JD.com: Transaction Level Data for the 2020 MSOM Data Driven Research Challenge

2019 ◽  
Author(s):  
Zuo-Jun Max Shen ◽  
Christopher S. Tang ◽  
Di Wu ◽  
Rong Yuan ◽  
Wei Zhou
Keyword(s):  
Data Driven ◽  
Level Data ◽  
Transaction Level ◽  
Research Challenge

JD.com: Transaction-Level Data for the 2020 MSOM Data Driven Research Challenge

2020 ◽  
Author(s):  
Max Shen ◽  
Christopher S. Tang ◽  
Di Wu ◽  
Rong Yuan ◽  
Wei Zhou
Keyword(s):  
Econometric Models ◽  
Data Driven ◽  
Level Data ◽  
Research Questions ◽  
Transaction Level ◽  
Research Challenge ◽  
Transactional Data

To support the 2020 MSOM Data Driven Research Challenge, JD.com, China’s largest retailer, offers transaction-level data to MSOM members for conducting data-driven research. This article describes the transactional data associated with over 2.5 million customers (457,298 made purchases) and 31,868 stock keeping units (SKUs) over the month of March in 2018. We also present potential research questions suggested by JD.com. Researchers are welcome to develop econometric models or data-driven models using this database to address some of the suggested questions or examine their own research questions.

RiRiShun Logistics: Home Appliance Delivery Data for the 2021 Manufacturing & Service Operations Management Data-Driven Research Challenge

2021 ◽  
Author(s):  
Xiaolong Guo ◽  
Yugang Yu ◽  
Gad Allon ◽  
Meiyan Wang ◽  
Zhentai Zhang
Keyword(s):  
Operations Management ◽  
Service Operations ◽  
Optimization Techniques ◽  
Data Driven ◽  
Analytical Models ◽  
Data Set ◽  
Service Operations Management ◽  
Level Data ◽  
Home Appliance ◽  
Research Challenge

To support the 2021 Manufacturing & Service Operations Management (MSOM) Data-Driven Research Challenge, RiRiShun Logistics (a Haier group subsidiary focusing on logistics service for home appliances) provides MSOM members with logistics operational-level data for data-driven research. This paper provides a detailed description of the data associated with over 14 million orders from 149 clients (the consigners) associated with 4.2 million end consumers (the recipients and end users of the appliances) in China, involving 18,000 stock keeping units operated at 103 warehouses. Researchers are welcomed to develop econometric models, data-driven optimization techniques, analytical models, and algorithm designs by using this data set to address questions suggested by company managers.

What's Happening to Profits at Cazenovia Creations?

2013 ◽  
Vol 8 (1) ◽  
pp. 24-29 ◽  
Author(s):  
Margaret Garnsey ◽  
Andrea Hotaling
Keyword(s):  
Financial Analysis ◽  
Financial Statements ◽  
Internal Controls ◽  
Net Income ◽  
Database Query ◽  
Microsoft Access ◽  
Level Data ◽  
Query Tool ◽  
Transaction Level

ABSTRACT In this case, students assume the role of an accounting professional asked by a client to investigate why net income is not as strong as expected. The students must first analyze a set of financial statements to identify areas of possible concern. After determining the areas to investigate, the students use a database query tool to see if they can determine causes by examining transaction level data. Finally, the students are asked to professionally communicate their findings and recommendations to their client. The case provides students with experience in using query-based approaches to answering business questions. It is appropriate for students with basic query and financial analysis skills and knowledge of internal controls. A Microsoft Access database with transaction details for the final seven months of the current year as well as financial statements for the current and prior year are provided.

Product-level profitability

2019 ◽  
Vol 33 (1) ◽  
pp. 214-237
Author(s):  
Hannu Hannila ◽  
Joni Koskinen ◽  
Janne Harkonen ◽  
Harri Haapasalo
Keyword(s):  
Business Processes ◽  
Data Driven ◽  
Product Portfolio ◽  
Data Governance ◽  
Content Type ◽  
Technical Product ◽  
Product Level ◽  
Level Data ◽  
Corporate Level

Purpose The purpose of this paper is to analyse current challenges and to articulate the preconditions for data-driven, fact-based product portfolio management (PPM) based on commercial and technical product structures, critical business processes, corporate business IT and company data assets. Here, data assets were classified from a PPM perspective in terms of (product/customer/supplier) master data, transaction data and Internet of Things data. The study also addresses the supporting role of corporate-level data governance. Design/methodology/approach The study combines a literature review and qualitative analysis of empirical data collected from eight international companies of varying size. Findings Companies’ current inability to analyse products effectively based on existing data is surprising. The present findings identify a number of preconditions for data-driven, fact-based PPM, including mutual understanding of company products (to establish a consistent commercial and technical product structure), product classification as strategic, supportive or non-strategic (to link commercial and technical product structures with product strategy) and a holistic, corporate-level data model for adjusting the company’s business IT (to support product portfolio visualisation). Practical implications The findings provide a logical and empirical basis for fact-based, product-level analysis of product profitability and analysis of the product portfolio over the product life cycle, supporting a data-driven approach to the optimisation of commercial and technical product structure, business IT systems and company product strategy. As a virtual representation of reality, the company data model facilitates product visualisation. The findings are of great practical value, as they demonstrate the significance of corporate-level data assets, data governance and business-critical data for managing a company’s products and portfolio. Originality/value The study contributes to the existing literature by specifying the preconditions for data-driven, fact-based PPM as a basis for product-level analysis and decision making, emphasising the role of company data assets and clarifying the links between business processes, information systems and data assets for PPM.

scholarly journals Geography of racial coresidence

2021 ◽  
Author(s):  
Anna Dmowska ◽  
Tomasz Stepinski
Keyword(s):  
Metropolitan Areas ◽  
Data Driven ◽  
Racial Groups ◽  
Census Block ◽  
Asian Populations ◽  
Racial Preferences ◽  
Level Data ◽  
Geographical Regions ◽  
Block Level ◽  
Us Cities

Whereas most work on residential race relations in US cities is based on the concept of segregation, our approach studies this issue from a who-lives-with-whom perspective. To this end, we study coresidence profiles – percentages of a given racial subpopulation living in different population zones. Population zones are data-driven divisions of a city based on characteristic racial compositions. We used 1990 and 2010 decennial census block-level data for 61 largest US metropolitan areas to calculate coresidence profiles for four major racial subpopulations in each city at both years. Profiles for each race/year combination were clustered into three archetypes. Cities, where given race profiles belong to the same archetype, have similar coresidence patterns with respect to this race. We present the geographic distributions of co-habitation profiles and show how they changed during the 1990-2010 period. Our results revealed that coresidence profiles depend not only on racial preferences but also on the availability of racial groups; cities in the different geographical regions have different coresidence profiles because they have different shares of White, Black, Hispanic, and Asian subpopulations. Temporal changes in coresidence profiles are linked to the increased share of Hispanic and Asian populations.

scholarly journals Determining Returns to Storage: Does Data Aggregation Matter?

2007 ◽  
Vol 39 (3) ◽  
pp. 571-579 ◽  
Author(s):  
Joni M. Klumpp ◽  
B. Wade Brorsen ◽  
Kim B. Anderson
Keyword(s):  
Data Aggregation ◽  
Aggregate Data ◽  
Grain Elevators ◽  
Level Data ◽  
The Mean ◽  
Transaction Level

Aggregate data are commonly used to determine returns to storage. However, recent studies have shown that aggregating data may lead to underestimated returns. This article compares aggregate and elevator data from Oklahoma to determine if aggregate data underestimate returns. We find no difference between the mean returns estimated with aggregate data and the mean returns estimated with transaction level data from grain elevators in Oklahoma.

scholarly journals Short-Term Traffic Prediction by Two-Level Data Driven Model in 5G-Enabled Edge Computing Networks

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 123981-123991 ◽  
Author(s):  
Yupin Huang ◽  
Liping Qian ◽  
Anqi Feng ◽  
Ningning Yu ◽  
Yuan Wu
Keyword(s):  
Edge Computing ◽  
Data Driven ◽  
Traffic Prediction ◽  
Short Term ◽  
Level Data

The Profitability of Revenue-Based Quotas Under Price Negotiation

2021 ◽  
Author(s):  
Pranav Jindal ◽  
Peter Newberry
Keyword(s):  
Reduced Form ◽  
Demand Model ◽  
Total Compensation ◽  
Price Negotiation ◽  
Level Data ◽  
Bargaining Position ◽  
Transaction Level ◽  
The Impact

We study how the presence of a monthly revenue-based quota impacts a retailer’s profits when prices are negotiated by a salesperson. Using transaction level data for refrigerators, we first provide reduced-form evidence that prices are impacted by the quota: the negotiated discounts are approximately 3.8% higher if the salesperson is 10% closer to reaching the quota in the final week of the month. Guided by this result, we specify and estimate a demand model that identifies the impact of the quota through two forces: the effort salespeople expend in order to sell the product and their bargaining position. Results indicate that, as salespeople get closer to reaching their quota, their effort increases regardless of the week, and their bargaining position weakens (i.e., they offer lower prices), but only in the final week of the month. We use these results to analyze the impact of the quota and find that, holding salespeoples’ total compensation fixed, eliminating quotas results in 8% lower profit for the retailer. This decrease stems primarily from the reduction in effort that outweighs any benefit from strengthening the salespeoples’ bargaining position. The change in profit is economically meaningful because eliminating both price negotiation (i.e., moving to fixed pricing) and the quota results in an up to 36% reduction in profit. This paper was accepted by Matthew Shum, marketing.

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