When operating in large Retail and GDO chains, inventory differences are one of the numerous problems that affect the budget of individual stores, destabilizing the correct progress towards the target turnover and representing a potential, significant economic loss.
For commercial enterprises, inventory differences represent all the merchandise that has been purchased and has not yet been sold and that, at the same time, is not physically in the warehouse or for sale.
In fact, it is not always possible to find the same quantities of goods loaded into the management databases; this, in addition to causing delays in ordinary work, causes accounting problems, negative consequences on the work of operators, and store profitability.
Warehouse shortages are an inevitable and highly variable phenomenon.
The main causes of warehouse shortages are:
- Human, such as small internal thefts (by managers and employees) or external thefts (by customers, couriers);
- Technological, such as data loading errors in logistics (goods loading/unloading)
- Accidental, recognized in breakages or poor production (this nature, however, is easily controllable, remembering to make the Return, a practice that is often forgotten by the operator and causes inventory differences).
Even if everything can be traced back to purely “manual” errors, logistics and warehouse management controls can be encouraged or increased, which would “technically” bring a mathematical decrease in the percentage of the possibility of the risk that the loss actually occurs. In plain English, the more people or managerial cross-checks are used, the less the chance of error.
In my opinion, the math is correct, but the uncontrollable variable dictated by humans makes these calculations feasible only on paper and not in reality. That’s why it’s important to find effective solutions to prevent these unpleasant situations, both for the company and for all the factors that revolve around it.
For tax purposes, they can trigger presumptions of transfer for VAT purposes provided for by DPR no. 441/97; for companies, reaching this point could ruin not only the course of their balance sheet but also determine responsibility gaps dictated by factors such as employees, suppliers, and customers.
The famous “inventory” is very important for preparing an accurate balance sheet.
The more detailed an inventory is, the more real the valuation or variation in the economic account of the stocks will be. Many companies, in fact, do not know the actual valuation of their stocks; taking inventory means counting the number of pieces, meters, or kilograms in the warehouse without leaving room for more or less vague interpretations.
Moreover, if the inventory is done well, important balance factors can be evaluated and one can say:
- If I have a high index of my profit, I reduce the value of the warehouse (even if the problem is postponed, it is not mitigated forever), I slightly reduce the profit and, consequently, pay fewer taxes;
- If I have a Low and/or Loss index, I increase the value of the warehouse, proportion the profit.
Logically, if all this is done in a controlled manner, this becomes a practice “tolerated” by the Italian Tax Authorities; otherwise, you can be sure of a check.
Warehouse stocks are indispensable both for the present and for the future of the company and its balance sheet; when the valuation of the remaining stocks is included in the balance sheet, it is wrong to think that any data or number can be written.
Within certain limits, varying data is normal, but exaggerating their proportion is a risk that no one should ever take; math does not forgive.
And let’s face it: “If there is no correct valuation of the warehouse, there is no total knowledge of the margins.”
The story we need to create now is towards technology, without being afraid to use it.
Companies such as Zara, Amazon, and Zalando are automating processes to reduce the minimum threshold of error percentage.
How to achieve this?
Eliminating “human” errors in software management and machines is the first step towards reducing the percentage of risk and its magnitude.
Trying to pinpoint the cause of a process error will no longer be a utopia, but a real opportunity to understand what happens to the company’s merchandise even before it is unloaded at the store.
A good starting point is certainly to automate, through merchandise recognition, the onboarding process before it is packed.
Then, rename and have the packed batch recognized directly by the Blockchain and monitor it to its destination from there.
Here, every technological risk is eliminated, and the only remaining error is the human/logistical one upon arrival, no longer upstream but downstream.
Human processes will end up being only two, if not one, the moment the means of shipment are also tracked in the blockchain through the automatic matching of data on the method used for loading the vehicle, vehicle data, destination, and transporter; consequently, the riskiest process would be unloading in the store.
It becomes imperative that in GDO/Retail procedures, the application or implementation of blockchain processes would become a must in a very short time; I briefly report a calculation I made, through the analysis of a Retail store with a turnover of approximately €5,500,000:
Inventory loss year x – €325,000
Inventory loss year x+1 – €360,000 Turnover: -7%
Inventory loss year x+2 – €389,000 Turnover: -8%
Initialization of merchandise loading processes from the main warehouse in the Blockchain, with a courier monitoring system:
Inventory loss year x+3 – €270,000 (with a trial period of the new system of about 4 months) Turnover: +3%
Inventory loss year x+4 – €90,000 Turnover: +9%
The decrease in inventory losses in the last two years is incredibly fast; with the increase in turnover, the company not only has the possibility of new hires but can also more easily manage one of the highest company risk factors.
The data in Grosseto shows how, thanks to a Blockchain data system, serious inventory losses can be avoided, and how the percentage of such losses has been decreasing over the years.
The increase in average turnover is guaranteed, as it changes the analysis of company risk, one of the values of the loss factors, from medium-high to low; initially, everything was calculated on management software and data that could vary depending on human intervention, but now it is based on the safety of blockchain technology and reduced human intervention.
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