Supply Chain Prescriptive Decision Automation: A Smarter Way to Optimize Logistics

In this article, we aim to eliminate confusion around the different types of analytics—predictive analytics, prescriptive analytics, and prescriptive decision automation—and highlight the tangible benefits of logistics optimization through prescriptive decision-making. We’ll contrast the old manual planning approach with the optimized, automated method, and spoiler alert: the benefits range from cost savings to improved customer service, thanks to tools like the LevelLoad automated prescriptive digital twin.

Understanding Supply Chain Analytics: Predictive vs. Prescriptive

What is Predictive Analytics?

• Purpose: Predictive analytics forecasts future events using historical data, correlations (e.g., more ice cream is sold on hot days), and trends.
• Methods: Employs statistical models, machine learning algorithms, and data mining to identify patterns and predict outcomes.
• Output: In supply chain management, this typically means forecasting metrics like demand (e.g., predicting sales of 500 units next week).
• Use Cases:
  

  • Demand forecasting
  • Customer behavior prediction
  • Risk assessment

• Example: Predicting customer sales from a distribution center based on historical trends.

What is Prescriptive Analytics?

• Purpose: Goes a step further by recommending actions to achieve desired outcomes. For example, if demand is increasing at a specific distribution center, prescriptive analytics can recommend sending more inventory there.
• Methods: Combines predictive analytics with optimization and simulation techniques to recommend the best course of action.
• Output: Provides actionable recommendations to optimize supply chain operations and resource allocation.
• Use Cases:
  

  • Optimizing inventory levels
  • Streamlining supply chain processes
  • Enhancing resource allocation

• Example: Recommending a production increase to meet growing demand in a specific region.

Key Differences Between Predictive and Prescriptive Analytics

Prescriptive Decision Automation: The Next Step in Supply Chain Analytics

Prescriptive analytics becomes even more powerful when automated. Known as prescriptive decision automation or performative automation, it reduces the need for human intervention by automatically applying recommendations based on predefined rules.

Features of Prescriptive Decision Automation

• Automation: Automatically applies recommended actions based on predefined algorithms.
• Real-Time Decisions: Optimizes operations in real-time, adjusting to changes as they occur.
• Integration: Connects seamlessly with systems like ERP, TMS, and WMS to enhance visibility and execution.
• Optimization: Uses AI-driven algorithms to achieve the best outcomes based on constraints, goals, and historical trends.

Use Cases of Automated Prescriptive Analytics

• Inventory Management: Automatically adjust stock levels based on forecasted demand and supply chain conditions.
• Dynamic Pricing: Real-time price adjustments based on customer behavior and market conditions.
• Carrier Management: Assigning loads to carriers to optimize cost and service.

Automating prescriptive analytics can significantly enhance operational efficiency, reduce costs, and improve decision-making accuracy.

Case Study: From Manual Planning to Prescriptive Decision Automation

The Old Way: Manual Planning Challenges

A large consumer products company generated 2,000 to 4,000 stock transfer shipments weekly. It employed a standard method for planning replenishment shipments that most companies would recognize. The company’s APS (Advanced Planning and Scheduling) system created replenishment requirements based on its understanding of needs and inventory availability. However, the day-to-day volume on each lane often fluctuated dramatically. 

These wildly fluctuating requirements were then organized into truckloads using a straightforward load-building tool that considered cube, weight, and pallet positions.  

When added to the ERP to create stock transfer orders (STOs), inventory availability was rechecked, and any potential out-of-stocks were removed from the STO. While, in theory, inventory should have been in stock, the APS and the ERP used different methods to calculate Available to Promise (ATP), resulting in discrepancies.

Once entered, the STO triggered the TMS to generate a request for a carrier (a tender) to pick up the load—usually a few days later. Often, when the volume on the lane was high, the first carrier (typically the lowest cost/best service carrier) refused some of the loads, and the remaining loads were sent to less desirable carriers until all were covered. Sometimes, this requires a transportation analyst to make phone calls if, for instance, contracted brokers cannot meet the demand or spot carriers are too expensive. While sending tenders earlier produced better results, the challenge was that supply planners had to complete loads sooner. They start work before sunrise—so earlier was indeed a stretch.

Meanwhile, during the time between tendering and the shipment’s departure, several unfortunate events often occurred.  

• There were inventory problems when, for instance, production failed to deliver the required quantities.
• Customer demand changed, so plans made several days ago included shipping the excess inventory while urgently needed products remained at the plant. In some cases, a careful planner could make last-minute adjustments, but the absence of the right inventory often led to customer service failures.
• The shipping and receiving sites and the wild fluctuations in demand required extra staffing. In some instances, service failures could again occur because unloading vans could be backlogged due to a lack of labor or available space inside the customer-facing distribution center.

In summary, the results were:

• Stressed staffing at both the planning and sites.
• Incremental transportation cost and degraded transportation service.
• Detention.
• Overtime and staffing issues at the receiving and shipping sites.
• Sub-optimal customer order fill.

Related Blog: Revolutionizing Supply Chain Planning: The Power of Automation

A Better Way: LevelLoad Prescriptive Decision Automation

Recognizing the inefficiencies of the manual process, this company adopted ProvisionAi’s LevelLoad automated prescriptive analytics tool. LevelLoad integrates data from APS, TMS, ERP, and WMS to create a digital twin of the supply chain and optimize operations.

Related Blog: The Role of Digital Twins in Modern Supply Chain Optimization

Here’s how LevelLoad automated and prescribed the process:

As before, the APS generates requirements. These now feed into LevelLoad along with data from the previously mentioned systems. Using advanced optimization and AI, LevelLoad calculates the flows by truckload along each lane for the entire network. There are two key points to note here:

1. Since large consumer product companies typically move full truckloads between sites, LevelLoad must determine what is on each truck.
2. It is essential to examine the entire network because adjusting one lane can affect many others. For example, if a plant warehouse is nearing capacity, moving products to other sites may be necessary.

The output from LevelLoad is a requirement for carrier capacity by day and lane for the upcoming month. Because LevelLoad knows the potential fluctuations and demand by day, it can smooth out the requirements over time. Please see the attached graphic.

Five days in advance, the system automatically sends a signal to the TMS through the ERP to request the necessary trucks for each lane. Again, there are some key points to consider here:

• Even though LevelLoad has determined what products should ship, the shipment contents have not been defined for the carrier or ERP. The carrier needs only the mode, origin, destination, and ship date.
• Since the day-to-day fluctuations have been removed and it’s five days out (this could be any number, but the company chose five), there is a high probability of receiving confirmation from the carrier that they will accept the load.

Carriers are now committed to a certain number of trucks on each lane. Just before the ship date, using the latest available inventory data, an optimizing load builder (AutoO2—automatic order optimization) automatically fills each load tendered five days earlier with the most needed product.

In this process, human intervention is minimal. LevelLoad runs unattended early in the morning. When planners come into work, the TMS automatically tendered all the loads. If this is Monday, the loads will ship on Friday. On Thursday, mid-morning, the only issues that need to be manually worked are after the load builder has run and some shipments have insufficient inventory to fill them above a set target. In this event, which happens less than 10% of the time, there is an intervention where the planner either cancels the load or finds inventory that can be used to fill out the vehicles.

Results and Benefits

The switch to LevelLoad yielded substantial improvements:

• Transportation Savings: Carrier tender acceptance increased to over 90%, cutting transportation costs by 2-5%.
• Reduced Detention: Improved efficiency at shipping and receiving sites minimized detention fees.
• Enhanced Customer Service: Shorter lead times and better load optimization improved order fill rates and reduced unexpected shortages.
• Higher Productivity: Planners now focus on strategic tasks rather than manual busywork, working normal hours instead of overtime.

Automated Prescriptive Analytics: A High-Profit Solution

Automated prescriptive analytics, like LevelLoad, delivers measurable benefits—cost savings, operational efficiency, and improved customer satisfaction—all while creating a scalable, repeatable process.

By integrating predictive insights with real-time optimization and automation, supply chains can achieve unprecedented levels of efficiency and resilience.

Ready to reduce costs, improve service, and automate your supply chain decision-making? Explore how automated prescriptive analytics can help your business thrive. Contact us today!