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The Current Situation

Retailers make hundreds of thousands of inventory decisions every day –for example, where do I fulfill the online order from; where do I place the inventory that I bought; what is trending and how does that impact my merchandise strategy; what and how much should I buy. There is an opportunity to make smarter decisions by applying science and listening to the insights from your data.

Science has been applied in the past with limited success. These inventory decisions were a combination of old forecasting methods and rules-based decision-making. Old methods of forecasting have been unable to address retail data challenges and combining this with rules-based decision-making has led to sub-optimal business results.

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Unrivaled machine learning technology

Developed to specifically address data modeling and prediction challenges across retail, the Celect Prediction & Optimization Platform is composed of proprietary innovations coupled with best-in-class open source components such as Docker, Spark, Kubernetes, and Airflow. 

With this unique framework, it is able to rapidly ingest large amounts of data, model the data, and expose the resulting predictive data through a set of flexible API’s for bulk and ad-hoc querying. 

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Ingest & Transform

The Celect Prediction & Optimization Platform consumes large amounts of data on-demand or on a schedule from various disparate sources to build a data store necessary to answer complex questions. 

Model 

The Celect Engine is the workhorse of the platform - built with computational speed and scale top of mind. As soon as transformed data enters the Engine, it begins building an exponential amount of predictive models from a library of algorithms. At any given time, the Celect Engine can process hundreds of millions of data points for analysis. 

Query & Visualize

The front-end UI and available APIs enable real-time querying, visualization, and reporting. As part of the Celect Prediction & Optimization Platform, retailers can leverage purpose-built solutions for critical insights and decision support for their business.  

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Celect takes a two-pronged approach to address inventory decisions: By combining a patented context-aware AI-based prediction with opportunity aware optimization, Celect technology can consider both leading indicators from structured and unstructured data to address retail's most pressing inventory challenges.

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Our science is all made available via Celect’s Inventory Optimization Platform which is a repeatable, cloud-based offering that delivers the prediction and optimization building blocks needed to support Celect’s Inventory Optimization Suite (Plan & Buy Optimization, Allocation Optimization and Fulfillment Optimization) and the ability to incorporate these capabilities into other As-a Service offerings.

Something Powerful

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Tell The Reader More

The headline and subheader tells us what you're offering, and the form header closes the deal. Over here you can explain why your offer is so great it's worth filling out a form for.

Remember:

  • Bullets are great
  • For spelling out benefits and
  • Turning visitors into leads.

Something Powerful

Tell The Reader More

The headline and subheader tells us what you're offering, and the form header closes the deal. Over here you can explain why your offer is so great it's worth filling out a form for.

Remember:

  • Bullets are great
  • For spelling out benefits and
  • Turning visitors into leads.

Something Powerful

Tell The Reader More

The headline and subheader tells us what you're offering, and the form header closes the deal. Over here you can explain why your offer is so great it's worth filling out a form for.

Remember:

  • Bullets are great
  • For spelling out benefits and
  • Turning visitors into leads.

Something Powerful

Tell The Reader More

The headline and subheader tells us what you're offering, and the form header closes the deal. Over here you can explain why your offer is so great it's worth filling out a form for.

Remember:

  • Bullets are great
  • For spelling out benefits and
  • Turning visitors into leads.

Celect’s AI-Based Optimization

Celect’s approach to optimization is unique because it considers four key areas: Opportunity Cost, Multiple Objectives at Scale, Assortment Context, and Robust Optimization.

OPPORTUNITY COST

Consider the tradeoffs for each relevant combination in sub-seconds. For example: customer expectation, cost, constraint, and demand combination.

ROBUST OPTIMIZATION

We make the optimization more accurate by considering the risk or uncertainty associated with the predicted demand.

ASSORTMENT CONTEXT

Consider the assortment implications - instead of optimizing for one product at a time. For example: this allows us to, again, investigate the future and tradeoff choosing to carry a product in a store versus sending more of that product to another store and avoiding an out-of-stock.

MULTIPLE OBJECTIVES AT SCALE

Make the best possible decision across multiple competing objectives at the scale of retail. For example: this allows us to investigate the future and tradeoff taking a future markdown versus fulfilling an online order from a store that might have a slightly higher transportation cost.

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