AWS TECHNOLOGY

A more sustainable and profitable retail with DData

15 of June of 2024

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A leading company in the retail sector, or one aspiring to be, must stand out for its ability to efficiently supply its stores with specific products, ensuring customer satisfaction and avoiding inventory problems. However, in this process, it may face challenges related to sales forecasting such as managing a high number of references, the dynamic introduction of new products, store segmentation, and variability in the impact of external factors on different products and locations.

 

This makes the approach to sales forecasting a significant challenge for companies in the sector. But nothing that can’t be overcome with expert help and cutting-edge technology.

 

Next, we’ll analyze a specific use case of a company facing this issue and how we helped them overcome it successfully.

 

 

 

Challenge: Perfecting Stock Forecasting

 

 

Oftentimes, when facing the challenge of perfecting stock forecasting, notable obstacles are encountered. In this case, the problems that arose when seeking an effective forecast were of three types:

 

1. Incorrect Forecasts with Insufficient Details
The lack of detailed information in manually made estimates directly affected the company, preventing the necessary precision due to the multitude of influencing factors.

 

Lack of Rolling Forecast: The absence of long-term forecasts limited the ability to anticipate and plan effectively.

 

2. Inefficient Decision-Making Due to

  • Influence of Human Factors: Decision-making was significantly influenced by human factors, such as sales incentives, leading to inconsistencies and a lack of objectivity in decisions.

 

  • Lack of Consistency Across Markets: The lack of standardization and disparity in teams across different markets led to costly and inefficient global decisions.

 

  • Challenge of Incorporating Internal Information: Inability to effectively integrate valuable internal information into forecasts, limiting the ability to make informed decisions.

 

3. Non-optimized Cash Flow

They had suboptimal cash flow management, leading to unreliable forecasts that caused occasional and punctual problems.

 

 

How We Achieved It

 

Through a strategic approach and specialized solutions, we addressed each challenge comprehensively:

 

Optimizing Details in Forecasts:

We implemented advanced Machine Learning models and data analysis to enhance accuracy through greater granularity and detail in forecasts.

 

Data-Driven Decision-Making:

We established a standardized and objective methodology, incorporating valuable internal information to drive informed and consistent decisions across all markets.

 

Efficient Cash Flow Management:

We developed more reliable forecasts by integrating key variables, thus improving cash flow management and avoiding punctual problems.

 

 

Good Data, Incredible Results

 

When we talk about the retail sector, where every prediction matters, the quality of data becomes the solid and indispensable foundation for achieving effective stock forecasting, exceptional personalized shopping, and skyrocketing sales. All of this in a sustainable and profitable manner, especially during critical moments like the holiday season.

 

At Daus Data, we not only assist with these challenges but transform them into great opportunities. With advanced AWS tools and our strategic approach, we build the future of retail every day, where anticipation, sustainability, and efficiency define the path to success.