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Overview

MySales is an app that helps you:

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In general, you should remember the following rules while choosing the predictors for your models:

1. Avoid including correlated predictors into the model, e.g. don't include 'Discount' and 'Discount %' as they have a strong correlation

2. Avoid including the predictors with non-linear (exponential, logarithmic, power) impact to your sales for a specific SKU or product group, instead include their regressed values, e.g. if the price has non-linear, but the exponential impact to your sales, include 'Regr price' instead of 'Price' predictor.

3. Exclude the predictors that do not impact your sales or their impact is too low, this will improve the model accuracy

Adding predictors

You can add more predictors, but this requires more work than just adding more models based on the combinations of already existing predictors. In order to add a predictor, first, you should prepare the data in the source system for download, add it into your 'sql.js' file and set it up in 'options.js' file. Often it also requires some coding to convert the predictor into the appropriate form of data.

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is also taken into account in price optimization module in cross-elasticity. In the price optimization module, can see the dependencies between specific products. Cross-elasticity is described in detail in the Cross-elasticity section.

Price elasticity

Price elasticity of demand - this is a measure of the change in the product demand, which is caused by a change in the price of the product.

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Using a Key Element in a Forecast

The system can take into account the effect of cannibalization of interchangeable and similarly priced positions in a direct way.

To do this, it is possible to combine several items that are interchangeable and close in price into one key element (KE). This join is done by adding the KEY_ID field to the PRODUCTS table where the list of SKUs is loaded. Also, it is possible to set up maintaining a directory of key elements in the MySales DB Editor. If several positions have the same key element (usually, this is the SKU_ID of the earliest position), then the forecast is built as follows:

• Initially, the system consolidates sales and inventory data for all key element (KE) positions
  Next, a forecast is built for the KE, i.e. forecast based on consolidated data

• For those weeks when each item of the KE was included in the assortment matrix (AM), or if the item was withdrawn from the AM, before the forecast week of the end of the balance, the system builds an independent forecast

• Further, the forecast of KE positions is adjusted so that the sum of the forecasts of all KE positions is equal to the total forecast for the KE. Thus, the KE forecast is redistributed to the forecast of each CI position.

• Those weeks where the “Daily adjustment” predictor worked are not adjusted
  Thus, if, for example, there were 3 KE positions in AM and sales were distributed between them, and one of the positions is withdrawn from AM, then the forecast of the remaining two will be increased. And vice versa, if there were 2 KE positions in AM, and a third one is entered, then the forecast of the previous two will be reduced.

Note 1: combining KE positions does not cancel the recommendation to put down an analogue. If there is no analogue for the new KE position, then the forecast for it will be built as an average for the group, adjusted for the price.

Note 2: It is recommended to test the forecast for positions combined by one KE, since such a combination may not always make a better forecast. For example, even if positions are close in price and consumer purpose, but differ in consumer preferences and each of them has its own target audience, i.e. in fact, one position cannibalizes the other only partially.

Price elasticity

Price elasticity of demand - this is a measure of the change in the product demand, which is caused by a change in the price of the product.

The price elasticity of demand shows how much the percentage of demand for a particular product will change when its price changes by 1%.

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• "New items with analogues;"

• The system calculates their sales forecast based on sales data for similar products. When building a forecast, the system takes into account the coefficient of influence of each of the analogues, which is specified when adding a new position. (details of adding analogs are described in the section New).

• "New items which analogs are not specified or which have no sales history or analogs at all;"

• In this case system automatically automatically finds the SKUs, which are in the assortment matrix, have less than 4 weeks of sales history and there are no analogs of them. Then algorithm forecasts them as an average position for the group, taking into account the price of the new position (if it is present if not, the forecast is calculated using the average value of the group). For this it takes sales of Group/Store level and divides them by the number of positions on the stock, and then recounts the group's forecast, taking into account the price of the new position, and then applies it to a new position.

• "New positions for which analogues are not specified, but there is a history of sales over the regions;"

• For forecasting this positions system uses "Expansion of distribution" algorithm. This option is used to predict a new position in the store more accurately, If new position has already been used in other region's stores assortment, then this option can be used.

  Expansion of the distribution is used if the number of weeks of historical sales for a new position in the store is less than a previously set number of weeks (the number of weeks is set using the forecast configuration file), but there are sales in the region for a new position. In this case, the system does not consider this position as completely new, but as an extension of the store's assortment.

  For positions that fall under the expansion of distribution, the system calculates the new position forecast as a forecast for the position from the region and applies it to the store level using the following proportion: Sales/Forecast value on SKU/Region level is multiplied by Sales/Forecast on Group/Store and divided by Sales/Forecast from Group/Region level.

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When testing each model, MySales builds the forecast for the past 52 weeks, and calculates Mean Absolute Percentage Error (MAPE) for the forecast on each model. Then based on your model settings, MySales adds the penalty for each model and chooses the model with minimum error including Penalty. The penalty is a percentage that you can use to reduce sensetivity sensitivity for a particular model.

You can see Penalty percentage for each model on SETTINGS page. To see the tested models and the selected models, use Models button on MY SALES page.

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Using past undefined weeks to evaluate the forecast

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There are multiple options, but the more generic solution is to take the forecasted quantity for the delivery period (from the nearest delivery day to the next delivery day), add Mean Absolute Deviation (MAD) or Mean Absolute Percentage Error (MAPE) as a safety stock. Your target stock quantity is ready, then just substract subtract the current stock and your quantity to order is ready. If the forecast accuracy is high, using MAPE is preferred when you need to increase the safety stock for the forecasted peaks. Using MAD is preferred when the forecast accuracy is less, or sales are low, or the sales expected to be more smooth without high peaks.

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