NextOrbit: Accelerating Growth and Enhancing Operational Effectiveness of Brands and Retailers

A man throughout his consulting career advised Retailer and CP brands. He lived in Plano TX in US. And in his journey, retailers consistently expressed a problem with out of stock/overstock. Most felt that it is a given in the business, they have accepted it as a cost of doing business, and cannot be solved. Most aspects of the supply chain had been optimized, except the last mile; the journey from the DC to the store. And this last leg was the root cause of out of stock.

At NextOrbit he discovered that application of AI(Artificial Intelligence) and Machine Learning to demand planning and store ordering – how much to order, and how much to send to each store – can have a big impact on reducing out of stock, improving inventory turns, improving basket sizes and eventually, improving customer experience. He performed an extensive research and keeping the entire state in mind, he built his own platform. Today, NextOrbit is one of the fastest growing cloud platform that uses AI (Artificial Intelligence) and Machine Learning to accelerate growth and enhance operational effectiveness of fashion brands and retailers in India.

The Creator of NextOrbit

With over 20 years of experience in consulting, product development, business development, Business Intelligence and Analytics, Mr. Kishore Rajagopal, is the Founder and an inspirational leader of NextOrbit. He holds a Bachelors degree in Mechanical Engineering and has completed his Masters in Management both from Indian Institute of Technology, Madras, which is a premier institute in India.

Kishore, during his 9+ years stint at Infosys in Plano TX, evangelized and developed technology consulting & technical architecture that repositioned Infosys as a consulting player. During his stint as a Global Head of Business Intelligence & Analytics at HCL, he helped rebrand the practice as a player focused on using Business Intelligence as a business enabler.

Kishore has also Co-founded Crowdanalytix – a platform to develop predictive models using a global crowdsourcing approach; enabled multi-million Series ‘A’ funding from reputed VCs. He says, “My primary strength is to bridge business and technology/analytics and use it for a useful purpose. My other natural strength is to inspire people and bring out the best in them.”

The Inspiration

“The opportunity to use AI and Machine Learning to solve a fundamental problem and to create a big competitive differentiation is not a delta innovation. We also knew that very few players are doing this kind of work. This inspired us to create this company.”

The Unique Player

Several key supply chain decisions are still taken by human judgment or rule-based software. Such decisions may be sub-optimal and result in- out of stock, overstock, higher product expiration, higher markdowns. NextOrbit uses AI and Machine Learning to improve sales for Retailers and brands. The company uses AI and Machine Learning to tell Retailers what to send to each store every day. NextOrbit Predictive Alerts informs store staff what COULD happen in the stores, and enables them to take preventive action before they lose customer sales opportunity.

NextOrbit demand plans enable retailers and brands to procure the optimal amount of a product.

In other words, we can say that NextOrbit helps Retailers and brand achieve the delicate balance between out of stock and overstock at a store and product level.


The NextOrbit platform for fashion and apparel retailers helps optimal demand plans and store allocations. The NextOrbit platform for grocery retailers helps in store allocations/store ordering, and provides Predictive out of stock alerts. And the NextOrbit product for online players enables optimal demand planning.

NextOrbit works for brands and retailers where customer experience and loyalty are important. The company uses all brand/retailer data such POS (Point-of-sale), DC Shipments, receipts at the store, store returns, customer returns, shrink, footfalls and plan-o-gram constraints. It also accelerates same-store-sales, enhances inventory turns, improves full-price-sell-through, and most of all, enhances customer experience.

The Belief in the Mission

According to NextOrbit, last year an investor committed to capital, but failed to infuse capital even after all the agreements had been signed. This led to a temporary slowdown. Some of the company’s key people left. Instead of letting this incident demoralize them, it made them even stronger. NextOrbit put together a new team, infused a fresh approach and continued the journey.

NextOrbit, with the use of AI and Machine Learning and by including external factors such as local events, national holidays, weather, macro-economic factors and social signals in the planning process sets itself apart from the competitors.

Taking Challenges as Opportunities

The technology that NextOrbit provides enables smaller retailers and brands to compete on a level playing field with larger players.

A few years ago, only a Target, Nordstrom or Walmart could acquire, engage and use technology involving AI, big data, Machine Learning, high velocity POS data and so on. With the advent of cloud, big data technologies, open source big data tools, open source Machine Learning technologes and datawarehousing appliances, even smaller retailers now can access that level of capability at a far lower price point. In other words, NextOrbit is leveling the playing field for Retailers and brands. Scale alone cannot be their differentiation. They can be small and yet compete effectively.

The challenges faced by NextOrbit were, Getting good, inspired talent, Getting to decision makers at our client place, Long decision cycles, and the initial stage of NextOrbit, where clients need to do the groundwork to give them data, which took time.

The Future Focus of NextOrbit

The future focus of NextOrbit is to continue enhancing the platform to accomplish their mission to level the playing field for Retailers and Brands, to be a world leader in applying AI and Machine Learning for fashion and apparel, to bring in fashion trends information from fashion sources to improve demand planning effectiveness, and enter new markets such as the Middle East and China.