Demand and supply planning in today’s Indian omnichannel environment

  • January 19, 2024

The Indian omnichannel environment is in a profound state of flux — coping with constant change is standard operating procedure. As customers rely on new ecommerce shopping options, they’re also demanding increased service levels. Moreover, they expect seamless shopping experiences across physical stores and online platforms.

Meanwhile, retailers find it increasingly difficult to deliver on consumer expectations as they deal with demand uncertainty throughout their supply chain networks. These circumstances are fertile ground for disruptions, instability and reduced supply chain effectiveness — leading to lost sales, compromised profitability and disgruntled shoppers.

What makes omnichannel planning so difficult?

It starts with the challenge of aligning business and planning goals. You need to decide what to forecast, how to forecast and then use that forecast to effectively plan supply. Complicating matters is that so many stakeholders still work in siloed compartments, hindering real-time inventory visibility.

This lack of visibility makes for inconsistent procedures, demand shortages, aging inventory and frequent stock-outs. You wind up with limited distribution locations because of a lack of systems that can automate the end-to-end process. Delayed deliveries and other negative effects are sure to follow.

Demand planning procedures must be able to deliver the right product, at the right price, in the right quantity and at the right time. This requires close integration with other supply chain processes, particularly sourcing and supply.

In this environment, there are three associated channels — buying, sourcing and fulfillment — where successful outcomes depend on the right combination of demand and supply planning.

Accurate forecasts can then inform buying channels and fulfillment operations of the inventory required to successfully fill customer orders.

Successful omnichannel demand planning and forecasting is always data-driven

  • Collect data at the most granular level possible. Consider product hierarchy, attributes, method of purchase, source of supply and mode of delivery.
  • Use zip codes for home deliveries and pickup locations. Group them according to clustering or neural network logic to the closest store or delivery warehouse. Place the newest stores in the nearest available cluster.


Clusters help align ideal fulfillment locations per nearest store for home delivery and pick-up orders through zip code algorithms.

Accurate warehouse and store sourcing generates reliable forecasts for each fulfillment method provided.

  • Provide all possible online fulfillment modes — cash-and-carry, pick-up from store and delivery to pick-up point options — as well as conventional home delivery.
  • Make ship-from-store (home delivery) and delivery to pick-up point options available for all in-store purchases.
  • The demand rate for each of these delivery modes should be consistently measured. Additionally, evaluate other potential fulfillment options for practical implementation.


Quantifying the throughput of each delivery mode makes just-in-time, cost-based, optimized supply planning possible

Use statistical forecasts — derived from traditional or advanced algorithms — for clusters identified as home delivery. These forecasts will identify optimal store or warehouse pick-up points for last-mile couriers

The omnichannel supply planning process

Supply planning strategies

  • Choose appropriate nodes for sourcing and distribution, and then build the delivery network
  • Group delivery locations based on order profile, customer base and demographic parameters
  • Use sales and profitability to determine product segmentation methods
  • Define a stocking policy based on product segmentation and lead time
  • Equip retail sites with warehousing spaces to serve as both distribution and sales locations
  • Distribute warehouse capacity in alignment with product segmentation

The adoption of coordinated and integrated methods is critical. Management faces increasingly complex omnichannel retail supply chains. In nearly all cases, these challenges result from the growing number of parties involved and steadily increasing service demands.

Improved supply-demand balances are possible through synchronizing information, material and financial flows. The ideal balance will improve customer service by reducing excess inventory, stock-outs, backorders and associated costs. Combined with clustering techniques and neural networks, these and other options will enable a thorough examination of the correlation between successful demand forecasting and consumer behavior.

Contact us to learn how NTT DATA Supply Chain Consulting’s Integrated Demand and Supply Planning team can help align your omnichannel operations.

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Ajay Singh

Ajay is a Senior Manager of Integrated Demand and Supply Planning in NTT DATA’s Supply Chain Consulting practice. He joined NTT DATA after prior postings at Capgemini, Eaton, Ecolab and Cummins. Ajay focuses on the development and implementation of supply and demand optimization solutions for clients in the omnichannel and ecommerce marketplace.

Susant Mansingh
As a Senior Manager of Integrated Demand and Supply Planning in NTT DATA’s Supply Chain Consulting practice, Susant brings two decades of experience driving consulting engagements in master replenishment and demand planning. Before joining NTT DATA, he oversaw projects in the U.S., Europe and Asia-Pacific for firms such as Accenture, Cognizant and TCS.

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