Businesses are transforming their supply chains by adopting next generation technologies, like artificial intelligence and machine learning. The strategic applications are rapidly expanding, enabling greater global efficiency, innovation and new supplier collaborations.
— By Gerald Donald
The time has finally arrived – the shakeup of supply chains with big data analytics and next generation technologies like artificial intelligence (AI) and machine learning. Human-machine collaboration is made possible by advanced technologies, but the challenge is utilizing the transformative capability in a way that creates value for various business functions, one of them being the supply chain.
Strategic supply chain planning and supply chain management are transforming processes from supplier selection to movement of goods around the world to identifying and managing risks.
Augmenting Supply Chains
There are three types of AI: Process automation, cognitive insight, and cognitive engagement according to professor Thomas Davenport and Deloitte consulting principal Rajeev Ronanki, authors of "Artificial Intelligence for the Real World." The last two are generally called augmentation AI.
Process automation AI is the automation of digital and physical tasks using robotic process information (RPA) technologies. Robots are actually codes located on a server that are capable of performing various tasks. In the supply chain arena, typical automation tasks including reading legal and contractual documents to find provisions while using NLP (natural language processing). This particular technology does not learn and improve at this point unless augmented with additional technologies.
The second type of AI is cognitive insight which is the use of algorithms to detect patterns in data and interpret the meaning of the patterns. It can detect fraud, analyze logistics data to identify inefficiencies, model future activities of suppliers, etc. The model’s ability to make predictions and categorize items improves over time. It can also identify probable data matches across databases. GE used this technology to integrate supplier data and saved $80 million the first year by eliminating redundancies and negotiating contracts once managed by business units.
Cognitive engagement is the highest AI and machine learning process. It is typically seen now as intelligent agents using NLP or as an internal employee support bot. Reaching full power in the supply chain enables human-machine collaboration, interactions with suppliers, matching suppliers for collaboration purposes, creating new value, enhancing supply chain scalability, and generating innovation. AI applications at a global level are already enabling supply chains to proactively anticipate, rather than react to, risks like natural hazards or operational disruptions.
What Humans Cannot Do
AI can make things happen that are humanly impossible – like accessing and analyzing huge volumes of data.
Lockheed Martin, a security and aerospace global corporation, adopted the sustainability mission "to foster innovation, integrity and security to protect the environment, strengthen communities and propel responsible growth." In its "2018 Science of Citizenship" sustainability report is the astonishing fact that global data will reach 40 zettabytes in another year or so. Another amazing statistic says only 10 percent of data is collected and maintained in a manner that allows for easy analysis and sharing.
For Lockheed Martin, harnessing the data and the advancing technologies like AI and machine learning is the only way to ensure the sustainability of the supply chain and to ensure the supply chain operations contribute to the company's mission and environmental, social, and governance goals. Using next-generation technologies is a crucial strategy for "pushing the boundaries of innovation" and delivering "products and services to improve lives now and for decades to come."
Lockheed Martin's complex supply chain is a good example of the high-level ways AI can be used in the supply chain. The company has 590-plus facilities in the U.S. and 52 other nations and territories. To achieve its goals of having a powerful and positive customer, economic, and social impact, data analytics and next generation technologies are applied in numerous ways. The supply chain is at the heart of the ability to meet goals. Various areas of application include fraud detection and possible ethical violations, product improvements through innovative designs, resource efficiency and environmental protection, information security, logistics, and risk identification and mitigation.
Intelligence in a Continuous Loop
AI offers intelligence in the supply chain, meaning it can offer new insights into different areas which are leveraged to improve things like customer service and operations.
Strategic supply chain planning uses a holistic perspective. It if not just about procurement. For example, AI adds contextual intelligence to the supply chain which enables better inventory, warehouse performance, and logistics information for the purpose of keeping customers better informed. AI can power visual inspection to identify damaged cargo which is robotically separated, minimizing delivery delays and the acceptance of faulty goods or materials.
Another important AI-driven feature is the ability to measure, track, and integrate all elements across all suppliers to forecast demand and identify risks. Additional applications include analyzing supplier factors for better decision-making and identifying potential collaborations and innovations. Forecasting with machine learning occurs in a continuous loop, meaning the algorithms and data are constantly integrated (like a machine collaboration).
Enriching the Supply Chain with Diversity
Diverse and small business suppliers can benefit from supplier selection systems using AI and machine learning processes.
One of the challenges of getting into a corporate supply chain is getting visibility and in-depth appraisal of capabilities. Machine learning and sophisticated predictive analytics can perform in-depth supplier assessments that are used to generate various supplier scenarios.
Using big data, AI can produce prescriptive analytics that assist in the development of a sourcing strategy that meets a set of requirements. Businesses having difficulty expanding their utilization of diverse suppliers can produce information that was difficult to access without AI assistance – like supplier scalability, business risks, innovation capabilities, products that are in best alignment with customer needs, and potential collaborators for current suppliers.
Cognitive sourcing is one of the newest AI applications, and it is likely to be a strategic sourcing game changer.
Innovative companies are developing systems that use AI to address supply chain challenges that are nearly impossible to solve with human actions. The company 3CE Technologies developed an AI-driven system that can read the standard international codes used to report goods to customs and government agencies. It covers 98 percent of international trade. The problem with the codes is that goods do not neatly fit into the classifications, creating a laborious and complex process. The 3CE AI-driven system does the complex reasoning, speeding up the process and adding consistency to the classification process while also providing an audit trail.
Intelligent Strategic Sourcing
Strategic sourcing using AI can identify specific problems and use massive volumes of data to create analytics for solutions. There is still a long way to go before the full power of AI and machine learning for strategic sourcing and supply chain management is reached.
What is known now is that these next generation technologies will transform supply chains around the world because the process has already started.