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The link between big data and analytics has already been established; namely, the extensive use of data, statistics, and quantitative algorithms for descriptive explanatory , predictive forecasting , and prescriptive optimization modeling and analyses for fact-based, analytic management. And through sensor technology, radio frequency identification, Total Asset Visibility, enterprise resource planning systems, and the Internet, information technology has now expanded to capture, track, monitor, and visualize data in near-real time across disparate, dislocated entities comprising an entire enterprise.
However, the Army has yet to fully integrate analytical architectures into its persisting enterprise system challenges. Complementary decision-support systems have not yet been developed that could capitalize on available enterprise data and, using analytically based methods, make sense of it all, enable improved decisions, and dramatically improve enterprise performance.
For a fixed demand, the three quantities shown in Figure F-1 inventory, capacity, and knowledge represent a trade space: If more of one of these quantities is available, then less of one or both of the others is necessary to reach the same level of system performance.
This trade-off suggests a fundamental truth: If the amount and timeliness of useful data and good information for actionable decisions improves i. For large, complex organizations, the greatest return on investment is derived from integrating relevant analytical tools and analysis with the appropriate information technology to provide actionable decisions support. This path achieves cost-effective, performance-oriented results aligned with strategic plans, organizational vision, and ultimately, the purpose for which the enterprise exists.
The goal should be the effective integration of analytics into organizational decision-making.
Improvements in data storage and processing continue at a rapid pace, but most organizations struggle to manage, analyze, apply, and transform data into useful information for knowledge creation and actionable decision options. The corporate world has come to realize that investment in new information technology systems, without first examining and implementing the necessary business process changes, simply automates existing inefficiencies and results in negligible benefits. Last accessed on September 22, Capacity, inventory, and knowledge.
Nonetheless, analytic management is often impeded by organizational pathologies: What must be created is the analytical capacity for insight, refinement, and better decision-making. Although studies linking analytical approaches and business performance are still relatively immature, one must be wary of so-called information technology solutions. It is imperative to realize that information systems, especially enterprise resource planning systems, must be connected to analytics in order to create decision support capabilities.
There is mounting evidence e. Figure F-2 shows some of the components of an effective management innovation technology system. Although information technology solutions have ubiquitous appeal and enormous investment levels , focusing only on information technology results in growing complexity and information overload that exceeds the interpretive capacities of the organizations responsible for developing and using information technologies.
Organizations need the analytical, integrative power of operations research to focus business process reengineering on desired outcomes. This has been termed the ingenuity gap Homer-Dixon, A complementary relationship, both symbiotic and synergistic, is needed between decision support systems and management information systems. Two distinctly different planning approaches can be distilled from the management literature: The traditional approach focuses on a short-term horizon, usually annual, where internal budget constraints and financial targets constitute the primary management objectives to defend and extend existing business.
For stable environments with growing market potential, this familiar incremental approach can yield steady business growth. In contrast, the transformational perspective focuses on penetrating other, perhaps emerging,. This externally focused approach views environmental conditions and future challenges as potential opportunities rather than constraints to existing business. Because past performance is now irrelevant as a benchmark for planning objectives, an organizational vision must be imagined for a future horizon, and creative plans developed to engineer progress toward this future vision.
Key ingredients for successfully pursuing a transformational strategy include an engine for innovation and strategic architectures for analysis, management, and planning. An engine for innovation is a virtual test bed that can provide a synthetic, nonintrusive environment for experimentation and evaluation of innovative ideas and concepts. In essence, such a capability functions as an engine for innovation to sustain continuous performance improvement.
The organizational construct for an engine for innovation is shown in Figure F The construct includes the following three main components:. A research model and supporting framework, including a strategic outreach mechanism, to function as a generator, magnet, conduit, filter, clearinghouse, and database for good ideas;.
A modeling, simulation, and analysis component that contains a rigorous analytical capacity to evaluate and assess potential impacts and associated costs of good ideas; and. An organizational implementation component to enable the transition of promising concepts into existing organizations, agencies, and companies by providing training, education, technical support, and risk reduction and mitigation methods to reduce operational and organizational risk during periods of inevitably disruptive transformational change.
Army, tactical units are renowned for pioneering and refining the after-action review concept as a continuous learning method to uncover, diagnose, and correct deficiencies that improve and. Yet, comparable diagnostic effort has not been prevalent at strategic levels within the institutional Army logistics bureaucracy. Because analytically rigorous root cause analysis and understanding of problems and effective response for management issues are not routinely performed at the strategic level to uncover ground truth and learn from mistakes, reactive crisis management seems to be the institutional response to visible symptoms.
In other words, the institution is always reacting to logistics problems rather than getting ahead of them. Institutional adaptation and agility requires a culture of innovation. However, sources for innovation must exist for the culture to embrace. An engine for innovation would provide a source of innovation by building a capacity for low-risk, low-cost experimentation using a synthetic environment where analytically rigorous cost-benefit analyses can be performed to differentiate between desirable objectives and attainable ones that can actually be implemented.
The purpose of this deliberate, cyclical discovery process is to sustain continuous improvement through experimentation, prototyping, field testing, feedback, and especially through rigorous analysis. Organizational vision and analytical tools must not be viewed as mutually exclusive paradigms. Rather, analytical tools should link organizational vision to operational results by defining and monitoring metrics tied to strategic enterprise objectives and aligning incentives to those objectives. In organizations with strong cultures, especially the military services, it is critical that incentives for behavior and performance be carefully and thoughtfully targeted to attain desired institutional outcomes.
Also, strategic planning and management frameworks are essential to enable learning within organizations and to ensure that strategies pursued achieve intended operational results.
Scott Czinkota, Michael R. Davis, Robert de Kluyver, Cornelis A. Ettenger, Kreg Everett, Heidi L. Foster, Carrie Fox, Gerald T.
For large, complex organizations, the greatest return on investment is derived from integrating relevant analytical tools and analysis with the appropriate information technology to provide actionable decisions support. Complementary decision-support systems have not yet been developed that could capitalize on available enterprise data and, using analytically based methods, make sense of it all, enable improved decisions, and dramatically improve enterprise performance. Jones, Lucy Judge, William Q. If the amount and timeliness of useful data and good information for actionable decisions improves i. Lowenstein, Michael Luck, Susan L. Wednesday September 12,
Gefen, David Geiger, Dale R. Gordy, Heather Gotsch, Susan D. Harrison, Julie Hartley, Janet L. Inkson, Kerr Inmon, William H. Iyer, Ananth Iyer, S. David Johnson, Mark W. Jones, Lucy Judge, William Q. Kaufmann, Lutz Kazeroony, Hamid H. Keys, Barry Killingsworth, William R. Kilner, Andrew Kimes, Sheryl E. Lowenstein, Michael Luck, Susan L. Martin, Dick Martin, Jeanette S. Moghaddam, Yassi Mohammed, Debbie A.
Pirson, Michael Pitcher, Graham S. Poulis, Athanasios Power, Daniel J. Rizomyliotis, Ioannis Robbins, Eric J. Schmid, Mariana Seago, W.