Data Analytics in Operations Management: A Review

@article{Mivsic2019DataAI,
  title={Data Analytics in Operations Management: A Review},
  author={Velibor V. Mivsi'c and Georgia Perakis},
  journal={SSRN Electronic Journal},
  year={2019}
}
Research in operations management has traditionally focused on models for understanding, mostly at a strategic level, how firms should operate. Spurred by the growing availability of data and recent advances in machine learning and optimization methodologies, there has been an increasing application of data analytics to problems in operations management. In this paper, we review recent applications of data analytics to operations management, in three major areas -- supply chain management… 
The role of data analytics within operational risk management: A systematic review from the financial services and energy sectors
Operational risks are increasingly prevalent and complex to manage in organisations, culminating in substantial financial and non-financial costs. Given the inefficiencies and biases of traditional
Is Investment in Data Analytics Always Profitable? The Case of Third-Party-Online-Promotion Marketplace
Studies show that merchants are heterogenous in profitability from offering promotions on third-party-online-promotion marketplaces who often charge a single commission rate. Using a data analytics
Procurement of New Products: Data-Driven Newsvendor with Profit Risk
Problem definition: We consider a retailer that needs to determine the optimal order quantities for new products, with the aim of maximizing the expected profit while ensuring that it could attain
A simulation-based evaluation of machine learning models for clinical decision support: application and analysis using hospital readmission
TLDR
Using data on unplanned emergency department surgical readmissions, it is shown that factors such as the provider’s schedule and postoperative prediction timing can have major effects on the pathway cohort size and potential cost reductions from preventing hospital readmissions.
Disruptive Technologies and Sustainable Supply Chain Management: A Review and Cross-Case Analysis
This study examines the relationship between disruptive technologies and their potential impacts on sustainable supply chain management (SSCM), with a focus on the following technologies: Big Data
Using transaction data and product margins to optimise weekly flyers
Customers increasingly expect companies to understand their wants and needs and to market to those desires. Unfortunately, such levels of personalisation can be difficult to accomplish for
Visualizing The Implicit Model Selection Tradeoff
TLDR
Methods for comparing predictive models in an interpretable manner are proposed that synthesize ideas from supervised learning, unsupervised learning, dimensionality reduction, and visualization and demonstrated how they can be used to inform the model selection process.
Revenue Management and the Rise of the Algorithmic Economy
TLDR
Revenue management has evolved over the years from its origins in the airline industry into a much broader discipline that analyzes algorithmic methods for demand and marketplace management, but how these methods are applied to the retail industry is still a work in progress.
...
1
2
3
...

References

SHOWING 1-10 OF 73 REFERENCES
From Predictive to Prescriptive Analytics
TLDR
This paper combines ideas from machine learning (ML) and operations research and management science (OR/MS) in developing a framework for using data to prescribe optimal decisions in OR/MS problems, and develops a metric P termed the coefficient of prescriptiveness to measure the prescriptive content of data and the efficacy of a policy from an operations perspective.
OM Forum - Causal Inference Models in Operations Management
TLDR
Five empirical tools that have been widely used in economics to address the challenges of endogeneity and selection bias are described and how they can be adopted by OM researchers.
Optimization-driven framework to understand health care network costs and resource allocation
TLDR
A general methodological optimization-driven framework based on linear programming that allows us to better understand network costs and provide strategic solutions to the aforementioned problems is developed.
The Big Data Newsvendor: Practical Insights from Machine Learning
TLDR
An innovative machine-learning approach to a classic problem solved by almost every company, every day, for inventory management, in which the best one-step, feature-based newsvendor algorithm is shown to beat the best-practice benchmark by 24% in the out-of-sample cost at a fraction of the speed.
Analytics for an Online Retailer: Demand Forecasting and Price Optimization
TLDR
This work develops an algorithm to efficiently solve the subsequent multiproduct price optimization that incorporates reference price effects, and creates and implements this algorithm into a pricing decision support tool for Rue La La's daily use.
Leveraging Comparables for New Product Sales Forecasting
TLDR
This work proposes a novel sales forecasting model that is estimated with data of comparable products introduced in the past and develops a fast and easy-to-use Excel tool that aids managers with forecasting and making decisions before a new product launch.
The Power and Limits of Predictive Approaches to Observational-Data-Driven Optimization
TLDR
Both the power and limits of predictive approaches to observational-data-driven optimization with a particular focus on pricing are studied and a new hypothesis test for causal-effect objective optimality is developed, applying it to interest-rate-setting data.
Systematic OR Block Allocation at a Large Academic Medical Center: Comprehensive Review on a Data-driven Surgical Scheduling Strategy
TLDR
This work describes the successful implementation of a data-driven scheduling strategy that increased the effective capacity of the surgical units and the use of the model as an instrument for change and strong managerial leadership was paramount to implement and sustain the new scheduling practices.
Mining Optimal Policies: A Pattern Recognition Approach to Model Analysis
TLDR
This project spawned from an admission control problem the author was working on for a major hospital in the Boston area, and tried to incorporate various aspects of the problem in a model, which resulted in a new admission control system.
Smart "Predict, then Optimize"
TLDR
Numerical experiments show that the SPO framework can lead to significant improvement under the predict-then-optimize paradigm, in particular, when the prediction model being trained is misspecified.
...
1
2
3
4
5
...