Watch the Low-Code/No-Code Summit on-demand sessions to learn how to successfully innovate and achieve efficiencies by upskilling and scaling citizen developers. Watch now.
More than ever, consumer goods, manufacturing and retail brands must rely heavily on their technology to unlock value – with only the artificial intelligence (AI) retail market poised to strike $31 billion by 2028. However, due to the massive fragmentation that exists in the AI ecosystem, companies in the retail and CPG categories are unable to generate the business impact they are looking for with their technology stacks.
To tackle these silos head on, in Mumbai and San Francisco Fractal AI developed an end-to-end AI platform called Asper A.I, which enables interconnected and automated decisions between supply and demand. By changing the way decisions are made, Asper aims to enable growth and transform organizations into adaptive intelligent enterprises.
Through its autonomous decision-making platform, Asper unifies demand planning, sales and distribution, inventory planning, and pricing and promotion. It works with data to not only make proactive decisions, but also make decisions that help customers achieve their potential – from their bottom-line results to optimizing their workflows.
“Business success today is determined by how quickly and seamlessly brands are able to make decisions,” said Mohit Agarwal, CEO of Asper. “Unfortunately, brands – especially in CPG – find that their efforts are constantly undermined by disconnected technology that hinders their success are not reinforced. Without interconnectedness, the promises of future AI technologies, since they debuted decades ago, would still be a long way off, instead of here, now. Asper aims to solve these challenges by driving interconnectedness through its autonomous decision-making platform.”
Asper’s parent company, Fractal, focuses on CPG, retail and manufacturing industries and has identified more than 10% potential growth opportunities in financial performance and more than 50% in decision-making automation.
The gaping hole that hinders efficiency and growth
The idea behind Asper dates back nearly five years, when Agarwal and his team began noticing a disturbing trend in the consumer goods, retail and manufacturing industries. They found that key business decisions were made in functional silos and lacked tactical consistency at the most granular level, resulting in missed revenue opportunities.
While Robotic Process Automation (RPA) is a thriving technology in the CPG world, he believes it has not led to productivity improvements beyond simple tasks. And it is still only up to “people” to make accurate decisions, taking into account a lot of available information and signals in real time. RPA has failed to deliver productivity improvements beyond simple tasks.
And even if companies were to turn to AI for this, building AI capabilities from the ground up takes significant time and investment, including building teams and processes across data science, engineering, and design.
“Our experience at Fractal in solving these problems for Fortune 500 customers gives us the building blocks to solve these problems,” said Agarwal. “With Asper AI, we are bringing this experience together and investing on behalf of our customers to create the next generation AI software platform for them to drive autonomous decisions in the enterprise.”
Essentially, what customers will see is an AI system that breaks down decision silos and evolves businesses to build automated ecosystems, redefining the roles of humans and AI to work with scalability and precision.
Asper’s dual platform
Asper’s current offering includes two modules: Dynamic Demand AI, which is used for demand planning and forecasting, and Revenue Management, a pricing and promotion platform.
With its demand planning and forecasting software, Asper aims to deliver a significant improvement in forecasting accuracy at the most granular levels for action. Not only can it drive autonomous forecast adjustments and rounding, but it also promotes collaborative consensus planning on risks and opportunities. The platform self-integrates on top of existing data and systems to deliver incremental financial growth through revenue, inventory optimization, and automation.
The platform is designed to support demand planners in their role through the following four user stories:
- Anticipate: Early warning of risks and opportunities with granular visibility on multiple levels and multiple horizons in real time.
- Quantify and Attribute: Quantify and prioritize risks and opportunities with a better understanding of demand drivers.
- Recommend and Collaborate: AI-led, self-learning prescriptive actions, recommendations for consensus planning adjustments.
- Automation and integration: Cognitive workflow setup with extensive automation and seamless integration with planning and execution systems.
On the other hand, the revenue management side is where most AI comes into play, especially for strategic and tactical decisions. It helps identify real-time opportunities and reduce the time for strategic price intervention to weeks instead of months. It features AI-based calendar optimization and per-account recommendations to enable KAMs to run promotions that meet internal and retailer KPIs. The platform can track and monitor revenue growth management (RGM violations, risks and opportunities).
The company claims that the platform can deliver 2-3% financial growth and 15-20% improvements in promotional ROIs. It is also said to cut customer negotiation and reconciliation time in half with a holistic view of internal, customer and consumer KPIs.
What does this mean in terms of real-world performance? According to Agarwal, the platform can help address four key issues:
- Revenue leakage at the intersection of supply and demand: The company brings together the right data strategy, AI and autonomous decision-making to exploit opportunities at the most granular level in real time at the intersection of supply and demand that are lost due to functional silos, slow response and human dependency.
- Complete dependence on people alone for decision-making is slow and inefficient: Asper builds the AI to make machine-first recommendations, but also designs the right tools and framework for human participation and intervention, leading to process transformation.
- The current analytics models only focus on limited drivers / KPIs: The AI models are built specifically to capture trends and signals from hundreds of internal and external signals / KPIs and identify the right drivers and data that are relevant and nuanced for each category.
- Difficult to take AI from experiment to scale deployment: Asper builds the AI software to generate value at scale at a fraction of the cost.
For example, Asper deployed its demand planning AI platform at a $5 billion food processing company in the US. The implementation is aimed at driving accuracy and autonomous forecasting at scale. They cover over 11,000 SKUs in a detailed distribution center.
“In the first year of the partnership, we have achieved an 8%+ improvement in forecast accuracy and are aiming for an additional 5% accuracy by the end of this year. We also enabled contactless forecast automation without human intervention for more than 40% of the portfolio, growing to 60% by the end of this year,” Agarwal told VentureBeat.
In addition to day-to-day efficiency and revenue optimization, Asper also provides additional flexibility for businesses to avoid getting stuck in a linear AI maturity curve. With Asper, companies are free to customize their AI journeys and success by giving them the ability to seamlessly jump in and out of their AI infrastructure to amplify the key components they need, without the wait times associated with linear development.
Through 2022, Asper has tested its platform with 5-10 customers and claims accuracy improvements of more than 10-15 points and up to 60% autonomous forecasting. The company has built a multidisciplinary team to innovate and advance AI software, bringing together leadership and talent in design, engineering, AI and business consulting. By the end of this year, the company is aiming for seven company-wide deployments and a 2x+ growth in revenue and ARR.
“Asper’s vision is to be the most preferred AI growth platform for CPGR and manufacturing. The team is aiming for a $250 million+ impact for every customer using their platform. With AI at its core and significant investment from Fractal to create a best-in-class AI platform, Asper aims to expand its wings by raising outside capital in the future,” said Agarwal.
VentureBeat’s mission is to become a digital city plaza where tech decision makers can learn about transformative business technology and execute transactions. Discover our Briefings.