How AI is providing supply chain agility in a turbulent climate
How AI is providing supply chain agility in a turbulent climate
By Will Dutton, Director of Manufacturing, Peak
There’s no denying that trade-offs are part and parcel of any supply chain strategy at some level, despite the aspirations of concepts such as ‘ambidexterity.’ However, there is another way of looking at them, and a different approach to be taken. It involves placing an added emphasis on switching between focuses, essentially decreasing the time between states of supply to give agility, enabled by artificial intelligence (AI).
However, the continued uncertainty that clouds the supply chain sector is resulting in teams being forced to make extremely complex, and potentially costly decisions. Today, most teams are forced to deal with multiple systems and spreadsheets, each taking up huge amounts of management time and resources. With AI it’s possible to model different scenarios and ask some crucial questions.
AI can be a real game-changer for supply chains in the current climate, powering smarter and faster, data-driven decision making, allowing businesses to ensure their supply chain is as agile as possible. Being flexible and dynamic enough to react to volatility, offers benefits that could be potentially more fruitful than trying to understand the volatility of the market in the first place.
Accurate data harvesting
The vast majority of regular information systems are built for more ‘steady-state environments.’ Given the huge amounts of volatility we’re currently seeing, the systems used to harvest data are under immense pressure and aren’t built for this kind of scenario. This results in more stressed supply chain teams, spending a lot of time running around trying to make the right trade-off decisions with little more than gut-feel to rely on. The answer to this problem lies in data. Yet despite it being a key source of intelligence, many businesses have little control or understanding the disparate data sources around their business. Companies can now use AI to collate large amounts of disparate data from sales, marketing, supply chains and elsewhere. They can use this data to create their own AI and train it to make smarter decisions faster.
For example, initial business plans may have meant optimising for resilience, revenue or profit. With AI, changing those optimisations is essentially a flick of a button or rerunning a particular model. As the surrounding environment continues to evolve, adjusting the way operations are running and adapting follows suit. This aids decision making around the configuration of supply, and takes away some of the hours supply chain teams would otherwise be losing stuck in spreadsheets, combing through lots of data manually.
Demand forecasting
Given the current fluctuating levels of demand, critical questions are having to be asked on a far more regular basis, across the business. For example, what products should be used? How often should machines be run? Which suppliers should be used? How much of certain materials are being ordered? These are all hugely complicated decisions, and it takes vast amounts of management resource and time to make the correct ones.
Demand forecasting is a hot topic at the moment, and it is no surprise that many may find themselves reaching towards a new demand forecasting solution in a bid to better understand current volatility and make the correct decisions around inventory and how much stock to hold. Whilst this may alleviate pressure in the short term, a wider perspective is required for success. The brutal truth is that there are some aspects of demand that are simply just random, that no system in the world can help forecast. Even with the most accurate forecast possible, levels of uncertainty will always be there. Rather than focusing on just one piece of the puzzle, the focus should be on agility as a wider concept.
Predicting supplier behaviour
Business leaders need to be able to predict supplier behaviour and know the demand for products at the same time, smoothing out all kinks in the supply chain. What does this mean in practice? It means safety stock levels being more dynamic while understanding profitability and trade-offs in the stock being held. In doing so, organisations will be optimised for the external and internal business conditions they find themselves in.
Enterprises can no longer operate on fixed rules built on forecasting or supply assumptions that are only updated infrequently. For increased agility and best practice, it’s now critical to constantly reassess what should be held based on a variety of factors such as potential demand, volatility, profit, and how the supply base is behaving. Adding AI into the mix will supercharge predicting supplier behaviours and feed this data back into safety stock to layer even more intelligence.
It is critical for businesses to be aware of fluctuating demand, potential bottlenecks in supply and to be agile amongst a mix of volatile external factors. With agility, teams will be able to answer questions of; what optimal level of stock should be held, what the optimal shift patterns or production runs are? The outcome of this, means AI is no longer just providing intelligence on data, but instead providing Decision Intelligence, using the technology to drive commercial performance. With this approach, and by investing in agility, business leaders can alleviate all their trade-off concerns whilst powering faster and smarter decision making across the entire supply chain.
