Five Ways Machine Learning Drives Competitive Advantage Through Supply Chain Speed, Accuracy, And Agility

David Sweetman

The supply chain is an integral part of business operations, and it drives tremendous competitive advantage. Its speed and agility come from quickly picking up subtle changes in demand and supply and adapting to those shifts to keep business humming along without disruption.

By tightening the procurement and supply chain synchronization, businesses can quickly realize this reality. However, even though they are focusing on improving the effectiveness and social consciousness of their procurement operation, very few procurement organizations have adequately integrated their supply chain into the fold. In fact, Deloitte’s “Global Chief Procurement Officers Survey 2018,” reported that only 23% of procurement leaders plan to use supplier collaboration as a strategy for delivering higher value – a slight decrease from 39% in 2016.

Tighter supply chain alignment in a world of automation

Dysfunctional behaviors across the procurement-supply chain relationship are introducing undesirable outcomes such as supply shortages, excess in slow-moving inventory, uncompetitive pricing, and delivery delays. For example, cost reduction overshadows the need for high-quality materials and products. Meanwhile, risk aversion allows larger suppliers to capture more business while excluding more qualified small and midsize providers from the selection process.

Thankfully, recent innovations in machine learning make it easier for procurement and supply chain organizations achieve a close-knit, family-like relationship that balances market opportunities with competitive challenges. In the end, these tight procurement-supply chain relationships are the ones that have close alignment with their organizational priorities.

How does this new operating model bring areas like procurement and supply chain team closer together? Here are five machine learning-enabled use cases that can bring the two organizations work in unison.

Use case #1: Predictive contract consumption and compliance

Visibility into purchasing contracts and their status – such as start and end dates, supplier requirements, designated materials, and current consumption rates – all are a critical part of optimizing the supplier relationship and ensuring business continuity. Establishing contractual trust requires active monitoring and alignment of performance against terms to ensure compliance on both sides and to restart contract negotiations before expensive expedites become necessary.

Contract consumption and compliance that leverage machine learning algorithms enables procurement specialists to automatically predict the date of the contract’s full consumption. The buyer can now identify contracts that should be renegotiated at the appropriate time, handle potential suboptimal conditions proactively, and avoid poorly negotiated prices and terms due to past contract overconsumption.

Use case #2: Predictive analytics for stock in transit

Companies that issue and receive goods need to follow the status of their materials and products in transit so they can address emerging delays or issues before they happen. By ensuring that every order is delivered on time, the company can avoid expediting activity to rush product across the supply chain, eliminating unnecessary payroll and logistics spend.

With integrated machine learning available in a mobile app, warehouse managers, dock employees, and drivers can access an overview of open shipments and goods movement based on predictive models, prebuilt automation triggers, and analytics based on real-time data. In turn, the process of forecasting stock in transit arrival is streamlined, automated, and responsive – leading to logistics planning and scheduling that are on-time, efficient, and reliable.

Use case #3: Automated supply assignment sourcing

Increasing the automation of supply assignment sourcing reduces the need for manual interactions while securing materials with the best price, fastest delivery time, and highest quality. The system must automatically create a bidding event when an internal source of supply is unavailable. In this case, artificial intelligence acts like a human purchaser and sends the bid invitation to a defined list of preferred suppliers.

Machine-learning algorithms assign the right source of supply to orders by using pattern recognition on historical data, even when a clearly defined origin is not present. Over time, the algorithms learn how to make decisions based on factors such as price, supplier evaluation score, and delivery time.

Use case #4: Intelligent creation of catalog items for free-text purchases

Proposing the creation of a new catalog item can yield many benefits. By reducing the number of one-off purchases based on the unique description from requests, better buying decisions and standardization of products can be made. These so-called “free-text” purchases are the ad-hoc descriptions that each requestor might use.

By using machine learning algorithms that look through the “free text,” requests can be compared against historical description patterns to recommend the addition of a product or service to the catalog. This enables prices for more than one-off purchase to be negotiated. New materials are made available in the catalog automatically if there is high user demand.

The advantages of controlling the free-text creation of new catalog items are potentially game-changing. Procurement areas can increase process efficiency, accelerate purchase-order creation, drive error-resistant transactions, and ease the handling of good and services from the internal catalog.

Use case #5: Intelligence assisted purchase requisition processing

Although non-automated supply chain requisitions are expensive from a process and resource perspective, they are sometimes necessary. Operational purchasers are facing long lists of open purchase requisitions from several sources – and they need a fast, efficient, and error-free way to handle all of them.

Machine-learning algorithms address this common challenge by recommending the best ways to process the purchase request. By optimizing requisition purchasing, the system suggests possible bundles, the most appropriate request for quotation document to be created, ways to avoid a specific exception in the future, and corrections for the right product category. The operational purchaser reviews the data to process the open purchase requisition with little effort.

Machine learning: A win-win for procurement and supply chain operations

As these five uses cases prove, procurement and supply chain organizations cannot afford to operate independently from each other. No matter how efficiently these functions run in their own domain, the overall business will inevitably experience suboptimal performance and unnecessarily wasteful practices.

With machine learning, procurement and supply chain leaders can finally bridge that risky divide. They will not only help ensure their strategic goals are fully aligned, but also become a unified front that protects the bottom line, reputation, and future growth of the business.

Discover how your business can take advantage of the latest machine learning scenarios. Download our white paper “Why Machine Learning – Why Now” and see first-hand a wide range of possibilities at SAPPHIRE NOW.

David Sweetman

About David Sweetman

David Sweetman is a Senior Director of Global Marketing at SAP. He is an accomplished software executive applying extensive business experience to develop and execute global product vertical and channel strategies that drive results. David has hands-on 360-degree experience of the software marketing, channels, sales, development and delivery processes.