Strategic Reasons Behind Accurate Forecasting

Many business companies use forecasting to predict future data, like sales, budget, or inventory. According to Lewis (1997), it is a scientific method of getting estimated values from analyzing past statistics. Accurate forecasting is important to improve business operations, as it helps managers plan better. Strategic reasons behind accurate forecasting can be seen by these 3 events: human resources, capacity, and supply-chain management.

Human Resources

The human resources department can derive a strategic plan in managing their manpower by having an accurate forecast. It is a cost-effective way as they are able to foresee the demands, hence knowing when to recruit or cut out workers. For example, when they know that the production for the next month is low, they will have to cut out some employees to save cost. Additionally, this can also mean that the workers are able to have adequate training as time management is properly planned, therefore quality of work is assured.

Capacity

As companies use forecasting to anticipate demands, prompt and effective judgment can be made for decision making on capacity. Managers can also make more realistic goals to achieve sales target. When there is adequate capacity, it can lead to customer satisfaction and loyalty. There will be lesser waste as excessive capacity can be avoided, hence, this will save time, effort and money, increasing productivity.

Supply-Chain Management

Companies also use forecasting to get data on supplies. According to Fredendall and Hill (2001), forecast can be made as a tool for communication within the supply chain. It tells how many products can be supplied, when can the supplies arrive, and/or the schedules of assembling the products. Accurate forecast can help the company plan their order schedule effectively using the lowest cost possible. For example, if they order a certain part but it can only be assembled in a later date, they are using too much space unnecessarily, which can lead to additional cost incurred for holding of inventory. Consequently, having accurate forecast is important to run a business efficiently. However, Fredendall and Hill (2001) mentioned that it is also a need to understand that forecasts are never perfect. It acts as a guideline for a company to plan accordingly. That is a reason why there are many types of forecast available and choosing a suitable technique is crucial. Fundamentally, there are 2 types of forecasting technique, qualitative and quantitative. Qualitative method uses a more subjective approach, whereas quantitative uses more mathematical based data which is calculated from past statistics. Some of the forecast techniques from the 2 approaches are, sales force composite, consumer market survey, and moving averages.

Sales Force Composite

Sales force composite is one of the 4 qualitative forecasting techniques. It requires all the salesperson to gather and estimate their future sales in their own area. The data will then be collected to compute an overall sales forecast, (Heizer & Render, 2014). The reason behind this is due to the fact that salesperson know their customers very well, hence they know how their market works. This is also a good way for them to understand what forecast is and at the same time, motivate them to reach a higher target each month. The advantages of this approach is that it is one of the lower cost method and the data comes from a credible first-hand account, which makes the forecast somewhat accurate. To increase the accuracy of this forecast, most companies alter their data by using another quantitative forecast method.

Consumer Market Survey

Consumer market survey is also a qualitative forecasting technique. This approach collects data from customer surveys, asking them the type and quantity of future purchase. According to Heizer and Render (2014), this approach has a few advantages other than providing forecast data. These feedbacks can be used for product improvement and designing of new products. However, as Pride and Ferrell (2010) mentioned, this technique is time consuming and costly. Collecting data from a survey regarding future sales is inaccurate, as these are just plans to purchase, and they might change their mind in the future.

Moving Averages

Moving average is a quantitative forecasting method. It requires taking the average of a past data from a certain period, to predict the future data for the next period. This method consists of 2 types of moving averages, simple and weighted moving average. Simple moving average uses the same weight of all data, for example, to forecast sales on the 4th day, we use a 3-day moving average forecast. Taking the sales from day 1 to 3, we divide the sum of it by 3 to find the average sales, making that the sales figure for day 4. A weighted moving average on the contrary, uses a weighted data to compute a more accurate forecast, with the most recent data having the most weight. However, there is no exact way of choosing a specific weight for each data, other than all of the weights have to add up to 1. As compared to simple moving average, this method computes a more reliable forecast as it gives more value to the most recent data, hence, it follows the trend more accurately.