Predictive analytics are a valuable technology in all business aspects, but they are particularly valuable to operations. COOs can use the data they collect to predict supply chain trends and behavioral patterns in human resources and customer satisfaction, and to assist with risk management. Leaders who integrate predictive analytics into their business models will see higher rates of success.

What is Predictive Analytics?

Predictive analytics looks at historical data to predict future outcomes. It collects data sets based on various business-related queries. It analyzes the data and delivers results via a report that can be reviewed by stakeholders.

There are various types of predictive analytics models as follows:

• Forecasting: A forecasting model looks at past performance to make predictions about inventory and demand.
• Marketing: Marketing analytics predict how consumers may react to a specific marketing strategy.
• Fraud Detection: A fraud detection model identifies fraud patterns and prevents them from occurring in the future.
• Supply Chain: Supply chain analytics can predict the likelihood of issues such as fuel cost increases, driver availability, and a shortage of goods so leaders can plan accordingly.
• Human Resources: These analytics predict whether an employee is likely to quit in a certain timeframe, whether they are a good match for your company, and how likely they are to be a top performer.

Predictive Analytics in Operations

As you can see, various predictive analytics models add value to the operations space. COOs can use them to:

Improve Supply Chain Operations

Supply chain operations play a huge role in a COO’s wheelhouse. Leaders can use predictive analytics to determine things like inventory shortages and demand. If they foresee a shortage in a specific market, they can order from different vendors to ensure they meet demand.

Logistics are also integral to the supply chain. COOs can use the data they collect to ensure they have the drivers and vehicles they need to deliver orders. They can also determine associated costs to create smart budgets.

Oversee Human Resources

Human resources are valuable in operations. COOs must ensure they have the manpower they need to operate supply chains and deliver goods. They also rely on managers, assistants, and analysts.

Predictive data provides insight into turnover rates. Leaders can use this information to hire accordingly. They can get ahead of the curve to ensure staff shortages don’t impact operations.

Ensure Customer Satisfaction

COOs have various long- and short-term goals, but the most critical may be customer satisfaction. Customers must be satisfied with the quality of products produced and the timeliness and way they are delivered. If customers aren’t satisfied, it can negatively impact profitability and brand reputation.

Leaders can use the data they collect to determine how customers will react to certain products and services. They can adjust their strategies to ensure they deliver the best service. With the right approach, they can make their company a leader in its industry.

Increase Efficiency

Predictive analytics help leaders foresee various obstacles and roadblocks. They can eliminate them in advance to ensure they don’t interfere with operations. Systems will run more efficiently allowing organizations to save time and money.

Risk Prevention

The technology identifies various risks such as potential supply and staff shortages, possible logistic interference, and other factors that indirectly affect operations, like fraud. This information allows leaders to determine vulnerabilities and protect their assets. It reduces risks and decreases disruptions.

Best Practices for Predictive Analytics Processing

Leaders will get the most from predictive analytics by following these best practices:

• Proper Interpretation: Leaders must know what they’re looking for to interpret data correctly.
• Data Quality: Not all data collected will be high-quality. Consider the data source to determine whether the information is accurate and usable.
• Data Bias: People have biases that may impact how they interpret data. Leaders should try to be impartial when reviewing information.
• Privacy and Security: Data mining often means going through personal accounts. Businesses must ensure they have legal access to the accounts. They must also protect the information they review.
• Maintenance: Predictive analytics will not be successful with a set-and-forget approach. You must update its parameters to ensure accurate reporting.

Want to learn how to use the latest technology to make your operations more efficient? Sign up for our newsletter today.