One of the key challenges in demand planning is the availability and quality of data. It’s an ongoing process to ensure you have the right information. Below are the critical data sources you’ll need and the attributes they should have.
Sales history is crucial. It will give you a view of past demand trends, helping you build a baseline forecast. This data typically includes:
Transaction dates: When the sales happened.
Quantities sold: How much of each product was purchased.
Sales channels: Where the sales came from (eCommerce, retail, etc.).
Customer details: Who bought the product.
You’ll usually find this in your ERP system’s sales module, where past sales orders and invoices are stored.
Order data tracks the details of customer orders and demand over time. It can differ slightly from sales data (due to cancellations or backorders). Typical data points include:
Order date: When the customer placed the order.
Quantity ordered: How much the customer requested.
Requested delivery date: When the customer expects the product.
You’ll find this in your ERP’s order management or sales order processing sections.
Shipped data tells you when and how much of a product actually made it to the customer. This data is critical for spotting gaps between what was ordered and what was delivered. It includes:
Ship date: When the product left the warehouse.
Quantity shipped: The actual amount shipped (which may differ from what was ordered due to stockouts or partial shipments).
Shipping destination: Where the product was sent.
This info typically sits in your ERP’s order management system.
Missing, incomplete, or inaccurate data can throw your demand forecast off. Common gaps include:
missing delivery dates
incorrect shipping quantities
incomplete sales histories.
Regular checks are essential to keep your data clean and ensure forecasts are built on solid information. Exception management in a demand planning system can actually also help with identifying and fixing these issues.
Once these basics are set, you can start to look at more advanced data sources like market trends or external factors to refine your forecasts further, if your system is able to incorporate these.
CRM systems can enrich your demand planning by offering insights about customers that typically only sales knows about. Here’s how:
Sales pipeline: Deals in progress or pending contracts can give you early warning signs of demand spikes or dips.
Promotions and campaigns: CRM data can reveal planned promotions or marketing efforts, allowing you to factor them into your forecasts. However, this info isn’t always directly available in the CRM.
PLM systems manage the entire lifecycle of a product, from concept through manufacturing. Here’s how they can support demand planning:
New product launches: PLM data helps predict demand for new products by providing specifications, launch dates, and expected volumes. This is crucial for aligning inventory and production ahead of the launch.
Phase-in/phase-out data: Knowing when a product will be phased out or replaced lets you adjust forecasts to prevent overproduction or stockouts in the early stages.
Design changes: Upcoming design modifications can impact demand, particularly if a new version is about to hit the market.
Caveat: Many systems are not integrated with a PLM, so this data might not be available in real-time.
ERP systems and sales orders: