Predictive analysis allows business owners to make decisions about the future direction of their companies, and in manufacturing and product sales, demand forecasting methods are commonly used to determine inventory levels. The goal of applying demand forecasting analysis to your data is to make decisions about finances, pricing, manufacturing, sales, marketing, labor, investment, and growth. Based on sales in the past, a business can make predictions for the future.
There are different types of demand forecasting methods – quantitative and qualitative. They are distinguished by the length of time examined, market size and detail level. Both work from historical sales data to examine which products sold and how many during certain periods of time. That information allows the person analyzing the data to make future demand predictions based on similar sets of circumstances. The qualitative method relies more on opinion and experience while the quantitative uses mathematical calculations and formulas.
Quantitative Methods of Forecasting
When a business has long periods of data to examine, it can plot out predictions over time for certain products undergoing the same conditions as those sold in the past.
This method looks at events occurring in the current moment to predict an outcome. For example, if the economy is strong, the business might determine sales will also be strong in the near future.
This technique applies complex formulas to determine demand based on factors that normally influence it. That formula typically includes historical information. Variables in the equation are calculated separately based on predictions and then applied to the equation.
Qualitative Methods of Forecasting
Delphi: This method is reminiscent of the decision-making process of trial jurors. Each person performs individual analysis and then the group discusses their conclusions and formulates a congruent decision based on the discussion.
Sales Force: The sales manager will ask salespeople in each region for an estimate of future sales based on past performance. Then the sales manager presents an estimate that accounts for all their input.
Market Research: A segment of the customer base is selected for a survey that asks for demographic information.
Demand forecasting models can be custom-made for individual businesses using software. That software pulls in inventory, sales, purchase order and other relevant information and generates reports based on historical information.
Demand forecasting techniques can be further defined:
An active demand forecast focuses on growth planning by predicting marketing and new product addition results based on what the competition is doing.
More popular with smaller businesses, the passive forecast makes very modest predictions for gradual growth.
To make decisions about the upcoming year, the business looks at patterns during certain seasons to predict customer demand fluctuations.
Mid to Long-Term:
To plan beyond the one-year point, businesses will strategize many activities leading up to a desired goal.
This technique is used to plan for new product or market directions based on external factors.
These forecasts rely on company data only to make future predictions.
Product Demand Forecasting
Product demand forecasting must consider the life cycle of the product as it is a determining factor of forecasting method. Data availability depends on the stage of the product. Historically, data planners have relied on spreadsheets and overlaid data from different sources to generate a forecast. Today, AI learning using real-time and manually entered data helps turn out more accurate predictions. The demand prediction process can incorporate new influencers as they become relevant.
Forecast and Demand Planning Techniques
Demand forecast and demand planning are linked together, but do not refer to the same thing. Forecasting is the act of making demand and sales predictions and plays a role in planning, but planning extends to include the application of a strategy of adjustments to adapt to the forecasts. Demand planning aims to reach certain budget and profitability targets.
The accuracy of both relies on the method chosen for calculations and there should be ample data to back the method chosen. If there is not enough history for the selected method to provide accurate data, forecasts will be off.
Just like weather forecasts, product forecasts may not be accurate. Seasoned planners will know how to perform calculations to produce the most accurate outcomes possible. The information used to make such predictions may include internal and external information such as Nielsen data, market trends, history sale information, competitive research, etc.
Inventory Forecasting Software
The ability to analyze inventory performance and deduce future behavior based on past sales is a key component of inventory planning. If your software is not tracking everything, your physical inventory may not match your records. To understand product performance, you need to know if profitability fluctuates because costs increased or a source of waste has yet to be identified. Having all the data at your disposal empowers you to make sound decisions.
How Forecasts are Applied
If a food manufacturer examines sales during the previous year, he might decide how much of a certain product to produce based on the quantities sold and profits realized on that product. If a business could use a crystal ball to make accurate predictions, there would be single method of forecasting. But predictions are only informed decisions based on input and while they can provide intelligent conclusions, they are still only estimates, and only as accurate as the data entered into them.
Application of Demand Forecasting
Where companies tend to err is predictive analysis is in the application of a single calculation across all products. More than half of all retail products are subject to seasonal fluctuations and if these fluctuations aren’t considered, the calculations will ultimately be inaccurate.
When assessing historical sales for any given period, your business must consider the following:
- Does the product sell consistently or experiencing a seasonal peak during that time frame?
- Were increased sales the result of a sale? If so, this sale activity should be excluded from calculations.
- Are you lumping too many products under the same predictive calculation?
To prevent the unfortunate ordering of too much inventory due to faulty calculations, your business should sort products and perform separate calculations according to sales performance for each product type.
Replenishing stock according to individual product behavior rather than applying the same rule across all products can help reduce overages and loss of profits.
To create the most accurate and consistent information company-wide, your business needs highly functional software to process and record the lifetime of every product that crosses your facility. Create accurate reports and data for more informed demand forecasting for tomorrow with SOS Inventory today.