Process management variability

Facilitating continuous processing to improve efficiency and manage variability. 

Process management, the next marketing-mix variable, refers to how Care-Rite Pharmacy incorporated the new Pharmacy Check-up Service into its existing practice. Workflow decisions were made regarding patient flow during the service. It was decided that the service would be by appointment only to minimize disruption of the other aspects of the practice. Schedules for pharmacists were created so that one pharmacist was responsible for clinic services while another covered the dispensing area. 

Mentre, F. Ebelin, M.E. Validation of population pharmacokinetic/pharmacodynamic analyses review of proposed approaches. In The Population Approach Measuring and Managing Variability in Response, Concentration and Dose Aarons, L., Balant, L.P., Danhof M., Gex-Fabry, M., Gundert-Remy, U.A., Karlsson, M.O., Mentre, F., Morselli, R.L., Rombut, F., Rowland, M., Steimer, J.-L., Voseh, S., Eds. Commission of the European Communities, European Cooperation in the Eield of Scientific and Technical Research Brussels, 1997 147-160. Ette, E.I. Williams, P. Sun, H. Fadiran, E. Ajayi, F.O. Onyiah, L.C. The process of knowledge discovery from large pharmacokinetic data sets. J. Clin. Pharmacol. 2001, 41 (1), 25-34. 

Real life field conditions are never reproducible. For the same site of interest, diverse activities, different process management strategies or various raw material compositions induce highly variable emissions. 

Figure 5 depicts the Baldrige Criteria for Performance Excellence (1) leadership (2) strategic planning (3) customer and market focus (4) information and analysis (5) human resource focus (6) process management and (7) business results (overarching—customer- and market-focused strategy and action plans). Compare and contrast these variables to the seven identified in Collins and Porras. 

Process control in multienzyme processes, variables such as pH and temperature are often controlled during the process in order to reduce the influence that they produce on the dynamics of other variables and enzymes. For modeling, the controlled variables need to be identified in order to limit the capabilities of the model. In this case, they are included as assumptions of the model. Process control can be divided into two basic control layers [43]. The first is known as the regulatory layer, which controls variables such as pH and temperature. In this case, a simple controller design can be implemented. The second is known as the supervisory layer, which manages variables with more impact on the process such as concentrations of the compounds. In this case, a more detailed controller design is required. For multienzyme processes, this issue is highly relevant especially to achieve process improvements. 

It is interesting to note that if an automatic feedback controller succeeds in keeping the PV at the desired SP in the presence of load disturbances then, by necessity, there will be changes in the MV dictated by the controller. So in effect, proem control takes variability from one place, and moves it to another. Thus, the trick to process control is understanding where variability can be tolerated and where it caimot, and designing schemes that manage variability to acceptable levels. 

Because of the wide variability in sludge characteristics and the variation in acceptability of treated sludges for ultimate disposal (this is a function of the location and characteristics of the ultimate disposal site), it is impossible to prescribe any particular sludge-management plan. In the sections below, general performance of individual sludge-treatment processes and operations is presented. 

With rapid advances in hardware, database management and information processing systems, efficient and competitive manufacturing has become an information-intensive activity. The amounts of data presently collected in the field on a routine basis are staggering, and it is not unusual to find plants where as many as 20,000 variables are continuously monitored and stored (Taylor, 1989). 

---