Forecasting is sometimes an overlooked part of business management. Other aspects, like small business inventory management, are already so time-consuming that there is little energy left to dedicate to it.
However, predicting future events can greatly help leaders make the best possible decisions. In order to boost your small business inventory management efficiency and leave some time for forecasting, you can start using a mobile inventory app.
You already took this step? Great. Then let’s take a look at how the business forecasting process usually occurs.
1. Identify the Problem
Defining the problem can seem simple at first because it looks like you are simply asking how will the market react to a new product, or how the company’s sales will look like in a few months. Even more so if you have a good forecasting tool for small business.
However, this step is quite tricky because there aren’t actually any tools that can help here. It requires you to know who the forecast is directed too, how the market works, and what your customer base and competition are.
You should spend some time evaluating these issues together with the people who will be responsible for maintaining databases and gathering the data.
2. Collect Information
We say information here, and not data, because data may not be available yet if for example the forecast is aimed at a new product. Having said this, the information comes essentially in two ways: the knowledge gathered by experts and actual data.
If no data is yet available, the information must come from the judgments made by experts in the area. If the forecast is based solely on judgment and no actual data, we are in the field of qualitative forecasting.
If data is available on the subject, a model is used to analyze the data and predict future values. This is called quantitative forecasting. A good example is predicting the sales for a given product in order to replenish stocks accordingly. This can even be done on a daily basis if you use a good forecasting tool for small business.
3. Perform a Preliminary Analysis
An early analysis of the data may tell you right away if the data is usable or not. It may also reveal patterns or trends that can then be helpful, for example, in choosing the model that best fits it.
Another thing that can be done here is to check for redundant data and cut it down or make some educated assumptions. By reducing the amount of data to analyze you can greatly simplify the entire process.
4. Choose the Forecasting Model
Once all the information is collected and treated, you may then choose the model you think will give you the best prediction possible. There is not one single model that works best in all situations, it all depends on the availability and nature of the available data.
Qualitative Forecasting
As we’ve seen before, we may not even have any historical data, in which case we have to use qualitative forecasting.
Two models that are commonly used in qualitative forecasting are a market research and the Delphi method. A market research is performed by enquiring a large number of people about their willingness to purchase a possible product or service.
The Delphi method consists of gathering forecasts from several different experts in a given area, and then compiling all that information into a single forecast. It relies on the assumption that a collective forecast is more accurate than that of a single person.
Quantitative Forecasting
If sufficient data is available, the human factor can be removed from the equation and a raw data analysis can be performed to predict future values. A lot of mathematical values exist to do these predictions, including regression models, exponential smoothing models, Box-Jenkins ARIMA models and others.
Some forecasting tools for small business, like DataQlick, use an Exponential Moving Average Calculation model to predict product sales.
5. Data analysis
This step is simple. After choosing a suitable model, run the data through it.
6. Verify Model Performance
When the time comes, it is very important to compare your forecast to the actual data. This allows you to evaluate the accuracy of not only the model, but the entire process, and change each step accordingly. Hopefully, if you use a good forecasting tool for small business, there won’t be much tweaking needed!
