Spherecast (YC S24)

Spherecast is growing 🎉 We are happy to welcome Leon and Felix to Spherecast! Leon joins Spherecast as a Founding Engineer. He studied Computer Science at 16, built machine learning models at appliedAI Initiative GmbH and applied data science methods at Amazon fulfillment centers. Recently coming back from Entrepreneur First, he decided to bring his entrepreneurial tech spirit into his role as a Founding Engineer at Spherecast, growing the product. Felix is joining Spherecast as a Founding Business Developer. He founded the local TechLabs entity in Mannheim and led partnership and GTM efforts to bring tech to everyone. Combined with his entrepreneurial experience at Technische Universität München, his hands-on engineering and tech consulting background make him the perfect match to build the early GTM engine at Spherecast. Excited for the future!

Spherecast is growing 🎉 We are happy to welcome Leon and Felix to Spherecast! Leon joins Spherecast as a Founding Engineer. He studied Computer Science at 16, built machine learning models at appliedAI Initiative GmbH and applied data science methods at Amazon fulfillment centers. Recently coming back from Entrepreneur First, he decided to bring his entrepreneurial tech spirit into his role as a Founding Engineer at Spherecast, growing the product. Felix is joining Spherecast as a Founding Business Developer. He founded the local TechLabs entity in Mannheim and led partnership and GTM efforts to bring tech to everyone. Combined with his entrepreneurial experience at Technische Universität München, his hands-on engineering and tech consulting background make him the perfect match to build the early GTM engine at Spherecast. Excited for the future!

📈 Bottom-up Forecasting in Spherecast Bottom-up forecasts are based on SKU history and its characteristics. Some of these characteristics drawn from the product’s history include: 1) Growth Rate 2) Seasonality 3) Demand Outliers ... and much more! A common challenge is extracting these characteristics automatically from product history and applying them to future projections. Excel sheets often face limitations in this regard, resulting in imprecise forecasts thus bad re-ordering decisions. The Planning feature in Spherecast allows you to: ✅ Select different forecasting methods per SKU & channel ✅ Track forecasting accuracy per SKU and category ✅ Automatically remove demand outliers and approximate stock-outs

📦 Multi-warehouse setups Multi-channel brands do not only have complexities on the sales forecast side of things, but also ask themselves: ⏱️ When should I send-in products from warehouse one to warehouse two? 🔢 What is my ideal reorder based on inventory needs in both warehouses? Find your answers in Spherecast: ✅ Understand the ideal time to replenish products ✅ Receive the ideal re-order & send-in amount per product ✅ Automate send-ins to Amazon FBA

🙌🏻 Marketing events in Spherecast Forecasting sales per SKU is definitely complex, especially when planned events come into play. The key to that is to have precise base forecasts and dynamically adjust them with planned events. Questions for e-com ops teams arise, such as 🔢 How can I integrate my events? 📊 What will my new forecast per SKU be based on my planned events? Check Spherecast 🔮 In Spherecast, you can ✅ Insert an event for every product & every channel ✅ Adjust the expected sales uplift for every event ✅ Manage events in one place

📈 Replenishment Simulator in Spherecast As an ops manager, you often think about your coverage for specific purchase orders. 🔢 For how long does the new PO cover my inventory? ❓How much should I order so that I can cover stock until a specific date? In Spherecast, you now can ✅ Get the ideal order quantity for any date per SKU ✅ Simulate the order quantity for different dates within a PO

📈 Supplier Analytics in Spherecast Operations manager often face the difficulty of analyzing supplier information, including 🔢 What is my actual lead-time based on past purchase orders? 🤝 How reliable are my suppliers? Check Spherecast 🔮 In Spherecast, you can ✅ Track the actual, average lead-time over multiple POs ✅ Get an understanding of revenue per supplier to assess risk ✅ Understand reliability of your suppliers based on timeliness #ecommercelogistics #supplychain #inventoryplanning #purchaseorders

📈 Understanding cash flow projections in Spherecast Finance teams in e-commerce brands require specific information from operations teams, including cash flow projections for supply chain decisions. Finding answers for that in Spherecast is easy 🔮 In Spherecast, you can ✅ Receive a purchase order plan for every product ✅ Get a list of recommended purchase orders for future weeks and months ✅ Project the cash outflow based on specific payment agreements #cashflow #ecommercelogistics #inventory #operations

📈 Tracking down forecast accuracy for your SKUs When operations manager look at their inventory, forecast accuracy is a key metric to track: Following questions come up on a regular basis: 🔢 Am I on track with my forecast? 📉 Am I under-forecasting thus running into a stockout risk? It is easy to find answers for that in Spherecast 🔮 In Spherecast, you can ✅ Track your current forecast accuracy of your best sellers ✅ Analyse the forecast accuracy over a past horizon for every SKU, for every channel ✅ Get the weighted forecast accuracy for each product category

Shared Minimum Order Quantity in Spherecast! When you think about re-ordering, suppliers often demand a Minimum Order Quantity (MOQ) on the parent level (e.g. in Fashion) – so you ask yourself 👕 What is the total order quantity for my variants (e.g. T-Shirt in XL) based on the MOQ of their parent product? You’ll get your answer in Spherecast! Here, you can ✅ Group SKUs on parent level on in the inventory tab ✅ Adjust the MOQ on the parent level of your variants and adjust the variant quantity automatically ✅ Convert total variant units to the right carton size requirement #ecommercelogistics #supplychain #moq #purchaseorder #inventory