Assessing demand

Unfortunately, most companies still rely on past-usage as a proxy for future demand. Such approaches suffer from deficient end-user demand data as it may hide the causes leading up to this data, ignorance of customer buying patterns and market potential, and inability to access actual demand at the point-of-sale. Clearly, the demand assessment approach needs to be broadened. 

An AMR study of 300 North American companies found that 56% of the companies took more than 2 weeks to sense true channel-demand (Cecere 2006), and only 26% could see channel demand in days or hours. In this context, the database of demand signals by attribute type can be a rich source for demand modeling approaches (Cecere 2009). Essentially, a demand model creates effective links between the sources of market data and customer buying behavior. Such links are not static and, therefore, to obtain a better sense of demand one must track the underlying attributes. Demand assessment must also include procedures to tie forecasts to shifts in demand insights. 

Demand response capability must ensure customer satisfaction and increase supplier s profit. Companies must first convert independent demand of products to determine the dependent demand for location-specific products. Failing to convert demand to each location may lead to over or under stocking cost (see Tesco s problem below). It may also incur unnecessary shipping expenses. Dependent demand focuses on demand at each location, demand in the factory, demand for specific parts for the suppliers, and the demand for logistics within the entire network. 

to ensure effective demand response, companies need to build capabilities for the following ensuring transparency between sales and operations (S OP) and the daily consumption, translating the dependent demand to material requirements, and keeping major component suppliers informed about production requirements. 

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Market survey to assess demand for a product and its availability,... 

Methodologies for Assessing Demand Driven Supply Chain... 

A realistic assessment of biomass as an energy resource is made by calculating average surface areas needed to produce sufficient biomass at different aimual yields to meet certain percentages of fuel demand for a particular country (Table 2). These required areas are then compared with surface areas available. The conditions of biomass production and conversion used ia Table 2 are either within the range of 1993 technology and agricultural practice, or are beheved to be attainable ia the future. 

EinaHy, the ecotoxicological studies, designed to assess the impact of the substance on the environment, embrace acute toxicity tests to fish and Daphnia, and a battery of tests for the biodegradabiUty of the substance and its biological oxygen demand characteristics. 

In the Verband Deutscher Elektrotechniker (VDE) regulations [1,4], no demands are made on the accuracy of the measured or calculated voltage drops in a rail network. An inaccuracy of 10% and, in difficult cases, up to 20%, should be permitted. A calculation of the annual mean values is required. If the necessary equipment is not available, a calculation is permitted over a shorter period (e.g., an average day). Voltage drops in the rail network only indicate the trend of the interference of buried installations. Assessment of the risk of corrosion of an installation can only be made by measuring the object/soil potential. A change in potential of 0.1 V can be taken as an indication of an inadmissible corrosion risk [5]. 

CCPS, 1989b, Process Equipment Reliability Data (Table 4.1-1) is a compilation of chemical and nuclear data. It assesses failure rates for 75 types of chemical process equipment. A taxonomic classification is established and data such as the mean, median, upper and lower (95% and 5%) values, source of information, failure by time and failure by demands are presented. 

The LER data base served as the primary source of DG failure data, while a data base for DG successes was formed from nuclear plant licensees responses to a USNRC questionnaire (Generic Letter 84-15). Estimates of DG failure on demand were calculated from the LER data, DG test data, and response data from the questionnaire. The questionnaire also provided data on DG performance during complete and partial LOSP and in response to safety injection actuation signals. Trends in DG performance are profiled. The effects of testing schedules on diesel reliability are assessed. Individual failures are identified in an appendix. 

The component failure rate data used as input to the fault tree model came from four basic sources plant records from Peach Bottom (a plant of similar design to Limerick), actual nuclear plant operating experience data as reported in LERs (to produce demand failure rates evaluated for pumps, diesels, and valves), General Electric BWR operating experience data on a wide variety of components (e.g., safety relief SRV valves, level sensors containment pressure sensors), and WASH-1400 assessed median values. 

Appendix III contains failure rate estimates for various genetic types of mechanical and electrical equipment. Included ate listings of failure rates with range estimates for specified component failure modes, demand probabilities, and times to maintain repair. It also contains some discussion on such special topics as human errors, aircraft crash probabilities, loss of electric power, and pipe breaks. Appendix III contains a great deal of general information of use to analysts on the methodology of data assessment for PRA. 

In designing the water storage capacity, account should be taken of the pattern of water usage for the premises and, where possible, to assess the likely frequency and duration of breakdown from the water authority mains. When dealing with domestic water storage, this is usually provided to meet a 24-hour demand. 

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