Maximizing your ERP: How AI drives predictive analytics

Since their inception enterprise resource planning systems (ERP) have been the backbone of modern business, especially in functional areas such as finance and supply chain management.

With artificial intelligence now able to be incorporated into most every feature and workflow in ERP, the platforms are moving from simply being the backbone to being the eyes, ears, and central nervous system of a business.

With integrated AI, ERP is quickly moving past its traditional role as a centralized repository into an intelligent platform that drives proactive, data-driven business strategies, such as predictive analytics.

What is predictive analytics?

Predictive analytics is a means of identifying patterns and trends to predict what might happen next. Using historical and current data, along with statistical algorithms and machine learning, a business can anticipate customer behavior, market shifts, or potential risks, enabling it to prepare for, and even get ahead of, changes, challenges, and trends.

With AI algorithms analyzing data from various ERP components (sales, inventory, finance) to generate forecasts, predictive analytics empowers businesses to make proactive decisions that can optimize inventory, reduce costs, improve resource allocation, and mitigate potential losses.

While these benefits apply to most functional area in any business, this article focuses on the specific benefits an organization gains in finance and supply chain management. More specifically, it details how specific ERP systems — Microsoft Dynamics 365 Finance and Dynamics 365 Supply Chain Management — benefit from AI-fueled predictive analytics systems.

Enhancing Dynamics 365 Finance with predictive analytics

Fluctuating markets, difficulty in predicting cash flow, reducing risk exposure, incomplete financial reporting – the challenges in finance a business would lean into predictive analytics to solve are essentially limitless. The benefits are equally limitless, but here are the top five when extending D365 Finance, specifically, with predictive analytics.

• Improved financial forecasting: Predictive models analyze historical financial data to forecast revenue, expenses, and cash flow with greater accuracy. This enables better budgeting, financial planning, and risk management, leading to more stable financial performance.
• Enhanced risk management: AI-driven analytics can identify potential financial risks, such as late payments, fraud, or market volatility, before they escalate. This proactive approach allows businesses to take preventative measures and minimize financial losses.
• Optimized cash flow: Predictive analytics forecasts cash flow patterns, enabling businesses to anticipate shortfalls or surpluses. This helps optimize working capital, improve liquidity, and make informed decisions regarding investments and expenses.
• Streamlined accounts receivable and payable: Predictive models can assess the likelihood of customers paying invoices on time, allowing for targeted collection efforts and reduced bad debt. Similarly, it can forecast payment obligations, enabling timely payments to suppliers and improved vendor relationships.
• Data-driven decision making: By providing actionable insights derived from vast financial data, predictive analytics empowers finance teams to make informed, strategic decisions. This leads to increased efficiency, reduced costs, and improved profitability.

Enhancing Dynamics 365 SCM with predictive analytics

In the supply chain, the only certainty is often uncertainty. Weather patterns interrupting shipping routes, material supplies getting more costly or running dry, increased global regulations and tariffs – in today’s supply chain, nothing moves in a straight line from A to B.

And while Dynamics 365 SCM enhanced with predictive analytics might not solve every single challenge, it plays a key role in navigating the modern supply chain. Here are five key supply chain advantages typically derived through the combination of Dynamics and predictive analytics.

• Demand forecasting and inventory optimization: Predictive models analyze historical sales data, market trends, and external factors to accurately forecast demand. This allows businesses to optimize inventory levels, minimizing stockouts and overstocking, reducing holding costs, and improving customer satisfaction.
• Supply chain risk mitigation: AI-driven analytics identify potential disruptions in the supply chain, such as supplier delays, transportation issues, or geopolitical events. This proactive approach enables businesses to develop contingency plans, diversify suppliers, and minimize the impact of disruptions.
• Optimized logistics and transportation: Predictive analytics optimizes transportation routes, delivery schedules, and warehouse operations. By analyzing factors like traffic patterns, weather conditions, and delivery times, businesses can reduce transportation costs and improve delivery efficiency.
• Enhanced supplier management: Predictive models assess supplier performance, identify potential risks, and forecast supplier reliability. This enables businesses to build stronger supplier relationships, negotiate better contracts, and ensure a stable supply of materials.
• Improved production planning: By forecasting demand and analyzing production capacity, predictive analytics enables businesses to optimize production schedules. This results in reduced lead times, improved production efficiency, and minimized waste.

The future of ERP is tied to AI-enabled predictive analytics. AI transforms static ERP data into dynamic, actionable insights. By forecasting trends and automating decisions, it enables proactive management, optimizing operations, and enhancing agility in an increasingly complex business landscape.

Want to learn more how you can maximize your ERP solutions with AI-driven predictive analytics? Contact us and get started today.