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In time series analysis, a fan chart is a chart that joins a simple line chart for observed past data, by showing ranges for possible values of future data together with a line showing a central estimate or most likely value for the future outcomes. As predictions become increasingly uncertain the further into the future one goes, these ...
Aggregate planning is a marketing activity that does an aggregate plan for the production process, in advance of 3 to 18 months, to give an idea to management as to what quantity of materials and other resources are to be procured and when, so that the total cost of operations of the organization is kept to the minimum over that period.
Due to software limitations, but especially the intense work required by the "master production schedulers", schedules do not include every aspect of production, but only key elements that have proven their control effectivity, such as forecast demand, production costs, inventory costs, lead time, working hours, capacity, inventory levels ...
There is a distinction between aggregate data and individual data. Aggregate data refers to individual data that are averaged by geographic area, by year, by service agency, or by other means. [2] Individual data are disaggregated individual results and are used to conduct analyses for estimation of subgroup differences. [2]
When information is transferred across time, often to specific points in time, the process is known as forecasting. Fully formed statistical models for stochastic simulation purposes, so as to generate alternative versions of the time series, representing what might happen over non-specific time-periods in the future
An aggregate project plan (APP) is the process of creating development goals and objectives and using these goals and objectives to improve productivity as well as development capabilities. The purpose of this process is generally to ensure that each project will accomplish its development goals and objectives.
An aggregate is a type of summary used in dimensional models of data warehouses to shorten the time it takes to provide answers to typical queries on large sets of data. The reason why aggregates can make such a dramatic increase in the performance of a data warehouse is the reduction of the number of rows to be accessed when responding to a query.
The time period of shipping activity should be compared against the forecast that was set for the time period a specific number of days/months prior which is call Lag. Lag is based on the leadtime from order placement to order delivery. For example, if the lead time of an order is three months, then the forecast snapshot should be Lag 3 months.