Deborah Nightingale

Enterprise Transformation: Why Are We Interested, What Is It, and What Are the Challenges?

The concept of enterprise transformation has become increasingly popular as companies recognize the need to achieve an integrated perspective within and across organizational boundaries to address complex challenges. Yet, there is little clarity concerning what constitutes an “enterprise” or indeed “enterprise transformation.” This article is conceived as an initial step along the journey towards this clarity. There is considerable work to be done in delineating this area of interest and this article is offered as a stimulus for debate on what constitutes enterprise transformation. Drawing on themes from the management and systems engineering disciplines, the article will propose four characteristics of “enterprise” as a unit for transformation and look at why this holistic unit of analysis has become critical to businesses. The article will also ask what constitutes transformation, and offer characterizing criteria to distinguish this magnitude of change from more incremental changes. A recent empirical case study will be examined to further elucidate challenges faced in defining, leading, and transforming multi-organizational enterprises. Finally, a near-term research agenda is outlined for the evolving discipline of enterprise transformation.

Architecting the system of systems enterprise: Enabling constructs and methods from the field of engineering systems

Engineering systems is a field of scholarship focused on developing fundamental theories and methods to address the challenges of large-scale complex systems in context of their socio-technical environments. The authors describe facets of their recent and ongoing research within the field of engineering systems to develop constructs and methods for architecting enterprises engaged in system-of-systems (SoS) engineering,. The ultimate goal of the research is to develop a framework for characterizing, designing, and evaluating SoS enterprise architectures throughout the system lifespan as various forces result in entering/exiting of constituent systems, changing environment, and shifting enterprise profile. The nature of systems-of-systems demands constructs for multi-dimensional architectural descriptions, as well as methods for design and evaluation that employ dynamic approaches. In this paper, two important elements in an emerging framework are described, including a holistic enterprise architecting framework and an epoch-based analysis method for examining possible futures of the SoS enterprise.

Predicting Emergency Department Inpatient Admissions to Improve Same-day Patient Flow

OBJECTIVES The objectives were to evaluate three models that use information gathered during triage to predict, in real time, the number of emergency department (ED) patients who subsequently will be admitted to a hospital inpatient unit (IU) and to introduce a new methodology for implementing these predictions in the hospital setting. METHODS Three simple methods were compared for predicting hospital admission at ED triage: expert opinion, naïve Bayes conditional probability, and a generalized linear regression model with a logit link function (logit-linear). Two months of data were gathered from the Boston VA Healthcare Systems 13-bed ED, which receives approximately 1,100 patients per month. Triage nurses were asked to estimate the likelihood that each of 767 triaged patients from that 2-month period would be admitted after their ED treatment, by placing them into one of six categories ranging from low to high likelihood. Logit-linear regression and naïve Bayes models also were developed using retrospective data and used to estimate admission probabilities for each patient who entered the ED within a 2-month time frame, during triage hours (1,160 patients). Predictors considered included patient age, primary complaint, provider, designation (ED or fast track), arrival mode, and urgency level (emergency severity index assigned at triage). RESULTS Of the three methods considered, logit-linear regression performed the best in predicting total bed need, with a receiver operating characteristic (ROC) area under the curve (AUC) of 0.887, an R(2) of 0.58, an average estimation error of 0.19 beds per day, and on average roughly 3.5 hours before peak demand occurred. Significant predictors were patient age, primary complaint, bed type designation, and arrival mode (p < 0.0001 for all factors). The naïve Bayesian model had similar positive predictive value, with an AUC of 0.841 and an R(2) of 0.58, but with average difference in total bed need of approximately 2.08 per day. Triage nurse expert opinion also had some predictive capability, with an R(2) of 0.52 and an average difference in total bed need of 1.87 per day. CONCLUSIONS Simple probability models can reasonably predict ED-to-IU patient volumes based on basic data gathered at triage. This predictive information could be used for improved real-time bed management, patient flow, and discharge processes. Both statistical models were reasonably accurate, using only a minimal number of readily available independent variables.

Real Options in Enterprise Architecture: A Holistic Mapping of Mechanisms and Types for Uncertainty Management

Uncertainty management is crucial for achieving high performance in enterprises that develop or operate complex engineering systems. This study focuses on flexibility as a means of managing uncertainties and builds upon real options analysis (ROA) that provides a foundation for quantifying the value of flexibility. ROA has found widespread applications ranging from strategic investments to product design. However, these applications are often isolated to specific domains. Furthermore, ROA is focused on valuation, rather than the identification of real options. In this paper, we introduce a framework for holistic consideration of real options in an enterprise context. First, to enable a holistic approach, we use a generalized enterprise architecture framework that considers eight views: strategy, policy, organization, process, product, service, knowledge, and information technology (IT). This expands upon the classical IT-centric view of enterprise architecture. Second, we characterize a real option as a mechanism and type. This characterization disambiguates among mechanisms that enable flexibility and types of flexibility to manage uncertainties. Third, we propose mapping of mechanisms and types to the enterprise architecture views. We leverage this mapping in an integrated real options framework and demonstrate its benefit over the traditional localized approach to ROA.

A Logical Approach to Real Options Identification With Application to UAV Systems

Complex systems are subject to uncertainties that may lead to suboptimal performance or even catastrophic failure if unmanaged. Uncertainties may be managed through real options that provide a decision maker with the right, but not the obligation, to exercise actions in the future. While real options analysis has traditionally been used to quantify the value of such flexibility, this paper is motivated by the need for a structured approach to identify where real options are or can be embedded for uncertainty management. We introduce a logical model-based approach to identification of real option mechanisms and types, where the mechanism is the enabler of the option, while the type refers to the flexibility provided by the option. First, we extend the classical design structure matrix and the more general multiple-domain matrix (MDM), commonly used in modeling and analyzing interdependencies in complex socio-technical systems, to the more expressive Logical-MDM that supports the representation of flexibility. Second, we show that, in addition to flexibility, two new properties, namely, optionability and realizability, are relevant to the identification of real options. We use the Logical-MDM to estimate flexibility, optionability, and realizability metrics. Finally, we introduce the Real Options Identification (ROI) method based on these metrics, where the identified options are valued using standard real options valuation methods to support decision making under uncertainty. The expressivity of the logic combined with the structure of the dependency model allows the effective representation and identification of mechanisms and types of real options across multiple domains and lifecycle phases of a system. We demonstrate this approach through a series of unmanned air vehicle scenarios.

Generalizability of a Simple Approach for Predicting Hospital Admission From an Emergency Department

OBJECTIVES The objective was to test the generalizability, across a range of hospital sizes and demographics, of a previously developed method for predicting and aggregating, in real time, the probabilities that emergency department (ED) patients will be admitted to a hospital inpatient unit. METHODS Logistic regression models were developed that estimate inpatient admission probabilities of each patient upon entering an ED. The models were based on retrospective development (n = 4,000 to 5,000 ED visits) and validation (n = 1,000 to 2,000 ED visits) data sets from four heterogeneous hospitals. Model performance was evaluated using retrospective test data sets (n = 1,000 to 2,000 ED visits). For one hospital the developed model also was applied prospectively to a test data set (n = 910 ED visits) coded by triage nurses in real time, to compare results to those from the retrospective single investigator-coded test data set. RESULTS The prediction models for each hospital performed reasonably well and typically involved just a few simple-to-collect variables, which differed for each hospital. Areas under receiver operating characteristic curves (AUC) ranged from 0.80 to 0.89, R(2) correlation coefficients between predicted and actual daily admissions ranged from 0.58 to 0.90, and Hosmer-Lemeshow goodness-of-fit statistics of model accuracy had p > 0.01 with one exception. Data coded prospectively by triage nurses produced comparable results. CONCLUSIONS The accuracy of regression models to predict ED patient admission likelihood was shown to be generalizable across hospitals of different sizes, populations, and administrative structures. Each hospital used a unique combination of predictive factors that may reflect these differences. This approach performed equally well when hospital staff coded patient data in real time versus the research team retrospectively.

Insights from enterprise assessment: How to analyze LESAT results for enterprise transformation

Due to the growing recognition of the importance of plasticity and adaptability in organizations, many enterprise leaders have sought to integrate transformation processes and continuous improvement goals into strategic planning efforts. Assessment tools provide the necessary insights to support and guide enterprise level transformation efforts. They contribute a multitude of information, including the current state of the organization, strengths and weakness, and team cohesion and prioritized needs; all of which assist in planning and guiding ongoing transformation efforts. In this paper, we examine a specific assessment tool LESAT, the Lean Enterprise Self-Assessment Tool, and associated ways of analyzing and interpreting assessment results in order to drive the transformation process. The insights draw from a combination of strategies including developing measurement tools, experiences collecting data, facilitating self-assessment exercises, and interpreting results in support of transformation planning. In addition, we examine common mistakes and threats to validity that may undermine or hurt the assessment analysis. This paper is designed to aid practitioners in choosing the most beneficial interpretation strategies to gain the greatest possible benefit from their assessment process.

Empirical Research on Systems Thinking and Practice in the Engineering Enterprise

The paper discusses recent and ongoing research on engineering systems thinking and practices within the Engineering Systems Division at the Massachusetts Institute of Technology. . The research seeks to impact the effectiveness of systems engineering in modern enterprises through development of new empirical-based knowledge related to systems thinking and practice in engineering. The paper will discuss research progress and outcomes to date as they apply to improving the effectiveness of systems engineering practice and competency development in industry, government and academia. The research involves highly collaborative engagement, use of grounded theory methods, and both quantitative and qualitative analysis. The challenges and lessons learned in performing research of this nature and applying non-traditional methods in systems engineering research are discussed.

Characterizing the value of predictive analytics in facilitating hospital patient flow

We apply discrete event simulation to characterize the patient flow affects of using admission predictions and current state information, generated in an Emergency Department (ED), to influence the prioritization of inpatient unit (IU) physicians between treating and discharging IU patients. Shared information includes crowding levels and total expected bed need (based on the sum of individual patients’ imperfect admission predictions and perfect admission predictions). It is found that sharing prediction and crowding information to influence inpatient staff priorities, using specific information sensitivity schedules, can result in statistically significant (p ≪ 0.05) reductions in boarding time (between 11.69% and 18.38% compared to baseline performance). The range of improvement is dependent on varying simulated hospital configurations.

Model-based estimation of flexibility and optionability in an integrated real options framework

Uncertainties can be managed through real options that provide a decision maker the right, but not the obligation, to exercise actions at a later time. In previous work [1] we introduced an integrated real options framework (IRF) that distinguishes among option mechanism and type. The mechanism is the enabler of the option, while the type refers to the flexibility provided by the option. The idea behind IRF is to use models of a system or enterprise as a coupled dependency structure matrix (C-DSM) in order to identify and value enablers and types of flexibility. In this paper, we first show how the distinction among mechanisms and types of options leads to the identification of some new “ilities”, such as optionability, that are relevant to the options identification problem. Second, we show that the semantics of a traditional dependency model does not allow for the representation and estimation of flexibility and optionability. Therefore, we extend the C-DSM model to a logical C-DSM that is capable of representing logical relations among dependencies. Finally, we present metrics for estimating flexibility and optionability from the logical C-DSM model. We discuss the results of applying these metrics to identify mechanisms and options in purchasing a swarm of uninhabited air vehicles.