Cost optimisation for supply chain
AI cost optimisation for supply chain reduces total costs by analysing workflows and identifying inefficiencies, allowing a logistics manager to ask which items are tied up in inventory and how to reduce lead time variance, then act to optimise the supply chain and reduce costs.
A logistics manager stares at a dashboard that shows a stable average lead time, but somehow the production line still gets held up by delayed shipments, and the warehouse is overflowing with inventory that nobody needs. This is the world of supply chain cost optimisation, where the numbers do not add up because they are the wrong numbers.
The unit cost trap
The mistake most people make is to focus on the unit cost of an item, without considering the total cost of the workflow that delivers it. This is a trap because it leads to optimising the wrong variable: the cost of a single item, rather than the cost of the entire supply chain. A lower unit cost can actually increase the total cost if it means more items are tied up in inventory or in transit.
A purchasing manager might negotiate a great price for a bulk order, but if that order sits in the warehouse for months, the total cost of carrying that inventory can outweigh any savings on the unit price. This is where AI cost optimisation for supply chain comes in, to help identify the real costs and make decisions that reduce them.
The real levers
There are three main levers that can be pulled to reduce the cost of a supply chain: caching, batching, and model routing. Caching involves storing frequently-used items in a convenient location, to reduce the time and cost of retrieving them. Batching involves grouping similar items together, to reduce the cost of processing and shipping them. Model routing involves using AI to optimise the route that items take through the supply chain, to reduce the cost of transportation and inventory.
These levers are not always easy to pull, because they require a deep understanding of the supply chain and its data. But when they are used effectively, they can have a significant impact on the total cost of the workflow.
Dependent on data
The effectiveness of AI cost optimisation for supply chain depends on the quality and availability of data. This includes purchase and delivery history, lead times, inventory by location, and demand signal. With this data, AI can identify patterns and trends that would be invisible to humans, and make predictions about future demand and supply.
A shift supervisor can use this data to compare the performance of different suppliers, and make informed decisions about which ones to use. For example, a supplier might offer a lower unit cost, but have a longer lead time, which could increase the total cost of the workflow.
What it changes
AI cost optimisation for supply chain can change the way that lead time variance is managed. Many companies focus on the average lead time, but this can hide a lot of variation and uncertainty. By using AI to analyse the data, companies can identify the sources of this variation and take steps to reduce it.
It can also change the way that inventory is managed. Many companies hold inventory against the wrong risk, such as holding too much inventory of a slow-moving item. By using AI to analyse the demand signal and lead times, companies can make more informed decisions about which items to hold in inventory and how much to hold.
Measuring success
The success of AI cost optimisation for supply chain can be measured in several ways, including lead time variance, fill rate, and inventory turns. Lead time variance measures the amount of variation in the time it takes for items to move through the supply chain. Fill rate measures the percentage of orders that are filled from inventory, rather than being backordered. Inventory turns measures the number of times that inventory is sold and replaced within a given period.
By tracking these metrics, companies can see the impact of AI cost optimisation on their supply chain and make adjustments as needed. For more information on how to implement AI cost optimisation, contact our operations team.
Limitations
AI cost optimisation for supply chain is not a silver bullet, and it is not the right choice for every company. It requires a significant amount of data and computational power, and it can be difficult to implement and integrate with existing systems. For some companies, the benefits of AI cost optimisation may not outweigh the costs and complexity of implementation.
For these companies, other approaches may be more effective, such as working with a logistics provider or implementing a more traditional cost reduction program. To discuss the options and determine which approach is best for your company, contact us.
Frequently asked questions
How can AI cost optimisation improve supply chain efficiency?
AI cost optimisation can improve supply chain efficiency by analysing workflows and identifying inefficiencies, allowing for data-driven decisions to reduce costs and improve lead times.
What data is required for effective AI cost optimisation in supply chain?
Effective AI cost optimisation in supply chain requires data on purchase and delivery history, lead times, inventory by location, and demand signals to identify patterns and trends.
How can AI cost optimisation help with inventory management?
AI cost optimisation can help with inventory management by analysing demand signals and lead times to determine which items to hold in inventory and how much to hold, reducing inventory costs.
What metrics can be used to measure the success of AI cost optimisation in supply chain?
The success of AI cost optimisation in supply chain can be measured by tracking metrics such as lead time variance, fill rate, and inventory turns to evaluate the impact on the supply chain.