A Machine Learning Approach for Predicting Inpatient Discharge at Central Maine Medical Center

Operating with a finite quantity of beds, medical resources, and physicians, hospitals are constantly allocating resources under conditions of scarcity. Misallocation of resources and operational inefficiencies are a substantial driver of the United States’ strikingly high healthcare costs. Accurately forecasting the duration which a specific patient will stay in a hospital, also known as a patient’s length of stay, could assist hospital decision makers in optimizing their workflow and allocating their resources efficiently. This paper demonstrates the superiority of a survival random forest approach over classical econometric techniques and current practice at the Central Maine Medical Center. Included in the discussion is an assessment of the strengths and weaknesses of the model, with the hope of informing the application of machine learning methods in the real world

The Copy-Exactly Ramp-Up Strategy: Trading-Off Learning With Process Change

Production ramp-up is the period of time during which a manufacturing process is scaled up from a small laboratory-like environment to high-volume production. During this scale-up, the firm needs to overcome the numerous discrepancies between how the process is specified to operate as written in the process recipe and how it actually is operated at large volume. The reduction of these discrepancies, a process that we will refer to as learning, will lead to improved production yields and higher output. In addition to its learning effort, however, the firm also attempts to change the process recipe itself, which can be in direct conflict with the learning objective. We formalize this intertemporal tradeoff between learning and process change in the form of a dynamic optimization problem. Our model explains the idea of a copy-exactly ramp-up, which freezes the process for some time period, i.e., does not allow for any change in the process. Mathematically, this corresponds to a process improvement policy which delays process changes, thereby exhibiting a nonmonotone trajectory, which we show to be optimal if the initial knowledge level is low, the lifecycle short and demand growth is steep, and learning is difficult

Optimal Product Launch Times in a Duopoly: Balancing Life-Cycle Revenues With Product Cost

We present a model describing the demand dynamics of two new products competing for a limited target market. The demand trajectories of the two products are driven by a market saturation effect and an imitation effect reflecting the product experience of previous adopters. In this general setting, we provide analytical results for the sales trajectories and life-cycle sales of the competing products. We use these results to study the impact of launch time on overall life-cycle sales. We consider the perspective of one of the competing products and model the trade-off between the lost revenues resulting from a delayed launch and the lower unit-production costs. We find that the profit-maximizing launch time exhibits a counterintuitive behavior. In particular, we show that a firm facing a launch time delay from a competing product might benefit from accelerating its own product launch, as opposed to using the softened competitive situation to further improve its cost position. We identify conditions under which a marginal cost-benefit analysis leads to suboptimal launch-time decisions. Finally, we analyze the Nash equilibrium in launch-time decisions of the two competing products

Pricing and Production Flexibility: An Empirical Analysis of the US Automotive Industry

We use a detailed data set from the U.S. auto industry spanning from 2002 to 2009 and a variety of econometric methods to characterize the relationship between the availability of production mix flexibility and firms’ use of responsive pricing. We find that production mix flexibility is associated with reductions in observed manufacturer discounts, resulting from the increased ability to match supply and demand. Under the observed market conditions, mix flexibility accounts for substantial average savings by reducing price discounting by approximately 10% of the average industry discount. We test three supplementary hypotheses and find that the reduction in discounts for vehicles manufactured at flexible plants is (1) higher for higher demand uncertainty, (2) higher for vehicles coproduced with vehicles that belong to a different segment, and (3) lower in situations with higher local competition

Communication and Uncertainty in Concurrent Engineering

We present an analytical model of concurrent engineering, where an upstream and a down-stream task are overlapped to minimize time-to-market. The gain from overlapping activities must be weighed against the delay from rework that results from proceeding in parallel based on preliminary information. Communication reduces the negative effect of rework at the expense of communication time. We derive the optimal levels of concurrency combined with communication, and we analyze how these two decisions interact in the presence of uncertainty and dependence. Uncertainty is modeled via the average rate of engineering changes, and its reduction via the change of the modification rate over time. In addition, we model dependence by the impact the modifications impose on the downstream task. The model yields three main results. First, we present a dynamic decision rule for determining the optimal meeting schedule. The optimal meeting frequency follows the frequency of engineering changes over time, and it increases with the levels of uncertainty and dependence. Second, we derive the optimal concurrency between activities when communication follows the optimal pattern described by our decision rule. Uncertainty and dependence make concurrency less attractive, reducing the optimal overlap. However, the speed of uncertainty reduction may increase or decrease optimal overlap. Third, choosing communication and concurrency separately prevents achieving the optimal time-to-market, resulting in a need for coordination

Managing the Process of Engineering Change Orders: The Case of the Climate Control System in Automobile Development

Engineering change orders (ECOs) are part of almost every development process, consuming a significant part of engineering capacity and contributing heavily to development and tool costs. Many companies use a support process to administer ECOs, which fundamentally determines ECO costs. This administrative process encompasses the emergence of a change (e.g., a problem or a market-driven feature change), the management approval of the change, up to the change\u27s final implementation. Despite the tremendous time pressure in development projects in general and in the ECO process in particular, this process can consume several weeks, several months, and in extreme cases even over 1 year. Based on an in-depth case study of the climate control system development in a vehicle, we identify five key contributors to long ECO lead times: a complex approval process, snowballing changes, scarce capacity and congestion, setups and batching, and organizational issues. Based on the case observations, we outline a number of improvement strategies an organization can follow to reduce its ECO lead times

Impact of Workload on Service Time and Patient Safety: An Econometric Analysis of Hospital Operations

Much of prior work in the area of service operations management has assumed service rates to be exogenous to the level of load on the system. Using operational data from patient transport services and cardiothoracic surgery—two vastly different health-care delivery services—we show that the processing speed of service workers is influenced by the system load. We find that workers accelerate the service rate as load increases. In particular, a 10% increase in load reduces length of stay by two days for cardiothoracic surgery patients, whereas a 20% increase in the load for patient transporters reduces the transport time by 30 seconds. Moreover, we show that such acceleration may not be sustainable. Long periods of increased load (overwork) have the effect of decreasing the service rate. In cardiothoracic surgery, an increase in overwork by 1% increases length of stay by six hours. Consistent with prior studies in the medical literature, we also find that overwork is associated with a reduction in quality of care in cardiothoracic surgery—an increase in overwork by 10% is associated with an increase in likelihood of mortality by 2%. We also find that load is associated with an early discharge of patients, which is in turn correlated with a small increase in mortality rate

The Economics of Yield-Driven Processes

The economic performance of many modern production processes is substantially influenced by process yields. Their first effect is on product cost — in some cases, low-yields can cause costs to double or worse. Yet measuring only costs can substantially underestimate the importance of yield improvement. We show that yields are especially important in periods of constrained capacity, such as new product ramp-up. Our analysis is illustrated with numerical examples taken from hard disk drive manufacturing. A three percentage point increase in yields can be worth about 6% of gross revenue and 17% of contribution. In fact, an eight percentage point improvement in process yields can outweigh a US$20/h increase in direct labor wages. Therefore, yields, in addition to or instead of labor costs, should be a focus of attention when making decisions such as new factory siting and type of automation. The paper also provides rules for when to rework, and shows that cost minimization logic can again give wrong answers

The Effects of Focus on Performance: Evidence from California Hospitals

We use hospital-level discharge data from cardiac patients in California to estimate the effects of focus on operational performance. We examine focus at three distinct levels of the organization—at the firm level, at the operating unit level, and at the process flow level. We find that focus at each of these levels is associated with improved outcomes, namely, faster services at higher levels of quality, as indicated by lower lengths of stay (LOS) and reduced mortality rates. We then analyze the extent to which the superior operational outcome is driven by focused hospitals truly excelling in their operations or by focused hospitals simply “cherry-picking” easy-to-treat patients. To do this, we use an instrumental variables estimation strategy that effectively randomizes the assignment of patients to hospitals. After controlling for selective patient admissions, the previously observed benefits of firm level focus disappear; focused hospitals no longer demonstrate a statistically significant reduction in LOS or mortality rate. However, at more granular measures of focus within the hospital (e.g., operating unit level), we find that more focus leads to a shorter LOS, even after controlling for selective admission effects