In continuation to our previous blogs that discussed leveraging analytics to drive customer intimacy and profitability in the Direct-to-Consumer (D2C) era, this blog looks at how advanced analytics can help overcome supply chain challenges.
The D2C business model calls for a re-imagination of traditional Consumer Packaged Goods (CPG) supply chains that have proven inadequate in the wake of the post-pandemic rapid-fire shifts in demand.
How can then CPG players navigate product shortages, increased costs from stock, inventory write-offs, and related inefficiencies up and down the value chain?
Re-defining D2C Supply Chain Planning with Data & Advanced Analytics
According to a research report, subscription business models are anticipated to grow from USD 650 Million in 2020 to USD 1.5 Trillion in 2025. The subscription economy continues to grow alongside D2C, leading to the rapid creation and adoption of package sizes, marketing and last-mile logistics.
If we view the challenges from the opportunity lens, we see three distinct possibilities:
Leveraging advanced analytics to predict customer behaviors: This effectively addresses the challenge of understanding the variability of future demand, which has become more volatile, granular and segmented. The right supply chain analytics solutions will enable CPG companies to anticipate and forecast changing consumer buying behaviors with a high degree of accuracy and successfully fulfill requirements across different categories and regional markets.
Integrating AI / ML and data to achieve network-wide inventory visibility and advanced optimization: This results in the pooling of inventory across e-commerce and brick-and-mortar channels, thereby eliminating sub-optimal levels of inventory across the distribution network of CPG companies. They can achieve a more granular segmentation, better prioritize trade spending for their most important retailers, monitor online prices in real-time to eliminate pricing conflicts very early and minimize downside impact.
Building automated advanced planning and execution workflows: This allows demand planners to shift focus to more complex issues and improve organizational efficiency. It creates an explicit link from forecast demand signals back to the production schedule to ensure sufficient raw materials are in place.
The right solutions will maximize product availability and production capacity while lowering the total cost-to-serve. Such predictive planning can model potential future scenarios, and simulate the impact and implications of various mitigation measures on the supply chain. Besides, machine learning methodologies help paint a clear picture of the entire supply chain for COOs to optimize for specific variables.
In the online world, gathering the right data to conduct revenue management analyses is difficult. Thus, advanced data management and analytical solutions become critical to simplifying direct-to-consumer supply chain complexities and providing quality insights. This will, in turn, drive enhanced consumer engagement, optimal operational costs and continuous improvement.
About WNS Triange:
WNS Triange (formerly WNS Research and Analytics practice) powers business growth and innovation for 120+ global companies with data, analytics and Artificial Intelligence (AI). Driven by a specialized team of over 4000 analysts, data scientists and domain experts, WNS Triange helps translate data into actionable insights for impactful decision-making. Built on the pillars of consulting (Triange Consult), future-ready platforms (Triange Nxt), and domain and technology (Triange CoE), WNS Triange seamlessly blends strategy, industry-specific nuances, AI and Machine Learning (ML) operations, and intelligent cloud platforms.
Driving a futuristic edge are WNS Triange’s modular cloud-based platforms and solutions leveraging advanced AI and ML to provide end-to-end integration and processing of data to actionable insights. WNS Triange leverages the combined strength of WNS’ domain expertise, co-creation labs, strategic partnerships and outcome-based engagement models.