Many businesses are seeking to develop and exploit analytics capabilities today. Using organizational information processing theory (OIPT), we study demand visibility and supply visibility as foundational resources for analytics capability, and organizational flexibility as a complementary capability. We further examine relationships among these factors under varying conditions of market volatility, a type of environmental uncertainty. The results from our analysis of data from 191 global firms indicate that both demand and supply visibility are associated with the development of analytics capability. In turn, analytics capability is shown to be more strongly associated with operational performance when supply chain organizations also possess organizational flexibility needed to act upon analytics-generated insights quickly and efficiently. Furthermore, the empirical results indicate that analytics capability and organizational flexibility are more valuable as complementary capabilities for firms who operate in volatile markets, rather than in stable ones. These findings extend OIPT to create a better understanding of contemporary applications of information processing technologies, while also providing theoretically grounded guidance to managers in the development of analytics capabilities within their firms.

This article is protected by copyright. All rights reserved.

An investigation of visibility and flexibility as complements to supply chain analytics: An organizational information processing theory perspective

Many real-world analytics problems involve two significant challenges: prediction and optimization. Because of the typically complex nature of each challenge, the standard paradigm is predict-then-...

Smart "Predict, then Optimize"

Many real-world analytics problems involve two significant challenges: prediction and optimization. Due to the typically complex nature of each challenge, the standard paradigm is predict-then-optimize. By and large, machine learning tools are intended to minimize prediction error and do not account for how the predictions will be used in the downstream optimization problem. In contrast, we propose a new and very general framework, called Smart "Predict, then Optimize" (SPO), which directly leverages the optimization problem structure, i.e., its objective and constraints, for designing better prediction models. A key component of our framework is the SPO loss function which measures the decision error induced by a prediction. 
Training a prediction model with respect to the SPO loss is computationally challenging, and thus we derive, using duality theory, a convex surrogate loss function which we call the SPO+ loss. Most importantly, we prove that the SPO+ loss is statistically consistent with respect to the SPO loss under mild conditions. Our SPO+ loss function can tractably handle any polyhedral, convex, or even mixed-integer optimization problem with a linear objective. Numerical experiments on shortest path and portfolio optimization problems 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. We find that linear models trained using SPO+ loss tend to dominate random forest algorithms, even when the ground truth is highly nonlinear.

https://robjhyndman.com/papers/temporalhierarchies.pdf

Forecasting with Temporal Hierarchies

We study how delivery data can be applied to improve the on-time performance of last-mile delivery services. Motivated by the delivery operations and data of a food delivery service provider, we di...

On-Time Last-Mile Delivery: Order Assignment with Travel-Time Predictors

In this paper, we describe both applied and analytical work in collaboration with a large multistate gas utility. The project addressed a major operational resource allocation challenge that is typical to the industry. We study the resource allocation problem in which some of the tasks are scheduled and known in advance, and some are unpredictable and have to be addressed as they appear. The utility has maintenance crews that perform both standard jobs each must be done before a specified deadline as well as respond to emergency gas leaks that occur randomly throughout the day and could disrupt the schedule and lead to significant overtime. The goal is to perform all the standard jobs by their respective deadlines, to address all emergency jobs in a timely manner, and to minimize maintenance crew overtime. We employ a novel decomposition approach that solves the problem in two phases. The first is a job scheduling phase, where standard jobs are scheduled over a time horizon. The second is a crew assignment phase, which solves a stochastic mixed integer program to assign jobs to maintenance crews under a stochastic number of future emergencies. For the first phase, we propose a heuristic based on the rounding of a linear programming relaxation formulation and prove an analytical worst-case performance guarantee. For the second phase, we propose an algorithm for assigning crews that is motivated by the structure of an optimal solution. We used our models and heuristics to develop a decision support tool that is being piloted in one of the utility's sites. Using the utility's data, we project that the tool will result in a 55% reduction in overtime hours.

This paper was accepted by Noah Gans, special issue on business analytics.

/pdf/business-analytics-for-flexible-resource-allocation-under-2qx9k2vuvo.pdf

Business Analytics for Flexible Resource Allocation Under Random Emergencies

This Electronic Companion contains supporting materials for the paper: Angalakudati, Balwani, Calzada, Chatterjee, Perakis, Raad, Uichanco (2014), “Business Analytics for Flexible Resource Allocation Under Random Emergencies”

Electronic Companion to: Business Analytics for Flexible Resource Allocation Under Random Emergencies

http://dspace.mit.edu/openaccess-disseminate/1721.1/99121

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Business Analytics for Flexible Resource Allocation Under Random Emergencies

Electronic Companion to: Business Analytics for Flexible Resource Allocation Under Random Emergencies

Business Analytics for Flexible Resource Allocation Under Random Emergencies