6 Keys to Achieving Success with Artificial Intelligence in Supply Chain
[activecampaign form=13] In the last several years, Artificial Intelligence has started to surface as a crucial business tool for industries of all kinds. Since supply chain management touches nearly every major industry in some way, it’s only logical that supply chain companies are investigating how they can use AI to save money, increase process efficiencies, and better manage resources. To understand how supply chain companies can best use AI, it is important first to understand what it is and what it is not.
Artificial Intelligence is a collective set of systems, processes, and programs that take a task that was previously completed by a human and assigns it to a machine. The machine mimics human behavior and decision making skills and subsequently uses this data to continuously refine and improve processes and decisions. Thisconcept is referred to as “machine learning.”
What Artificial Intelligence cannot do is replace interactions or processes that require a high degree of human interaction. A chat-bot can be programmed to answer simple, commonly-asked questions, for example, but for a complicated service or logistics issue, human interaction is required to ensure the best possible outcome. How do supply chain companies decide which of their processes are best served by AI? First, they must consider how Artificial Intelligence fits most naturally within the supply chain and then
1. Access to real-time and community data
AI is only as intelligent as the data it receives. Many supply chain companies make the mistake of mining “near real-time” data that is days or weeks old and assuming it will serve the needs of its AI system. Instead, this stale data creates deficiencies in the decision-making process and the need for human intervention. Without correcting the issue of old data, a snowball effect of bad decisions will occur. Supply chain logistics happen quickly and from moment to moment. It therefore stands to reason that for an AI system to achieve optimal results and make the best decisions, it must be fueled by accurate, up-to-date information.
Secondly, the AI system needs access to data outside the supply chain platform. Without this key, downstream data, machine learning algorithms will be operating with a handicap and in a silo. This leads to costly course corrections that waste valuable time and consume additional resources.
2. Predictive analytics for demand forecasting
One of the areas in which artificial intelligence can best support the supply chain is in accurately predicting demand, based on a variety of factors like weather predictions, fluctuations in commodities and trade across the globe, among others. AI takes historical data and couples it with real-time information and predicted events to help companies increase operational efficiency and ensure that logistics needs are met across its entire supply chain.
The key to success is identifying all potential factors that may impact demand and then providing the right data sources to feed machine learning processes. As the AI system collects large amounts of data over time, it will continue to predict demand with greater accuracy and better results.
3. Support for consumer-driven business objectives
The foremost goal of any supply chain organization is to meet service levels in the most efficient way possible, at the lowest possible cost to the organization. AI supports this goal by taking service level data into consideration and making decisions based on the path of least resistance (and expense) to provide the best outcomes.
Cost and efficiency simply can’t be the only two factors considered for AI. Consumer-driven objectives and customer expectations at all points across the supply chain must also be included as non-negotiable, foundational elements for AI algorithms.
4. Autonomous AI engines
Many Artificial Intelligence strategies are carefully designed and implemented to make intelligent business decisions, but can’t execute on those decisions without human intervention. It defeats the purpose of AI as a vehicle for efficiency and better business outcomes.
While supply chain teams certainly need visibility on the status and outcomes of AI processes with the ability to override decisions, it should be the exception– not the rule. Instead of letting AI almost reach the finish line, supply chain organizations must have the courage to let it cross and execute crucial business decisions while they still have the opportunity to make an impact.
5. Agile and continuous decision-making
While the cost of change must always be considered, an AI system must have the ability to weigh the costs against the potential benefits when making decisions. AI systems must be empowered with the right historical and real-time data to remain agile and prompt change when necessary.In supply chain management, data is constantly fluctuating and changing. AI systems must measure data and potential outcomes on a continuous basis to feed the correct decisions and improve over time. Real-time data is the fuel for true machine learning
6. A scalable AI strategy
Supply chain networks are intricate webs, with many connected strands within the community of suppliers, consumers, and logistics providers. To be successful, organizations must be able to manipulate large quantities of data quickly to make on-the-spot decisions.
Artificial Intelligence systems should not be constrained by data processing limitations. As supply organizations grow, they must implement AI processes and platforms that can grow with them and scale easily. Supply chain organizations must design machine learning algorithms to effortlessly accommodate times of explosive growth so that the right decisions are made without the need for re-programming or costly downtime.
The potential for AI to support supply chain management is exciting and limitless. Whether it’s warehouse management, inventory controls or circumstance monitoring, the sky is the limit in this brave new world of machine learning. By opening doors for machines to use vast amounts of crucial data in a way that humans simply can’t, supply chain organizations will reap the benefits of Artificial Intelligence as a vehicle for efficiency and higher profitability.