How To Select The Right Forecasting Technique?

jaro education
11, August 2024
5:30 pm

Introduction

In the fast-changing world of manufacturing, one has to be a step ahead. Among various steps toward this, accurate forecasting plays a vital role. A good technique in forecasting allows the manager to predict the trends in the market, proper resource allocation, and proper decision-making, among many other benefits. Now, in such a wide sea of methods and techniques, how can you tell which one is the right way? This blog delves into details of various types of forecasts, and the alternatives in methods, comprehensively helping manufacturing managers in their choice.

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A Review of forecasting techniques

Forecasting is an estimate of some future events based on knowledge of past events and analysis. The forecasting in the subject of manufacturing management is quite strategic since demand, production planning, and inventory are the periphery of success.

Qualitative Forecasting Techniques

The qualitative forecasting technique involves judgment and opinion as opposed to numerical data. These methods are particularly used when historical data is of little value, or the activity is new, for instance, the launching of a new product.

Delphi Method

The Delphi approach in forecasting methods is one in which a panel of experts provides their forecasts independently of what the others have to say. These forecasts are then aggregated and, finally, the panel settles the forecast through repeated rounds of discussions. This approach is less influenced by any particular dominant person, hence providing a wholesome forecast.

Market Research

Market research involves obtaining relevant information from the potential customers or population by using tools like surveys, interviews, focus groups, etc. More so, this is extremely useful in informing the tastes and preferences of customers for new products being launched.

Quantitative Forecasting Methods

In quantitative forecasting techniques, one can predict future events through the use of statistics or numerical data. These techniques have, however, been considered more accurate than many of those in the first group.

Time Series Analysis

Time series analysis is the examination of historical data points to identify trends and patterns. Moving averages, exponential smoothing, and the application of ARIMA models are very common forecasting methods for time series analysis.

Causal Models

Causal models are sometimes called econometric models. They examine the relationships among variables and establish cause-and-effect relationships. For instance, one likely would use a causal model to discover how changes in economic variables such as gross domestic product or unemployment rates impact demand for a product.

Choosing the Right Forecasting Technique

An appropriate forecasting method depends on many factors: the nature of the data, the time horizon, and the needs of an organization. It determines the accuracy and power required of the techniques and hence governs selection, as mentioned in a report by Harvard Business Review.

Nature of Data

The nature and quality of available data greatly influence the selection of the type of forecasting technique to be used. If you have a long history of data, quantitative methods such as time series or causal models are appropriate. However, when data are limited, one may resort to qualitative methods like the Delphi technique or marketing research.

Time Horizon

The time horizon of the forecasting technique also serves to play a vital role. Short-term forecasts(up to one year) usually focus on immediate trends and patterns hence based on time series analysis. Long-term forecasts (beyond one year) usually base their negotiations on causal models to account for the economic factors bringing a broad objective of the markets.

Specific Needs of the Organization

Think about which approach is better suited to the unique requirements of your organization. Market research might be the best solution if you need to make projections for demand for a newly released product. Time-series analysis is a better way to go if the more important thing is ensuring the optimal level of inventory based on historical sales information.

Combining Forecasting Methods

In some cases, the union of many different forecasting techniques can give a much more accurate answer than anyone can do alone. This is a composite or ensemble method of forecasting in general: a forecasting method in which the better parts of each can be synthesized to give a better overall feel for the prediction.

For example, a manufacturing manager can utilize time series analysis in forecasting short-term demand and then combine it with some market research insights, say imminent changes in customers’ preferences, to supplement the previous portion of his prediction.

Practical Application in Manufacturing Management

Now, let’s take the example of one manufacturing company that manufactures consumer electronics. Suppose this case is related to the forecasting demand for a new model of smartphones.

Step 1: Market Research

The firm will have undertaken market research and will have gathered customer knowledge in terms of those who associate with features, price points, and the intent to purchase. Through the qualitative data it has collected, the initial demand for the new model of smartphones will be learned and accordingly understood.

Step 2: Time Series Analysis

Next, time series analysis is used to analyze the historical sales data for previous smartphone models. Using a quantitative technique, the analysis can identify trends and patterns, which can be used for guiding the forecast of the demand for the new model.

Step 3: Combining Methods

By incorporating qualitative market research insights along with historical quantitative data analysis, a more comparatively accurate forecast can be determined. This composite approach is important for the company in planning its production, managing inventory, and allocating resources correctly.

Advanced Forecasting Techniques

Organizations that need more complex forecasting techniques have various advanced solutions at their disposal, including machine learning and artificial intelligence.

Machine Learning

Machine learning algorithms can analyze, identify, and predict highly complex patterns based on large data using regression analysis, neural networks, and decision tree techniques. Some common forecasting methods in machine learning include regression analysis, neural networks, and decision tree techniques.

Artificial Intelligence

AI-powered forecasting systems can continuously learn and adapt to new data, improving their accuracy over time. These systems can handle complex scenarios, such as predicting the impact of external factors like economic shifts or competitor actions on demand.

Conclusion

Manufacturing managers consider the selection of the right forecasting technique to be of vital importance. Understanding the different forecasting techniques available increases the value of the manager’s selection decision-making. Whether it is qualitative techniques such as the Delphi model and market research, or quantitative approaches like time series analysis and causal model, each technique has its strengths and its related fields of application.