Artificial intelligence (AI) is a transformative technology for manufacturing and logistics organizations and the C-suite is under a great deal of pressure to ensure their organizations have deployed it. AI is currently one of the hottest enterprise technologies. However, AI is not a magic, shiny toy that’s a set it and forget it kind of technology. Getting real value from an AI project requires a well-developed and comprehensive strategy that’s crafted to achieve a specific business goal.
Does shipping need to optimize routes for the delivery of goods to reduce costs and get them to customers faster? Or maybe AI could enable predictive maintenance of factory equipment, increasing uptime and lengthening the useful life of critical machines. Is the supply chain a priority? AI can assist with managing a multi-sourcing strategy to increase resilience in the face of shortages and volatility. Or perhaps your greatest need is to accurately forecast demand, so you don’t end up with excess inventory or not enough supply to fill all your customer orders.
The most successful transformative projects begin with identifying where AI could have the greatest impact first and then crafting a strategy to attain the desired business outcome. To succeed, AI must be supported by four fundamental pillars:
- the right people
- the right processes
- the right systems
- the right data.
The Right People
AI is a complex technology, and getting full value from it requires a mix of specialized skills, beginning with a data scientist or data analyst. AI requires a great deal of relevant data, formatted for its use in order to train and produce accurate insights. The AI must be closely managed to ensure that biases do not creep in over time that skew the accuracy of its predictions.
The team will need a technology advisor to help choose, deploy, integrate and maintain the software, hardware and networking required to support the AI. AI requires high-performance machines to process massive amounts of data quickly enough to produce timely insights. All that data will need to be stored in a scalable infrastructure and networked so it can be rapidly delivered. AI will need to be integrated with many other systems. Both to pull information out of them and deliver insights.
Finally, as noted earlier, AI should be deployed in the service of a specific business goal. Success requires a deep understanding of the business and its strategy to ensure that the insights AI is delivering are relevant to achieving those goals. This implies that process owners across the organization work in a collaborative manner to identify functional in support of overall business goals.
With collaboration across the organization, many roles should form a cohesive transformation team that will advise the organization on how to leverage AI and data to move the company forward.
The Right Processes
Processes are a series of steps whose aim is to achieve a specific goal and without the right processes, AI will at best produce insights on which no one ever acts. If those insights aren’t acted on they may as well not exist. It’s important to map out the steps that an organization will follow to achieve the results or business objectives and map where AI is best able to help achieve these goals and objectives. It’s critical that people understand their roles and how they contribute to achieving the end goal. People are not mindless cogs in a machine and we all work better when we fully comprehend both what we’re expected to do and why our tasks are important.
It is also important to define appropriate measurements and feedback loops to assess how well the process is working. AI is still a fairly new technology, and there will always be room for improving processes, tweaking the data and honing the questions AI is addressing. It must be done in a cohesive manner that enables meeting business objectives.
The Right Systems
The technical requirements for AI can be demanding, depending on the use case to which you are applying it. In advanced use cases, the organization may need high-performance computing (HPC) clusters powered by graphical processing units (GPUs), with data stored in data lakes or even a data lake house. IT will need to evaluate whether to deploy on premises, in the cloud or on a hybrid model. These resources will all need to be in a cohesive architecture that provides security, resiliency and performance.
It's easy to overpay and overprovision an AI deployment, but without the right underlying systems, the AI will not scale and will not add value to the business in a timely manner. Striking that balance and building an AI infrastructure that can scale as an organization’s needs grow is a tricky challenge that requires a great deal of thought and planning. It’s critical to a project’s success to define and understand the vision and the mission of the AI deployment.
Training a generic AI to a specific purpose is a time consuming, complex task that requires specialized skills to do well. Organizations can reduce effort and risk by choosing an AI platform that’s pre trained and specifically developed for the use case in question. By purchasing a purpose-built AI from a well-regarded vendor, an organization can partner with the AI provider and rely on their expertise and counsel regarding the underlying resources it will require.
The Right Data
AI requires a lot of data and it’s important that it is the right kind of data. Typically, in addition to volume, it also needs a wide variety of data types to provide the most accurate insights. Before embarking on an AI project, an organization should make it a priority to digitize internal information to the highest degree possible. Purchase orders, HR information, shipping information, none of this data can be used by AI if it’s stored on paper forms in a file cabinet. Ensure that all data is reliable, meaningful and actionable. Data that allows measuring and actioning against the organizational objectives are key. Using the wrong data creates noise and can inhibit efficient decision making that could add risk in meeting the organization’s objectives.
Internet of Things (IoT) can be another extremely useful technology for collecting a wide array of data on temperature, machine performance, inventory, asset movement and more. The more relevant data you have, the more value you can extract from it.
Relevant external data sources can also be used to enrich internal data to produce better results and provide more meaningful insights. Weather patterns can have an enormous effect on the supply chain. A bad flu or COVID-19 season in the Southern Hemisphere could signal labor shortages due to illness six months later in the north, and both macro and micro economic data can be very useful in predicting demand.
AI can bring great benefits and significant competitive advantage to manufacturing and logistics companies, but only if there’s a clear goal in mind and solid strategy to achieve it. Taking the time to ensure the fundamentals are in place is well worth the time and investment, because only then will an organization be in a position to succeed with AI. Embracing the four dimensions of change: people, processes, systems and data, will drive business transformation and success.