Infuse Accuracy and Speed into your Supply Chain Planning

Supply chains remain vulnerable to shocks and disruptions, making planning and execution challenging. Demand is difficult to forecast as the bullwhip effect continues for those contending with supply side shortages and logistic capacity constraints. Consumer expectations are driving increased pressure for end-to-end planning solutions to improve supply chain performance.

Companies are working hard to build momentum by diversifying their supply chain footprint and modernizing their technology to build advanced planning capabilities. They are developing strategies to gain deeper insights into consumer behavior patterns to increase revenue and optimize inventory performance. In these uncertain times, companies that lead in customer experience will outperform those who do not.

The rising adoption of predictive analytics is allowing companies to make smarter decisions about their supply chains. Predictive analytics is now becoming more mainstream and affordable so companies can fine-tune their supply chains more easily. Research has shown that these advanced planning capabilities can provide a valuable solution to infuse accuracy and speed into supply chain planning.

What Is Supply Chain Predictive Analytics?

Predictive analytics is the ability to use historical data to predict future supply chain activities. It is a form of advanced analytics that leverages a combination of statistics, data mining, and machine learning algorithms. Predictive analytics enhances your forecasting with mathematical models to accurately forecast future trends and predict what will happen given certain variables. Predictive analytics uses sophisticated technology to improve accuracy and speed so users can make data-driven decisions with greater confidence. Enterprise resource planning can be more efficient and accurate through demand forecasting that predicts future market trends and inventory supply.

 Why Predictive Analytics?

 It is a way to boost your supply chain analytics and is essential in generating better forecasts for demand, optimizing inventory, and reducing costs. It uses sophisticated models to determine inventory requirements by region, location, and usage, allowing companies to determine the optimal inventory levels to satisfy the demand while minimizing stock. Predictive analytics combines data from different various sources to paint a comprehensive picture of what may happen soon. The challenge is to understand what happened and why, learn from it, and then make decisions based on what you think will happen next.

 Artificial intelligence (AI) is at the heart of predictive analytics in supply chains. Augmented analytics is a collection of features enabled by AI and ML that perform complicated tasks to enable advanced analytics. It can automate demand forecasting, production planning, and optimize inventory levels with little or no human intervention. Companies need prescriptive models capable of using multiple variables like sales reports, manufacturing data, transportation information, weather forecasts, consumer sentiment on social networks, and other external factors that may impact supply chains.

Data is the backbone of a strong supply chain. Organizations must cultivate a data literacy culture because data-driven decision-making is a critical element in supply chains. Intelligent supply chains are powered by custom prescriptive and cognitive solutions, instead of simple predictive analysis software. Four types of data analytics are:

1. Descriptive analytics tells what has happened.
2. Diagnostic analytics tells why it had happened.
3. Predictive analytics tells you what will happen.
4. Prescriptive analytics tells you how you should act to make it happen or prevent it from happening.

Companies are focusing on how they can leverage supply chain data for competitive advantage. Using predictive analytics for inventory optimization can help maintain the appropriate level of inventory at all times lowering investment costs. End-to-end visibility, inclusive of a connected planning platform, is the road map for a digital supply chain.

 The benefits of predictive analysis include:

• Improved decision-making
• Cost savings
• Revenue maximization
• Risk reduction
• Increased customer service

Companies using predictive analytics have a better understanding of where supply chain bottlenecks occur, where delays are happening, and where quality might be impacted. Exploring how specific changes will impact supply chain operations, to recognize where improvements can be made.

Smart Predict uses SAP machine-learning algorithms to evaluate the relationships in the data, to then build a formula for a predictive scenario. The beauty of this is that the users themselves build advanced models, allowing the focus to be on finding the best insights to solve the business question at hand. Little knowledge of statistics and machine learning is required. There are three types of scenarios available in Smart Predict: classification, regression, and time series. To learn further details, read this short blog that explains how SAP enhanced the machinery behind the scenes during a March 2021 initiative.

Leverage these additional resources to learn more about predictive analytics:

• Discover how the machine-learning application of predictive planning is a key to success. Explore the Smart Predict functionality of SAP Analytics Cloud (SAC) to build predictive analytics models.
• Businesses seeking to successfully meet today’s challenges are ready for advanced analytics to help drive their actions. Discover the SAP Analytics Cloud solution to leverage intelligent technology and learn how you can use predictive model to prepare for the future.
• Explore how supply chains are a key to profitability. The elements of people, processes, and data are the key elements to optimizing business workflows and simplifying the supply chain. By developing a culture that continuously reviews and adjusts companies can run more efficiently and deliver financial value from their supply chains.

What does this mean for SAPinsiders:

1. Empower business users, data scientists, and machine learning experts. Because Smart Predict models are built by the business users, it puts more trust in the model, which leads to solid decision-making and greater profitability. In addition, having qualified business workers on-site to execute complex AI projects will give you a competitive advantage. Data scientists and machine learning experts can still provide support for more advanced analytic models.
2. Champion data collection, storage, and preparation. It is critically important to invest time and resources into collecting data, and information about business processes. Machine learning algorithms need clean and organized data for proper modeling. The higher the quality of data, the better insights they can produce.
3. Deploy predictive analytics. Consider what type of digital technology is most appropriate for your organization. Cloud, big data, robotic process automation (RPA), artificial intelligence (AI), or machine learning (ML) can be used to transform the quality of your planning decisions. Explore the possibilities for your investment.