Customer Stories / Retail / Egypt

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MaxAB Improves Demand Forecasting by 60% for Optimized Pricing Using AWS

Fast-growing startup MaxAB, offers a B2B ecommerce platform for small grocery stores in Egypt and Morocco. Working with MaxAB, grocery retailers—who are faced with wafer-thin margins—are able to purchase stock direct from the company only when needed, cutting out multiple supply-chain intermediaries.

9 hours to 30 minutes

demand forecasting model training time reduction


faster model iteration speeds


improvement in demand forecasting model accuracy rates

Cost minimized


Because the company had already built its entire cloud infrastructure on Amazon Web Services (AWS), it decided to use AWS to develop and improve its demand forecasting model performance. Using AWS MaxAB has seen a reduction of more than 94 percent in the time it takes to train its demand forecasting models. The company’s data team was supported in model development through a regional initiative, the AWS Prototyping Program. Built on AWS, MaxAB has seen an increase in model iteration times by a factor of 8. It has also improved the accuracy rate of models by 60 percent. With its first demand forecasting model for daily sales spike detection in operation, its marketplace team can now regulate product pricing by understanding how each spike occurred. Using this information, the company can quickly see product demand changes, alter pricing structures, and protect product supply.

Opportunity | Using AWS to Optimize Demand Forecasting Development

MaxAB, founded in 2018 and based in Egypt, offers an app-based and web channel ordering system for grocery store owners. Small grocery stores make up the majority of this type of retailer across Egypt and Morocco. Each store is usually dependent on deliveries from individual suppliers, making stock replenishment slow. Using MaxAB, orders can be placed on a daily basis, with deliveries received in just 24 hours.

The company provides grocery stores with stock from its network of 30 warehouses across both countries, with 3,000 product lines in dry goods, chilled goods, and beverages. End-product costs are further reduced by aggregating orders and buying warehouse stock in bulk. Fast warehouse product turnover also means fresher products and improved customer cash flow.

MaxAB had been developing its own demand forecasting models in several areas. These were primarily to improve predictions for customer sales demand, manage product pricing levels, offer special pricing for retailers, and optimize warehouse stock levels and product holding costs. The company needed to streamline model development and iterate at faster speeds. “Our data team was using AWS for our demand forecasting models, but we needed them to be more accurate,” says Amr Faisal, chief technology officer (CTO) at MaxAB. “Working closely with AWS helped us to refine and speed development, and realize the full potential of what we could achieve.”


Using AWS, we can continually enhance demand forecasting accuracy and get better results—it’s not just a one-time thing.”

Amr Faisal
Chief Technology Officer, MaxAB

Solution | MaxAB Cuts Demand Forecasting Model Training Time by 94% Using Amazon SageMaker Studio

When the MaxAB data team began working on its demand forecasting models using AWS, it developed them manually in silos. This meant it was difficult to collaborate on model development, resulting in slow model iteration speeds and a lack of model interdependency.

MaxAB benefited from a regional initiative, the AWS Prototyping Program, to help it build a new model development structure using Amazon SageMaker Studio, the fully integrated development environment for machine learning (ML). Gaining access to targeted AWS support helped the company streamline its processes. “Working closely with AWS on this program was a big enabler for us,” says Faisal. “It allowed our data team to understand which of the AWS services we already used would best support the building and optimization of all demand forecasting models in development.”

Using Amazon SageMaker Studio, the company has reduced demand forecasting model completion times by over 94 percent, taking it down from 9 hours to just 30 minutes. To pull in data from multiple sources across the company and support model training, the company uses Amazon Athena, which allows it to query data instantly. The company has cut manual data ingestion processes by linking Athena to Amazon Simple Storage Service (Amazon S3), allowing it to retrieve any amount of data from anywhere.

MaxAB has increased model accuracy from 50 to 80 percent—a 60 percent improvement—by building on AWS. The company has also been able to increase model iteration speeds by a factor of 8. This means that its team can spend more time on model development by applying different model features and algorithms to further improve accuracy rates. “By freeing up time, it’s much easier to experiment,” says Faisal. “We can continually enhance demand forecasting accuracy on and get better results—it’s not just a one-time thing.”

Using AWS, the company has developed its first operational model to track product sales spikes on the same day. This allows its marketplace team to understand why spikes have occurred, revisit pricing structures, and limit sales of specific products to protect supply. With an almost immediate understanding of changes in product demand, the company can create special sales’ pricing offers for its retailer customers.

Daily demand predictions also help in the use of on-demand workers. “Along with full-time staff, we also employ temporary workers for our warehouses to fulfil customer orders,” says Faisal. “Using AWS means we can more accurately predict how many people we need on a given day and lower our employment costs.”

Outcome | Continual Enhancement of Demand-Forecasting Models Improves Predictions

MaxAB has also developed two additional demand forecasting models that can provide demand predictions for either the next-day or for 7 days ahead, which it plans to begin using soon. This will allow it to connect warehouses and make demand predictions on a regional level and connect each model to further improve demand-and-supply forecasts.

The company’s data team is now primed to develop its demand forecasting capability further and support future growth using AWS. “The entire experience of working closely with AWS to build our new demand forecasting development structure really energized the entire data team,” says Faisal. “We felt our vision and goals were fully supported throughout the whole process, and now we’re ready to grow on a much smarter and better-informed basis.”

About MaxAB

MaxAB’s B2B ecommerce platform offers just-in-time product deliveries for small grocery stores in Egypt and Morocco from its network of 30 warehouses. Using the MaxAB app, grocery store retailers can replenish stock within just 24 hours.

AWS Services Used

Amazon S3

Amazon Simple Storage Service (Amazon S3) is an object storage service offering industry-leading scalability, data availability, security, and performance.

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Amazon SageMaker Studio

Amazon SageMaker Studio provides a single, web-based visual interface where you can perform all ML development steps, improving data science team productivity by up to 10x..

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Amazon Athena

Amazon Athena is a serverless, interactive analytics service built on open-source frameworks, supporting open-table and file formats.

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