In the past, accurate supply chain forecasting has relied on huge amounts of manual work, lots of intuition and a bit of luck.
But due to the sheer amount of data companies now have to analyze and increasingly volatile markets, the process of predicting future parts demand is hard for manufacturers to get right. The speed at which we need to make decisions, the drive to reduce cycle-times, the speed of operations, and the speed in making continuous improvement. AI in the supply chain is here to stay and disrupt for years to come.
So what exactly is AI in supply chain? Its simple – AI is organizations investing in digital solutions to optimize their supply chain operations which might include everything from vision system robots performing inventory checks in warehouses to learning algorithms that predict the on time arrival of a part at a service event.
For some years we’ve seen a shift towards the use of AI in supply chain forecasting. By 2024, 60% of Forbes Global 2,000 manufacturers will depend on on AI for their supply chain.
Most supply chain forecasting is done with siloed data and inconsistent planning tools, but transcending these manual processes means you can:
- Enable more accurate demand prediction
- Account in advance for parts price fluctuations or uncertainty
- Minimize the need for costly FSLs and storage
- Improve your sustainability
Planning teams are working with one hand tied behind their back, and this impacts the entire business. Incorrect information = incorrect decision-making.
According to McKinsey, companies that use AI-powered forecasting can cut errors in supply chain networks by up to 50%. The predictive, data-enriched capabilities it brings enable your organization to plan faster, more accurately and more granularly.
What’s the result of this? Your organization stays agile, and keeps your clients happy without scrambling to manually react to each change that comes your way.
Let’s take a look at four ways AI can transform your supply chain forecasting for the better.
1. Consistently keep up with demand
Supply chain forecasting relies on an accurate prediction of future demand. Overshoot and you end up with a surplus of parts—underestimate and you end up with a deficit. Either way, both end up losing you money and potential customers.
A smart AI solution can use a bank of your historical and current supply data to paint an accurate picture of future demand for parts through predictive analytics.
On top of this, AI can pick up on past and current market trends and signals and integrate them into this picture.
How would this work in practice?
You may notice you’re selling 90,000 parts in an average month across all your locations; you can prepare to meet this need. However, an AI will also notice your company is slowly expanding, growing in sales by 5% monthly, and simultaneously calculate what this expansion would mean for sales over the next 12 months. From there it can forecast future demand accordingly and adjust how much stock you should order, whether you need to hire more staff to keep up, etc.
2. Reduce complexity in seconds
Effective supply chain forecasting needs to be able to account for and interpret multiple, ever-fluctuating factors at once. A tall order for your standard manual process.
Smartphone manufacturers have a LOT to consider when manufacturing and selling their products.
For example, the prices of raw materials such as steel, copper and plastics fluctuate all the time. If a company doesn’t have the power to foresee these fluctuations and stabilize their prices in advance, there will inevitably be a loss of profit.
AI can analyze the historical data and identify patterns of previous price fluctuations instantly, learning from them to forecast any potential future changes.
Processing multiple forecasting factors at once and creating plans in advance to stabilize your service supply chain crucially means customers don’t end up feeling the wrath of reactive and steep price adjustments.
3. Navigate uncertainty
All you have to do is look at the COVID-19 pandemic to put this benefit into perspective.
Companies didn’t have large sets of historical data to delve into to predict how their supply chains would be affected by the pandemic, as it was a situation the majority of them had never faced before.
AI-powered supply chain forecasting can help with this.
When uncertainty hits, AI can make instant and smart decisions on inventory management optimization. Helping your company answer important questions such as:
- How much new stock should be produced?
- What orders should be prioritized over others?
- What’s the minimum amount of stock you should keep to meet new demand?
4. Improve sustainability and cost-effectiveness
When your supply chain forecasting is informed by incorrect data, planning departments end up ordering parts they don’t need and eating into finite resources. This lands your supply chain into unavoidable habits of waste, unnecessary spending and acute frustration.
Being able to order inventory to near 100% accuracy through AI forecasting reduces:
- Unnecessary truck rolls
- Surplus in inventory
- Unnecessary spending on extra storage space and FSLs
This, in turn, helps you reduce your carbon footprint, streamline your supply chain and keep profit margins balanced.
The way forward for supply chain management
By keeping up with demand, automating complex decision-making, navigating uncertainty, and boosting your supply chain efficiency—your business is better prepared with more time, resources and confidence to provide an outstanding customer experience.
Leading companies are already utilizing AI-powered supply chain forecasting to achieve this, kicking legacy methods to the curb and stepping into a more competitive digital environment.