The CPG industry and its unique operational challenges have been very close to my heart and professional experience. I’ve been working with consumer goods companies for close to 20 years across multiple areas and roles.
I started my career in advanced supply chain planning systems with i2 Technologies (developing and deploying advanced demand planning, replenishment planning, inventory optimization, and merchandise planning & allocation solutions) in the early 2000s and from there shifted to the demand chain side with Oracle Demantra (a CPG focussed Trade Promotions Optimization solution). At Tibco Spitfire, I gained invaluable experience in the advanced visualization space for CPG sales and marketing and with Mu Sigma, I engaged in strategic CPG analytics projects applying the latest advances in Big Data and Analytics across many problems areas and functions including sales, marketing, manufacturing and supply chain. Most recently, at Noodle.ai, I led the definition and development of cost and efficient focused supply chain and manufacturing Enterprise AI applications.
These experiences have given me a unique vantage point to apply different solution paradigms at the intersection of demand and supply operations – planning systems, visualization platforms, big data analytics and now AI and Machine Learning – and have helped me understand the underlying assumptions, functional and technical architectures of different solution methodologies, and their strengths and weaknesses.
Although many solution paradigms exist, I find CPG companies are still facing tremendous inefficiency at the intersection of demand and supply that results in a loss of 8-10% revenue growth potential. And this is in the face of saturating growth, the need for greater agility and the imperative to drive revenue.
This is what drove me to co-found Samya.ai along with Shelly and Pavan. We’re obsessed with solving these challenges for CPG companies using our deep AI/ML based SaaS approach to recapture revenue growth opportunities.
While many of us think about AI in terms of robotics, or computer vision, chat-bots, Alexa/Google echo type devices etc., AI also has a very unique role to play in enterprises with day-to-day decision-making scenarios for planning and execution. Complexity and volatility put a tremendous amount of stress and anxiety on CPG companies and their front-line operations professionals such as demand planners, inventory planners, sales planners, and trade & revenue growth management teams.
These teams are always trying to catch up and react to new situations and exceptions – changes in pricing, promotions, weather, competitive actions, pandemics, shifts in consumer preferences, production downtimes, supplier delays, material quality problems, and more. In this environment, it becomes increasingly difficult for staff to consider all the complex factors in a particular situation, especially when there’s uncertainty in decision levers and possible factors. Most teams are left moving from one exception to another with little bandwidth left to focus on high impact tasks. Long tail activities and exceptions consume most of their time and resources.
This complexity and volatility cause significant operational blind spots and an imbalance in day-to-day operations and decision-making, resulting in value leakage, tremendous anxiety and operating in ‘fire-fighting’ mode. AI/ML has a huge role to play in helping organizations and the front-line team better tackle this environment.
AI/ML can make CPG companies and their employees better at anticipating these exceptions and blind spots well in advance, while also understanding the associated risks, opportunities, insights, predictions and recommendations that give a complete picture of potential revenue growth. This will help them become truly agile and proactively respond to situations even before they become exceptions.
This is the true promise of AI/ML in the enterprise, to empower human intelligence to anticipate and respond better.
One key principle we realized early on is that the challenge of unlocking revenue growth lies at the intersection of demand and supply operations. The core mission critical teams that operate at this intersection are:
In most cases, these teams operate on different systems, have different processes and optimize for different metrics. When the teams do come together at meetings, the focus tends to be reactive and subject to a great deal of bias due to multiple inputs and different levels of information availability. While a significant amount of data is available, it resides in different systems or in excel spreadsheets.
So, the key aspect of our AI/ML technology platform, approach and applications is to concretely interconnect the data and information from each of these areas to drive our AI/ML based insights, predictions and recommendations. As an example, we leverage information and data from each of these areas (along with external data sources) to drive better demand anticipation:
Based on our experience of existing solution paradigms and after talking to over 100 CPG stakeholders, we realized that there are several key gaps in traditional ERP, planning and forecasting systems:
To solve these challenges, we created a new intelligence paradigm that can help CPG organizations:
To support our intelligence paradigm, we bring together state-of-the-art AI/ML approaches of Deep Learning (to address complex latent and non-linear interactions), Probabilistic Machine Learning (to capture variability of the inputs and outputs) and
Reinforcement Learning (to drive the right prescriptive recommendations in the presence of complexity and variability).
Samya is the only Revenue Growth AI company operating at the intersection of demand and supply operations. In building our Revenue Growth.ai platform we kept three key principles in mind – Interconnected AI Applications, System of Intelligence over Existing Systems and a Scalable & Agile Technology Foundation.
Interconnected AI Applications
Our vision is to help CPG companies unlock revenue growth and we are doing this with four concrete interconnected AI applications. Each application is focussed on moving key operational and outcome levers in the CPG organization.
This month, we’ve launched our Dynamic Demand.ai application to help CPG companies improve the granularity of anticipated demand, increase the frequency of demand sensing and forecasting and address primary and secondary sales estimates in the short and long term.
In the coming months, we will release Dynamic Inventory.ai., Dynamic Pricing & Promotions.ai and Dynamic Sales & Distribution.ai. Dynamic Inventory.ai addresses dynamic inventory rebalancing and allocation to drive better fill rates, reduce out of stocks and reduce lost sales. Dynamic Pricing & Promotions.ai adjusts and optimizes promotion tactics and plans to maximize ROI on promotion spend and improve margins. And Dynamic Sales & Distribution.ai will adjusts sales and distribution activities to drive volume growth through the channels and outlets aligned with the latest demand patterns.
The key is that each of these applications is interconnected in terms of data, insights and predictions. As an example, the output of Dynamic Demand.ai – the forecasts, contributions of different levers and elasticities – feeds each of the other three applications to help them optimize for their purpose, taking into account a holistic view.
System of Intelligence over Existing Systems
Instead of disrupting existing systems and existing user workflows our Revenue Growth.ai platform and applications complement existing systems and workflows by sitting on top or beside them. We connect and integrate with existing systems to leverage relevant data and send back predictions, insights and recommendations as necessary.
Scalable, Secure & Agile Technology Foundation
We’ve built our Revenue Growth.ai platform on a very strong technology foundation to adapt to different levels of scale, speed, time-to-market and data availability scenarios. The technology foundation includes:
Dynamic Demand.ai was built with a deep understanding of the pain points and challenges of demand planners, their existing systems and processes. Demand planners and demand planning teams have a unique place in CPG organizations at the intersection of demand and supply operations. As stewards of the forecasts, demand planners face daily struggles as they strive to determine and forecast product demand with accuracy.
Demand planners spend their time reviewing historical actuals, demand forecast performance and error metrics, cleaning histories, adjusting forecasts, managing splits, and making decisions on forecast rollovers. They collaborate with sales, marketing, and supply chain teams to fill in the details needed for their forecast. Some demand planners in larger companies must handle SKUs into the tens of thousands for multiple locations and distribution centers, forcing the planner into triage mode.
As a result, they may miss potential risks and opportunities. Especially as they prepare forecasts that address multiple levels as well as multiple horizons, both in the shorter three to five-month term and in a longer 12-24-month time frame. And as we already discussed, demand planners are working with legacy planning systems that are heavily rules-based and reliant on linear planning.
It’s in this context that we built Dynamic Demand.ai. Ultimately, we’re empowering demand planners with:
Welcome to agile demand planning. Our engine blends intelligent cognitive automation and collaborative workflows to spot human bias, create transparency, and uncover previously hidden probabilities that will impact future inventory, promotions, and sales.