The retail landscape has probably changed more in the past year than in the past decade. While e-commerce has been disrupting traditional brick-and-mortar retail for more than a decade, nothing could have prepared retailers for the disruptions and uncertainties that emerged in 2020. The combined impact of the Covid pandemic, supply chain constraints, and shifting consumer expectations have created high levels of uncertainty, variability, and complexity within supply chains.
As communities grappled with implementing lockdowns and public safety measures, retailers had to immediately pivot to new planning scenarios, distribution channels, and fulfillment methods to meet consumer needs and comply with COVID restrictions. As a result, omnichannel distribution models combined traditional retail and D2C sales through brand stores and digital avenues took center stage for many retailers.
But as economies slowly emerge from the pandemic’s peak, there will be continued opportunities for omnichannel, specifically in e-commerce and direct-to-consumer (D2C) channels. For example, a retailer’s direct consumer interaction can result in the following:
- Creating a tailored brand experience.
- Greater product ranges that can be offered.
- Promotions that are targeted and personalized to customer segments.
- Gathering first-party consumer preferences, purchasing behavior, and feedback that can lead to new product innovation.
Additionally, omnichannel distribution can significantly increase customer reach, resonate with current customers while expanding into new customer segments, and drive higher margins. Retailers that continue to embrace an omnichannel approach that encompasses in-store experiences, multiple pick-up options, and D2C channels are likely to become industry innovators and leaders in the new world of retail.
Omnichannel isn’t without its challenges
Omnichannel distribution is a viable way for retailers to pivot and can be very beneficial from a marketing and product innovation perspective. However, this distribution model can have drawbacks from a supply chain standpoint. Omnichannel distribution can be challenging because of an increasingly complex supply chain that includes multiple modes (cross-docking, VMI, drop-shipment, etc.). Additionally, today’s customers are expecting a high-quality fulfillment experience (i.e., free 2-day delivery) with multiple pick-up/return options such as:
- Direct-to-consumer (D2C)
- Reserve-Online-Pick-up-In-Store (ROPIS)
- Buy-Online-Pick-up-In-Store (BOPIS)
- Traditional Online Return
- Buy-Online-Return-In-Store (BORIS)
One of the ways to mitigate challenges in omnichannel distribution is by ensuring transparency and integration across all associated functions throughout the supply chain.
The role of integrated planning
Retail demand planning is also evolving into a more functional approach, shifting away from seasonality/schedule towards event/exception as the main process driver. This approach can create a more seamless retail supply chain if a tighter integration accompanies it:
- Functional integration would better align supply chain planning with merchandise financial planning.
- Business unit integration draws connections across data structures, planning, and governance across all market, product, and brand dimensions.
- Channel integration connects B2B and B2C channels to support a unified vertical.
- Process integration removes siloes in the planning processes across an organization.
While an integrated planning approach will help create alignment across a retailer’s organization and supply chain, building an integrated planning system will require complex technology to achieve optimal business outcomes.
As larger volumes of data become more prevalent due to the availability of data related to weather, local events, mobility, e-commerce shopping carts, and website glance views, retailers can make better-informed decisions. The challenge is converting these vast amounts of data into actionable insights aligned with business goals. AI/ML technologies can make all the difference. Retailers can effectively manage an integrated omnichannel strategy by leveraging advanced analytics, demand sensing, and other AI use cases to understand customer needs and demand better.
Demand sensing allows retailers’ planning teams to incorporate vast amounts of external demand data into their planning processes. AI/ML algorithms can connect data points, enabling a much more granular and accurate forecast. This allows companies to automate execution, better collaborate with upstream and downstream suppliers, and increase customer engagement by understanding their buying intent.
Building an effective omnichannel is worth the investment
Building a robust omnichannel model takes time and effort. Still, in the long run, it can set retailers on a path to navigate today’s challenges while building a robust distribution model that will keep up with future needs. As part of their omnichannel journey, retailers can deploy and benefit from the following capabilities:
- Leverage advanced analytics to manage uncertainty.
- Use AI to understand consumer demand and right-size production to meet demand more accurately.
- Provide visibility of inventory across all locations and transition inventory segregation policies to more holistic ways of allocation and fulfillment.
For retailers ready to invest in building an effective omnichannel model, a truly integrated business planning platform can help them take planning and decision-making to the next level.
Get free industry updates
Each quarter, we'll send you o9 Magazine with the latest supply chain industry news, trends, and o9 knowledge. Don’t miss out!
About the author
Vikram MurthiVice President Industry Strategy
Vikram Murthi, Vice President of Industry Strategy at o9 Solutions, engages with companies to understand their merchandising and supply chain challenges and helps shape their investment strategy and transformation roadmaps. He has extensive experience in supply chain transformation initiatives focusing on business case development, strategic roadmap planning, leading client workshops and solution definition. Vikram is interested in helping consumer-facing businesses leverage Big Data, Artificial Intelligence, Machine Learning and Optimization techniques to improve merchandising, forecasting, inventory planning, omni-channel fulfillment and new product introductions. Vikram has a B.Tech in Electrical Engineering from Indian Institute of Technology (IIT) in Kanpur, India and an M.S. in Computer and Systems Engineering from Rensselaer Polytechnic Institute.