA-GTMax-Min algorithm are makespan, cloud utilization ratio, gain cost is the cost earned by the CSP for successful completion of the tasks, and penalty cost the CSP pays to the customer for violation of SLA. The experimental results illustrate clearly algorithm SLA-GTMax-Min performs a better balance among makespan, gain cost, and penalty cost than existing algorithms.

Pedestrian simulation and PLOS analysis in the subway station

Abstract: Today, pedestrian traffic is highly regarded because every moving in any way will include walking partly, so improving the pedestrian walkways service level is considered very important. In this research, the service level of walkways and corridors of Sadeghiyeh urban train station in Tehran was examined by using two software, Aimsun and Path Finder. The pedestrian traffic volumes within the scope of the study are gathered through subway organization statistics as well as manual census in the study area. After simulating pedestrian traffic in the two above-mentioned software, the results, including traffic time, pedestrian density, speed, volume, level of service, etc., have been analyzed and the data of these two software are compared with each other. The simulation results also showed that some sections such as corridors, waiting platforms and ticket gates were not predicted based on the pedestrian volume in design year. Therefore, the need to reconsider these spaces is seen based on qualitative criteria for pedestrian (passengers). Final results show that the main hall level of service in the station is between D and E, the North Entrance level of service in station is C and the South Corridor LOS in the station between C and D.

Bi-level programming for modeling inventory sharing in decentralized supply chains

Abstract: This paper deals with inventory sharing in the context of a decentralized supply chain. The supply chain consists of a manufacturer who distributes a set of products through a network of independents Points of Sale (POS). The primary decision each player has to make is to optimize its service level while maintaining a minimum total cost. The POS share their inventory allowing transshipment to enhance their ability to face demand and to avoid shortages. The problem is modelled as a 1-leader, n-followers Stackelberg game. A mixed integer bi-level program is developed considering that the manufacturer decides first on inventory levels and routes to be constructed knowing each follower\x92s (POS) response function arising from its own minimization of its total cost. In this game, a manufacturer incurs the vehicle routing cost for regular shipments. In addition to their own holding costs, it is assumed that the manufacturer and the POS each are willing to incur both a part of the cost of lost sales associated with the products shortage and a part of the cost of transshipment. To address the combinatorial complexity of the problem and to provide efficient solutions for large size instances, a hybrid Genetic Algorithm coupled with deep reinforcement learning is developed. Results show that Stackelberg mechanism for inventory sharing under certain conditions allows the network as a whole to achieve savings and improve its service level.

AI-based industrial full-service offerings: A model for payment structure selection considering predictive power

Abstract: Artificial Intelligence and servitization reshape the way that manufacturing companies derive value. Aiming to sustain competitive advantage and intensify customer loyalty, full-service providers offer the use of their products as a service to achieve continuous revenues. For this purpose, companies implement AI classification algorithms to enable high levels of service at controllable costs. However, traditional asset sellers who become service providers require previously atypical payment structures, as classic payment methods involving a one-time fee for production costs and profit margins are unsuitable. In addition, a low predictive power of the implemented classification algorithm can lead to misclassifications, which diminish the achievable level of service and the intended net present value of the resultant service. While previous works focus solely on the costs of such misclassifications, our decision model highlights implications for payment structures, service levels, and – ultimately – the net present value of such data-driven service offerings. Our research suggests that predictive power can be a major factor in selecting a suitable payment structure and the overall design of service level agreements. Therefore, we compare common payment structures for data-driven services and investigate their relationship to predictive power. We develop our model using a design science methodology and iteratively evaluate our results using a four-step approach that includes interviews with industry experts and the application of our model to a real-world use case. In summary, our research extends the existing knowledge of servitization and data-driven services in the manufacturing industry through a quantitative decision model.

Ordering behavior in a supply chain with customers that respond to changes in service level

Abstract: The presence of service-level-dependent demand has been empirically observed in industry and is well-documented in the literature. How does the implicit contractual existence of such service dependency impact the ordering decisions of upstream suppliers? We conduct three controlled laboratory experiments to study the impact a service-reward mechanism may have on the upstream ordering decisions of those charged with inventory ordering decisions. The multistudy approach provides representations of decision dynamics across a variety of scenarios, from one-shot buys to long-term supply-chain relationships. Our combined empirical results consistently suggest that the service-reward mechanism significantly and systematically elevates order levels and order variability in a manner that increases departure from optimal ordering. This effect is observed even when decision-makers have incentives to maintain a steady ordering pattern with their suppliers. Our findings shed much-needed light on individual ordering responses to, and the associated risks of, service-reward mechanisms.

Supply chain coordination with strategic customers: Yield uncertainty and replenishment tactic

Abstract: Supply capability and strategic customer behavior jointly influence the operations management in a supply chain (SC). Few studies have been conducted to reflect such a composite impact from the supply side and the demand side. The aim of our article is to fill this gap by focusing on the SC facing strategic customers under demand and yield uncertainty. Specially, we emphasize the importance of replenishment tactic in keeping a stable supply level, and discuss the corresponding impact on the input quantity decision of the SC under yield uncertainty. Two scenarios are investigated: (i) without the market service-level (MSL) constraint and (ii) with the MSL constraint. The SCs under different scenarios are coordinated by various contracts. Meanwhile, by adjusting corresponding contract parameters, the SC members can achieve Pareto improvement. The numerical analyses are used to examine the validity of the conclusions derived.

Factorial Design with Simulation for the Optimization of the Level of Service in the Platform-Train Interface of Metro Stations—A Pilot Study

Abstract: Metro stations are considered complex areas of pedestrian mobility due to the increasing congestion, due to the a high level of demand of different circulation spaces. Given this situation and the limited physical spaces remaining to develop transport systems in urban areas, railways acquire greater relevance given the need to mobilize pedestrians. Within the stations, the most problematic area is the platform-train interface (PTI) due to the high number of interactions between passengers boarding and alighting. The objective of this study is to identify the PTI configuration that maximizes the level of service for passengers, safeguards the operational continuity of the system and improves user experience by reducing dissatisfaction and delay times. For this, a pedestrian microsimulation model is used in order to recreate the reality of a generic metro station and its different scenarios given the combinations of two factors: the platform configurations (topology) and the traffic control elements. Subsequently, these scenarios are analyzed through a factorial design, looking for the situation that optimizes the combination of metrics chosen in a weighted way. Finally, it is found that the PTI configuration that maximizes the level of service for users is the mixed station with signaling. It is this which includes the factors with the greatest positive effect on the chosen metrics.

Achieving High Individual Service Levels Without Safety Stock? Optimal Rationing Policy of Pooled Resources

Abstract: In “Achieving High Individual Service-Levels without Safety Stock? Optimal Rationing Policy of Pooled Resources,” Jiang, Wang, and Zhang analyze a resource rationing problem with service level constraints. They present a general framework to study the two-stage problem when customers require individual and possibly different service levels: (1) the capacity level of pooled resources in anticipation of random demand of multiple customers and (2) how the capacity should be allocated to fulfill customer demands after demand realization. The modeling framework generalizes and unifies many existing models in the literature and includes second-stage allocation costs. The authors propose a simple randomized rationing policy for any fixed feasible capacity level and show the optimality of this policy for very general service level constraints, including type I and type II constraints and beyond. They also discuss the optimality of index policies.

An Intense Study on Intelligent Service Provisioning for Multi-Cloud Based on Machine Learning Techniques

Abstract: Cloud computing is evolving as a paradigm that models “everything-as-a-service.” Organizations are migrating from single cloud environment to multi-cloud environment for issuing streamlined services for multi-tenants. The lock-in effect is a scenario in which the consumers feel difficulty in migrating from one cloud to another without inconvenience which is solved by the deployment of multi-cloud. Service provisioning is the process of delivering the virtualized physical resources, applications, and infrastructure. There is a need for intelligent service provisioning (ISP) in multi-cloud because the cloud user faces difficulty in choosing the appropriate services from variety of services provided by a large number of cloud providers. By providing with appropriate cloud services to the user, the service level agreements (SLAs) are fulfilled. Quality of Service (QoS) is also satisfied through ISP. Intelligent Cloud Broker (ICB) is often used for simplifying the services selection task. ICB acts as an efficient mediator between cloud service provider (CSP) and cloud user. It acts as a tool for provisioning of services. ISP is made possible through the incorporation of machine learning (ML) techniques. Machine learning (ML) technique is widely used in many applications for making the system to produce intelligent decisions.

Capacity and resource allocation in flexible production networks by a game theory model

Abstract: Abstract In many production applications, plants that produce multiple products with random demands share the required items among suppliers. The decision of how to allocate requests between suppliers to achieve the desired level of customer service is relevant to the efficiency of the production network. The literature highlighted how the long chain has the same level of performance as the full flexible network. This research proposes a decision model based on the game theory model to improve the performance of the production network. The model uses the Gale-Shapley algorithm with low computational complexity to share the demand among the suppliers. A simulation environment allows the evaluation of the proposed model in different conditions, and the model is compared to the dedicated, full flexibility, and long chain models. The numerical results show how the proposed model improves the efficiency of the production environment by keeping the number of connections with the supplier closer to the long chain model.

Spare Parts Inventory Management System in a Service Sector Company

Abstract: Spare parts inventory management is crucial in the success of a service providing company. In this study, the spare parts of a service provider in the automotive sector are classified according to their characteristics in groups and different inventory control policies are applied to the categorized groups. The Analytical Hierarchy Process (AHP), one of the Multi-Criteria Decision Making (MCDM) methods, is used to classify the spare parts into groups. As a result of the application of AHP, classes of spare parts are determined according to the VED analysis, classifying the spare parts according to their criticality. Furthermore, the ABC analysis performed by the company was improved by using cost and demand criteria. After performing both analysis, three new classes of spare parts are determined with the combination of ABC and VED classification techniques. For each class, an appropriate inventory control policy is decided according to the spare parts importance and criticality. Based on the literature review, the \(({\varvec{R}},{\varvec{S}},{\varvec{s}})\) inventory control policy is chosen to be applied in each class, taking into consideration the review period, order up-to-level and reorder point of items. In the inventory control model, the review period for the same class items is assumed to be constant based on the information provided by the company. For verification purposes, necessary cost calculations including total ordering and holding costs are performed by means of Microsoft Excel. In order to be able to vastly observe the system behavior, different cost scenarios are generated by increasing and decreasing the service level and review period of the system. Using, OptQuest, an optimization tool, embedded into ARENA simulation software, the different scenarios were analyzed and the total minimum cost is reached. For supporting the daily operations of the company, a user-friendly decision support system is built, where the end-user can easily add/remove spare parts to/from the system, classify them and compare the results of inventory control policies with the current system. The DSS will also assist the company to manage and control their real-time inventory and perform spare parts stock level tracking and decide when to place orders.

Multi-Level Stock Management System

Abstract: In the petrochemical, chemical and pharmaceutical industries, supply chains generally consist of multiple phases of production facilities, inventory/distribution centers and customers. Supply chain staging in the face of various market and technical uncertainties is usually measured by service level, i.e., the expected fraction of demand that the supply chain can satisfy within a predefined allowable delivery time window. Safety stock is introduced into supply chains against uncertainty to provide customers with an assured service level. Although a higher safety stock level guarantees a better service level, it does increase the supply chain operating cost and thus these levels must be suitably optimized.