Context: company for automation
Retail chain of grocery stores in two regions of Ukraine, 18 points of sale, about 80 employees. The owner is an entrepreneur who has developed the business from one store over 8 years.
At the time of the appeal to SPOC, the network operated as follows: each store had its own cash register program, inventory was maintained separately in a common database, the accounting department used another program, and all data was collected manually every month — the chief accountant would send a request to each store, receive Excel files, and compile them into one table.
Month-end closing took 14+ working days — essentially three weeks with weekends. During this time, the accounting department could not do anything else.
What problems needed to be solved
1. The closing time is critically long. The owner received reports three weeks after the end of the month — essentially too late for making management decisions.
2. Reconciliation errors. Data from stores and the warehouse often did not match by 5-10%. The cause of the discrepancies had to be investigated separately.
3. Lack of operational information. The owner did not know the current balances, sales, or margins — all of this appeared post-factum after weeks.
4. Difficulty of scaling. Opening new stores amidst the existing chaos was risky — the workload on the accounting department was increasing non-linearly.
5. Errors in retail reporting - periodic "misunderstandings" with the tax authorities due to untimely or incorrect data from cash registers.
What was done: SPOC decision
It was proposed to implement a unified accounting system that will consolidate all modules: store cash registers, inventory, purchasing, reporting, and analytics.
Key project decisions: a unified database for the entire network, online synchronization of cash registers with the PRRO, automatic movement of goods between locations with inventory control, integration with the client bank and tax authority, a dashboard for the owner with key metrics in real time.
Implementation Stages
Stage 1 (3 weeks). Examination and description of processes. It was found that 30% of the "accounting problems" are actually process issues. An optimized model was proposed.
Stage 2 (2 weeks). Preparation of BRD, specifications, and coordination.
Stage 3 (2 months). Configuration of the central system and data migration. The challenging part was cleaning the product catalog — there were duplicate entries in the stores with different names for the same product.
Stage 4 (1 month). Gradual connection of stores. First the first pilot, then 3 more, then the rest — to avoid stopping the operation of the entire network at once.
Stage 5 (3 weeks). Employee training. Separately: cashiers, store managers, accounting, management.
Stage 6. Stabilization — 2 months of active support after the launch.
What was difficult
Resistance of cashiers. Many people had worked in the network for over 5 years and were used to the old programs. What helped: separate training for each location, simple instructions, a hotline in the first weeks, ongoing first line support 16/7
Old equipment. In 4 stores, the computers couldn't handle the modern system. It was necessary to upgrade the hardware — the client additionally invested about 80,000 UAH.
Old server. The old system used the client's own servers, which had not been updated for a long time. We switched to modern cloud resources that are automatically optimized for needs.
Unstable internet. In two locations in small towns, the internet periodically went down. We set up operations in offline mode with synchronization upon restoring the connection.
Results for the first year
Month-end closing time: from 14+ to 3 business days. The weeks of freed-up time for the accounting department each month are essentially a "bonus" employee without additional costs.
Accuracy of accounting: discrepancies between cash data and inventory have decreased from 5-10% to 0.1-0.3%. In numbers, this means savings of about 200,000 UAH per year on "missing" goods and erroneous write-offs.
Data operability: the owner can now see sales, margins, and inventory in real-time through a mobile dashboard. This has allowed for quicker responses to sales declines and timely ordering of products.
Analytics: for the first time, there was an opportunity to see the margin by product groups. It turned out that 12% of the assortment was actually sold at a loss — after reviewing prices and supplies, the network's margin increased.
Scaling: over the year, the network opened 5 more stores — without expanding the accounting staff. Previously, every 3-4 new locations required an additional employee.
What happened next
After stabilization, the client continued to develop: added a loyalty program module, integrated with courier services for delivery, and launched an online store with a single warehouse.
Today, the network has been operating in a unified system for over 2 years, growing from 18 to 27 stores, and the staff has increased from 80 to 115 employees. The accounting department remains the same with those 3 people.
Conclusions for Retail Business
1. Closing the month in 14+ days is not "normal for retail." It is a signal that accounting is being done inefficiently. An adequate timeframe for a network of this scale is 3-5 days.
2. Time savings for accounting is not the only benefit. The timeliness of data for management decisions is equally important.
3. Team resistance is a normal phenomenon. Allow 2-3 months for adaptation and invest in training.
4. The payback period for automation in retail is usually 8-15 months due to improved accounting accuracy and freed-up time.
5. Do not start with all stores at the same time. A pilot launch at one location, then at 2-3 more locations, reveals issues that can be fixed before scaling up.
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