ANJANA INVESTMENTS

Procurement planning determines when to order and how much to order, taking into account factors such as forecast demand, lead time and the service level to be achieved.

Incorrect procurement planning can cause the company to incur in inefficient situations that undermine its profitability. If the stock is much higher than necessary, it can generate liquidity tensions due to excess capital tied up. If stock is lower than necessary, stock-outs will occur, resulting in an increase in lost sales, with an associated reduction in revenue and customer satisfaction.

The uncertainty of future demand together with the uncertainty of supply lead times means that a probabilistic analysis is required in order to adequately size the stock. Kajal’s probabilistic engine takes uncertainty into account, modeling with advanced algorithms the demand forecast and the necessary amount of inventory.

Problem

The company

Anjana Investments is the owner of the commercial brands: Aurgi, Motortown and Autingo, which develop their business in the automotive sector.

Since the start of operations in 2009, the company’s growth has been exponential. Anjana currently has a total of 147 points of sale of products, accessories and services related to the automotive world and its maintenance, distributed throughout Spain. Of this total, 62 are Aurgi centers, 55 are Motortown centers (automobile centers located within El Corte Inglés), 33 are on the shelves of El Corte Inglés Hipercor centers and 2 are on the shelves of two Supercor centers. Not to mention the online platform Autingo (acquisition made by the company in March 2020).

Hand in hand with this great business growth, comes the need to continue advancing in leadership over its competitors, so it seeks to develop different technological solutions to sustain this growth and make the company more competitive.Camilo González Quirós, Aurgi’s Purchasing Director.

Project challenges

The project was highly complex due to the following aspects:

• Daily management of more than 100,000 plant-product combinations.
• Consideration of external variables in forecasting models (promotions, calendars, commercial budgets, seasonality, etc.).
• Management of different supply flows in the distribution network: direct flow from the supplier, internal transfer from the warehouse, direct procurement from the supplier.
• Application of multiple ordering policies.
• Ordering plan adjusted to the service schedule of the centers.
• Hardship management in a distribution center.

Results

At the end of the project, our client obtained the following results and benefits:

• Daily updated order plan for more than 100,000 SKUs.
• Advanced calculation engine, which performs individualized processing per SKU: automatic selection of forecast and inventory models and allocation of procurement policy.
• Hardship management in the distribution center.
• Interface for visualization of demand forecasting and procurement results.
• Monitoring the evolution of stock in the distribution network and the service offered to customers.
• Cloud management, which facilitates information accessibility and system reliability.