ERP analytics leverages ERP software and services to gather and analyze data produced by organizations’ activities. It involves parsing data generated by business operations and presents reports, dashboards and visualizations in the form of graphs, charts and maps to communicate performance metrics, trends and patterns. It helps users derive insights by streamlining their efforts to search, merge and query data to make robust business decisions.

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For instance, a manufacturing company that needs to manage its supply chain leverages ERP analytics capabilities to gauge discrepancies in the shipping process that cause delays. With the help of analytics, you can figure out the best routes to avoid delays and deliver the product at a predefined time. In another example, a sales team can consolidate sales data obtained from ERP solutions to create a dynamic dashboard displaying sales in each region, average weekly sales, average revenue per unit, customer acquisition, profits and more.

Users leverage ERP analytics capabilities to drive change, create efficient processes, eliminate redundancies and adapt to dynamic market conditions.

Components

ERP analytics entails processes and methods that collect, store and analyze data obtained from business operations to optimize performance. It provides a comprehensive state of the business to the stakeholders to help them make actionable decisions.

The components of ERP analytics include:

• Data Mining: Data mining is the process of finding trends, patterns, correlations and anomalies within large datasets. It uses a broad range of techniques like machine learning and statistics to uncover patterns, and insights are used to increase business revenue, cut costs, reduce risks and boost customer relationships.
• Reporting: Collaborate with stakeholders to present data analysis in the form of reports so they can make informed decisions. The reports consist of charts, graphs and visualizations to present findings in a single source of truth.
• Performance Metrics: Compare current performance data with historical data to track performance against predefined goals using customized dashboards.
• Descriptive Analytics: Descriptive analytics is a statistical method that summarizes historical data to identify trends and patterns.
• Data Querying: Ask data-specific questions in natural language to extract specific information or answers from complex datasets.
• Statistical Analysis: Obtain results from descriptive analytics and further explore data using inferential statistics to gauge how a trend happened and why.
• Data Visualization: Create visual data representations such as charts, graphs, maps, histograms and more to convey meaningful insights.
• Visual Analysis: Explore data through visual storytelling to communicate insights on the fly.
• Data Preparation: Data preparation involves combining data from multiple sources, cleaning it to eliminate discrepancies, identifying dimensions and measurements and preparing it for data analysis.

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Primary Benefits

Let’s look at the benefits of ERP Analytics:

Better Demand Forecasting

Demand forecasting typically involves identifying patterns and trends in historical data that reveal sales fluctuations over time. Users can dig deeper to help organizations determine how soon to stock up on in-demand inventory. Users assess weekly, monthly and yearly seasonality factors to provide requisite guidance to the business.

For example, an influencer with a huge follower base, posting on Instagram about a product can increase sales. It’s straightforward to see the benefits of ERP analytics in the retail industry, but the advantages expand into manufacturing and customer service. For instance, increased demand for an item that needs a difficult-to-acquire component might require an organization or its supply chain partners to expand their network to source the item or look elsewhere for alternatives.

If a manager is in charge of production schedules for beverage factories and the sales graph shows robust month-over-month growth in a particular region, they can approve shifts to ensure production meets demand.

Understand Consumer Preferences

ERP analytics captures and analyzes consumer data to understand consumer preferences and make better decisions. It provides useful insights into consumer behavior, such as the products they prefer to buy and how they perceive marketing, sales and customer service offers.

Analysis of customer behavior from the data obtained from ERP systems is known as consumer analytics. The primary objectives of consumer analytics are to identify, attract and retain the most profitable customers.

Consumer analytics involves:

• Creating a unified view of customers called customer personas to make robust decisions about acquiring and retaining customers.
• Identifying valuable customer segments.
• Interacting with them proactively to better understand their preferences.

The better you can gauge current customer preferences and buying patterns, the better forecasts you can make about their behavior with the help of predictive analytics capabilities.

Provides Real-Time Insights

ERP analytics offers real-time insights into customer data. It tracks customer behavior and formulates strategies to raise their satisfaction levels. Real-time analytics improves profitability, reduces costs and propels business towards exponential growth.

It predicts market fluctuations, identifies opportunities and drives management to make appropriate decisions. With immediate remedies and solutions at their disposal, businesses can predict with confidence and optimize data to drive competitive business advantages.

Fuels Growth

Predictive analytics lets you formulate data-driven strategies to deal with challenges and leverage possibilities for business growth. Knowing that you’re seeing the whole picture can help you understand how changes will influence customers, vendors, stakeholders and departments. Leveraging predictive analytics keeps guesses and speculations out of the game and uses data to make difficult decisions.

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Measuring Success with ERP KPIs

Key performance indicators (KPIs) are defined as quantifiable values used to gauge the organization’s overall performance in terms of financial, strategic and operational achievements compared to competitors. KPIs measure a company’s success against predefined targets or objectives it aims to achieve within a stipulated period. Whereas KPI management is the process of defining, executing, monitoring and analyzing KPIs that help businesses achieve their objectives.

Before you formulate a robust ERP implementation plan, it is vital to understand how to measure ERP success. That’s where the role of KPIs comes into play. You should be able to measure these KPIs during and after ERP implementation. While there are many KPIs to consider for ERP implementation, a few of the important ones are mentioned below:

Improved Customer Experience

One of the most important reasons businesses invest in ERP is to facilitate collaboration between the new and existing customers and boost customer experiences. ERP systems streamline business processes that eventually improve business functions related to customer service. For example, if you own an e-commerce store, the ERP solution helps you organize customer orders and shipping information in a centralized database. It empowers you to ship orders on time and improve the consumer experience.

Consistency is another factor that is vital to customer service. If a business delivers positive experiences to its customers every time they associate with it, they are likely to return and make a purchase. The best way to measure customer satisfaction levels is to gauge online reviews, send out surveys, listen and proactively resolve customer complaints.

Increased Inventory Turnover

Inventory turnover measures the number of times inventory is sold during a particular period. The ERP solution can increase inventory turnover by providing better visibility into the inventory and supply chain management processes.

For example, suppose you always have excess inventory for a particular product. The ERP solution can use its predictive analytics capabilities to see how many products should be stocked for future use. It can also predict consumer buying behavior to stock products accordingly, minimize wastage and reduce costs.

Revenue Growth

After implementing the ERP solution, your business is likely to experience revenue growth due to increased sales and reduced costs. These solutions reduce costs by optimizing business processes and increase efficiency. It provides access to real-time data to make smarter decisions to cut down on costs and increase sales.

Real-Time Data Access

Businesses implement software to gain real-time access to data to make robust decisions while leveraging reporting and dashboard capabilities. While the business intelligence capabilities may differ from system to system in terms of complexities, it helps users obtain useful insights and make sense of data.

For example, on the factory floor, ERP systems with advanced business intelligence capabilities convey which machines are due for maintenance. They empower users to conduct advanced planning, minimize downtime and set realistic expectations.

Increased Purchasing Power

ERP solutions increase purchasing power by sourcing the right suppliers. It leverages supplier performance management capabilities to identify weak points and negotiate better terms with suppliers to source the right ones.

They use ERP analytics to analyze suppliers and determine the ones costing you more than necessary. Replace the costly suppliers with the cheaper ones that provide the same quality raw materials. This way, they help reduce material costs and boost productivity.

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Conclusion

ERP analytics can go a long way to generate useful business insights and deliver a competitive advantage. With these tools, it is possible to aggregate data from different sources and generate reports and dashboards for specific business departments or businesses. You can leverage systems with prepackaged analytics or integrate with other BI tools to facilitate advanced analytics.

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