Published on Friday, April 15, 2022
Whether you’re an industry veteran or manufacturing novice, you’re likely well aware of how challenging it can be to maintain proper stock levels to meet ongoing customer demand. Even with a haul of both historic and real-time data, numerous third-party factors must be taken into consideration to ensure smooth operations.
These factors include but are not limited to, seasonality of sales, unexpected customer demands, available personnel, and access to equipment. Fortunately, with the correct manufacturing forecasting techniques, you can automate your operations to better anticipate and meet ongoing consumer demand.
Forecasting in manufacturing can help predict the current market for your products, all while autonomously conducting demand planning to properly fulfill such predictions. Read on to learn more about the importance of forecasting in manufacturing and the leading forecasting methods you should be using in the manufacturing industry.
Why is Forecasting Important in Manufacturing?
Forecasting in manufacturing is a critical operational component that allows managers to conduct demand planning to ensure that product inventory neither greatly surpasses forecasted demand nor goes out of stock. Inventory forecasting methods help manufacturing businesses meet their goals by saving on operational costs while ensuring customer demand is met.
Manufacturing forecasts additionally help businesses better identify and respond to budget variances that can be brought on by manufacturing costs, business climate, and labor variances. Combining current budget variances with different demand forecasting techniques allows managers to remain increasingly accurate with calculations and efficiently respond to unexpected impacts.
What are the Benefits of Manufacturing Forecasts?
Conducting manufacturing forecasting will benefit a business’ operations in many different ways. In fact, manufacturing demand forecasting plays a significant role in the push and pull components of supply chain management, which is at the heart of multiple manufacturing processes.
On one hand, manufacturing forecasting helps ensure optimal inventory levels and peak usage of factory capacity. On the other hand, it also helps streamline outgoing distribution efforts to support customer satisfaction. Hand-in-hand, forecasting techniques support a manufacturing operation from a variety of angles.
Likewise, forecasting in manufacturing helps further reduce unnecessary product costs, reduce over inventory of raw materials and/or finished products, and generally increase ROI. Forecasting techniques heavily support customer management operations as well by allowing companies to better adhere to delivery commitments by determining the operational capacity of their factory.
How Do Manufacturers Forecast Demand?
To conduct accurate forecasting in manufacturing, you must first understand the types of quantitative and qualitative factors that dictate forecasting methods. One of the largest influences on forecasting is the method of manufacturing being used. Depending on the specific operation, a product will be either made to order or made to stock. Made-to-stock environments will need to use historical data to conduct forecasting, whereas made-to-order will rely on current order data.
Another factor that influences manufacturing forecasts is the multiple timelines that accompany operations; the average timeline of production can significantly sway forecasting outcomes. If a business experiences an increased demand for a product, but the manufacturer only has certain production capacity over a set forecasting period, these timeline factors must be considered for accurate forecasting.
Similarly, many manufacturing operations will leverage historical factors, such as past trends, sales cycles, and seasonality, to more thoroughly conduct production forecasting. While such quantitative factors can’t provide full accuracy in a forecast outcome, combining qualitative data like previous sales and production helps create a more targeted and broad-angle of necessary future production.
What Are the Demand Forecasting Methods in the Manufacturing Industry?
With the right data in hand, you should now be ready to conduct manufacturing forecasting. There are four general manufacturing demand forecasting techniques that are used by managers. Understanding each one helps you understand which forecasting method(s) will work best for you.
1. Push Systems
The push-based manufacturing forecasting method works to predict inventory requirements over a set amount of time using demand data. By assessing current demand, a manufacturing business will predict which of its products are expected to be purchased and in what capacity. Bear in mind, there is a high risk of solely leveraging demand data for forecasting, as demand can vary significantly from year to year for a multitude of reasons.
2. Sales Driven Forecasts
Compared to push system type forecasting, sales-driven manufacturing forecasts offer a much safer and calculated result. Instead of relying solely on current demand data, a sales-driven forecast uses pipeline data to gain an expectation of what manufacturing needs must be met over a set period of time. Current sales pipeline data will be analyzed to understand the likelihood of closing different opportunities and determine future manufacturing needs.
When conducting sales-driven manufacturing forecasting techniques, it’s important to use a strong revenue realization management solution. Forecasting solutions with Salesforce integrations, like revVana, provide great insight into pipeline and customer data for all-around enhanced support.
3. Production Driven Forecasts
Rather than use sales pipeline data to conduct forecasting in manufacturing, some managers opt to focus on production data. A production-driven forecast relies on year-to-year production data to determine the expected manufacturing needs over an upcoming period of time. As with the above demand forecasting types, production-driven forecasting techniques have operational risks due to a lack of sales funnel visibility and yearly changes to market and consumer conditions and behaviors.
4. Pull Systems
Compared to a sales-driven forecast, which focuses on potential deal closings, a pull system forecasting method uses data only from what has been sold to conduct production forecasting needs. With the addition of previous sales data, a well-constructed pull-based forecasting system uses previous consumer data to lower excess inventory while simultaneously improving cash flow.
However, this method can be difficult, as it requires proper planning and ongoing system support. A lack of either could result in a forecast being thrown off balance, which creates negative impacts across a manufacturing operation.