Why Equal Split Forecasting Quietly Hurts Your Supply Plan?
A pattern one sees often in planning implementations: the monthly forecast gets disaggregated to weeks using an equal split. 400 units in the month?
That becomes 100 / 100 / 100 / 100 across the four weeks. Clean. Simple.
✨ And in the example below, it lets the open forecast key figure (max (Forecast − SO, 0) with forward consumption) calculate neatly against actual sales orders.
✅ 𝗪𝗵𝘆 𝗱𝗲𝗳𝗮𝘂𝗹𝘁 𝘁𝗼 𝗲𝗾𝘂𝗮𝗹 𝘀𝗽𝗹𝗶𝘁:
⚡ Fast to configure, easy to explain to the business
🧮 Keeps monthly totals intact, so S&OP numbers reconcile cleanly
📈 Works reasonably well for products with stable, non-seasonal weekly demand
🔄 Simplifies forecast consumption logic when sales orders arrive lumpy
⚠️ 𝗪𝗵𝗲𝗿𝗲 𝗶𝘁 𝗾𝘂𝗶𝗲𝘁𝗹𝘆 𝗵𝘂𝗿𝘁𝘀:
🎢 Real demand is rarely flat – promotions, paydays, month-end pushes, and customer ordering patterns create weekly shape that an equal split erases. And sometimes these patterns could be predictable. Weekly modeling and forecasting could show week of the month patterns
🏭 Supply plans inherit that flat signal, which can mistime production, inventory builds, and deployment
🎯 Forecast accuracy at the weekly level looks worse than it should, because you’re measuring a smooth signal against a lumpy reality
🙈 It hides the conversation the planning team should be having: “what does this month actually look like week by week?”
💡 An acceptable middle ground is using historical weekly profiles (or a planner-maintained weekly pattern) to disaggregate and reserve equal split for genuinely flat SKUs or new products without history.
Equal split isn’t wrong. Make sure this is a deliberate choice for patterns that can use this 🎯
𝑰𝒓𝒐𝒏𝒚: 𝑰 𝒉𝒂𝒗𝒆 𝒔𝒆𝒆𝒏 𝒔𝒚𝒔𝒕𝒆𝒎𝒔 𝒄𝒐𝒏𝒇𝒊𝒈𝒖𝒓𝒆𝒅 𝒊𝒏 𝒔𝒖𝒄𝒉 𝒂 𝒘𝒂𝒚 𝒕𝒉𝒂𝒕 𝒂 𝒇𝒐𝒓𝒆𝒄𝒂𝒔𝒕 𝒘𝒊𝒕𝒉 𝒘𝒆𝒆𝒌𝒍𝒚 𝒑𝒂𝒕𝒕𝒆𝒓𝒏𝒔 𝒄𝒐𝒏𝒗𝒆𝒓𝒕𝒆𝒅 𝒃𝒚 𝒕𝒉𝒆 𝒔𝒖𝒑𝒑𝒍𝒚 𝒑𝒍𝒂𝒏𝒏𝒊𝒏𝒈 𝒔𝒚𝒔𝒕𝒆𝒎 𝒊𝒏𝒕𝒐 𝒂𝒏 𝒆𝒗𝒆𝒏 𝒔𝒑𝒍𝒊𝒕 𝒇𝒐𝒓𝒆𝒄𝒂𝒔𝒕 𝒘𝒉𝒆𝒏 𝒔𝒖𝒑𝒑𝒍𝒚 𝒑𝒍𝒂𝒏𝒏𝒆𝒓𝒔 𝒑𝒍𝒂𝒏! 𝑾𝒉𝒚?!
Comment your experience with the weekly splits of the monthly plan.
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