How the distributor of Nestle, Mars and P&G in CIS saves up
about 16 000 000 $ a year with forecasting algorithms
about 16 000 000 $ a year with forecasting algorithms
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How to plan and forecast on the unstable market
Novo Forecast Enterprise implementation case
- The company profileALIDI is a distributor of Nestle, Mars and P&G in Russia, Belarus and Kazakhstan.
The company supplies more than 20 000 of goods to 160 000 of customers – to retailers and other intermediaries.
The company in numbers- The turnover in 2020 amounted to 80 billion rubles
- It is represented in 62 regions where 18 distribution centers are situated
- Supplies to 100+ stores
- The staff amounts to 9000 employees
- The project problemsManagers used their own methods of sales forecasting. Everything was quite primitive: calculations were made in Excel, sales plans and promo actions were negotiated via e-mail. The accuracy hardly reached 40%.
The employees from various departments and suppliers were not included into the process and it also spoiled forecasting.
The proportion of unsaleable and out-of-stock goods was high and the distributor was losing money. The lower the forecast accuracy, the more money the company loses.
If the scales of ALIDI activity are taken into account, it is a question of hundreds of millions of rubles a year. - The project tasks
- To increase the forecast accuracy
- To decrease the level of uncertainty and to set up the forecasting processes
- To cut down expenses of the business
Execution of these tasks would help ALIDI- To build more stable relations with suppliers
- To increase clients’ satisfaction: to reduce the percentage of unsaleable goods that was about 20% of the total turnover, to minimize out-of-stock cases
- To build inner processes effectively. For example, not to buy odd goods “just in case”
What was done
How Novo BI solved the customer’s problem
A supply chain contains many participants and factors. It depends on them where, when and how many goods will be at the customer’s at particular time. It’s quite difficult to forecast such a system with a large amount of data without artificial intelligence and machine learning.
That’s why ALIDI inquired for implementation of Novo Forecast Enterprise. The software optimizes the process of planning and forecasts the demand with high accuracy. The system gets data from all the participants of the supply chain, analyzes it taking external factors into account and forecasts the needed quantity of the goods.
The algorithms can be accurate up to 99% for separate categories as well as it can forecast which product should be promoted and when.
- ALIDI contacted Novo BI, we discussed the tasks and started the project. We decided to implement Novo Forecast Enterprise – a system of forecasting automation, joint planning and optimization of supply chains
- The whole integration took about 6 months, but it was compensated in 12 months
- The forecast accuracy increased during the first month
- In 6 months the forecast accuracy grew, but the company needed an instrument that would have combined the work of employees from different departments and specialists from the side of end customers and suppliers. Thus, a new stage in work started, it was customization of Novo Forecast Enterprise for customer’s needs.
- We created a forecasting system of the company with full automation of business processes, forecast calculation and multi-user environment for joint planning of factors influencing the forecast accuracy
How Novo BI solved the customer’s problem
A supply chain contains many participants and factors. It depends on them where, when and how many goods will be at the customer’s at particular time. It’s quite difficult to forecast such a system with a large amount of data without artificial intelligence and machine learning.
That’s why ALIDI inquired for implementation of Novo Forecast Enterprise. The software optimizes the process of planning and forecasts the demand with high accuracy. The system gets data from all the participants of the supply chain, analyzes it taking external factors into account and forecasts the needed quantity of the goods.
The algorithms can be accurate up to 99% for separate categories as well as it can forecast which product should be promoted and when.
The results
The forecasting process has been adjusted:
The forecasting accuracy has increased:
The company has reduced its expenses:
All these solutions help to save up about 16 000 000 $ a year.
The forecasting process has been adjusted:
- Now more than 200 employees from different countries are included into the process, they fill in data and make plans, that increases the accuracy of the final forecast
- The calculation speed has increased tenfold. There’s no need to wait for a reply by mail anymore — everything is automated
- The goods are not bought “just in case”.
The forecasting accuracy has increased:
- + 40%, from 45% to 85%
The company has reduced its expenses:
- the amount of unsaleable goods has reduced by 50 %
- out-of-stock cases have reduced by 10%
All these solutions help to save up about 16 000 000 $ a year.
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