Machine Learning Revolutionising the Supply Chain Management

Machine learning (ML), which typically uses observations or data, is a type of artificial intelligence that allows an algorithm, system or piece of software to learn and adjust, allowing technology to be taught over time in an attempt to improve operations.

Modern international supply chain generates vast amounts of data, which can be analyzed, and the findings can be used to enhance the supply chain management. ML helps to discover the implicitly existing patterns in the supply chain data by relying on algorithms, without needing manual intervention. The algorithms, usually based on constraint-based modeling, iteratively query data to find the core set of factors with the greatest predictive accuracy. Thus, the new knowledge and the insights from ML are revolutionizing Supply chain management in numerous ways.

The dysfunctional nature of the behaviors across the procurement-supply chain relationship is leading to undesirable outcomes such as uncompetitive pricing, supply shortages, excess in slow-moving inventory, and delivery delays. Consider for example the case of cost reduction where it overshadows the need for high-quality materials and products. Another case of risk aversion where it allows larger suppliers to capture more business while excluding more qualified small and midsize providers from the selection process. ML algorithms make it easier for the procurement and supply chain organizations to achieve a close-knit, family-like relationship that balances market opportunities with competitive challenges.

Mentioned are the ways in which ML algorithms can benefit these two entities of the supply chain management system.

Use case #1: Predictive contract consumption and compliance

Visibility into purchasing contracts and their statuses – 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 enable 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 monitor the status of their in-transit materials and items so they can fix emerging delays or issues before they happen.
Through ensuring that each order is delivered on time, the organization can avoid speeding manufacturing production across the supply chain, reducing excessive payroll and logistics

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 the procurement of supply assignments eliminates the need for manual interactions while obtaining products with the best price, the quickest delivery time and the best quality. When an internal source of supply is not available, the program must automatically create a bidding case. In this case, artificial intelligence serves as a human buyer and sends an invitation to tender to a certain 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 purchases 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 goods 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.

Machine learning can prove to be a bridge the risky divide between the procurement and the supply chain operations and make it a win-win situation for both. It will not only help to ensure that their strategic goals are fully aligned but also help to protect the bottom line, reputation and future growth of the business.

Operations and Global Supply chain disruption by COVID – 19

The Corona Virus has had a large impact on the global supply chain industry. As per the report from Dun & Bradsheet – “ 938 of the Fortune 1000 companies have a tier 1 or tier 2 supplier that has been affected by the virus”. We’ve heard in news and through different social networking sites, the brands like Apple, Microsoft, Nissan, and JCB about the disruption they’ve felt as their factories in China are either closed or are operating with limited production capacity.

Effect of COVID 19 on Global Supply chain

China is the global center of production and is a major source of finished goods and products. With China being locked down, the global pipeline of parts and components, has become emptier. This pipeline feeds directly to producers, manufacturers, and distributors worldwide. Thus, if the disruption continues, many manufacturers and retailers can see their operations suspended and businesses coming to a standstill.

China is also a major market for consumable goods and services. The COVID- 19 event has come down as a major impact on the demand of these goods and services for the relevant industries spread across the world, the United States of America and Europe in particular.

Global Supply chain: Fragile nature

There have been many instances in the past which have disrupted the global supply chain earlier as well. Consider for instance the 1999 Taiwan Earthquake, 2003 SARS epidemic, 2011 Japan Earthquake. With Taiwan being a major exporter of memory chips in 2011, the earthquake cut off a major source of memory chips for computer manufacturers, driving up RAM prices drastically in the international market. The 2011 earthquake that struck Japan affected many automotive manufacturers who relied heavily on inputs coming from Japanese factories, thus disrupting the demand and supply in the market.

An analysis of the global supply chain shows that it is very fragile and the following are the major reasons which are responsible to lend it to this character.

· Reduced inventory levels: The concept of JIT manufacturing (just-in-time) increases efficiency and lowers the cost of the supply chain, but it also leaves supply chains less resilient to these kinds of sudden shocks and shortages occurring in the world.

· Rigid supply chains: This wouldn’t be a problem if companies were running flexible supply chains. Thus in times of stress, they may move order volumes to alternative suppliers. However, very few companies don’t do so, leaving them unable to locate and communicate with alternative suppliers when unexpected disruptions arise in their supply chain leading to a tangible impact on production.

· Manual supply chain management: A key reason supply chains become rigid is because they are managed manually. Changing orders or shifting suppliers is a lengthy and complex process and is a luxury few businesses have in times of stress

· Lack of supply chain transparency: Businesses are still unaware of what’s happening in the first tier in their supply chain, so they can’t figure out where production capacity risks occur. So that makes effective control of a situation like the COVID-19 outbreak almost impossible.

· Consolidated center’s of production: The globalization of supply chains has led to the creation of specialist production zones — towns or countries specializing in the manufacture of certain main goods. These have helped ensure the availability of key supply chain components and lowered the overall production cost. And while this is useful when times are healthy, when there is uncertainty it may cause problems That’s because there isn’t the capacity in other parts of the world to plug the gap in supply.

What to look at now

Leading companies are taking several actions to combat the effects of COVID – 19

• The leading companies are considering to shift there available inventory from the present quarantined zones to the areas which are either away from the quarantine zones or near the ports.

• The companies are constantly working with Tier 2 and Tier 3 suppliers by either securing allocated supplies or by ensuring overtime assembly capacity .

• Firms are taking proactive measures by keeping inventory and raw materials which are currently in shortage in impacted areas.

• Firms are also negotiating for future air transportation as capacity becomes available to avoid the ocean freight based lead times.

• The Major players are actively looking to substitute the components or the raw materials in situations where the primary supplier has been affected and secondary source has not been affected.

• Few of the Industry giants are also considering to redesign the product or alter the material certification where the reliable alternate sources are not readily available.

• Industries who currently have their manufacturing units in China are considering to introduce the new products to the alternate manufacturing locations.

Where to focus next

Improve supply chain visibility

There is an urgent need to ensure a greater supply chain visibility in the system. It is needed to provide a sight into the capacity constraints in the first, second-and third-tier suppliers. When looking further into their supply chains, multinational producers will achieve a more complete and comprehensive supply chain mapping of the assemblies and sub-assemblies.

Model new risks and costs

There is an urgent need to get visibility of the supply chain in the network and provide a line of sight and capability. There is a need for new tools and technology that can provide more information. Risk assessment tools that use machine learning, for example, may identify trends that may suggest futuristic opportunities or threats in geopolitical and global health, macroeconomic and other related areas.

Focus on resilience
The persistent nature of such outbreaks would continue to shift the focus of the global supply chain operations toward more a rigorous proactive modelling and multiple dimensions. The COVID19 outbreak is likely to result in a long-term reconfigurations in the supply chain globally to create an inbuilt resilience in the system.