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Retail & Enterprise
End-to-end AI-based solutions for Supply Chain forecasting, optimization and management.
Service Platform
The integrated Supply Chain solution that supports every decision, from strategic planning to distribution.
Service
Platform
Platform
The integrated Supply Chain solution that supports every decision, from strategic planning to distribution.
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Retail
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FMCG
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Manufacturing
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Health & Pharma
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Food & Beverage
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Transportation & Logistic
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MMR/GDO
AI & Supply Chain
Predicting the future is never easy, but it’s a challenge we need to embrace, to face it in the best way and keep looking ahead.
That’s why harnessing the power of AI is a competitive advantage, capable of shaping the operations and overall profitability of your business, helping you take a step into the future.
Forecast Accuracy
95 %
Margin Growth
15 %
Lost Sales Reduction
87 %
Inventory Size
50 %
Production Time Reduction
15 %
Artificial Intelligence is driving the revolution of integrated Supply Chains with increasingly innovative solutions for optimizing every End-to-End process.
Our goal is to support this transformation, improving managers’ forecasting ability and reducing business risk, helping you act quickly, proactively and reactively across the processes you manage.
Thanks to the huge amount of data AI algorithms can handle, they provide concrete support in sharpening your vision. Whether it’s market trends, shipping timelines or future sales forecasts, we can help you capture every relationship – even the weakest and most unpredictable ones – to make better decisions.
But we know that in your day-to-day work, things don’t always go as planned. That’s why at Deda Ai we pay close attention to the power of forecasts, also measuring the uncertainty tied to the forecast itself – a key aspect ofSupply Chain Risk Management that lets you understand the impact of different options on expected performance, supporting timely, informed decisions by simulating the scenarios you envision.
Whether it’s long-term strategy or more tactical activity planning, being able to rely on accurate data can build trust and value across the entire chain, from production to distribution, wherever you want to maximize efficiency and minimize unsold-inventory costs.
We can help you accurately estimate sales at a territorial level, right down to setting effective allocation for individual stores or the most peripheral distribution nodes, guaranteeing your end customer the certainty of finding the product they’re looking for in every store.
Digital Twin
We have built a software platform capable of replicating and virtualizing all company operations as a Digital Twin.
Thanks to’Artificial Intelligence, you can simulate the impact of any operation across the entire Supply Chain through millions of modeled decisions and constraints – optimizing processes, reducing risks and synchronizing supply and demand for goods and services in a fully integrated, end-to-end way.
At its core is a conscious Data Culture because good data delivers better results, sparking continuous, steady improvement that can make all the difference. .
Modular and integrated solutions, at every stage.
Store Benchmark
Analyze sales performance by classifying every variable according to your needs, from color variants to local promotions and store positioning. Compare hundreds of characteristics to get better forecasts.
Robo Buyer
Financial portfolio analysis lets you optimize purchases and budgets. Analyze sales to optimize warehouse stock, ordering only what you need and maximizing expected margin based on sales probability.
Use Cases
Image Similarity
A sales forecasting tool based on neural networks, built for new product launches. AI identifies the similarity between the new product and others already on the market, breaking down and weighting quantitative and qualitative attributes such as technical specs (materials and features), images (shapes and colors) and layout (positioning and past sales data) to build the most reliable forecast scenario possible.
Sales Peaks
To improve forecast accuracy and seasonality, demand analysis also factors in sporadic and recurring events, such as holidays or specific promotional activities, that can influence demand.
Performance Monitor
AI techniques to monitor and identify system performance issues, flagging any anomalies and contextual situations through aproactive alerting system.
Store Value
We help complex international retail networks turn each store into an active part of the distribution process.
Our solution suggests how to move products between stores without impacting warehouses, while collecting hundreds of variables — from staff ratings to merchandise display — to improve simulations and strengthen algorithm performance.
Innovation ideas
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Next Best Action -
Know Your Customer -
Proposal Explainability -
Advanced Monitoring -
Digital Marketing
What is the next best action?
Sharing “best experiences” across the organization is crucial. This process can be supported by a recommendation system that helps identify the most effective next actions to take, applying the same criteria and approaches behind the identified best practices.
Thanks to AI, it is possible to efficiently leverage information contained in unstructured data sources (e.g., paper documents) to maximize customer lifetime value.
By enriching and aggregating available information, you can improve performance and reduce processing times.
How can you overcome data collection limitations?
Being a Predictive Company means using predictive models for every decision that can benefit from them.
→ For example, predicting how a customer will react to a market event can help anticipate that customer’s needs in specific circumstances. It can also help identify which customers or customer segments are likely to behave similarly, enabling you to recommend a more suitable action or product.
Can recommendation tools be understood and explained?
One of the most important aspects of models that generate recommendations is understanding their logic and, where possible, the reasons behind their suggestions.
Implementing AI algorithms that complement predictions by helping explain and interpret them can improve your work and enhance the customer experience.
Can decision efficiency be measured?
The success of a service also depends on the efficiency with which it is delivered. Data Envelopment Analysis (DEA) algorithms can help your business measure commercial and operational performance, identify benchmarks and best-in-class practices, and define commercial targets and budgets that are objectively achievable, measurable, and aligned with your potential.
→ Transparent and holistic multi-level assessment
→ Multi-input and multi-output analysis
→ Clear and transparent evaluation of clusters
→ Consideration of uncontrollable resources
→ Definition of cost/performance improvement targets
How can you maximize contacts and reach new customers?
Traditional marketing tools often struggle to transform structured and unstructured data into effective actions. Text analysis and interaction analysis, combined with the extensive use of Unsupervised Machine Learning techniques, are the foundation of a strategy capable of converting many more contacts into leads and new customers.
Activate multiple (dozens of) cluster analyses on texts and metrics.
A contact, lead, or customer can be included in different groups depending on the type of analysis and the messages to be addressed (e.g., an executive passionate about cooking may belong to a business group as well as a wine enthusiast group).
Next Best Action
What is the next best action?
Sharing “best experiences” across the organization is crucial. This process can be supported by a recommendation system that helps identify the most effective next actions to take, applying the same criteria and approaches behind the identified best practices.
Thanks to AI, it is possible to efficiently leverage information contained in unstructured data sources (e.g., paper documents) to maximize customer lifetime value.
By enriching and aggregating available information, you can improve performance and reduce processing times.
Know Your Customer
How can you overcome data collection limitations?
Being a Predictive Company means using predictive models for every decision that can benefit from them.
→ For example, predicting how a customer will react to a market event can help anticipate that customer’s needs in specific circumstances. It can also help identify which customers or customer segments are likely to behave similarly, enabling you to recommend a more suitable action or product.
Proposal Explainability
Can recommendation tools be understood and explained?
One of the most important aspects of models that generate recommendations is understanding their logic and, where possible, the reasons behind their suggestions.
Implementing AI algorithms that complement predictions by helping explain and interpret them can improve your work and enhance the customer experience.
Advanced Monitoring
Can decision efficiency be measured?
The success of a service also depends on the efficiency with which it is delivered. Data Envelopment Analysis (DEA) algorithms can help your business measure commercial and operational performance, identify benchmarks and best-in-class practices, and define commercial targets and budgets that are objectively achievable, measurable, and aligned with your potential.
→ Transparent and holistic multi-level assessment
→ Multi-input and multi-output analysis
→ Clear and transparent evaluation of clusters
→ Consideration of uncontrollable resources
→ Definition of cost/performance improvement targets
Digital Marketing
How can you maximize contacts and reach new customers?
Traditional marketing tools often struggle to transform structured and unstructured data into effective actions. Text analysis and interaction analysis, combined with the extensive use of Unsupervised Machine Learning techniques, are the foundation of a strategy capable of converting many more contacts into leads and new customers.
Activate multiple (dozens of) cluster analyses on texts and metrics.
A contact, lead, or customer can be included in different groups depending on the type of analysis and the messages to be addressed (e.g., an executive passionate about cooking may belong to a business group as well as a wine enthusiast group).
