What’s holding back data-driven supply chains?
A new study has found that the forecasting techniques most commonly used by wholesalers are often outdated and lack integration of more advanced data analytics.
Blue Ridge, a provider of supply chain solutions, surveyed more than 100 NAW (National Association of Wholesaler-Distributors) SmartBrief readers and Blue Ridge customers on their 2018 challenges.
Of those surveyed, 37.4 percent said their biggest challenge in the next three years would be complex demand patterns. That was closely followed the 30.6 percent who agreed that demand volatility from new competitors, customers and digital e-commerce would be their biggest challenge.
One finding was that increasingly complex demand patterns — largely caused to the expansion of online selling — are making it necessary for firms to hold more inventories. Unfortunately, while increasing on-hand inventories should address volatile demand, the increase is failing to translate into higher sales and at the same time increasing working capital.
“This could be driven by several factors,” Blue Ridge wrote in its report. “Because demand is more complex, supply chain managers simply can’t increase inventory across the board. Traditional approaches like rule of thumb and gut feeling normally lead to increased inventory and may not actually help improve sales.”
Another finding was that traditional demand factors, such as price change impact (used by 75.3 percent) and programs or promotions (71 percent), are still driving forecast models that are often critical to managing promotions. Only five percent use advanced machine learning techniques and artificial intelligence to forecast demand.
Asked which strategic capabilities they most valued in inventory planning and optimization solutions for their organization, the top answer was rich analytical capability, 39.4 percent; following by provides customer insights, 26.3 percent; automation, 20 percent; intuitive user interface and workflows, 16.1 percent; and tools to collaborate with suppliers, 12.1 percent.
Overall, less than 35 percent felt their organization was using analytics effectively to management inventory.
The study concluded that more sophisticated techniques, incorporating advanced machine learning and Big Data strategies, can better incorporate price changes, type of promotions, event dynamics and weather influences to enhance the accuracy of forecasting.
“Order promising, increased availability, reduced delivery or shipping time, and returns management are becoming less of a differentiator and basic expectations,” Blue Ridge wrote.
- 2018 State Of Wholesale Distribution Supply Chan Report – Blue Ridge
- Less Than 35 percent Of Supply Chain Execs Use Analytics Insights To Manage Inventory – Retail Touchpoints
DISCUSSION QUESTIONS: Is demand volatility caused by online selling overwhelming retail’s traditional forecasting tools? Is infusing analytics into the inventory management process a bigger hurdle than into merchandising, marketing and other areas? What are the unique pain points in such implementation?