Pricing and Design of After-Sales Service Contract: The Value of Mining Asymmetric Sales Cost Information

2017 ◽  
Vol 34 (01) ◽  
pp. 1740002 ◽  
Author(s):  
Yanfei Lan ◽  
Zhibing Liu ◽  
Baozhuang Niu
Keyword(s):  
Big Data ◽  
Private Information ◽  
Investment Decision ◽  
Wholesale Price ◽  
Service Level ◽  
Equivalent Model ◽  
Cost Information ◽  
Service Contract ◽  
Symmetric Information ◽  
Asymmetric Cost

In this paper, we study a pricing and after-sales service contract design problem, where a retailer purchases products from a manufacturer and then sells to the consumers. The sales cost is the retailer’s private information and might be mined by the manufacturer via advanced learning algorithms and related big data techniques. We first develop a crisp equivalent model, based on which the optimal contracts and the supply chain parties’ profits under asymmetric information are derived. We show that, compared with the optimal wholesale price and after-sales service level with symmetric information, asymmetric cost information makes the wholesale price distorted upward when the retailer’s sales cost is low. When the retailer’s cost is high, the after-sales service level is distorted downward. We characterize the manufacturer’s loss and the retailer’s gains due to asymmetric sales cost information. This helps the manufacturer make the investment decision of big data techniques. Interestingly, we find that the retailer might voluntary disclose the sales cost information, which results in a win-win situation for the manufacturer and the retailer. This makes the manufacturer less favor big data techniques. Finally, we conduct extensive sensitivity analysis with respect to the retailer’s sales cost, the consumer’s sensitivity to the retailer’s after-sale service level, and the fraction of high-type retailers in the market.

Pricing and service decision of dual-channel operations in an O2O closed-loop supply chain

2017 ◽  
Vol 117 (8) ◽  
pp. 1567-1588 ◽  
Author(s):  
Lingcheng Kong ◽  
Zhiyang Liu ◽  
Yafei Pan ◽  
Jiaping Xie ◽  
Guang Yang
Keyword(s):  
Supply Chain ◽  
Closed Loop ◽  
Wholesale Price ◽  
Revenue Sharing ◽  
Service Level ◽  
Return Rate ◽  
Transfer Payment ◽  
Channel Conflict ◽  
Content Type ◽  
Dual Channel

Purpose The online direct selling mode has been widely accepted by enterprises in the O2O era. However, the dual-channel (online/offline, forward/backward) operations of the closed-loop supply chain (CLSC) changed the relationship between manufacturers and retailers, thus resulting in channel conflict. The purpose of this paper is to take a dual-channel operations of CLSC as the research target, where a manufacturer sells a single product through a direct e-channel as well as a conventional retail channel; the retailer are responsible for collecting used products in the reverse supply chain and the manufacturer are responsible for remanufacturing. Design/methodology/approach The authors build a benchmark model of dual-channel price and service competition and take the return rate, which is considered to be related to the service level of the retailer, as the function of the service level to extend the model in the reverse SC. The authors then analyze the optimal pricing and service decision under centralization and decentralization, respectively. Finally, with the revenue-sharing factor, wholesale price and recycling price transfer payment coefficient as contract parameters, the paper also designs a revenue-sharing contract led by the manufacturer and explores in what situation the contract could realize the Pareto optimization of all players. Findings In the baseline model, the results show that optimal price and service level correlate positively in centralization; however, the relation relies on consumers’ price sensitivity in decentralization. In the extension model, the relationship between price and service level also relies on the relative value of increased service cost and remanufacturing saved cost. When the return rate correlates with the service level, a recycling transfer payment can elevate the service level and thus raise the return rate. Through analyzing the parameters in revenue-sharing contract, a point can be reached where lowering the wholesale price and raising the transfer payment coefficient will promote retailers to share revenue. Practical implications Many enterprises establish the dual-channel distribution system both online and offline, which need to understand how to resolve their channel conflict. The conflict is especially strong in CLSC with remanufacturing. The result helps the node enterprises realize the coordination of the dual-channel CLSC. Originality/value It takes into account the fact that there are two complementary relationships, such as online selling and offline delivery; used product recycling and remanufacturing. The authors optimize the strategy of product pricing and service level in order to solve channel conflict and double marginalization in the closed-loop dual-channel distribution network.

Sharing Demand Information with Retailer Under Upstream Competition

2021 ◽  
Author(s):  
Aditya Jain
Keyword(s):  
Information Sharing ◽  
Private Information ◽  
Operations Management ◽  
Information Acquisition ◽  
Wholesale Price ◽  
Substitutable Products ◽  
Downward Pressure ◽  
Demand Information ◽  
Intensity Of Competition ◽  
Share Information

We analyze demand information sharing collaboration between two manufacturers and a retailer under upstream competition. The manufacturers produce partially substitutable products, which are stocked by the retailer that sells them in the market characterized by random demand. The manufacturers are privately informed about uncertain demand and decide on whether to share this information with the retailer. We show that by not sharing information, a manufacturer ends up distorting its wholesale price upward to signal its private information to the retailer, and under upstream competition, this distortion is propagated to the competing manufacturer. Thus, although a manufacturer’s decision to not share information may benefit or hurt its own profit, this always benefits the competing manufacturer. Under low intensity of competition, signaling-driven distortions exacerbate double marginalization and hurt all parties, whereas under more intense competition, these distortions help manufacturers offset downward pressure on wholesale prices. Thus, in equilibrium similarly informed manufacturers share information in the former case but not in the latter case. Additionally, when manufacturers differ in their information accuracies, only the better-informed manufacturer shares information. The retailer always benefits from both manufacturers sharing information, and its benefits are larger when the better-informed manufacturer shares information. We show existence of a contracting mechanism the retailer can employ to enable information sharing. Finally, we analyze manufacturers’ information acquisition decisions and find that under competition, two manufacturers acquire minimal information so that they are better off not sharing information in the information sharing game. This paper was accepted by Vishal Gaur, operations management.

scholarly journals Incorporating costing study results into district and service planning to enhance immunization programme performance: a Zambian case study

2019 ◽  
Vol 34 (5) ◽  
pp. 327-336 ◽  
Author(s):  
Isabelle Feldhaus ◽  
Carl Schütte ◽  
Francis D Mwansa ◽  
Masauso Undi ◽  
Stanley Banda ◽  
...  
Keyword(s):  
Organizational Context ◽  
Service Level ◽  
Service Planning ◽  
Quality Data ◽  
Structured Interviews ◽  
Cost Information ◽  
International Agencies ◽  
Immunization Programme ◽  
Planning And Management ◽  
Study Results

Abstract Donors, researchers and international agencies have made significant investments in collection of high-quality data on immunization costs, aiming to improve the efficiency and sustainability of services. However, improved quality and routine dissemination of costing information to local managers may not lead to enhanced programme performance. This study explored how district- and service-level managers can use costing information to enhance planning and management to increase immunization outputs and coverage. Data on the use of costing information in the planning and management of Zambia’s immunization programme was obtained through individual and group semi-structured interviews with planners and managers at national, provincial and district levels. Document review revealed the organizational context within which managers operated. Qualitative results described managers’ ability to use costing information to generate cost and efficiency indicators not provided by existing systems. These, in turn, would allow them to understand the relative cost of vaccines and other resources, increase awareness of resource use and management, benchmark against other facilities and districts, and modify strategies to improve performance. Managers indicated that costing information highlighted priorities for more efficient use of human resources, vaccines and outreach for immunization programming. Despite decentralization, there were limitations on managers’ decision-making to improve programme efficiency in practice: major resource allocation decisions were made centrally and planning tools did not focus on vaccine costs. Unreliable budgets and disbursements also undermined managers’ ability to use systems and information. Routine generation and use of immunization cost information may have limited impact on managing efficiency in many Zambian districts, but opportunities were evident for using existing capacity and systems to improve efficiency. Simpler approaches, such as improving reliability and use of routine immunization and staffing indicators, drawing on general insights from periodic costing studies, and focusing on maximizing coverage with available resources, may be more feasible in the short-term.

The influence of big data analytics management capabilities on supply chain preparedness, alertness and agility

2019 ◽  
Vol 32 (2) ◽  
pp. 297-318 ◽  
Author(s):  
Santanu Mandal
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Supply Chain ◽  
Data Analytics ◽  
Investment Decision ◽  
Big Data Analytics ◽  
Content Type ◽  
Investment Decision Making ◽  
Management Capabilities ◽  
The Impact

Purpose The importance of big data analytics (BDA) on the development of supply chain (SC) resilience is not clearly understood. To address this, the purpose of this paper is to explore the impact of BDA management capabilities, namely, BDA planning, BDA investment decision making, BDA coordination and BDA control on SC resilience dimensions, namely, SC preparedness, SC alertness and SC agility. Design/methodology/approach The study relied on perceptual measures to test the proposed associations. Using extant measures, the scales for all the constructs were contextualized based on expert feedback. Using online survey, 249 complete responses were collected and were analyzed using partial least squares in SmartPLS 2.0.M3. The study targeted professionals with sufficient experience in analytics in different industry sectors for survey participation. Findings Results indicate BDA planning, BDA coordination and BDA control are critical enablers of SC preparedness, SC alertness and SC agility. BDA investment decision making did not have any prominent influence on any of the SC resilience dimensions. Originality/value The study is important as it addresses the contribution of BDA capabilities on the development of SC resilience, an important gap in the extant literature.

scholarly journals Service and Price Decisions of a Supply Chain with Optional After-Sale Service

2016 ◽  
Vol 2016 ◽  
pp. 1-11
Author(s):  
Xiaochen Sun ◽  
Qingshuai Zhang ◽  
Yancong Zhou
Keyword(s):  
Quality Assurance ◽  
Supply Chain ◽  
Wholesale Price ◽  
Service Level ◽  
Retail Price ◽  
High Quality ◽  
Optional Service ◽  
Durable Products ◽  
Purchase Behaviors

For durable products, the high quality after-sales service has been playing an increasingly important role in consumers’ purchase behaviors. We mainly study a supply chain composed of a manufacturer and a retailer. In a process of products sales, the manufacturer will provide a basic free quality assurance service. On this basis, the retailer provides paid optional quality assurance service to consumers to promote sales. Users are divided into two categories in this paper: users with no optional service and users with optional services. We derive the equilibrium decisions between the manufacturer and the retailer under the following two cases: (i) the optional after-sales service level and the wholesale price determined by the manufacturer and the retail price determined by the retailer; (ii) the wholesale price determined by the manufacturer and the optional after-sales service level and the retail price determined by the retailer.

scholarly journals Distributed Storage Strategy and Visual Analysis for Economic Big Data

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Xiangli Chang ◽  
Hailang Cui
Keyword(s):  
Big Data ◽  
Data Storage ◽  
Visual Analysis ◽  
Distributed Storage ◽  
Storage System ◽  
Service Level Agreement ◽  
Wiener Filter ◽  
Service Level ◽  
Prototype System ◽  
Data Types

With the increasing popularity of a large number of Internet-based services and a large number of services hosted on cloud platforms, a more powerful back-end storage system is needed to support these services. At present, it is very difficult or impossible to implement a distributed storage to meet all the above assumptions. Therefore, the focus of research is to limit different characteristics to design different distributed storage solutions to meet different usage scenarios. Economic big data should have the basic requirements of high storage efficiency and fast retrieval speed. The large number of small files and the diversity of file types make the storage and retrieval of economic big data face severe challenges. This paper is oriented to the application requirements of cross-modal analysis of economic big data. According to the source and characteristics of economic big data, the data types are analyzed and the database storage architecture and data storage structure of economic big data are designed. Taking into account the spatial, temporal, and semantic characteristics of economic big data, this paper proposes a unified coding method based on the spatiotemporal data multilevel division strategy combined with Geohash and Hilbert and spatiotemporal semantic constraints. A prototype system was constructed based on Mongo DB, and the performance of the multilevel partition algorithm proposed in this paper was verified by the prototype system based on the realization of data storage management functions. The Wiener distributed memory based on the principle of Wiener filter is used to store the workload of each workload distributed storage window in a distributed manner. For distributed storage workloads, this article adopts specific types of workloads. According to its periodicity, the workload is divided into distributed storage windows of specific duration. At the beginning of each distributed storage window, distributed storage is distributed to the next distributed storage window. Experiments and tests have verified the distributed storage strategy proposed in this article, which proves that the Wiener distributed storage solution can save platform resources and configuration costs while ensuring Service Level Agreement (SLA).

scholarly journals Research on Dual Channel Supply Chain Decision-making of New Retailing Enterprises Considering Service Behavior in the era of Big Data

2022 ◽  
Vol 30 (9) ◽  
pp. 0-0
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Supply Chain ◽  
Service Level ◽  
Sustainable Competitive Advantage ◽  
Dual Channel ◽  
Decentralized Decision Making ◽  
Starting Point ◽  
Dual Channel Supply Chain ◽  
Channel Service

Drawing from extant retailing and supply chain research, this paper studies the dual channel supply chain decision-making of member channel, and obtains the optimal price strategy, maximum demand and maximum total revenue of the supply chain of network channel and retailing channel under the centralized decision-making and decentralized decision-making respectively. The contributions of this study identify that investing in big data within a certain threshold can improve the channel service level, reduce the channel price and improve the income of the supply chain. Supply chain members improve the channel service level and increase the corresponding channel price. The supply chain can get the most advantages when manufacturers and retailers make centralized decisions. This paper provides a starting point for new retailing academic and practical research in a domain that is deficient in empirical research, provides the theoretical framework to new retailing enterprises and decision-making model for their sustainable competitive advantage.

scholarly journals An Efficient Data Stream Analytics Model for Real Time Internet of Things (Iot) Applications

2020 ◽  
Vol 8 (6) ◽  
pp. 495-500
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Real Time ◽  
Private Information ◽  
Large Scale ◽  
Layer System ◽  
Collection Data ◽  
Efficient Data ◽  
Multiple Environments ◽  
Iot Devices

Internet of Things (IoT), data analytics is supporting multiple applications. These numerous applications try to gather data from different environments, here the gathered data may be homogeneous or heterogeneous, but most of the data collected from multiple environments were heterogeneous, the task of gathering, processing, storing and the analysis that is being performed on data are still challenging. Providing security to all these things is also a challenging task due to untrusted networks and big data. Big data management in the ever-expanding network may rise several non-trivial concerns on data collection, data-efficient processing, analytics, and security. However, the above said scenarios depends on large scale sensor deployed. Sensors continuously transmit data to clouds for real time use, which can raise the issue of privacy disclosure because IoT devices may gather data including a kind of sensitive private information. In this context, we propose a two-layer system or model for analyzing IoT data, collected from multiple applications. The first layer is mainly used for gathering data from multiple environments and acts as a service-oriented interface to ingest data. The second layer is responsible for storing and analyses data securely. The Proposed solutions are implemented by the use of open source components.

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