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Current Problem
I think it would be a good idea to create a report to show which products are low moving and are not going out as much as others, this would mean i was able to show my customers which products to push sales on so they can move more stock. |
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| Idea to resolve Problem | New report |
The way I do it is I created a simple custom Report by going into the builder and selecting the type as StockFlow then I added 3 columns SKU, Product Name, Quantity. Export everything to a CSV file and use a simple =SUMIF formula in excel and this consolidated everything for me per SKU. I use this method for the exactly same reason mentioned above to see which products are slow moving so I know where I need to push the sales. Works great however it would be a lot easier and save some time if there was a pre-built report that already calculated all off this on Mintsoft without having to work with formulas in excel.
Hope this helps you guys.
If the original user that posted this idea has found a work around I would appreciate it if you could add a comment to point me the right direction
The Custom Report Builder only gets you so far.
MintSoft has all the data to hand that I need but it's not possible to extrapolate it in a format that consolidates each SKU into one line of information.
For example, if you use the "Last Picked" field it gives you a separate date and time for each time an order is fulfilled. All I need is for the total number of picked items to be combined down into one line for each SKU within a chosen date range.
Agreed!
It would be useful to be able choose a date range, bulk export all lines to show the consolidated number of units picked for each SKU within that timeframe.
This would give usable data for slow/no moving stock items for review; either to be shifted to a stock location further away from packing stations or even to be discontinued. Also gives the option to flag with the sales team to offer discounts on items with high quantities of remaining stock.
Likewise, SKUs with a higher pick rate can be moved closer to packing stations.
Overall this data would give clear insight to streamline picking and increase productivity.