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We find that practitioners are not always sure about the meanings of these words, which can impede effective business communication, particularly when implementing demand planning and forecasting software. \u00a0Sometimes two companies (supplier and customer, supply chain software vendor and customer, etc.) use the same word but apply different meanings to it.\u00a0 This post aims to clarify some basic definitions.<\/p>\n
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How close the forecast is to the actual.\u00a0 Forecast accuracy should be expressed as \u201cforecast error\u201d and determined as follows:\u00a0 Forecast \u2013 Actual.\u00a0 To determine the percentage error use: (Forecast \u2013 Actual)\/Actual. It is not recommended to use the forecast as the denominator (i.e. (forecast \u2013 actual)\/forecast)) when computing accuracy since forecast accuracy can never be worse than 100% and the planner won\u2019t properly communicate the true error associated with the demand forecasting process.<\/p>\n
The number of historical data\u00a0points used in moving average computations. See\u00a0<\/em>also <\/em>moving average.<\/p>\n <\/p>\n <\/p>\n A statistical forecasting technique\u00a0based on sampling at random (with replacement)\u00a0from historical values. These resampled data\u00a0values are combined to form realistic demand\u00a0scenarios that resemble the actual historical\u00a0demand patterns without exactly repeating them.<\/p>\n An exponential smoothing\u00a0forecasting method appropriate for data with trend\u00a0but without seasonality. Also known as DOUBLE\u00a0exponential smoothing<\/p>\n <\/p>\n A measure of the relative\u00a0variability of the data values stored in a variable,\u00a0computed as 100 times the standard deviation\u00a0divided by the mean.<\/p>\n A measure of the degree of linearrelationship between two variables, ranging from -1\u00a0to +1, with 0 indicating no linear relationship.Large values may suggest a leading indicatorrelationship useful in regression analysis.<\/p>\n The correlation between oneseries and another one that is shifted forward or\u00a0backward in time. See <\/em>correlation.<\/p>\n Data organized by\u00a0administrative units rather than by time periods(for example, company sales measured over all\u00a0sales territories during one time period).<\/p>\n The forecast of the total of\u00a0all the data values over the forecast horizon. Use\u00a0this number when your goal is to estimate\u00a0percentiles of the cumulative demand. The\u00a0cumulative forecast differs from the forecast sum\u00a0when the service level is not 50%. See <\/em>forecast\u00a0sum.<\/p>\n <\/p>\n The frequency with which data are\u00a0collected. If the data are collected in monthly\u00a0\u201cbuckets,\u201d the data cycle is 12. Quarterly data have\u00a0a data cycle of 4. Accounting months have a data\u00a0cycle of 13. If you have a web site and you want to\u00a0track usage by the hour, you may want to use a data\u00a0cycle of 24.<\/p>\n A statistical procedure that\u00a0decomposes a time series into trend, seasonal, and\u00a0irregular components. In multiplicative\u00a0decomposition, data = trend x seasonal x irregular.\u00a0In additive decomposition, data = trend + seasonal+ irregular. See also <\/em>seasonal adjustment.<\/p>\n Degrees of freedom. Used in computing the\u00a0statistical significance of an F-statistic in a\u00a0regression analysis.<\/p>\n In regression analysis, the\u00a0variable to be predicted from the predictor or\u00a0independent variable(s). Also known as the\u00a0forecast variable. In the equation Y = 1 + 2 X, the\u00a0dependent variable is Y.<\/p>\n A forecasting\u00a0method appropriate for data with trend but without\u00a0seasonality. Also known as Brown\u2019s method.<\/p>\n A variable that takes on only the\u00a0values of zero or one. This type of variable is often\u00a0used in regression analysis to indicate the presence\u00a0or absence of some factor, such as a price\u00a0promotion or a snowstorm. These play a role\u00a0similar to event variables in promo\/event\u00a0modeling.<\/p>\n In regression analysis,\u00a0an indicator of the degree of autocorrelation in theresiduals. Values near 2 indicate no\u00a0autocorrelation, values between 0 and 2 indicate\u00a0positive autocorrelation, and values between 2 and\u00a04 indicate negative autocorrelation. If the DW is\u00a0not near 2, the regression equation may still be\u00a0useful for forecasting purposes, but it is not the\u00a0best summary you could make of the historical\u00a0relationships among your variables; furthermore,\u00a0the estimates of uncertainty will be inaccurate.<\/p>\n <\/p>\n Economic Order Quantity models.\u00a0A class of models designed to find efficient\u00a0inventory control procedures. For instance, one\u00a0type of EOQ model computes the best values for\u00a0when to reorder and how much to reorder, so as to\u00a0balance inventory carrying costs against the cost of\u00a0losing sales due to stockouts.<\/p>\n Forecast \u2013 Actual. To determine the percentage error use: (Forecast \u2013 Actual)\/Actual. It is not recommended to use the forecast as the denominator (i.e. (forecast \u2013 actual)\/forecast)) when computing accuracy since forecast accuracy can never be worse than 100% and the planner won\u2019t properly communicate the true error associated with the demand forecasting process.<\/p>\n A forecasting model used in Promo\u00a0forecasting that adjusts the data for the effects of\u00a0unusual events, then applies exponential\u00a0smoothing. Events include sales promotions, bad\u00a0weather, power outages, and other unusual\u00a0circumstances of short duration.<\/p>\n A class of forecasting\u00a0methods that averages out the random variation in\u00a0a data series. Exponential smoothing computes a\u00a0weighted average of all the historical data in a\u00a0series. It gets its name from the way it weights the\u00a0data, giving each data point a fixed percentage of\u00a0the weight assigned to its successor.\u00a0SmartForecasts uses the following four exponential\u00a0smoothing methods: single, double, Winters\u2019\u00a0additive and Winters\u2019 multiplicative.<\/p>\n <\/p>\n In regression analysis, the\u00a0statistical approximation to the actual value of the\u00a0forecast or dependent variable.<\/p>\n A prediction about the future value of a\u00a0variable.<\/p>\n In SmartForecasts, the range of\u00a0values indicating the margin of error in the\u00a0forecast.<\/p>\n In regression analysis, an indicator of\u00a0the collective ability of all the predictor\u00a0(independent) variables to predict the forecast\u00a0(dependent) variable. The F-statistic is used to\u00a0assess the chance that a high value of R-square is\u00a0nothing more than a statistical fluke. It is possible\u00a0to get a fairly high value of R-square just by\u00a0accident, especially when you have a small number\u00a0of data cases relative to the number of predictor\u00a0variables. With more data, you might get a much\u00a0lower R-square. The statistical significance of a\u00a0given value of the F-statistic must be assessed\u00a0manually using a table of critical values and the\u00a0degrees of freedom reported by SmartForecasts. If\u00a0the F-statistic is not statistically significant, then\u00a0none of the predictors is useful. Examine the tstatistics \u00a0for individual regression coefficients only\u00a0if the F-statistic is significant. See also <\/em>d.f.statistical significance, coefficient, and t-statistic.<\/p>\n <\/p>\n A judgmentally specified deceleration of a\u00a0statistical trend.<\/p>\n A method for making realistic assessments of forecast uncertainty. This method uses older data values to forecast newer values, then compares the forecasted and actual values. This mimics reality, since the forecasts are assessed against data that were not involved in their creation. There are two versions. A simple holdout analysis compares the most recent actual data values against forecasts using all the previous data. A sliding simulation uses a portion of the historical data to forecast the next several values, then slides forward one period and repeats the process, and continues in this way until using all but the last data value to predict one step ahead.<\/p>\n [\/et_pb_text][\/et_pb_column][et_pb_column type=”1_3″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.16″ text_line_height=”2.04em” header_3_text_color=”#8f8f8f” header_3_font_size=”19px” header_4_font_size=”23px” background_size=”initial” background_position=”top_left” background_repeat=”repeat” z_index_tablet=”500″ text_text_shadow_horizontal_length_tablet=”0px” text_text_shadow_vertical_length_tablet=”0px” text_text_shadow_blur_strength_tablet=”1px” link_text_shadow_horizontal_length_tablet=”0px” link_text_shadow_vertical_length_tablet=”0px” link_text_shadow_blur_strength_tablet=”1px” ul_text_shadow_horizontal_length_tablet=”0px” ul_text_shadow_vertical_length_tablet=”0px” ul_text_shadow_blur_strength_tablet=”1px” ol_text_shadow_horizontal_length_tablet=”0px” ol_text_shadow_vertical_length_tablet=”0px” ol_text_shadow_blur_strength_tablet=”1px” quote_text_shadow_horizontal_length_tablet=”0px” quote_text_shadow_vertical_length_tablet=”0px” quote_text_shadow_blur_strength_tablet=”1px” header_text_shadow_horizontal_length_tablet=”0px” header_text_shadow_vertical_length_tablet=”0px” header_text_shadow_blur_strength_tablet=”1px” header_2_text_shadow_horizontal_length_tablet=”0px” header_2_text_shadow_vertical_length_tablet=”0px” header_2_text_shadow_blur_strength_tablet=”1px” header_3_text_shadow_horizontal_length_tablet=”0px” header_3_text_shadow_vertical_length_tablet=”0px” header_3_text_shadow_blur_strength_tablet=”1px” header_4_text_shadow_horizontal_length_tablet=”0px” header_4_text_shadow_vertical_length_tablet=”0px” header_4_text_shadow_blur_strength_tablet=”1px” header_5_text_shadow_horizontal_length_tablet=”0px” header_5_text_shadow_vertical_length_tablet=”0px” header_5_text_shadow_blur_strength_tablet=”1px” header_6_text_shadow_horizontal_length_tablet=”0px” header_6_text_shadow_vertical_length_tablet=”0px” header_6_text_shadow_blur_strength_tablet=”1px” box_shadow_horizontal_tablet=”0px” box_shadow_vertical_tablet=”0px” box_shadow_blur_tablet=”40px” box_shadow_spread_tablet=”0px” global_colors_info=”{}”]<\/p>\n See <\/em>predictor variable.<\/p>\n <\/p>\n Demand that is mostly zero,but takes random nonzero values at random times.\u00a0Intermittent demand is characteristic of spare parts\u00a0and high-priced capital goods. Also known as\u00a0\u201cslow moving demand,\u201d \u201clumpy demand,\u201d\u00a0\u201cirregular demand.\u201d and \u201csporadic demand.\u201d<\/p>\n <\/p>\n A forecast expressed as a range,\u00a0for example, there is a 90% chance that sales next\u00a0month will be between 100 and 150 units. See\u00a0<\/em>point forecast.<\/p>\n <\/p>\n The process of computing\u00a0the minimal inventory needed to provide a desired\u00a0inventory service level.<\/p>\n <\/p>\n The random variation in a\u00a0time series that remains after trend and seasonality\u00a0have been accounted for. The irregular component\u00a0measures the effect of other influences on the\u00a0series, such as snowstorms, strikes, power failures,\u00a0and other nonrecurring events. See <\/em>decomposition.<\/p>\n <\/p>\n The number of time periods that old values are\u00a0shifted forward to pair with current values when\u00a0computing an autocorrelation.<\/p>\n The number of time periods that future values\u00a0are shifted backward to pair with current values\u00a0when computing an autocorrelation.<\/p>\n In inventory management, the time\u00a0between placing and receiving an inventory\u00a0replenishment order. The inventory manager may\u00a0wish to insure that there are no stockouts during\u00a0this period.<\/p>\n The total of the demands in\u00a0each period of the lead time.<\/p>\n An item or variable whose\u00a0changes anticipate changes in some other variable.For example, knowing current new car sales may\u00a0let you better predict next quarter\u2019s sales of\u00a0automotive aftermarket accessories. A leadingindicator can be used as a predictor variable in\u00a0regression analysis.<\/p>\n A method of estimating regression\u00a0coefficients. This method selects values for theregression constant and coefficients so as tominimize the sum of squared residuals. See also\u00a0<\/em>regression and residual.<\/p>\n A feature of a time series indicating its longtermaverage value.<\/p>\n linear moving average A forecasting method\u00a0appropriate for data with trend but without\u00a0seasonality; also useful for removing seasonality\u00a0from trending data. Sometimes referred to as \u00a0double moving average.<\/p>\n <\/p>\n A measure of the typical level of a variable,computed as the arithmetic average of the data\u00a0values stored in the variable.<\/p>\n <\/p>\n A numeric value to be forecasted. It\u00a0might be unit sales, liters, dollars, and so on.<\/p>\n <\/p>\n A measure of the typical level of a\u00a0variable, determined by sorting the values stored in\u00a0a variable from smallest to largest and identifying\u00a0the middle value (if there are an odd number of\u00a0data values) or the average of the two middle\u00a0values (if there are an even number of data values).<\/p>\n <\/p>\n A class of forecasting methods\u00a0for averaging out the random variation in a data\u00a0series. A moving average uses only the most recent\u00a0to form the total, forecasts the total, then pro-rates\u00a0the forecasts of the total down to the individual\u00a0items.<\/p>\n <\/p>\n A term describing the net effect of any\u00a0number of random factors that influence the value\u00a0of a variable. The Decompose feature separates\u00a0noise from the trend and seasonality in a variable.<\/p>\n A data value that is much larger or smaller\u00a0than the other values of the same variable. An\u00a0outlier could be caused by an error in data\u00a0recording or an unusual circumstance.<\/p>\n <\/p>\n In regression analysis, the probability\u00a0that a regression coefficient would achieve its\u00a0observed value or an even greater value solely by\u00a0chance, if the true coefficient value were zero. A\u00a0low value of P(2-tail) suggests that the coefficient\u00a0is not zero. See <\/em>statistically significant.<\/p>\n <\/p>\n A number that equals or exceeds a\u00a0specified percentage of all the values of a variable.\u00a0For instance, the median is the 50th percentile,\u00a0since 50% of the data values are less than or equal\u00a0to the median.<\/p>\n <\/p>\n A forecast expressed as a single\u00a0number, for example, sales next month will most\u00a0likely be 125 units. See <\/em>interval forecast.<\/p>\n <\/p>\n In regression analysis, a\u00a0variable used to forecast the dependent or forecastvariable. Also known as an independent variable.\u00a0In the equation Y = 1 + 2X, the predictor variable\u00a0is X.<\/p>\n <\/p>\n A special feature of\u00a0SmartForecasts that combines automatic\u00a0forecasting with adjustments for sales promotions\u00a0and other special events that influence sales, such\u00a0as blizzards and strikes.<\/p>\n <\/p>\n A number that divides the distribution of\u00a0data values into quarters. The lower quartile is\u00a0greater than 25% of the data values, the middle\u00a0quartile (that is, the median) is greater than 50%,\u00a0and the upper quartile is greater than 75%.<\/p>\n <\/p>\n A statistical analysis that creates an\u00a0equation to convert the values of predictor or\u00a0independent variables into estimates of the values\u00a0of a forecast or dependent variable<\/p>\n <\/p>\n In regression analysis, the difference\u00a0between the actual value of the forecast, or\u00a0dependent variable, and its fitted value.<\/p>\n <\/p>\n In regression analysis, a measure of\u00a0goodness of fit between the observed and fitted\u00a0values of the forecast or dependent variable. Rsquare\u00a0values range from 0% to 100%, with 100%\u00a0\u00a0indicating a perfect fit. If the R-square is close to\u00a00%, then your predictors are essentially worthless\u00a0for forecasting purposes. See <\/em>adjusted R-square<\/p>\n [\/et_pb_text][\/et_pb_column][et_pb_column type=”1_3″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.16″ text_line_height=”1.69em” header_3_text_color=”#8f8f8f” header_3_font_size=”19px” background_size=”initial” background_position=”top_left” background_repeat=”repeat” z_index_tablet=”500″ text_text_shadow_horizontal_length_tablet=”0px” text_text_shadow_vertical_length_tablet=”0px” text_text_shadow_blur_strength_tablet=”1px” link_text_shadow_horizontal_length_tablet=”0px” link_text_shadow_vertical_length_tablet=”0px” link_text_shadow_blur_strength_tablet=”1px” ul_text_shadow_horizontal_length_tablet=”0px” ul_text_shadow_vertical_length_tablet=”0px” ul_text_shadow_blur_strength_tablet=”1px” ol_text_shadow_horizontal_length_tablet=”0px” ol_text_shadow_vertical_length_tablet=”0px” ol_text_shadow_blur_strength_tablet=”1px” quote_text_shadow_horizontal_length_tablet=”0px” quote_text_shadow_vertical_length_tablet=”0px” quote_text_shadow_blur_strength_tablet=”1px” header_text_shadow_horizontal_length_tablet=”0px” header_text_shadow_vertical_length_tablet=”0px” header_text_shadow_blur_strength_tablet=”1px” header_2_text_shadow_horizontal_length_tablet=”0px” header_2_text_shadow_vertical_length_tablet=”0px” header_2_text_shadow_blur_strength_tablet=”1px” header_3_text_shadow_horizontal_length_tablet=”0px” header_3_text_shadow_vertical_length_tablet=”0px” header_3_text_shadow_blur_strength_tablet=”1px” header_4_text_shadow_horizontal_length_tablet=”0px” header_4_text_shadow_vertical_length_tablet=”0px” header_4_text_shadow_blur_strength_tablet=”1px” header_5_text_shadow_horizontal_length_tablet=”0px” header_5_text_shadow_vertical_length_tablet=”0px” header_5_text_shadow_blur_strength_tablet=”1px” header_6_text_shadow_horizontal_length_tablet=”0px” header_6_text_shadow_vertical_length_tablet=”0px” header_6_text_shadow_blur_strength_tablet=”1px” box_shadow_horizontal_tablet=”0px” box_shadow_vertical_tablet=”0px” box_shadow_blur_tablet=”40px” box_shadow_spread_tablet=”0px” global_colors_info=”{}”]<\/p>\n An inventory buffer or investment\u00a0that must be added to the most likely estimate of\u00a0future demand (that is, the expected forecast) to protect against demand variablity<\/p>\n <\/p>\n A graph of cross-sectional data, with\u00a0the independent variable on the horizontal (X) axis\u00a0and the dependent variable on the vertical (Y) axis.\u00a0Also known as XY plot.<\/p>\n <\/p>\n A statistical procedure to\u00a0cancel out a predictable pattern of monthly,\u00a0quarterly, or other periodic variation in a data\u00a0series. If the seasonality is multiplicative, seasonal\u00a0adjustment divides the data by seasonal\u00a0multipliers. If the seasonality is additive, seasonal\u00a0adjustment subtracts seasonal add-factors from the\u00a0data. The Decompose command performs seasonal\u00a0adjustment to leave only the influences of the trend\u00a0and irregular components. See <\/em>decomposition<\/p>\n <\/p>\n The repetitive cyclical\u00a0influence on a time series. The Decompose\u00a0command estimates the trend, seasonal, and\u00a0irregular components.<\/p>\n <\/p>\n A feature of a time series\u00a0characterized by repetitive cycles of high and low\u00a0values; often found in quarterly data (cycles of\u00a0period 4) and monthly data (cycles of period 12).<\/p>\n <\/p>\n A sequence of measurements or\u00a0observations on a variable.<\/p>\n <\/p>\n The probability, expressed as a\u00a0percentage, of meeting total customer demand for a\u00a0particular product item during a future period out\u00a0of available inventory (for example, inventory nextperiod will be sufficient to maintain a 95% or 99%\u00a0customer service level). Also known as inventory\u00a0service level.<\/p>\n <\/p>\n Forecasts of a high\u00a0percentile of the distribution of demand. Usually,\u00a0forecasts aim for the average or expected valueHowever, in inventory control applications, it is important to plan around the upper end of the\u00a0demand distribution; this helps to minimize the\u00a0occurrence of stockouts or backorders.<\/p>\n <\/p>\n A forecasting method\u00a0appropriate for data with neither trend nor\u00a0seasonality; also useful for removing seasonality\u00a0from data without trend.<\/p>\n <\/p>\n A method of understanding the\u00a0randomness in a variable by creating scenarios or\u00a0possible futures. Used to estimate the distribution\u00a0of lead time demand when forecasting intermittent\u00a0data.<\/p>\n <\/p>\n exponential smoothing A forecasting\u00a0method appropriate for data with neither trend nor\u00a0seasonality.<\/p>\n <\/p>\n Abbreviation for Stock Keeping Unit, the\u00a0most disaggregated level of inventory tracked in a manufacturing company.<\/p>\n <\/p>\n In forecasting method\u00a0using exponential smoothing or moving averages,\u00a0the statistical approximation to the actual value of\u00a0the forecast variable.<\/p>\n <\/p>\n The percentage weight given to\u00a0the most recent data value in exponential\u00a0smoothing computations. A lower weight implies\u00a0more smoothing.<\/p>\n <\/p>\n The percentage of variables containing\u00a0historical data. If sparcity is eliminated when\u00a0connecting a database to SmartForecasts, only\u00a0rows with forecastable historical data are imported\u00a0into the SmartForecasts\u2019 data table.<\/p>\n <\/p>\n A measure of the variability\u00a0of the data values stored in a variable (computed\u00a0using n-1 in the denominator).<\/p>\n <\/p>\n In regression analysis, a measure\u00a0of the sampling uncertainty in the value of a\u00a0regression coefficient. If you were to analyze a\u00a0different set of data on the same subject, you would\u00a0inevitably get somewhat different regression\u00a0coefficients. The standard errors show how\u00a0different the regression coefficients might be. With\u00a0different data, you could expect to get regression\u00a0coefficients that are one or even two or more\u00a0standard errors different from those computed from\u00a0your present dataset.<\/p>\n <\/p>\n In regression\u00a0analysis, a measure of the typical size of the\u00a0discrepancies between the actual data values and\u00a0the values predicted by the regression equation,\u00a0that is, the residuals. Also known as the root mean\u00a0square error.<\/p>\n <\/p>\n A result that is beyond the \u00a0typical range of chance variability. For example, if\u00a0you flip 100 fair coins, getting 53 heads and 47\u00a0tails is not a statistically significant deviation from\u00a0the 50:50 ideal, since this small a discrepancy is\u00a0easily explained by chance. In contrast, a 90:10\u00a0split would be statistically significant, since this\u00a0large a discrepancy would almost never arise by\u00a0chance if the coins were fair. In regressionanalysis, SmartForecasts tests the statistical\u00a0significance of the regression coefficients against\u00a0the null hypothesis that they are zero. See also <\/em>P(2-tail)<\/p>\n <\/p>\n <\/p>\n <\/p>\n A graph of data values plotted against\u00a0time. It is a good idea to examine a timeplot before\u00a0forecasting to look for trends, seasonality, and\u00a0unusual values (outliers). Also known as a line\u00a0graph in Excel.<\/p>\n An ordered succession of numbers\u00a0representing the values of a particular variable over\u00a0a given period of time (for example, monthly sales\u00a0figures for l999). Synonymous with data series.\u00a0Contrast with cross-sectional data.<\/p>\n A table that contains textual attributes<\/p>\n Application of a mathematical\u00a0function to a variable. For instance, log(X) is a\u00a0transformation of X. Transformations are used to\u00a0make a skewed distribution more symmetric or to\u00a0make a nonlinear relationship more linear. The\u00a0Scatterplot command can transform either or both\u00a0of the X and Y variables to create a better straight\u00a0line fit and then save the transformed variables in\u00a0the data table. The Define command can also\u00a0create and save new, transformed variables.<\/p>\n A feature of a time series marked by more or less steady increases or decreases in the level of the series<\/p>\n The slow, long-term change in \u00a0the level of a time series. The Decompose\u00a0command estimates the trend, seasonal, and\u00a0irregular components<\/p>\n The average unit change per period\u00a0divided by average units per period multiplied by\u00a0It is available as a statistic in the describe\u00a0function and for numerical filtering to identify\u00a0variables with positive, negative, or zero trend and\u00a0to compare average trends of one series with\u00a0another.<\/p>\n In regression analysis, the ratio of a\u00a0coefficient value to its standard error.\u00a0SmartForecasts uses the t-statistic to compute the\u00a0value of P(2-tail) when testing the hypothesis that\u00a0the coefficient value would be zero except for\u00a0chance variation. Roughly speaking, values of the\u00a0t-statistic beyond \u00b1 2 suggest that the\u00a0corresponding predictor variable is useful for\u00a0predicting the forecast variable. See <\/em>P(2-tail) and\u00a0statistically significant<\/p>\n <\/p>\n A parameter in an exponential smoothing\u00a0method that determines the balance of influence\u00a0between recent and older data values. Higher\u00a0values for weights give more influence to more\u00a0recent data values. This permits faster adjustment\u00a0to changing conditions at the cost of less\u00a0smoothing of noise when conditions are stable.<\/p>\n A\u00a0forecasting method appropriate for data with both\u00a0trend and seasonality; both additive and\u00a0multiplicative forms of Winters\u2019 method are \u00a0available in SmartForecasts.<\/p>\n [\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ custom_padding_last_edited=”on|desktop” admin_label=”section” module_id=”archives” _builder_version=”4.16″ custom_padding_tablet=”50px|0|50px|0″ custom_padding_phone=”” transparent_background=”off” padding_mobile=”off” make_fullwidth=”off” use_custom_width=”off” width_unit=”on” locked=”off” global_colors_info=”{}”][et_pb_row admin_label=”row” _builder_version=”4.16″ background_size=”initial” background_position=”top_left” background_repeat=”repeat” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_text admin_label=”Featured Articles” _builder_version=”4.16″ header_font_size=”24px” background_size=”initial” background_position=”top_left” background_repeat=”repeat” z_index_tablet=”500″ text_text_shadow_horizontal_length_tablet=”0px” text_text_shadow_vertical_length_tablet=”0px” text_text_shadow_blur_strength_tablet=”1px” link_text_shadow_horizontal_length_tablet=”0px” link_text_shadow_vertical_length_tablet=”0px” link_text_shadow_blur_strength_tablet=”1px” ul_text_shadow_horizontal_length_tablet=”0px” ul_text_shadow_vertical_length_tablet=”0px” ul_text_shadow_blur_strength_tablet=”1px” ol_text_shadow_horizontal_length_tablet=”0px” ol_text_shadow_vertical_length_tablet=”0px” ol_text_shadow_blur_strength_tablet=”1px” quote_text_shadow_horizontal_length_tablet=”0px” quote_text_shadow_vertical_length_tablet=”0px” quote_text_shadow_blur_strength_tablet=”1px” header_text_shadow_horizontal_length_tablet=”0px” header_text_shadow_vertical_length_tablet=”0px” header_text_shadow_blur_strength_tablet=”1px” header_2_text_shadow_horizontal_length_tablet=”0px” header_2_text_shadow_vertical_length_tablet=”0px” header_2_text_shadow_blur_strength_tablet=”1px” header_3_text_shadow_horizontal_length_tablet=”0px” header_3_text_shadow_vertical_length_tablet=”0px” header_3_text_shadow_blur_strength_tablet=”1px” header_4_text_shadow_horizontal_length_tablet=”0px” header_4_text_shadow_vertical_length_tablet=”0px” header_4_text_shadow_blur_strength_tablet=”1px” header_5_text_shadow_horizontal_length_tablet=”0px” header_5_text_shadow_vertical_length_tablet=”0px” header_5_text_shadow_blur_strength_tablet=”1px” header_6_text_shadow_horizontal_length_tablet=”0px” header_6_text_shadow_vertical_length_tablet=”0px” header_6_text_shadow_blur_strength_tablet=”1px” box_shadow_horizontal_tablet=”0px” box_shadow_vertical_tablet=”0px” box_shadow_blur_tablet=”40px” box_shadow_spread_tablet=”0px” use_border_color=”off” global_colors_info=”{}”]<\/p>\n [\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row admin_label=”row” _builder_version=”4.16″ background_size=”initial” background_position=”top_left” background_repeat=”repeat” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_tabs active_tab_background_color=”#eeeeee” inactive_tab_background_color=”#ffffff” admin_label=”Article tabs” _builder_version=”4.25.1″ background_color=”#edf2f7″ background_size=”initial” background_position=”top_left” background_repeat=”repeat” hover_enabled=”0″ z_index_tablet=”500″ body_text_shadow_horizontal_length_tablet=”0px” body_text_shadow_vertical_length_tablet=”0px” body_text_shadow_blur_strength_tablet=”1px” tab_text_shadow_horizontal_length_tablet=”0px” tab_text_shadow_vertical_length_tablet=”0px” tab_text_shadow_blur_strength_tablet=”1px” body_link_text_shadow_horizontal_length_tablet=”0px” body_link_text_shadow_vertical_length_tablet=”0px” body_link_text_shadow_blur_strength_tablet=”1px” body_ul_text_shadow_horizontal_length_tablet=”0px” body_ul_text_shadow_vertical_length_tablet=”0px” body_ul_text_shadow_blur_strength_tablet=”1px” body_ol_text_shadow_horizontal_length_tablet=”0px” body_ol_text_shadow_vertical_length_tablet=”0px” body_ol_text_shadow_blur_strength_tablet=”1px” body_quote_text_shadow_horizontal_length_tablet=”0px” body_quote_text_shadow_vertical_length_tablet=”0px” body_quote_text_shadow_blur_strength_tablet=”1px” box_shadow_horizontal_tablet=”0px” box_shadow_vertical_tablet=”0px” box_shadow_blur_tablet=”40px” box_shadow_spread_tablet=”0px” use_border_color=”off” locked=”off” global_colors_info=”{}” sticky_enabled=”0″][et_pb_tab title=”2024″ _builder_version=”4.25.1″ _module_preset=”default” hover_enabled=”0″ global_colors_info=”{}” sticky_enabled=”0″]<\/p>\n Smart Software is excited to announce that we are joining Epicor,\u00a0<\/span><\/strong>a global leader of\u00a0industry-specific enterprise software<\/a>.\u00a0The acquisition brings together two companies tightly aligned in helping organizations get to the right insights at the right time and take action to maximize business performance.<\/p>\n In joining Epicor, Smart Software customers will benefit from significant scale, development, and investment in our inventory planning and optimization solutions, over time giving you even more capabilities and product options. In acquiring Smart Software, Epicor is complementing and strengthening its portfolio of best-in-class ERP solutions, helping makers, movers, and sellers worldwide streamline and simplify their supply chains to gain a competitive advantage.\u00a0As your strategic business partner, our top priority as we integrate the organizations in the coming months is to continue to provide you with the highest level of service and support you expect.<\/p>\n For more information on the news, please visit the\u00a0Epicor Newsroom<\/a><\/p>\n [\/et_pb_tab][et_pb_tab title=”2023″ _builder_version=”4.21.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n Smart Software is pleased to announce the award of US Patent 11,656,887<\/strong>.<\/a> \u201cSystem And Method to Simulate Demand and Optimize Control Parameters for a Technology Platform.\u201d<\/p>\n The patent directs \u201ctechnical solutions for analyzing historical demand data of resources in a technology platform to facilitate management of an automated process in the platform.\u201d One important application is optimization of parts inventories.<\/p>\n [\/et_pb_tab][et_pb_tab title=”2022″ _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n Smart Software named a Microsoft Co-sell-ready partner<\/strong><\/a> as a leading demand planning and inventory optimization solutions provider.\u00a0 Microsoft customers leverage Smart\u2019s web-native platform for Inventory Planning and Optimization (Smart IP&O) to develop consensus forecasts, manage demand, and optimize stocking policies.<\/p>\n [\/et_pb_tab][et_pb_tab title=”2021″ _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n We are so grateful to announce Smart Software\u2019s 40th anniversary. Forty years of Innovation for Demand Forecasting, Inventory Planning, and Supply Chain Analytics. Smart, Hartunian, and Willemain was incorporated in June 1981<\/a> by Charles Smart, Thomas Willemain, and Nelson Hartunian, our visionary founders.<\/p>\n [\/et_pb_tab][et_pb_tab title=”2020″ _builder_version=”4.16″ background_color_gradient_direction_tablet=”180deg” background_color_gradient_direction_phone=”180deg” background_color_gradient_stops_tablet=”#2b87da 0%|#29c4a9 100%” background_color_gradient_stops_phone=”#2b87da 0%|#29c4a9 100%” background_color_gradient_start_position_tablet=”0%” background_color_gradient_start_position_phone=”0%” background_color_gradient_end_position_tablet=”100%” background_color_gradient_end_position_phone=”100%” hover_transition_duration_tablet=”300ms” hover_transition_duration_phone=”300ms” hover_transition_delay_tablet=”0ms” hover_transition_delay_phone=”0ms” body_text_shadow_horizontal_length_tablet=”0px” body_text_shadow_horizontal_length_phone=”0px” body_text_shadow_vertical_length_tablet=”0px” body_text_shadow_vertical_length_phone=”0px” body_text_shadow_blur_strength_tablet=”1px” body_text_shadow_blur_strength_phone=”1px” tab_text_shadow_horizontal_length_tablet=”0px” tab_text_shadow_horizontal_length_phone=”0px” tab_text_shadow_vertical_length_tablet=”0px” tab_text_shadow_vertical_length_phone=”0px” tab_text_shadow_blur_strength_tablet=”1px” tab_text_shadow_blur_strength_phone=”1px” body_link_text_shadow_horizontal_length_tablet=”0px” body_link_text_shadow_horizontal_length_phone=”0px” body_link_text_shadow_vertical_length_tablet=”0px” body_link_text_shadow_vertical_length_phone=”0px” body_link_text_shadow_blur_strength_tablet=”1px” body_link_text_shadow_blur_strength_phone=”1px” body_ul_text_shadow_horizontal_length_tablet=”0px” body_ul_text_shadow_horizontal_length_phone=”0px” body_ul_text_shadow_vertical_length_tablet=”0px” body_ul_text_shadow_vertical_length_phone=”0px” body_ul_text_shadow_blur_strength_tablet=”1px” body_ul_text_shadow_blur_strength_phone=”1px” body_ol_text_shadow_horizontal_length_tablet=”0px” body_ol_text_shadow_horizontal_length_phone=”0px” body_ol_text_shadow_vertical_length_tablet=”0px” body_ol_text_shadow_vertical_length_phone=”0px” body_ol_text_shadow_blur_strength_tablet=”1px” body_ol_text_shadow_blur_strength_phone=”1px” body_quote_text_shadow_horizontal_length_tablet=”0px” body_quote_text_shadow_horizontal_length_phone=”0px” body_quote_text_shadow_vertical_length_tablet=”0px” body_quote_text_shadow_vertical_length_phone=”0px” body_quote_text_shadow_blur_strength_tablet=”1px” body_quote_text_shadow_blur_strength_phone=”1px” global_colors_info=”{}”]<\/p>\n Supply and Demand Chain Executive 2020 Online Magazine features an article about inventory optimization at BC Transit, entitled \u201cOptimizing Parts Management at BC Transit.<\/a>\u201d <\/strong>\u00a0Eric Nelson, Director for Supply Services at BC Transit explains how Smart IP&O has helped ensure that they have the right part in the right place at the right time to equip their entire service network with 35 repair locations.<\/p>\n [\/et_pb_tab][et_pb_tab title=”2019″ _builder_version=”4.16″ background_color_gradient_direction_tablet=”180deg” background_color_gradient_direction_phone=”180deg” background_color_gradient_stops_tablet=”#2b87da 0%|#29c4a9 100%” background_color_gradient_stops_phone=”#2b87da 0%|#29c4a9 100%” background_color_gradient_start_position_tablet=”0%” background_color_gradient_start_position_phone=”0%” background_color_gradient_end_position_tablet=”100%” background_color_gradient_end_position_phone=”100%” hover_transition_duration_tablet=”300ms” hover_transition_duration_phone=”300ms” hover_transition_delay_tablet=”0ms” hover_transition_delay_phone=”0ms” body_text_shadow_horizontal_length_tablet=”0px” body_text_shadow_horizontal_length_phone=”0px” body_text_shadow_vertical_length_tablet=”0px” body_text_shadow_vertical_length_phone=”0px” body_text_shadow_blur_strength_tablet=”1px” body_text_shadow_blur_strength_phone=”1px” tab_text_shadow_horizontal_length_tablet=”0px” tab_text_shadow_horizontal_length_phone=”0px” tab_text_shadow_vertical_length_tablet=”0px” tab_text_shadow_vertical_length_phone=”0px” tab_text_shadow_blur_strength_tablet=”1px” tab_text_shadow_blur_strength_phone=”1px” body_link_text_shadow_horizontal_length_tablet=”0px” body_link_text_shadow_horizontal_length_phone=”0px” body_link_text_shadow_vertical_length_tablet=”0px” body_link_text_shadow_vertical_length_phone=”0px” body_link_text_shadow_blur_strength_tablet=”1px” body_link_text_shadow_blur_strength_phone=”1px” body_ul_text_shadow_horizontal_length_tablet=”0px” body_ul_text_shadow_horizontal_length_phone=”0px” body_ul_text_shadow_vertical_length_tablet=”0px” body_ul_text_shadow_vertical_length_phone=”0px” body_ul_text_shadow_blur_strength_tablet=”1px” body_ul_text_shadow_blur_strength_phone=”1px” body_ol_text_shadow_horizontal_length_tablet=”0px” body_ol_text_shadow_horizontal_length_phone=”0px” body_ol_text_shadow_vertical_length_tablet=”0px” body_ol_text_shadow_vertical_length_phone=”0px” body_ol_text_shadow_blur_strength_tablet=”1px” body_ol_text_shadow_blur_strength_phone=”1px” body_quote_text_shadow_horizontal_length_tablet=”0px” body_quote_text_shadow_horizontal_length_phone=”0px” body_quote_text_shadow_vertical_length_tablet=”0px” body_quote_text_shadow_vertical_length_phone=”0px” body_quote_text_shadow_blur_strength_tablet=”1px” body_quote_text_shadow_blur_strength_phone=”1px” global_colors_info=”{}”]<\/p>\n Don\u2019t Be a Victim of your Forecast Models \u2013 Special Section in Foresight Magazine<\/a>.<\/strong> In this feature section, Tom Willemain provokes a good deal of thought about the role of statistical models in supply chain forecasting, a field that he believes lags far behind finance in embracing algorithms over gut instinct. Download Special Section on Forecast Modeling in Spring 2019 Issue of Foresight Magazine<\/a><\/strong><\/p>\n [\/et_pb_tab][et_pb_tab title=”2018 and prior” _builder_version=”4.17.6″ global_colors_info=”{}”]<\/p>\n Right-Sizing Inventory with Demand Forecasting,\u201d interview with Chief Material Officer at Metro-North Railroad. <\/a><\/strong> See \u201cFour Steps to Service Level-Driven Inventory Planning\u201d in SupplyChain Management Review.<\/a><\/strong> Minnesota\u2019s Metro Transit adopts service-level driven method of inventory planning from Smart Software.<\/a><\/strong> Video Interview with SupplyChainBrain, \u201cService Level-Driven Demand Planning\u201d<\/a><\/strong> Conquering Supply Chain Variability at Rev-A-Shelf,\u201d in Supply & Demand Chain Executive Online<\/a><\/strong> Now You See It, Now You Don\u2019t Inventory,\u201d New technologies to address intermittent demand, in Aviation Week Online<\/a><\/strong> Webinar Replay: Agile Demand Forecasting, the Rev-A-Shelf Experience.<\/a><\/strong> Feature Article: MCI\u2019s Parts Planning Excels with Smart Software\u201d in BusRide Maintenance Magazine<\/a><\/strong> Webinar replay: CFO View of Demand Forecasting, with Bud Schultz, NKK Switches. Originally broadcast 9\/26\/2013.<\/a><\/strong> Featured Review: \u201cSmart Software for Service-level Driven Forecasting\u201d by Technology Evaluation Centers.<\/a><\/strong> Feature Article: \u201cForecasting \u2013 It\u2019s More than Inventory Management,\u201d in Supply & Demand Chain Executive.<\/a><\/strong> Feature Article: \u201cSmall Operational Changes Bring Big Cost-Cutting Results for Transit Agencies\u201d in APTA Passenger Transport Magazine<\/a><\/strong> Feature Article: \u201cMeeting the Service Parts Inventory Planning Challenge\u201d in BUSride Maintenance Magazine<\/a><\/strong> SmartForecasts Review: \u201cSmarter Inventory Management: Forecasting public transportation needs with the complete software package\u201d in APICS Magazine<\/a><\/strong> Transit Talk Blog: \u201cHow to Discover Hidden Cost Savings in Your Transit Operations\u201d<\/a><\/strong> Cover Story: A Northeast Commuter Railroad Struggles to Optimize Its Spare Parts Inventory<\/a><\/strong> “Prestolite Charges up Its Inventory Management”<\/a><\/strong> “How to Survive the Economic Crunch”<\/a><\/strong> “Smooth Ride: Forecasting solution improves product deployment and reduces transportation costs”<\/a><\/strong> “Prevost Parts increases replacement part availability and reduces inventory through improved demand forecasting” by Alex Daudelin.<\/a><\/strong> “The Right Tools” by Charles Smart, president of Smart Software.<\/strong><\/a> “Forecasting-As Important as Ever” by Charles Smart, president of Smart Software.<\/strong><\/a>B<\/h4>\n
bootstrapping<\/h3>\n
Brown\u2019s method<\/h3>\n
C<\/h4>\n
coefficient of variation<\/h3>\n
correlation<\/h3>\n
crosscorrelation<\/h3>\n
cross-sectional data<\/h3>\n
cumulative forecast<\/h3>\n
D<\/h4>\n
data cycle<\/h3>\n
decomposition<\/h3>\n
d.f.<\/h3>\n
dependent variable<\/h3>\n
.DOUBLE exponential smoothing<\/h3>\n
dummy variable<\/h3>\n
Durbin-Watson statistic<\/h3>\n
E<\/h4>\n
EOQ models<\/h3>\n
error<\/h3>\n
event model<\/h3>\n
\u00a0exponential smoothing<\/h3>\n
F<\/h4>\n
fit or fitted value<\/h3>\n
forecast<\/h3>\n
forecast interval<\/h3>\n
F-statistic<\/h3>\n
H<\/h4>\n
hedge<\/h3>\n
holdout analysis<\/h3>\n
I<\/h4>\n
independent variable<\/h3>\n
intermittent demand<\/h3>\n
interval forecast<\/h3>\n
inventory optimization<\/h3>\n
irregular component<\/h3>\n
L<\/h4>\n
lag<\/h3>\n
.lead<\/h3>\n
lead time<\/h3>\n
lead time demand<\/h3>\n
leading indicator<\/h3>\n
least squares<\/h3>\n
level<\/h3>\n
M<\/h4>\n
mean<\/h3>\n
measure<\/h3>\n
median<\/h3>\n
moving average<\/h3>\n
N<\/h4>\n
noise<\/h3>\n
outlier<\/h3>\n
P<\/h4>\n
P(2-tail)<\/h3>\n
percentile<\/h3>\n
point forecast<\/h3>\n
predictor variable<\/h3>\n
Promotional forecasting<\/h3>\n
Q<\/h4>\n
quartile<\/h3>\n
R<\/h4>\n
regression<\/h3>\n
residual<\/h3>\n
R-square<\/h3>\n
S<\/h4>\n
safety stock<\/h3>\n
scatterplot<\/h3>\n
seasonal adjustment<\/h3>\n
seasonal component<\/h3>\n
seasonality<\/h3>\n
Series<\/h3>\n
service level<\/h3>\n
service level forecasts<\/h3>\n
SIMPLE moving average<\/h3>\n
simulation<\/h3>\n
SINGLE<\/h3>\n
SKU<\/h3>\n
smoothed data value<\/h3>\n
smoothing weight<\/h3>\n
sparcity<\/h3>\n
standard deviation<\/h3>\n
standard error<\/h3>\n
standard error of estimate<\/h3>\n
statistically significant<\/h3>\n
T<\/h4>\n
timeplot<\/h3>\n
time series<\/h3>\n
time table<\/h3>\n
transformation<\/h3>\n
trend<\/h3>\n
trend component<\/h3>\n
trend percent<\/h3>\n
t-statistic<\/h3>\n
W<\/h4>\n
weight<\/h3>\n
\u00a0WINTERS’ exponential smoothing<\/h3>\n
Article Archives<\/h2>\n
Supply & Demand Chain Executive<\/em> \u2014 June 26<\/p>\n
SupplyChain Management Review<\/em> \u2014 April 12, 2015<\/p>\n
Mass Transit Magazine<\/em> \u2014 February 2015<\/p>\n
SupplyChainBrain<\/em> \u2014 June 30, 2014<\/p>\n
Supply & Demand Chain Executive<\/em> \u2014 April 9, 2014<\/p>\n
Aviation Week<\/em> \u2014 April 7, 2014<\/p>\n
SmartSoftware Inc.<\/em> \u2014 January 2014<\/p>\n
BusRide Maintenance Magazine<\/em> \u2014 September 2013<\/p>\n
SmartSoftware Inc.<\/em> \u2014 September 2013<\/p>\n
Technology Evaluation Centers<\/em> \u2014 May 2013<\/p>\n
Supply & Demand Chain Executive<\/em> \u2014 February 2013<\/p>\n
APTA Passenger Transport Magazine<\/em> \u2014 June 2012<\/p>\n
BUSride Maintenance Magazine<\/em> \u2014 April 2012<\/p>\n
APICS magazine<\/em> \u2014 January\/February 2012<\/p>\n
Mass Transit Magazine<\/em> \u2014 September, 2011<\/p>\n
SupplyChainBrain<\/em> \u2014 May\/June 2011<\/p>\n
When retirements drained its planning expertise and a bad mix of too much inventory put a strain on cash flow, the company brought in a new forecasting tool to power its planning process. S&DCE Aug\/Sep 2010<\/strong><\/p>\n
Current economic conditions are challenging everyone\u2019s business. This is especially true for companies in the manufacturing and distribution sectors that have a large percentage of their supply chain resources tied up in inventory. With credit tight, these companies are looking for ways to preserve as much cash as possible. Because inventory is cash sitting on the shelves, a prime way to reduce costs and increase cash flows is to improve the forecasting and planning of inventory requirements. SDCExec.com May 2009<\/strong><\/p>\n
See how Prevost Parts, a division of Canadian bus manufacturer Prevost Car, has used SmartForecasts to improve inventory allocation and distribution effectiveness in the North American aftermarket for its 25,000 motorcoach and urban bus parts. Learn about Prevost Parts’ evaluation criteria in selecting SmartForecasts over five other applications considered, and the importance of Smart’s unique intermittent demand forecasting capability in solving Prevost’s forecasting and safety stock estimation problems. APICS May 2005<\/strong><\/p>\n
Read an article about how Prevost Parts, the parts division of Canadian bus manufacturer Prevost Car, uses SmartForecasts to more effectively distribute replacement parts for the North American motorcoach and transit bus markets. Learn why the company selected SmartForecasts over SAP’s demand planning application and now expects both a 25% increase in forecast accuracy and close to a 20% reduction in inventory stocking levels. Geston Logistique\u00a0March 2004<\/strong><\/p>\n
Learn how the right statistical forecasting tools combined with collaborative inputs from management, salespeople and customers can greatly improve forecast accuracy and streamline a company’s demand planning process. APICS May 2003<\/strong><\/p>\n
Read why demand forecasting is still essential in this new manufacturing age. APICS Nov\/Dec 2001<\/strong><\/p>\n