Search PubMed⌕ Search

PubMed · 10662000

How process enterprises really work.

Abstract

Many companies have succeeded in reengineering their core processes, combining related activities from different departments and cutting out ones that don't add value. Few, though, have aligned their organizations with their processes. The result is a form of cognitive dissonance as the new, integrated processes pull people in one direction and the old, fragmented management structures pull them in another. That's not the way it has to be. In recent years, forward-thinking companies like IBM, Texas Instruments, and Duke Power have begun to make the leap from process redesign to process management. They've appointed some of their best managers to be process owners, giving them real authority over work and budgets. They've shifted the focus of their measurement and compensation systems from unit goals to process goals. They've changed the way they assign and train employees, emphasizing whole processes rather than narrow tasks. They've thought carefully about the strategic trade-offs between adopting uniform processes and allowing different units to do things their own way. And they've made subtle but fundamental cultural changes, stressing teamwork and customers over turf and hierarchy. These companies are emerging from all those changes as true process enterprises--businesses whose management structures are in harmony, rather than at war, with their core processes. And their organizations are becoming much more flexible, adaptive, and responsive as a result.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

M Hammer, S Stanton. How process enterprises really work.. https://pubmed.ncbi.nlm.nih.gov/10662000/

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

Predictive modeling of mixed microbial populations in food products: evaluation of two-species models.

Predictive microbiology is an emerging research domain in which biological and mathematical knowledge is combined to develop models for the prediction of microbial proliferation in foods. To provide accurate predictions, models must incorporate essential factors controlling microbial growth. Current models often take into account environmental conditions such as temperature, pH and water activity. One factor which has not been included in many models is the influence of a background microflora, which brings along microbial interactions. The present research explores the potential of autonomous continuous-time/two-species models to describe mixed population growth in foods. A set of four basic requirements, which a model should satisfy to be of use for this particular application, is specified. Further, a number of models originating from research fields outside predictive microbiology, but all dealing with interacting species, are evaluated with respect to the formulated model requirements by means of both graphical and analytical techniques. The analysis reveals that of the investigated models, the classical Lotka-Volterra model for two species in competition and several extensions of this model fulfill three of the four requirements. However, none of the models is in agreement with all requirements. Moreover, from the analytical approach, it is clear that the development of a model satisfying all requirements, within a framework of two autonomous differential equations, is not straightforward. Therefore, a novel prototype model structure, extending the Lotka-Volterra model with two differential equations describing two additional state variables, is proposed to describe mixed microbial populations in foods.

Evaluation Studies as Topic↗

Evaluation of time-of-flight mass spectrometric detection for fast gas chromatography.

Separations below 1 s of a mixture of organic compounds ranging from C5 to C8 have been performed to investigate the performance of a time-of-flight mass spectrometer in fast gas chromatography. The gaseous samples were focussed on a cold trap, and then injected after thermal desorption to obtain the required narrow input band-widths. Also, to obtain a very fast separation, a short narrow bore column was used, operated at above-optimum inlet pressures. With this system, it was possible to identify ten compounds within 500 ms, showing peak-widths (2.354sigma) as narrow as 12 ms. The spectral acquisition rate used for these analyses was 500 Hz. The quality of the recorded spectra and the comparison with library spectra was very high. Deconvolution algorithms offer the possibility of identifying overlapping peaks. It is shown that the spectral scan speed of the time-of-flight mass spectrometer is high enough for very fast separations.

Evaluation Studies as Topic↗