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For more information please contact Prof. Bob Batt, bob.batt@wisc.edu.

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This is joint work with Tinglong Dai (Johns Hopkins University) and Rongzhu Ke (Hong Kong Baptist University).

Looking for the Needle in the Haystack: Evidence of the Superforecasting Hypothesis When Time and Samples are Limited

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Image RemovedProf. Kostas Nikolopoulos, Professor, Bangor Business School, Bangor University

The success of the Good Judgmental Project in harnessing the power of superforecasting naturally leads to the question as to how one can implement that approach on a smaller scale with more limited resources as in less time and fewer participants. Small(er) corporate environments and SME-type decision structures are prime examples where the modified superforecasting approach can be used. In this research we focus on a hybrid approach of judgmental forecasting on special events where we combine training of superforecasters-to-be via the concept of a modified version of structured analogies (s-SA), a staple of judgmental forecasting in the literature. We call the resulting approach structured superforecasting and illustrate its efficacy over samples of participants from the wider public sector and the academic community. In particular, with a proper experimental design that includes a training and a control group, we apply the above methodology and compare performances. More importantly we do find evidence of the superforecasting hypothesis even when we are working with smaller samples – a few hundred experts - and when the selection of super forecasters needs to be done much faster – in less than a year

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Leasing, Modularity, and the Circular Economy

Prof. Ataly Atalay Atasu, Professor, Scheller College of Business, Georgia Tech

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The main take-away from this paper is that for all the talk on the potential of the circular economy, there is a lot to be done to test and verify its broad, sweeping claims, and much of this needs an academic perspective.

Surfacing the Submerged State: Operational Transparency Increases Trust in and Engagement with Government

Image AddedProf. Ryan Buell, Associate Professor, Harvard Business School, Harvard University

As trust in government reaches historic lows, frustration with government performance approaches record highs. We propose that peoples’ perceptions of government and their levels of engagement with it can be reshaped and enhanced by increasing government’s operational transparency – that is, by designing service interactions so that citizens can see the often-hidden work that government performs. Across three studies, we find that revealing the “submerged state” through operational transparency impacts citizens’ attitudes and behavior. In Study 1, viewing a five-minute computer simulation highlighting the work performed by the government of an archetypal town increased trust in government and support for government services. In Study 2, residents of Boston, Massachusetts who interacted with a website that visualized service requests (e.g., potholes and broken street lamps), and efforts by the city’s government to address them became 14% more trusting and 12% more supportive of government. Moreover, residents who additionally received transparency into the growing backlog of service requests that government was failing to fulfill were no less trusting and supportive of government than residents who received no transparency at all. Study 3 leveraged proprietary data from a mobile phone application developed by the city of Boston through which residents can submit service requests; the city’s goal was to increase engagement with the app. Users who received photos of government meeting their service requests submitted 60% more requests and in 40% more categories over the ensuing 13 months than users who did not receive such photos. These significant gains in engagement persisted for 11 months following users’ initial exposure to operational transparency, and were highest for users who previously had experienced government to be moderately effective in responding to their service requests. Taken together, our results suggest that revealing the submerged state through operational transparency can shape both attitudes and behavior – results with potential implications for a broad array of service domains where operations are hidden and levels of consumer trust and engagement are faltering.

Dual Sourcing and Smoothing under Non-Stationary Demand Time Series: Re-shoring with SpeedFactories

Image ModifiedProf. Jan Van Mieghem, Professor, Kellogg School of Management, Northwestern UniversityImage RemovedProf. Beril Toktay, Professor, Scheller College of Business, Georgia Tech

We investigate the emerging trend of near-shoring a small part of the global production back to local SpeedFactories. The short lead time of the responsive SpeedFactory reduces the risk of making large volumes in advance, yet it does not involve a complete re-shoring of demand. Using a breakeven analysis we investigate the lead time, demand, and cost characteristics that make dual sourcing with a SpeedFactory desirable compared to off-shoring to a single supplier. We propose order rules that extend the celebrated inventory optimal order-up-to replenishment policy to settings where capacity costs exist and demonstrate their excellent performance. We highlight the significant impact of autocorrelated and non-stationary demand series, which are prevalent in practice yet challenging to analyze, on the economic benefit of re-shoring. Methodologically, we adopt Z−transforms and present an exact analysis of several discrete-time linear inventory models. 

Image AddedProf. Tinglong Dai, Associate Professor, Carey Business School, Johns Hopkins University

Few issues in the healthcare ecosystem are more salient than the utilization of medical tests. By some estimates, up to 30% of medical-testing decisions are deemed inappropriate, which may entail either over- or under-testing. All too frequently, the public attention has centered on over-testing. By comparison, under-testing has received little media coverage, but frequently appears in the medical literature. In addition, contrary to popular belief, the US trails most OECD countries in terms of the utilization of medical tests.

In this talk, I discuss several recent modeling efforts aimed at understanding physician decision-making leading to over- and under-testing. These efforts, motivated by ophthalmology and interventional cardiology practices, reflect clinical, financial, and operational incentives. I will also highlight implications for policymakers and healthcare executives.

My talk will draw from three papers:

Tinglong Dai, Shubhranshu Singh. 2018. Conspicuous by Its Absence: Diagnostic Expert Testing under Uncertainty. Johns Hopkins University Working Paper.

Tinglong Dai, Mustafa Akan, Sridhar Tayur. 2017. Imaging Room and Beyond: The Underlying Economics behind Physicians’ Test-Ordering Behavior in Outpatient Services. Manufacturing & Service Operations Management 19(1) 99–113.

Tinglong Dai, Xiaofang Wang, Chao-Wei Hwang. 2018. Clinical Ambiguity and Conflicts of Interests in Interventional Cardiology Decision-Making. Johns Hopkins University Working Paper.

Integrated Anesthesiologist and Room Scheduling for Surgeries: Methodology and Application

Image ModifiedProf. Kumar Rajaram, Professor, Anderson School of Management, UCLA

We consider the problem of minimizing daily expected resource usage and overtime costs across multiple parallel resources such as anesthesiologists and operating rooms, which are used to conduct a variety of surgical procedures at large multispecialty hospitals. To address this problem, we develop a two-stage, mixed-integer stochastic dynamic programming model with recourse. The first stage allocates these resources across multiple surgeries with uncertain durations and prescribes the sequence of surgeries to these resources. The second stage determines actual start times to surgeries based on realized durations of preceding surgeries and assigns overtime to resources to ensure all surgeries are completed using the allocation and sequence determined in the first stage. We develop a data-driven robust optimization method that solves large-scale real-sized versions of this model close to optimality. We validate and implement this model as a decision support system at the UCLA Ronald Reagan Medical Center. This system effectively incorporates the flexibility in the resources and uncertainty in surgical durations, and explicitly trades off resource usage and overtime costs. This has increased the average daily utilization of the anesthesiologists by 3.5% and of the operating rooms by 3.8%. This has led to an average daily cost savings of around 7% or estimated to be $2.2 million on an annual basis. In addition, the insights based on this model have significantly influenced decision making at the operating services department at this hospital.