Blog > Supply chains automatization in SPAR Tula

Supply chains automatization in SPAR Tula

“Regular promos are our key tool in the fight for the customer. To receive the maximum effect we need to maintain the safety stock of the optimal size, reacting to seasonal deviations and holidays in advance. To not raise the price of the logistics it is important to maintain the rhythm of the supply. Simple automated ordering systems were not satisfying us- we were searching the solution with strong demand forecasting capabilities. We found tools we were searching for in RS.SCM demand forecasting and replenishment system…”CEO of Tula SPAR Andrey Anosenko

SPAR is one of the largest supermarket chains in the world with 13,700 stores, serving 9 million customers daily. Commodity circulation exceeds 27 billion euro. SPAR unites independent wholesalers and retailers under one name and works in 35 countries worldwide. SPAR Tula has 50 own supermarkets and sub franchises. It operates in Tula, Ryazan, Kaluga, and Moscow regions. The company was founded in 2004.

Preparation for implementation

Retailer’s project team developed goals and indicated desired results for supply chain automation management project:

  • Automate order calculation on all levels of the supply chain;
  • Optimize stock in stores and DC;
  • Increase of automated ordering and store orders management quality;
  • Decrease in waste and logistics expenditures;

SPAR Tula works with 19 000 SKU of active assortment and possesses a high level of process organization. This is why special demands were made not only regarding functional capabilities, but also performance and reliability of the software.

The accuracy of incoming data is crucial for any automation system. This is why to prepare the company for RS.SCM implementation the retailer applied a set of organizational measures to increase the quality of data in the registry system. As a result:

  • Incomes are registered on time and stock counts are regularly carried out for problematic products;
  • Stock level is always relevant;
  • Goods are introduced and withdrawn from stock at the right time and store range is kept in the relevant condition.

Progress of the project

At first, the system was tested on the limited product range for a smooth and painless transition to the new work technology. From May to August of 2017 product categories of “Beer” and “Pasta” were automatically ordered for all stores, without involving distribution centers. As a result, product availability in stores was increased and stock decreased throughout the entire chain.

For “Beer” category:

  • Stock before RS.SCM implementation: Was 25 days — > Became 17 days
  • Product availability in stores before RS.SCM implementation: Was 86% — > Became 96.8%

For “Pasta” category:

  • Stock by the end of the period: Was 35 days — > Became 21 days
  • Product availability in stores by the end of the period: Was 90% — > Became 95.2%

An increase in these indicators was possible due to the increase in demand forecasting quality. RS.SCM automatically chooses the best forecasting method for every product category based on existing data, stability of sales, forecasting errors and comparisons to factual sales. Now category managers from SPAR receive cleaned sales statistics, eliminating out-of-stocks, promo, clearance and accidental spikes in sales. The system corrects demand forecasts on product categories and calculates the required number of products in the order based on statistics. The job of the stock manager is to approve the precalculated orders.  After approval, the SCM system automatically sends orders to suppliers and DCs.

The economic effect of the pilot project satisfied managers of the retail chain. Over the next six months the entire product range was transferred to the automated ordering system. 

The system is a decision support tool. it identifies and analyzes supply chain KPIs, allowing the manager to tune replenishment parameters from the same analytical interface. 

RS.SCM is designed to work with large quantities of data specific to retail chains. This is why the system overtakes more labor-intensive operations, such as calculations for stock replenishment in stores and DCs. Most optimal ways of safety stock calculation are automatically applied for different product groups. As a decision support tool, it identifies and analyzes supply chain KPIs, allowing the manager to tune replenishment parameters from the same analytical interface. 

Project results

System automatically forecasts and replenishes stock in the retailer’s entire supply chain.

Orders for stores are automatically generated for the entire range. Less than 5% of regular orders may need corrections. Promo orders make up 20% of regular orders and less than half of them may need corrections.

Before RS.SCM implementation it took 12 managers to manually generate orders and 4 managers to generate delivery orders for DCs. With RS.SCM the number of managers reduced to 4 and 2 respectively.

Stock for the entire product range decreased from 25.6 days to 20.7 days. The decrease in stock led to the release of additional capital and decreased product waste. Availability of products increased from 78% to 94.3%. The decrease in the number of Out-Of-Stock decreased lost sales and increased the loyalty of customers towards the retailer.

According to the CEO of SPAR Tulа, Andrey Anosenko, who was managing the project of supply chains automation, implementation of the system decreased stock, increased service quality and made order schedule management more comfortable. This, in turn, decreased the number of scheduling confusions, specifically during the temporary transition from direct deliveries to deliveries through DCs.

Building effective supply chains is a complex project, heavily depending on the maturity of the retailer’s business processes and the quality of supplier’s services. Powerful analytical capabilities of RS.SCM along with continuous work with suppliers to improve the service level allow SPAR Tula to refine the supply chain KPI.