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The goal? To identify where institutional complexity is an issue, where complexity caused by factors such as a lack of role clarity or poor processes is a problem, and what’s responsible for the complexity in each area. Companies can then boost organizational effectiveness through a combination of two things: removing complexity that doesn’t add value and channeling what’s left to employees who can either handle it naturally or be trained to cope with it.

In this article, we review the experience of a multinational consumer goods manufacturer that applied this approach in several regions and functions and consequently halved the time it needed to make decisions in critical processes.

Armed with the survey data, the manufacturer constructed several “heat maps” to help senior managers pinpoint where, and why, complexity was causing trouble for employees. Each map showed a particular breakdown—a region or function, for example—and how much complexity of various kinds was occurring there, as well as the level of coping skills employees possessed.

This ethnographic study investigates how a project group deals with the contradiction between distributed knowledge in boundary-spanning collaborative processes and the expectation that software systems will provide unified, codified knowledge. Group and individual activities were observed over a period of 18 months, to examine the ways knowledge was presented, recognized, shared, or otherwise managed during joint design of business process and IT systems change. The study explores how knowledge and expertise were translated across organizational boundaries, and identifies four stages in the development of group understanding of how to manage sensemaking and expertise across knowledge boundaries: focus on defining shared goals; acknowledging and sharing tacit knowledge about organizational practice; identifying external influences; and explicit knowledge generation.

This course advances the concept that humans and their organizations are an integral part of the engineering paradigm and that it is up to engineering to learn how to better integrate considerations of people into engineering systems of all types. This course focuses this concept on the assessment and management of the risks associated with engineered systems during their life-cycle (concept development through decommissioning). Risks (likelihoods and consequences) are addressed in the contexts of the desired quality from an engineered system including serviceability (fitness for purpose), safety (freedom from undue exposure to harm), compatibility (on time, on budget, with happy customers including the environment), and durability (freedom from unexpected degradations in the other quality characteristics). Reliability is introduced to enable assessment of the wide variety of hazards, uncertainties, and variabilities that are present during the life-cycle of an engineered system. Proactive (get ahead of the challenges), Reactive (learn the lessons from successes and failures), and Interactive (realtime assessment and management of unknown knowables and unknown unknowables) strategies are advanced and illustrated to assist engineers in the assessment and management of risks.

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Bea categorizes disasters into four groups. One such group is when an organization simply ignores warning signs through overconfidence and incompetence. He thinks the BP spill falls into that category. Bea pointed to congressional testimony that BP ignored problems with a dead battery, leaky cement job and loose hydraulic fittings.

AutoMap is a text mining tool that enables the extraction of network data from texts. AutoMap can extract content analytic data (words and frequencies), semantic networks, and meta-networks from unstructured texts developed by CASOS at Carnegie Mellon.  Pre-processors for handling pdf’s and other text formats exist.  Post-processors for linking to gazateers and belief inference also exist. The main functions of AutoMap are to extract, analyze, and compare texts in terms of concepts, themes, sentiment, semantic networks and the meta-networks extracted from the texts. AutoMap exports data in DyNetML and can be used interoperably with *ORA. AutoMap uses parts of speech tagging and proximity analysis to do computer-assisted Network Text Analysis (NTA). NTA encodes the links among words in a text and constructs a network of the linked words. AutoMap subsumes classical Content Analysis by analyzing the existence, frequencies, and covariance of terms and themes. AutoMap has been implemented in Java 1.5.0_07. It can operate in both a front end with gui, and backend mode. Main functionalities of AutoMap are: Extract, analyze and compare mental models of individuals and groups. Reveal structure of social and organizational systems from texts. AutoMap also offers a variety of techniques for pre-processing Natural Language: Named-Entity Recognition Stemming (Porter, KStem) Collocation (Bigram) Detection Extraction routines for dates, events, parts of speech Deletion Thesaurus development and application Flexible ontology usage Parts of Speech Tagging