July 8, 2021

Written by

Collective[i]

  • Posted in
  • Sales Forecasting
  • Sales Forecasting Process

Why sales forecasting is essential in manufacturing

As businesses grow, so does the need to keep track of sales — both past and present — through sales forecasting. What is sales forecasting? Sales forecasting is the process of estimating future sales revenue by acquiring and analyzing data from past sales cycles and trends in current market data. Most sales teams maintain some sort of process to estimate how their sales cycles will perform so that they can make more impactful decisions about the future.

For manufacturers juggling suppliers, inventory, machine time, and a growing list of customers, sales forecasting is an essential tactic.

What is the importance of sales forecasting?

The sales forecasting process sheds important light on how a business or company can expect sales cycles to perform. This can be a daily, weekly, monthly, quarterly, or even annually driven estimation, which can be then used to build better decisions for the future. The difficulties of sales forecasting are rooted in the methods used to capture an accurate estimate of future sales cycles.

What is forecasting in manufacturing?

In manufacturing, forecasting is used to determine how to prepare for future sales cycles by providing sales and operations leaders an accurate and impactful projection of future revenue.

Why is forecasting important in manufacturing?

Forecasting can provide insight into how much production materials to order, how to staff a manufacturing plant, and more. The ability to predict the future for a business is why sales forecasting is essential in manufacturing. To put it bluntly, sales forecasting is the driving force behind most all decisions that a manufacturing business makes. Production managers will use sales forecasting to make critical decisions on material orders, staffing requirements, and more. The more accurate their forecasting results are, the better a manufacturer can plan for the future, reduce waste margins, and maintain efficiency across plants.

Types of sales forecasting

There are many unique ways to develop sales forecasting techniques and processes. Many businesses use a combination of qualitative (opinion-based) and quantitative (data-based) forecasting methods to arrive at various estimates for future sales. Another nascent type of sales forecasting leverages artificial intelligence to build upon both of these evolutionary steps towards better sales forecasting.

Qualitative sales forecasting represents the oldest type of sales forecasting, and leans on expert opinions to make assessments about the expected state of an industry. For manufacturers, polling experts within the industry about incoming market trends, seasonal demand, and even material costs can help a business verify quantitative data, or even provide additional considerations that data-alone might not be able to account for.

A frequently used qualitative sales forecasting example would be the Delphi Method, which involves surveying experts through a series of panels in an effort to draw out shared conclusions and allow productive conversation around unique perspectives and insights. Qualitative methods are useful for gauging general expert consensus, but not coupling these insights with real data can easily become a disadvantage of sales forecasting.

Quantitative sales forecasting is the next step in forecasting and focuses on data-driven analysis of historical cycles, test marketing, consumer polling, and more to understand a general (or precise) expectation for current or future cycles. These estimations are driven by data and science to complete simple and complex math to gather conclusions. Historical data can’t always tell a full story for the future, which makes real numbers only a contributing factor affecting sales forecasting.

AI-enabled, prescriptive sales forecasting represents the latest leap forward in sales forecasting. It puts the power of smart machine learning to work managing and analyzing data across multiple data sources. Artificial intelligence gives businesses the keen ability to collect data, automatically update that data, compare it to inputs from other key competitors within the manufacturing space, and consider current events to provide daily forecast updates. This can translate to CRM automation, connected networks of data across many businesses, and even improved data collection and analysis. That, in turn, leads to truly prescriptive forecasting that can provide sellers with adaptive forecasts that provide automated best next steps to drive revenue.

Smarter forecasting for a smarter future

Hindsight is often 20/20, but AI-enabled, prescriptive sales forecasting tools take the guesswork out of planning for the future. Sellers in the manufacturing industry know that buyer behavior is influenced by market-wide fluctuations that can have a big impact on how they buy. Consider the recent spike in lumber costs associated with the booming housing market; any sales forecast for a business selling to manufacturers who deal in lumber that was made before lumber prices began to skyrocket would be operating under incorrect assumptions about buyer budgets.

This is why it’s so important for modern sales teams to leverage technology that can account for real-time movements like these. With accurate, daily forecast updates and real-time customer sales information, Collective[i]’s forecasting platform helps manufacturers boost their efficiency, make better plans for the future, and cut margins on labor and materials.

Our smart platform synthesizes all collected data to produce clear recommendations for improving sales figures and closing deals. Ready to see what that kind of clarity can bring? Click here to explore Collective[i] for yourself.

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