Thank you Bill Shube and Zack Napolitano, CSCP for this accurate breakdown. From small/medium businesses to large enterprises, brands still rely on Excel or Google Sheets for inventory planning. This approach requires as much time and effort for predictable SKUs as it does for strategically important areas of the product assortment. Without retail-specific machine learning algorithms necessary for accurate forecasting, planners generally base demand forecasts on last year's sales, which can be highly unreliable. Last year's data typically doesn't account for stock-outs or the impact of organic and paid marketing events that won't be relevant in the future. As you mentioned, Zack, this results in stock-outs and overstock issues, hurting brands and their fulfillment partners. Additionally, it puts unnecessary pressure on factory relationships and often leads to more air vs. sea shipping, negatively impacting margins and their environmental footprint. At Flagship, we're helping $20M+ revenue brands on Shopify solve for this.
A topic that keeps popping up in my mid-year conversations with brands and fulfillment providers: Forecasting This has been a consistent pain point for the fulfillment provider. They are not being provided with consistent forecasts, don't get advance notice regarding planned sales events, and struggle to properly plan labor and achieve service levels because of it. From the brand side, they are struggling to develop accurate forecasts to share with their partner(s). When they do, there's often a gap in effectively relaying this information to the fulfillment partner. The impact to the fulfillment partner? They end up carrying too much labor to ensure service, driving up their labor spend and eroding margins. Or they run lean to save on labor and end up missing service targets, negatively impacting the brand and putting a strain on their relationship. Neither are good options. For the brand, the inability to accurately forecast demand impacts sourcing and ultimately in-stock, lost sales, increased costs from their fulfillment provider because they need to carry more labor, etc. The good news is that brands are investing in technology to drive improved forecast accuracy while building internal control towers to improve end-to-end visibility, provide centralized data solutions and improving collaboration with their fulfillment providers. Meanwhile, fulfillment providers are better utilizing historical data to generate baseline demand and building labor plans around this and leveraging AMs to improve communication with the brand. I've been helping both sides build out effective strategies to manage through these challenges with a focus on improving communication and collaboration. I talk a lot about simplifying things in operations and this is a prime example of that. There's little value in the data if you're not able to effectively communicate it. Focus on getting it into the hands of those who rely on it to makes decisions and run your operations and let the magic happen. #forecasting #3PL #distribution #fulfillment
