How are Analytics Used in Demand Forecasting?

how are analytics used in demand forecasting

The Relationship Between Analytics and Demand Forecasting

Demand forecasting is an essential function of demand planning that uses historical sales data to estimate future customer demand. Business processes ranging from supply chain management to decision making are influenced by demand forecasting and demand planning accuracy.

Predictive analytics is a category of data analytics that extracts information from data sources using a combination of machine learning algorithms and traditional statistical forecasting in order to predict future outcomes. Predictive analytics goes beyond historical data analysis, evaluating why or what factors drive customer demand and the potential future impact of these drivers on a specific business.

As big data systems become more common, predictive analytics and machine learning tools have become more accessible to business professionals as a result. Businesses are increasingly utilizing data mining techniques for predictive insights including during their traditional forecasting and demand planning processes.

Differences Between Analytics and Demand Forecasting

Both demand forecasting and data analytics benefit crucial business operations. However, there are vast differences between demand forecasting and data analytics that business professionals should be aware of.

Instead of relying primarily on historical sales data to predict future customer demand, predictive analytics provides a deeper understanding of why a consumer buys a product. Gaining an intimate understanding of customer preferences gives businesses an edge over their competitors.

Predictive models analyze historical data and current data concurrently to formulate a more comprehensive understanding of products, partners, and customers. Partner, product, and customer insights can then be intergrated into both short term and long term risk assessment and decision making processes.

Predictive analytics looks for opportunities where demand planning forecasting focuses on requirements. While demand planning forecasting uses time series and solely seeks to predict future customer demand, predictive analytics uses big data to generate multiple solutions and insights.

How Analytics is Beneficial to Helping Forecast Demand

Proper use of data analytics including predictive analytics increases the accuracy of demand forecasting by incorporating a wide range of external factors that influence customer buying decisions and preferences. These external factors can range from weather changes to economic expansion.

For example, a snowstorm could quickly develop into a supply chain disaster if vendors are unable to deliver inventory to a business by a preestablished expected date. Without access to the materials or products that customers ordered, businesses will be unable to execute order fulfillment.

When customers are not able to obtain the products they visited an online or in-store business to purchase, customer satisfaction rates plummet. These negative customer service experiences can result in decreased customer retention and damaging social media reviews that can negatively impact a business's reputation long term.

While traditional forecasting would be focused on historical data primarily, data analytics seek to foresee potential weather hazards, allowing a business to make alternative arrangements to avoid supply chain management issues. For instance, an alternative supplier could be located and contracted, avoiding the entire supply chain management issue from ever transpiring.

With the improved risk management techniques that data analysis supplies, business operations can continue running as smoothly as possible. As a result, customers and employees will have an overall better experience with your business.

Why Analytics are Important in Ensuring Accurate Demand Results

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With less than 30% of businesses accurately predicting future deals, data analytics can completely revolutionize demand planning and demand forecasting processes.

While traditional forecasting techniques use historical data to predict the future, innovative business professionals are increasingly recognizing that the future is always evolving in unpredictable and unprecedented ways.

As such, businesses are recognizing the potential for big data to grant a clearer vision of the future and are identifying opportunities to use data in their decision making processes. Data driven decision making decreases the likelihood of businesses making bad investments or missing important opportunities.

Data analytics are useful for supply chain management and identifying new market opportunities. As businesses become more technologically advanced and more future oriented than ever before, data analytics will become even more commonplace.

Predictive analytics are valuable for a long term demand forecast but are not viable for short term forecasting. When using predictive analytics make sure to forecast demand for at least a month in advance.

Using the wealth of information that big data supplies, businesses will find that their demand planning and demand forecasting techniques are increasingly more accurate. Businesses can then redirect the labor costs and employee time that would have been otherwise wasted due to an inaccurate demand forecast on developing their next innovative new product or expanding existing business operations.

Key Takeaways

  • Demand forecasting is a function of demand planning that predicts future customer demand using historical sales data.
  • Predictive analytics is a category of data analytics that seeks to predict future outcomes using a combination of traditional statistical forecasting and machine learning algorithms.
  • While demand forecasting uses time series to predict future customer demand, predictive analytics uses big data to generate numerous insights and solutions.
  • Predictive analytics increases demand forecasting accuracy by analyzing a wide range of factors simultaneously such as weather events or economic expansion.
  • Data driven decision making and the ability to identify potential market opportunities are increased when businesses invest in data analytics techniques.

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