Supply Chain

Artificial Intelligence and the Supply Chain

The role of Artificial Intelligence (AI) is the new focus of supply chain possibilities.  AI holds the promise of creating unlimited capability to optimize product flows, financial controls, and decision support across the supply chain.

Ned Blinick

February 9, 2018

The role of Artificial Intelligence (AI) is the new focus of supply chain possibilities. That is along side blockchain. Whereas blockchain is transactional and holds out promise for validating and auditing information across the extended supply chain at exceptional value, AI holds the promise of creating unlimited capability to optimize product flows, financial controls, and decision support across the supply chain.

To appreciate the promise of AI, we must understand what it is, and what is required to exploit its potential.

AI is a very broad term with many applications. At its core AI is about using information to program machines to do work done by humans. A simple application of AI is robotics. Simple robots are programmed to do repetitive manual functions, like assembly line work. Another example of relatively simple robots are the self-directed vacuum.

The current autonomous automobile is an example of a much higher form of AI. However, as AI is currently employed is autonomous vehicles it is still using vast amounts of data to execute preprogrammed decisions.

At its extreme, Artificial Intelligence is the application of information by machines to self-learn and make highly complex, real-time decisions. IBM’s Watson, Apple’s SIRI, or Amazon’s Alexa are examples of early stage higher forms of AI. However, AI is still in its early stages and the promises and concerns around AI are considerable.

To achieve advanced AI, and particularly the higher forms, requires incredible amounts of information to support automated self-learning, real-time analysis and decision support. For AI to reach its full potential in the supply chain, it requires what are now being called data lakes. Data lakes are the aggregation of vastly more data than is maintained in current big data structures.

5 Key Areas where Artificial Intelligence impacts the Supply Chain

The possibilities for AI in the supply chain are vast. For companies that deal in products - from raw materials to finished goods - the opportunities for the use of AI are infinite. This blog, highlight some of the areas where AI has the potential to reshape and dramatically impact the supply chain. While AI has powerful potential in each of the five (5) areas listed below, the results from using AI on a holistic supply chain perspective are exponentially more powerful.

1. Artificial Intelligence and Forecasting:

Forecasting depends on data - data accuracy, data timelines, and data volume. The greater the amount and timeliness of meaningful data available for analysis the better the forecast outcome. The type of data necessary to create supply chain forecasts are anchored in product/SKU demand and supply related information, logistics and transportation information, regulatory data, financial transaction data, etc.

AI is said to increase forecast accuracy by upwards of 33%. This has a dramatic impact on both the inventory and financial requirements.

2. Artificial Intelligence and Inventory Optimization

Inventory Optimization occurs when the right product is available in the right quantity to meet immediate demand at any specific node across an organization’s supply chain. To achieve this requires many supply chain factors to converge.

Organizations live or die on their ability to optimize inventory and respond to customer/consumer demands to achieve its business goals. A great company, large or small, understand this and is focused on this quest. Inventory optimization is a journey, not a destination.

Effectively using ai to optimize inventory requires vast amounts of real-time product/sku information from all available supply chain sources - forecasts, supply availability (Purchasing and logistics), cross-network physical inventory (at each node), demand signals (multi-channel) trade compliance and product costs. Aggregating and managing this level of supply chain data demands a fully integrated supply chain platform that digitizes a product/sku from all information sources.

3. Artificial Intelligence and supply chain risk mitigation

There are several key risk factors that severely disrupt organizational behaviour when they occur. Although it may be rare, the impact of a severe disruption in a supply chain can have significantly damaging and potentially catastrophic results. How a company mitigates the effects of the supply chain disruption is directly linked to its ability to see its physical and financial global position and manage the outcomes.

Disruptive events, are by definition, unplanned. Not all disruptive events are catastrophic. But, in today’s business environment the chances of a company experiencing a critical disruption are increasing. How a company reacts to a disruptive event can have meaningful impact on its ability to survive.

Business continuity, in the event of massively disruptive events, requires the ability to quickly analyze the impact of the disruption and implement a recovery plan. AI provides the ability to look at voluminous amounts of data, contextualize the information, and suggest alternative options for continuity.

4. Artificial Intelligence and Network Optimization

Supply Chain network optimization focuses on managing the delivery of product to the customer at the lowest cost. It encompasses multi-echelon distribution requirements and the configuration of sourcing, transportation, and manufacturing/distribution facilities.

Supply chains are in constant flux and supply and demand inputs are often rapidly changing. A company that can quickly review and analyze its current network based on changing supply and demand inputs can reduce total landed cost of a product/SKU without affecting fulfillment objectives.

AI enables the capability to optimize global supply chain networks by analyzing and constantly monitoring the vast data elements. This analysis can ultimately provide various scenarios to support decision processes.

5. Artificial Intelligence and the financial Supply Chain

The Financial supply chain is a reflection of the physical supply chain, but layered with financial transactions, trade terms, settlement mechanisms, payment terms, exchange rates. Gaining visibility into the myriad financial transactions in an accurate sequence is a major challenge to managing a business. Financial planning and execution requires accurate details as to the cash inflows and commitments related to the physical supply chain.

For a company with any degree of supply chain complexity the amount of information to manage is overwhelming. ai is a cornerstone requirement to manage the financial supply chain and provide cfo’s with the accurate perspectives required to manage current and future cash position of the company.

The volume of supply chain information is exploding exponentially. The accuracy and real-time availability of information is increasing significantly with capabilities like the blockchain. The supply chain is truly digitized and those companies that can aggregate the data and make sense of the “lakes” of data are truly at a significant competitive advantage. It is impossible to effectively manage all this data without the help of automation Data is the frame on which companies interpret their supply chains. AI is quickly evolving to allow companies to interpret that data.