Research

A First Step Towards Strong AI

By Larry Bookman, Oct 16, 2018

A common view in the press and in artificial intelligence research is that sentient and intelligent machines are just on the horizon. How much longer can it be before they surpass our intelligence and take our jobs? Before we decide if machines can surpass our intelligence, let us first define two terms that will help us get a better handle on this topic: Weak AI and Strong AI.

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Bumps and Triumphs on the Road to Optimization at UPS

By Jack Levis, Aug 21, 2018

Available to Research & Advisory Network Clients Only

United Parcel Service (UPS) is now well known for instituting ORION, an optimized routing system for UPS drivers that has been called the world’s largest operations research project. ORION determines the most efficient delivery route for every driver, every day, resulting in savings of more than 100 million driving miles. The project serves as an excellent case study on how embedding analytics and models within the day-to-day operations of the business—in this case, package delivery—can reap huge cost savings benefits.

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2017 Analytics Symposium - Chicago Summary and Recordings

By Jack Phillips, Oct 31, 2017

Available to Research & Advisory Network Clients Only

Earlier this month, IIA held its 8th Analytics Symposium and awarded the 2017 ANNY Excellence in Analytics Award to Cisco Systems in a packed house at the Gleacher Center in Chicago. Read the key themes of the event and watch the session recordings on demand.

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The Road to Optimization

Oct 31, 2017

Available to Research & Advisory Network Clients Only

2017 Analytics Symposium - Chicago Session Recording

UPS has gone through a long evolution in moving up the analytical hierarchy which required organizational commitment and significant process change. UPS has seen a reduction of 185 million miles driven per year by integrating analytics within its operations’ systems. Its award-winning dispatch optimization tool ORION (On Road Integrated Optimization and Navigation) completed deployment in 2016 and is saving $300M to $400M annually. Jack Levis shares his experiences and best practices to compete with analytics, requiring organizational support in the form of data, tools, and senior management commitment.

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Optimization of Inventory Allocation

By Dr. Chris Holloman, Leigh Helsel, Tayler Blake, Aug 23, 2017

Available to Research & Advisory Network Clients Only

In 2016, U.S. e-commerce sales totaled an estimated $394.9 billion, accounting for 8.1 percent of total annual sales. This total was a 15 percent increase from 2015. Advances in technology and adoption of the internet have forced the retail industry to make dramatic shifts toward e-commerce. While this change presents a tremendous opportunity for business growth, the cost associated with inefficiencies in supply chains makes optimally allocating inventory to fulfillment centers integral to retailers’ success. In this research brief, we describe a method to determine the best allocation of inventory to fulfillment centers after a total buy has been determined.

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Inquiry Response: Tips for Linking Retail Outlet Sales Back to Digital Marketing Efforts

By Greg Bonsib, Aug 21, 2017

Available to Research & Advisory Network Clients Only

Inquiry:

A large part of our business is in consumer packaged goods sold through mass-channel outlets such as Wal-Mart. We’d like some insights into how we can use analytics to help us understand the marketing-driven revenue on the retail end. Is there a way we can link POS revenue back to our digital marketing efforts?

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Five Big Data Analytics Pitfalls to Be Aware of (And Avoid!)

By Bill Franks, Jun 28, 2017

Available to Research & Advisory Network Clients Only

Many people think that in the age of big data, we always have more than enough information to build robust analytics for almost every situation. Unfortunately, this isn’t the case. In fact, there are situations where even massive amounts of data still don’t enable basic predictions to be made with confidence. In many cases, there isn’t much that can be done other than to recognize the facts and stick to basic analytics instead of getting fancy. However, it is critical to recognize the situation before expending a lot of effort in a wasteful attempt to get predictive analytics to work in a situation where success isn’t in the cards. This challenge of big data that can’t be used to predict seems like an impossible paradox at first, but, as you’ll see, it is not.

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The Manufacturer’s Dilemma

By Geoffrey Moore, Jun 20, 2017

There is a lot of serious talk in America these days about improving the state of our manufacturing sector. Smart products, Internet of things, robotics, predictive maintenance—all great stuff. But none of it addresses the most fundamental challenge facing the sector: how to deal with a demand/supply inversion which has made the customer king.

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Understanding Power in the Digital Economy

By Geoffrey Moore, May 09, 2017

We are all stakeholders in the economic systems within which we live and work, and the better we can understand their dynamics, the more likely we are to navigate them successfully. For the most developed economies of today, this means understanding the transition from an industrial to a digital economy, and specifically, how economic power is migrating from familiar to unfamiliar sites.

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Move Your Analytics Operation from Artisanal to Autonomous

By Thomas H. Davenport, May 02, 2017

Many organizations today are wondering how to get into machine learning, and what it means for their existing analytics operation. There are many different types of machine learning, and a variety of definitions of the term. I view machine learning as any data-driven approach to explanations, classifications, and predictions that uses automation to construct a model. The computer constructing the model “learns” during the construction process what model best fits the data. Some machine learning models continue to improve their results over time, but most don’t. Machine learning, in other words, is a form of automating your analytics. And it has the potential to make human analysts wildly more productive.

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