Demand Forecasting: Bringing Together Algorithms and Technology

April 4, 2017 - by Synoptek

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Demand forecasting serves as the central warehouse for predicting future demands cost-effectively. But is that all? Optimal forecasting must not only help in increasing ROI, it should use the most appropriate methods to forecast demand or sales accurately to best serve the particular dynamics of an organization. All products and markets are different and so are their methods of accurate forecasting using diverse algorithms and models. This article highlights the importance of accurate forecasting through the use of advanced forecasting models.

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Many finance heads or CFOs find forecasting a challenge as it encompasses forecasting future probabilities and getting answers to questions like:

1) Is my forecasting adequately reliable to plan future procurement, production, inventory, and logistics?

2) Is my company still using basic forecasting methods and tools which are insufficient?

3) How can I use advanced forecasting algorithms and technology, which offers accuracy in future predictions, with less investment?

Forecasting comes with its own set of challenges, but is top priority for decision makers as better and precise demand forecasting helps improve cost saving and availability in the supply chain for their businesses.

Business leaders, mostly finance heads, look for predictive insights that will help them stay away from snags. Organizations are attaining ways to replace manual forecasting processes with a fresh set of technologies driven by data science, which is based on predictive analytics – a combination of statistics, data mining and modelling, and machine learning. According to Entrepreneur Magazine, businesses that use predictive analytics report levels of forecast accuracy to be as high as 82% on a deal-by-deal basis.

In a predictive analysis model, several statistical, data mining, and machine-learning algorithms are available. You can choose from a variety of algorithms and forecasting tools to address business apprehensions; we’ve listed some of the popular ones here:

1) Regression Algorithms: Regression analysis is commonly used for prediction and forecasting purposes, with substantial usage in the field of machine learning.

2) Classification Problems: Classification is the process of detecting the set of categories a new observation belongs to, based on a training set of data containing observations/instances with a known category membership.

3) Time Series: Time series analysis embraces time series data analyzing methods to extract meaningful statistics and other characteristics of the available data. This model is used for forecasting to predict future data/values based on previously observed data/values.

Various techniques for carrying out these algorithms or models have been developed over the years including linear regression which is used in forecasting problems where the label is continuous in nature e.g. sales forecast of a retail chain; decision trees that are used for classification of problems and building a tree of predictive decisions based on actual values of attributes in the data; and, support vector machines algorithms which can be used for both classification or regression challenges and best segregates the two classes. In case the two classes can’t be evidently parted, the algorithms find the best boundary they can. Other popular algorithms/models include Logistic Regression, Clustering, HoltWinters, and ARIMA.

Predictive business intelligence tools are used to produce accurate and statistics-based demand forecasts that leverage past and current (or external) information by pulling data from different sources or repositories. R programming language is a well-known statistical packages used for predictive analytics, and its use has been growing rapidly over the past few years.

Even though many tools come with a full suite of algorithms, there are a number of tools available with a specific focus that performs one algorithm well. These tools offer usability and visualization features, but are used for one type of algorithm only, like in case of decision trees.

Full-function code-based tools provide access to the largest array of forecasting tools and modeling algorithms, and thus, can be utilized for all the required data processing and management, but do not offer great usability and visualizations. The latest market entrants are offering predictive analytics through cloud solutions with web-based modeling interfaces, where data storage and processing, model building, and prediction is done on the cloud with pay-per-use model.

According to an article on Tech Republic, business intelligence tools offer decision makers complete access to information that provides a detailed portrait of past data, and boost your sales and marketing efforts. With various predictive analytics tools available in the market, you can optimize demand forecasting to increase sales, avoid item overstocks, manage inventory to provision omni-channel sales, deploy more targeted campaigns for sales growth, predict future behavior of customers, and more.

With accurate predictions, it is easier for you to visualize the future and facilitate smarter decision making, uncovering new business opportunities. So, put an end to guessing and start using smart algorithms and technology to achieve accuracy in your forecasting.

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