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242 Cards in this Set
- Front
- Back
associative model
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Forecasting technique that uses explanatory variables to predict future demand.
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bias
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Persistent tendency for forecasts to be greater or less than the actual values of a time series.
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centered moving average
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A moving average positioned at the center of the data that were used to compute it.
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control chart
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A visual tool for monitoring forecast errors.
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correlation
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A measure of the strength and direction of relationship between two variables.
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cycle
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Wavelike variations lasting more than one year.
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Delphi method
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An iterative process in which managers and staff complete a series of questionnaires
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error
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Difference between the actual value and the value that was predicted for a given period.
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exponential smoothing
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A weighted averaging method based on previous forecast plus a percentage of the forecast error.
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forecast
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A statement about the future value of a variable of interest.
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irregular variation
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Caused by unusual circumstances
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judgmental forecasts
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Forecasts that use subjective inputs such as opinions from consumer surveys
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least squares line
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Minimizes the sum of the squared vertical deviations around the line.
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linear trend equation
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Ft = a + bt
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mean absolute deviation (MAD)
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The average absolute forecast error.
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mean absolute percent error (MAPE)
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The average absolute percent error.
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mean squared error (MSE)
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The average of squared forecast errors.
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moving average
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Technique that averages a number of recent actual values
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naive forecast
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A forecast for any period that equals the previous period's actual value.
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predictor variables
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Variables that can be used to predict values of the variable of interest.
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random variations
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Residual variations after all other behaviors are accounted for.
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regression
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Technique for fitting a line to a set of points.
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seasonal relative
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Percentage of average or trend.
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seasonal variations
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Regularly repeating movements in series values that can be tied to recurring events.
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seasonality
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Short-term regular variations related to the calendar or time of day.
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standard error of estimate
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A measure of the scatter of points around a regression line.
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time series
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A time-ordered sequence of observations taken at regular intervals.
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time-series forecasts
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Forecasts that project patterns identified in recent time-series observations.
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tracking signal
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The ratio of cumulative forecast error to the corresponding value of MAD
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trend
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A long-term upward or downward movement in data.
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trend-adjusted exponential smoothing
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Variation of exponential smoothing used when a time series exhibits a linear trend.
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weighted average
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More recent values in a series are given more weight in computing a forecast.
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A-B-C approach
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Classifying inventory according to some measure of importance
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cycle counting
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A physical count of items in inventory.
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cycle stock
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The amount of inventory needed to meet expected demand.
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economic order quantity (EOQ)
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The order size that minimizes total annual cost.
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excess cost
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Difference between purchase cost and salvage value of items left over at the end of a period.
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fill rate
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The percentage of demand filled by the stock on hand.
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fixed-order-interval (FOI) model
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Orders are placed at fixed time intervals.
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holding (carrying) cost
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Cost to carry an item in inventory for a length of time
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inventory
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A stock or store of goods.
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inventory turnover
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Ratio of average cost of goods sold to average inventory investment.
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lead time
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Time interval between ordering and receiving the order.
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Little's Law
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The average amount of inventory in a system is equal to the product of the average demand rate and the average time a unit is in the system.
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ordering costs
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Costs of ordering and receiving inventory.
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periodic system
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Physical count of items in inventory made at periodic intervals (weekly
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perpetual inventory system
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System that keeps track of removals from inventory continuously
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point-of-sale (POS) system
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Record items at time of sale.
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purchase cost
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The amount paid to buy the inventory.
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quantity discounts
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Price reductions for larger orders.
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reorder point (ROP)
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When the quantity on hand of an item drops to this amount
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safety stock
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Extra inventory carried to reduce the probability of a stockout due to demand and/ or lead time variability.
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service level
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Probability that demand will not exceed supply during lead time.
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setup costs
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The costs involved in preparing equipment for a job.
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shortage costs
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Costs resulting when demand exceeds the supply of inventory; often unrealized profit per unit.
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single-period model
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Model for ordering of perishables and other items with limited useful lives.
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two-bin system
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Two containers of inventory; reorder when the first is empty.
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universal product code (UPC)
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Bar code printed on a label that has information about the item to which it is attached.
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backflushing
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Exploding an end item's BOM to determine the quantities of the components that were used to make the item.
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bill of materials (BOM)
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One of the three primary inputs of MRP; a listing of all of the raw materials
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capacity requirements planning
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The process of determining short-range capacity requirements.
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changes
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Revisions of due dates or order quantities
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cumulative lead time
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The sum of the lead times that sequential phases of a process require
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dependent demand
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Demand for items that are subassemblies or component parts to be used in the production of finished goods.
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distribution resource planning (DRP)
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A method used for planning orders in a supply chain.
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enterprise resource planning (ERP)
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Integration of financial
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exception reports
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Data on any major discrepancies encountered.
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gross requirements
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Total expected demand for an item or raw material in a time period.
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inventory records
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One of the three primary inputs in MRP; includes information on the status of each item by time period.
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load reports
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Department or work center reports that compare known and expected future capacity requirements with projected capacity availability.
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lot sizing
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Choosing a lot size for ordering or production.
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low-level coding
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Restructuring the bill of materials so that multiple occurrences of a component all coincide with the lowest level at which the component occurs.
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manufacturing resources planning (MRP II)
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Expanded approach to production resource planning
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master schedule
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One of three primary inputs in MRP; states which end items are to be produced
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material requirements planning (MRP)
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A computer-based information system that translates master schedule requirements for end items into time-phased requirements for subassemblies
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net-change system
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Approach that updates MRP records continuously.
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net requirements
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The actual amount needed in each time period.
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order releases
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Authorization for the execution of planned orders.
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pegging
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The process of identifying the parent items that have generated a given set of material requirements for an item.
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performance-control reports
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Evaluation of system operation
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planned-order receipts
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Quantity expected to be received by the beginning of the period in which it is shown.
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planned-order releases
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Planned amount to order in each time period; planned-order receipts offset by lead time.
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planned orders
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Schedule indicating the amount and timing of future orders.
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planning reports
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Data useful for assessing future material requirements.
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product structure tree
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A visual depiction of the requirements in a bill of materials
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projected on hand
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Expected amount of inventory that will be on hand at the beginning of each time period.
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regenerative system
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Approach that updates MRP records periodically.
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scheduled receipts
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Open orders scheduled to arrive from vendors or elsewhere in the pipeline.
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time fences
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Series of time intervals during which order changes are allowed or restricted; the nearest fence is most restrictive to change
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Activities
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Project steps that consume resources and/or time.
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Activity-on-arrow (AOA)
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Network diagram convention in which arrows designate activities.
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Activity-on-node (AON)
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Network diagram convention in which nodes designate activities.
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Beta distribution
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Used to describe the inherent variability in activity time estimates.
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CPM
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Critical path method
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Crash
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Shortening activity durations.
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Critical activities
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Activities on the critical path.
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Critical path
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The longest path; determines expected project duration.
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Deterministic
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Time estimates that are fairly certain.
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Events
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The starting and finishing of activities
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Independence
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Assumption that path duration times are independent of each other; requiring that activity times be independent
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Most likely time
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The most probable length of time that will be required.
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Network (precedence) diagram
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Diagram of project activities that shows sequential relationships by use of arrows and nodes.
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Optimistic time
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The length of time required under optimal conditions.
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Path
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A sequence of activities that leads from the starting node to the finishing node.
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PERT
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Program evaluation and review technique
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Pessimistic time
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The length of time required under the worst conditions.
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Probabilistic
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Estimates of times that allow for variation.
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Project champion
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A person who promotes and supports a project.
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Projects
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Unique
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Slack
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Allowable slippage for a path; the difference between the length of a path and the length of the critical path.
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Virtual project teams
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Some or all of the team members are geographically separated.
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Work breakdown structure (WBS)
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A hierarchical listing of what must be done during a project.
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Avoidance
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Finding ways to minimize the number of items that are returned.
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Bullwhip effect
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Inventory oscillations become progressively larger looking backward through the supply chain.
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Centralized purchasing
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Purchasing is handled by one special department.
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Closed-loop supply chain
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A manufacturer controls both the forward and reverse shipment of product.
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Cross-docking
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A technique whereby goods arriving at a warehouse from a supplier are unloaded from the supplier’s truck and loaded onto outbound trucks
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Decentralized purchasing
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Individual departments or separate locations handle their own purchasing requirements.
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Delayed differentiation
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Production of standard components and subassemblies
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Disintermediation
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Reducing one or more steps in a supply chain by cutting out one or more intermediaries.
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E-business
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The use of electronic technology to facilitate business transactions.
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Event management
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The ability to detect and respond to unplanned events.
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Fill rate
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The percentage of demand filled from stock on hand.
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Gatekeeping
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Screening returned goods to prevent incorrect acceptance of goods.
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Information velocity
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The speed at which information is communicated in a supply chain.
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Inventory velocity
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The speed at which goods move through a supply chain.
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Logistics
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The movement of materials
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Order fulfillment
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The processes involved in responding to customer orders.
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Purchasing cycle
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Series of steps that begin with a request for purchase and end with notification of shipment received in satisfactory condition.
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Reverse logistics
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The process of transporting returned items.
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Radio frequency identification (RFID)
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A technology that uses radio waves to identify objects
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Strategic partnering
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Two or more business organizations that have complementary products or services join so that each may realize a strategic benefit.
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Strategic sourcing
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Analyzing the procurement process to lower costs by reducing waste and nonvalue-added activities
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Supply chain
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A sequence of organizations—their facilities
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Supply chain management
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The strategic coordination of the supply chain for the purpose of integrating supply and demand management.
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Supply chain visibility
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A major trading partner can connect to its supply chain to access data in real time.
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Third-party logistics (3-PL)
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The outsourcing of logistics management.
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Traffic management
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Overseeing the shipment of incoming and outgoing goods.
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Vendor analysis
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Evaluating the sources of supply in terms of price
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Vendor-managed inventory (VMI)
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Vendors monitor goods and replenish retail inventories when supplies are low.
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What is a Forecast?
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A statement about the future value of a variable of interest such as demand
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What happens when under forecast?
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lose customers
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What happens when over forecast?
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too much inventory
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What are three characteristics of forecasts?
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1. Rarely perfect because of randomness
2. More accurate for groups vs. individuals 3. Less accurate as time horizon increases |
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Good forecasts are:
5 characteristics |
timely
reliable accurate meaningful easy to use |
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True or False: Forecasts needs to be continuously monitored for accuracy
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true
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What happens when results are under forecast?
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Shortages and excesses throughout the supply chain. Shortages of materials, parts, and services can lead to missed deliveries, work disruption, and poor customer service. Ultimately, could lose customers.
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What happens when results are over forecast?
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Excesses of materials and or capacity, which increase costs.
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How do we reduce the occurrences of inaccurate forecasts?
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Develop the best possible forecast, collaborative planning and forecasting with major supply chain partners, information sharing among partners and perhaps increasing supply chain visibility by allowing supply chain partners to have real-time access to sales and inventory information, rapid communication about poor forecasts as well as unplanned events that disrupt operations, and changes in plans.
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Why are forecasts rarely perfect?
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The presence of randomness precludes a perfect forecast. Allowances should be made for forecast errors.
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Are forecasts more accurate for groups of items or individual items?
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More accurate for groups of items because forecasting errors among items in a group usually have a cancelling effect.
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Good forecasts are:
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timely, reliable, accurate, meaningful, and easy to use
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What are the six elements of a good forecast?
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(1) Forecast horizon must cover the time necessary to implement possible changes.
(2) accurate and the degree of accuracy stated. (3) reliable and work consistently. (4) expressed in meaningful units. (5) in writing so that they can be evaluated once data is in. (6) techniques used should be simple to understand and use. |
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How do you improve the accuracy of forecasts?
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Continuously monitor them, work aggregately across functions to develop them
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What are the two most important factors affecting a forecast?
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Cost and Accuracy. Consider how much money is budgeted for generating the forecast, what are possible costs of errors, and what are the benefits that might accrue from an accurate forecast. The higher the cost, the higher the accuracy.
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Aside from cost and accuracy, what are some other factors that affect forecasts?
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historical data
availability of computer software the time needed to gather and analyze the data the forecast horizon |
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Which type of forecasts use a short horizon? Why?
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Moving Average and Exponential Smoothing since they produce forecasts for the next period.
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Which type of forecasts use a long horizon?
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Trend Equations
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What is helpful in determining what forecasting method to use?
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Plotting the data
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List the three types of forecasts:
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Judgmental
Time Series Associative Models |
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Which type of forecast uses subjective inputs?
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Judgmental
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Which type of forecast uses historical data?
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Time Series
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Which type of forecast uses explanatory variables to predict the future?
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Associative Models
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List the subjective inputs used in a Judgmental forecast.
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Executive opinions
sales force opinions consumer surveys outside opinions Delphi method opinions of managers and staff |
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What does a judgmental forecast achieve?
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it achieves a consensus forecast.
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What are the components of a time series forecast?
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Trend
Seasonality Cycle Irregular Variations Random Variations |
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What causes random variations?
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Chance
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What are the 5 Time Series Models of Forecasting?
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Naïve
Moving Average (MA) Weighted Moving Average (WMA) Exponential Smoothing (ES) Simple Linear Regression |
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If the time series data contains random variation, but does not contain a trend, seasonality, or cycles, which Time Series Model should be used?
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Moving Average (MA)
Weighted Moving Average (WMA) Exponential Smoothing models |
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If the time series data contains random variation and a trend which Time Series Model should be used?
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Simple Linear Regression
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If a time series data contains random variation, trend, and seasonality which Time Series Model should be used?
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Simple Linear Regression and Seasonal Factors
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What is the formula for a moving average?
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It is the unweighted mean of the previous "n" datum points. Determine the frequency of the average (i.e. 3 month, 5 month, etc.). Sum the data points for the specified frequency then divide by the frequency.
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What is the formula for a weighted moving average?
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The same as a moving average, except each data point is weighted. Typically the most recent data point has the most weight.
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What is the error term in Exponential Smoothing?
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(A-F)
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What is the smoothing constant in Exponential Smoothing?
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Sigma: a
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What is the premise behind Exponential Smoothing?
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The most recent observations might have the highest predictive value. Therefore we should give more weight to the more recent time periods when forecasting.
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What is the formula for Exponential Smoothing?
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Ft = Ft-1 + α(At-1 - Ft-1)
F is the forecast A is the actual t is the time period. |
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What is the goal of Simple Linear Regression?
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to find a Trend Line to best fit the Time Series.
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What is a forecast error?
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It is the difference between the actual value and the predicted value.
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What are the three most commonly used measures for summarizing historical errors? What is the difference between these measures?
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Mean Absolute Deviation (MAD)
Mean Squared Error (MSE) Mean Absolute Percent Error (MAPE) The difference between these measures is that MAD weights all errors evenly, MSE weights errors according to their squared values, and MAPE weights according to relative error. |
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What is Mean Absolute Deviation (MAD)?
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It is the average absolute forecast error.
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What is Mean Squared Error (MSE)?
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It is the average of the squared error.
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What is Mean Absolute Percent Error (MAPE)?
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It is the average absolute percent error.
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What is the purpose of MAD, MSE and MAPE?
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To compare the accuracy of alternative forecasting methods, to track error performance over time,
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When should MAD, MSE, and MAPE should be used?
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MAD is the easiest to compute, but weights errors linearly. MSE squares the errors, thereby giving more weight to larger errors, which typically cause more problems. MAPE should be used when there is a need to put errors into perspective.
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What does a control chart detect?
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It detects non-randomness in errors.
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Which is better: MAPE or a Control Chart?
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Control Chart
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When using a Control Chart, how is it determined that the errors are in control?
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All errors are within the control limits, and no patterns such as trends or cycles are present.
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What are four features common to all forecasts?
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(1) Forecasts generally assume that the same underlying causal system that existed in the past will continue to exist in the future.
(2) Forecasts are not perfect; actual results usually differ from predicted values. (3) Forecasts for groups of items tend to be more accurate than forecasts for individual items (4) Forecast accuracy decreases as the time period covered by the forecast--the time horizon--increases. Generally speaking, short-range forecasts must contend with fewer uncertainties than longer-range forecasts, so they tend to be more accurate. |
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What are the six basic steps in the forecasting process?
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(1) Determine the purpose of the forecast.
(2) Establish a time horizon. (3) Obtain, clean, and analyze appropriate data. (4) Select a forecasting technique. (5) Make the forecast. (6) Monitor the forecast. |
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What is the caveat to forecasting?
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Accuracy and control of forecast is a vital aspect of forecasting so forecasters want to minimize forecast errors. However, the complex nature of most real-world variables makes it almost impossible to correctly predict future values of those variables on a regular basis.
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What is the Delphi method useful for?
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It is useful for technological forecasting, that is for assessing changes in technology and their impact on an organization. Often the goal is to predict when a certain event will occur.
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When can the Naïve forecast approach be used?
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It can be used with a stable series (variations around an average), with seasonal variations, or with a trend. The forecast is the last data point for each series. Example: the forecast for this season is equal to the value of the series last season.
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What are the advantages and disadvantages of the Naïve forecast method?
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Advantage: virtually no cost, it is quick and easy to prepare because data analysis is nonexistent, and is easily understandable.
Disadvantage: inability to provide highly accurate forecasts. |
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Which forecast technique can be used to serve as a standard of comparison against which to judge the cost and accuracy of other techniques?
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The Naïve Forecasting Technique
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What is the purpose of Averaging Techniques?
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It smooth's fluctuations in a time series because the individual highs and lows in the data offset each other when they are combined into an average. It provides a forecast based on an average thus tends to exhibit less variability than the original data.
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What is an ideal method of analyzing data? Why is this not practical?
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Completely remove randomness from the data and leave only "real" variations such as changes in demand. However it is usually impossible to distinguish between these two kinds of variations, so it is best to hope that the small variations are random and the large variations are "real".
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Why are average techniques useful?
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Many of the movements on a trend line merely reflect random variability rather than a true change in the series.
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What type of forecasts does averaging techniques present?
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Generates forecasts that reflect recent values of a time series.
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When do Averaging techniques work best?
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When a series tends to vary around an average, although they can also handle step changes or gradual changes in the level of the series.
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What happens in a moving average forecast as each new actual value becomes available?
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The forecast is updated by adding the newest value and dropping the oldest and then recomputing the average. Consequently, the forecast "moves" by reflecting only the most recent values.
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What does the number of data points in the average determine?
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The sensitivity to each new data point: The fewer the data points in an average, the more sensitive (responsive) the average tends to be.
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What should be used if a responsive forecast is important? Why?
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A moving average forecast with relatively few data points. This will permit quick adjustments to a step change in the data, and also will cause the forecast to be somewhat responsive to random variations.
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If smoothing of data is more important than forecast responsiveness what is important to consider?
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You must weigh the costs of responding more slowly to changes in the data against the cost of responding to what might simply be random variation.
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What are the advantages and disadvantages of the Moving Average Forecast?
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Advantage: easy to compute, easy to understand.
Disadvantage: all values in the average are weighted equally, slow to react to changes in the series. |
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What is the advantages and disadvantages of the Weighted Moving Average Forecast?
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Advantage: the weighted average is more reflective of the most recent occurrences.
Disadvantage: the choice of weights is arbitrary. |
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What is the quickness of forecast adjustment to error determined by?
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the smoothing constant, a. The closer it's value to zero, the slower the forecast will be to adjust to forecast errors (i.e. the greater the smoothing). The closer the value of a is to 1.00, the greater the responsiveness and the less the smoothing.
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What is the goal in selecting a smoothing constant?
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To balance the benefits of smoothing random variations with the benefits of responding to real changes if and when they occur.
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When are low and high variables of a used in Exponential Smoothing?
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Low values are used when the underlying average tends to be stable; higher values are used when the underlying average is susceptible to change.
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When should exponential smoothing forecasts begin?
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They should begin several time periods back to enable the forecasts to adjust to the data.
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What is analysis of trend?
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Developing an equation that will suitable describe the trend (assuming that trend is present in the data). The trend component may or may not be linear.
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What are the two techniques used to develop forecasts when trend is present?
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Trend Equation, and an Extension of Exponential Smoothing
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What is the formula for a trend equation?
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Ft = a + bt.
F = forecast for period t a = value of Ft at t = 0 b = slope of the line t = specified number of time periods from t = 0 |
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What does the slope mean or say in a trend equation?
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The value of Ft will change by b units for each time period.
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What is the simplest and most widely used form of regression?
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Simple Linear Regression, which involves a linear relationship between two variables.
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What is the objective in Simple Linear Regression?
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To obtain an equation of a straight line that minimizes the sum squared vertical deviations of data points from the line (i.e., the least squares criterion).
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What are indicators of how accurate a prediction might be for a linear regression line?
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The amount of scatter of the data points around the line. If the data points tend to be relatively close to the line, predictions using the linear equation will tend to be more accurate than if the data points are widely scattered.
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What is an application of regression?
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the use of indicators, which are uncontrollable variables that tend to lead or precede changes in a variable of interest.
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What conditions are required for an indicator to be valid?
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(1) the relationship between movements of an indicator and movements of the variable should have a logical explanation.
(2) Movements of the indicator must precede movements of the dependent variable by enough time so that the forecast isn't outdated before it can be acted upon. (3) A fairly high correlation should exist between the two variables. |
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What does the square of the correlation coefficient provide?
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It provides a measure of the percentage of variability in the values of Y that is "explained" by the independent variable. The closer the r^2 is to 1.00, the greater the percentage of explained variation.
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What are three assumptions that must be satisfied in the use of simple regression?
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(1) variations around the line are random. If they are random, no patterns such as cycles or trends should be apparent when the line and data are plotted.
(2) Deviations around the average value (i.e., the line) should be normally distributed. A concentration of values close to the line with a small proportion of larger deviations supports the assumption of normality. (3) Predictions are being made only within the range of observed values. |
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To obtain the best results in Linear Regression Analysis:
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(1) Plot the data to verify that a linear relationship is appropriate.
(2) Data may be time-dependent. (3) A small correlation may imply that the other variables are important. |
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What are some weakness of Linear Regression analysis?
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(1) it applies to only linear relationships with one independent variable.
(2) A considerable amount of data is needed to establish the relationship (20 or more observations) (3) All observations are weighted equally. |
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How are errors plotted on a control chart?
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In the order in which they occur
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How is a control chart constructed?
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First compute the MSE then compute the UCL and the LCL. Plot the errors in the order of occurrence on the chart.
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Which is superior, Control Chart or a Tracking Signal? Why?
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Control Chart. A tracking signal uses cumulative errors. Individual errors can be obscured so that large positive and negative values cancel each other. In Control Charts, each error is judged individually.
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What are the advantages of better short-term forecasts?
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enhance profits through lower inventory levels
fewer shortages improved customer service enhance forecast credibility |
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Are lean systems more dependent on short-term forecasts than traditional systems?
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No, a lean system is demand driven; goods are produced to fulfill orders rather than to hold in inventory until demand arises.
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What forecasting techniques are generally used by sales?
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Subjective techniques such as focus groups and surveys.
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What forecasting techniques are generally used by operations?
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Objective techniques such as using data, historical information
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What forecasting techniques are generally used by finance?
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Generally the same tools as operations
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What forecasting techniques are generally used by marketing?
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Judgmental forecasts
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How does accounting use forecasts?
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cost estimates for new products or process, profit projections, and cash management
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How does finance use forecasts?
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evaluate equipment and equipment replacement needs, timing and amount of funding and/or borrowing needs.
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How does human resources use forecasting?
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Hiring activities such as recruitment, interviewing and training; layoff planning; outplacement counseling.
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How does marketing use forecasts?
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Pricing and promotion; e-business strategies; global competition strategies
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How does MIS use forecasts?
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New and/or revised information systems; internet services
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How does product/service design use forecasts?
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revision of current features; design of new products and/or services
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What are executive opinions used for in forecasting?
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Long range planning and new product development. It brings together the knowledge and talents of various managers. Disadvantage: groupthink
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What are sales force opinions used for in forecasting?
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Sales force may be aware of customers future plans. Disadvantages: unable to distinguish what a customer wants and actually does, may be overly influenced by recent experiences, estimates may be low if used to establish sales quotas.
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What are consumer surveys used for in forecasting?
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They can tap into data that may not be available elsewhere. They can be expensive and time consuming and require skills to administer and interpret results. They must contend with consumer irrational behavior methods.
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What is focus forecasting?
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Involves the use of several forecasting methods all being applied to the last few months of historical data after any irregular variations have been removed. The method with the highest accuracy is used to make the forecast for the next month.
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