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One of the main aspects verified during the tax inspections is the analysis of the tax treatment applied to inventory losses incurred during the economic activity (e.g. damaged goods, expired goods, perishables, inventory differences etc.) and related procedures existing at the level of the taxpayers.
EY easyInventory is a machine learning solution that automatically cluster together similar inventory items and net the differences.
You may benefit of a favorable tax treatment, both from a corporate income tax and VAT perspective, depending on the nature of losses and on the compliance with certain conditions.
On the contrary, in the lack of proper ocumentation for inventory losses control and monitoring, the tax associated costs can be significant, representing more than one-third of the value of the inventory losses.
What we offer:
Analysis of inventory losses
Inventory losses offset
Assistance with other stock adjustments
Analysis of the categories of inventory losses incurred by the company, the procedures, the available documentation, the associated tax costs and provide comments regarding the identified risks / opportunities.
Review / prepare internal procedures and supporting documentation in order to reduce the tax costs associated with inventory losses, in line with the applicable law and based on practical experience gained during the tax audits.
Analysis of the causes of inventory differences – pluses and minuses.
Prepare the list of similar products that can be offset, including based on an automated solution.
Provide support in offsetting the inventory pluses with minuses, based on an automated solution.
Provide support in preparing the necessary documentation (e.g.procedures, decisions) for sustaining a favorable tax treatment.
Analysis of the internal procedures for monitoring and control of other inventory adjustments.
Review the available supporting documentation for each type of adjustment and provide recommendations for their update, in order to apply a favorable tax treatment, in line with the applicable tax legislation.
Under certain conditions, the legislative framework regarding inventory management provides for the possibility of performing offsets of inventory differences for accounting purposes:
losses are not due to felonies, thefts, negligence of the persons responsible with inventory management, etc.;
there is a confusion risk among the type of products to be offset due to their resemblance regarding the exterior appearance, such as: colour, drawings, design, size, packaging or other items;
the inventory differences are related to the same period and same management unit.
Following the inventory offsets, the taxpayer is required to recognise into its accounting books the result of the inventory count as taxable revenues (positive results) or non-deductible expenses, subject to VAT (negative results). Incorrect offsets of inventory differences and/or incomplete documentation could trigger significant additional tax liabilities upon a tax audit.
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| Inventory loss services
EY easyInventory
Inventory loss digital solution
About the solution
EY easyInventory is a machine learning solution that automatically cluster together similar inventory items and net the differences. It Includes text processing and standardization, extraction of measurement units and quantities, machine learning clustering and optimal netting of the opposite sign inventory items. More so, supplemental, erroneous inputs are detected and automatic text correction functions can be implemented.
EY easyInventory steps:
The segmentation of the inventory items is performed hierarchically, depending on the district, category, subcategory, and, finally, depending on the description, the unit of measurement of the quantity and value.
The sensitivity of the grouping of most discreet subcategory is configurable, so different criteria can be used for each district.
Initial data processing ensures the elimination of duplicate items and identifies (and eliminates, if necessary) erroneous or inconsistent entries.
By processing the descriptive text, the units of measurement and the quantities related to each inventory object can be extracted (except for the bulk ones); you can also extract the number of individual items in a package.
The classification is performed multicriteria, using advanced natural language processing and machine learning algorithms.
The algorithm for grouping the inventory objects determines both the optimal number of categories and their component.
The difference compensation algorithm is robust and fast ensuring optimal results.