New-Age Retailing: Digitally Transform or Perish
New-Age Retailing: Digitally Transform or Perish

The organized sector of Indian retailing is evolving rapidly with new entrants, which span fashion and apparel, beauty, electronics, and furniture. With increased competition and investment in retail, consumers will benefit through lower prices, and wider and more localized assortments, while driving improvement in supply chain logistics. 

These new-age retailers are under tremendous pressure to grow rapidly, and at the same time, deliver on aggressive margin goals demanded by their investors. To gain this operational efficiency, they need to offer assortments that align with the needs of the consumer at specific store locations and make changes to merchandising and supply chain processes, to deliver on this hypergrowth. 

This requires a digital transformation in planning and decision-making across stakeholders, processes, and technologies. New-age retailers need to upend their current manual and largely spreadsheet-driven operating model, with a digital operating model, so that they can quickly analyze, optimize, and evaluate interconnected decisions before taking action. 

Here are some key areas of retail where digital can play a transformative role: 

Merchandizing - Merchandise Financial Plans are mostly manual with sales and receipt projections planned with growth assumptions over the previous year. This needs to be digitally transformed with planners providing inputs such as new store openings, new product introductions, marketing spending, and promotion calendars so that the system can build robust plans. Planners can make overrides and adjustments and manage by exception. This will take the manual decision-making and bias away from this process and provide a perfect balance of art and science. Planners can now spend time devising strategies for merchandise categories keeping the end consumer in mind, instead of building plans manually.

Assortment Planning - Hyper-localized assortments, considering local consumer demographics, are as important in new-age retail as it is in traditional retail outlets. Curating the right product ranges to the right stores to provide a personalized assortment to consumers is critical, and needs to be digitally transformed, with advanced clustering techniques based on multivariate algorithms that consider many inputs, including past-buying patterns, income levels, seasonality, and regional variations. 

Replenishment and Allocation - With thousands of items and many channels (buy online/home delivery, click-and-collect, store purchase), it is difficult for allocators and replenishment planners to manage each item and store combination, yet achieve the most optimal results with the right product at the right location. Digital transformation requires a forecast-driven approach to allocation and replenishment, freeing up a lot of time for planning teams and allowing them to strategize the inventory placements to the locations where it can sell quickly.

Demand Forecasting - Demand uncertainty of innovative items, seasonal, short lifecycle, or high-fashion products is a big challenge for new-age retailers.  Using newer forecasting techniques available with Artificial Intelligence and Machine Learning, and leveraging external data such as incomes, interest rates, weather patterns, and demographic data, reduces forecast error at the point of purchase. This means increased revenue due to lower stockouts, more satisfied customers, and lower supply chain inventory and transportation costs. In addition, digital transformation drives automation with touchless forecasting and replenishment, with planner attention and inputs driven by exceptions. 

The Emergence of Integrated Retail Data and Analytics Platforms

It becomes clear that the key asset that needs to be leveraged is data across the retail enterprise that must be converted to knowledge and insights. Planners, merchants, and allocators spend a lot of time and effort collecting data spread across multiple systems such as spreadsheets, data marts, ERPs, and data lakes to glean insights. 

A data platform to store this data across multiple systems in one place would allow the planning teams to focus on the high-value-add tasks instead of spending time gathering data, and cleansing, aggregating, and maintaining it. Such platforms also model the assortment and the retail supply chain to provide insights and recommendations on assortments by location, optimal price points, promotions and markdowns, and the timing thereof, as well as the amount of inventory to be held at every node in the supply chain. 

New-age retailers have no choice but to invest in such data and analytics platforms, as well as the talent with the necessary skills, to digitally transform the way their businesses are run to deliver profitable hyper-growth.

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