Prescriptive Analytics in the Supply Chain: Go Beyond Forecasting

The role of advanced analytics is becoming essential across supply chains as they become global, more complex and extensively data driven. Advanced analytics analyzes data in real-time, predicts future scenarios and prescribes the best course of action, reducing the need for guesswork.

Leading businesses leverage the entire spectrum of advanced analytics to optimize their supply chain and procurement processes, navigate volatility and drive savings. Here is a quick overview of the four types of analytics:

Descriptive analytics: It provides clarity by stating facts about things happening within the supply chain through KPIs such as delivery time and freight cost per unit.

Diagnostic analytics: This form of analytics that sets out the reason behind what is happening. It can help prevent the recurrence of problems or identify past scenarios which yielded good results and which can be recreated.

Predictive analytics: These are helpful for risk management, and present the most probable outcomes and business implications in any given scenario.

Prescriptive analytics: They take predictive analytics a step ahead and tell you what needs to be done for achieving operational and financial goals in the supply chain.

Amongst these four types of analytics, prescriptive analytics is also known as the ‘future of data analytics’, and rightly so because it is not just limited to forecasting but leverages data to suggest the best course of action moving forward.

What is Prescriptive Analytics?

Prescriptive analytics analyzes raw historical data and performance data through machine learning to determine the best probable courses of action or strategies to take at any given point in time. It works on the results given by predictive analytics. Therefore, predictive analytics is essential for prescriptive-based planning.

However, only 10% of companies reported using prescriptive analytics in a Gartner survey.

Prescriptive analytics is highly efficient in empowering supply chains. Some of the crucial scenarios that prescriptive analytics allows companies to answer include:

• What does the customer want?
• What type of offer would be best for a customer?
• What should be the ideal shipment strategy?
• How should the supplier perform?
• How much should you scale up a business?

4 Ways Prescriptive Analytics Can Supercharge Supply Chain

1. Supply Chain Network Design

A good supply chain network design focuses on two things. First, determining your supply chain’s footprint, i.e., where to place facilities and their size. And second, determining the product flow through the supply chain.

The complexity and size of today’s supply chain networks call for stakeholders to make multi-variable choices in their network design like choosing the cost of labor, customer locations and best logistics networks. Prescriptive analytics makes these choices easy. Leading network design solutions often have optimization solvers in the backend that can take the business’ complex constraints into account to deliver an optimal answer.

2. Inventory Optimization

Prescriptive analytics helps businesses maintain optimum inventory levels. It does so by analyzing historical data and predicting future patterns factoring in variables such as lead times, seasonality and ordering patterns.

Even today, most businesses use spreadsheets to optimize inventory.

However, this is not ideal for the requirements of today. Prescriptive analysis helps businesses go beyond the basic logging and itemizing of inventory and mitigates guesswork. It leverages data to create scenarios for different inventory policies and their impact.

Such modeling helps communicate with all stakeholders inside and outside the supply chain the inventory the consequences of their business choices.

Prescriptive analytics in supply chain and scenario modeling benefit a business in the following ways:

• A significant reduction in out-of-stock occurrences
• Reducing inventory and potential service risks
• Lowers losses
• Increases efficiency

3. Supplier Management

Prescriptive analysis plays a vital role in enhancing supplier management in multiple ways. One essential area is choosing the right suppliers. Businesses can identify the best suppliers for each product line depending on factors like reliability, and sustainability apart from cost and quality. This assistance can help boost profits, improve product quality, reduce supply chain risks, and create sustainable ecosystems.

Additionally, prescriptive analysis enables businesses to track supplier performance management. This involves tracking and monitoring supplier KPIs like delivery timeliness, lead time, compliance, defect rate and sustainability KPIs like carbon emissions. More importantly, it helps identifying areas for improvement.

Lastly, prescriptive analytics can effectively facilitate collaboration with suppliers and other internal stakeholders to share information on production schedules, inventory levels, demand forecasts and more. This can lead to improved supply chain visibility, reduced lead times, a boost in overall supply chain performance, better supplier relationships and better overall supply chain management.

4. Warehouse Optimization

Warehouse operations are complex with dynamic and intricate processes. To successfully operate warehouses, businesses need prescriptive analytics tools like mathematical optimization. Prescriptive mathematical optimization helps businesses in the following ways:

• Identify the best location for products
• Define optimal inventory levels
• Define the optimal stacking configuration for pallets (mathematical models are sometimes used in combination with automated palletizing systems to achieve this)
• Optimize schedules for worker schedules
• Streamline order picking activities and choose path length

Prescriptive analysis in warehouse management helps increase warehouse capacity, and productivity of staff and streamlines processes.

3 Challenges of Using Prescriptive Analytics in Supply Chain

1. Data Quality

Since prescriptive analytics relies heavily on the accuracy and reliability of data, low-quality data can thwart its ability to generate insights and recommendations. With supply chain integrating with multiple systems like enterprise resource planning (ERP), warehouse management systems (WMS), and transportation management systems (TMS), ensuring that they share accurate data is difficult.

Moreover, the modern-day supply chain is complex and dynamic with multiple variables that have an impact on its operations, like weather, geopolitical events, and shifts in demand – prescriptive analytics might struggle to consider all data from all these variables for accurate recommendations.

2. Resistance to Change

The implementation of prescriptive analytics calls for modifications in processes and workflows. This can get challenging, as employees could be resistant to the training needed to adapt to a technology-driven way of working.

3. Cost and ROI

Deploying prescriptive analytics requires investments in technology, data infrastructure and training. Businesses may also need to show a positive return on investment (ROI) to justify investments in prescriptive analysis.

Summing Up

With modern-day supply chains getting more complex and going global, using prescriptive analytics in supply chains have become essential. They leverage data and machine learning to not only forecast but recommend the best course of action to take. It helps the supply chain in multiple ways -- from fulfilling customer demands and warehouse optimization to supplier collaboration.