Supply Chain Forecasting with AI: Build Agile and Cost-Efficient Supply Chain Networks
A little unpredictability in life is normal. But if you’re waking up almost every other day to rude surprises, you’ll be left feeling disheveled and shaken up.
That’s how life has been for supply chain professionals ever since the pandemic. First COVID and then the unrest across several countries, the Suez Canal situation, and hurricanes, the sense of control that once came with being able to considerably predict and prepare for the future went right out of the window.
Forecasting has forever been at the heart of efficient supply chain operations – driving the much needed predictability to source raw materials, manufacture products and fulfill customer needs at the right time, right place and in the right quantity.
But with volatile demand patterns and global disruptions, the gaps in supply chain forecasting accuracy have widened so much that profitability, customer satisfaction and your mental peace have fallen right through that ginormous crack!
Without being able to accurately forecast supply situations, it has become extremely difficult to orchestrate continuous collaboration between supply and demand, planning and fulfillment, expectations and customer satisfaction, leading to frequent stock outs, overstocking, missed deadlines, customer dissatisfaction and lost sales opportunities.
Clearly, supply chain forecasting has become more complex. Yet, you know the success and future of efficient supply chain operations lies in being able to sharpen up and hone one’s forecasting capabilities to adapt to these changes, which is primarily why AI and machine learning in supply chain forecasting is booming.
Traditional Supply Chain Forecasting Is Falling Short
The spreadsheet and calculator method of forecasting is familiar but they aren’t cutting it in the new normal. Cost overruns, stock outs and delays leading to customer dissatisfaction is becoming commonplace. That isn’t healthy for a business that is practically looking to grow and sustain.
Multiple present day challenges are bogging your traditional supply chain forecasting down.
Here’s a look at them all.
- Demand Volatility: Fluctuating consumer behavior, changing market trends, and unpredictable external factors contribute to demand volatility. Supply chain professionals are struggling to accurately anticipate and respond to these shifts, leading to imbalanced inventory levels, stock outs, and excessive carrying costs.
- Global Disruptions: Unpredictable disruptions such as natural disasters, geopolitical issues, and trade conflicts are significantly impacting supply chain operations, making it challenging to maintain continuity and meet customer demands.
- Complex Networks:– Supply chains have become increasingly complex, involving multiple stakeholders, geographies, and interconnected processes. Coordinating activities across the network and aligning demand with supply is posing a significant challenge, especially when relying on outdated forecasting methods.
- Incomplete or siloed data:Data is the key to solving the supply chain forecasting challenges. But with data sitting siloed in different systems and with different teams, compounded by the inability of traditional systems to integrate and orchestrate this data, the resultant forecast is going to be inaccurate or incomplete.
How AI in Supply Chain Forecasting is the Enabler in Uncertain Times & Beyond
To get a hold on your supply chain costs and to create agility to face demand volatility and global disruptions, supply chain forecasting needs the computing power and intelligent recommendations of AI.
Here’s the simplest way to understand the difference between traditional and AI-led forecasting.
Remember how we traveled before the advent of Google Maps. You either trusted people on the road to guide you or you trusted your memory to take you to the right place, if you’ve been to that destination before. But neither your sharp memory nor the people you meet on the road or the manual sign boards could predict traffic congestions, diversions, a fallen tree blocking the road or an accident. Most likely, you’ll still reach your destination but you’d have missed a meeting or a wedding, spent extra on fuel or tired yourself out with all the extra traveling you had to do! Things became simpler with Google Maps.
That’s what AI promises in supply chain forecasting – the just perfect addition to human intelligence to improve your ability to effectively balance supply and demand. Here’s how AI can add value to your human intelligence driven supply chain forecasting.
AI can consolidate data from multiple sources, break through the silos and get value out of the consolidated data faster than a human can. In fact, using an AI tool, you can also bring external data into consideration alongside internal data for more accurate forecasting.
You can ingest large amounts of data in no time and identify patterns, trends and anomalies that’ll help you make an informed decision faster.
When you’re not manually sifting through tons of data to make sense of it, you’re also saving a lot of time. Plus, AI can automate repetitive and time-consuming tasks involved in supply chain forecasting.
For example, AI algorithms can automatically generate and distribute reports, update forecast models, trigger alerts for anomalies or deviations, and initiate corrective actions. By automating these tasks, AI frees up valuable time for you to focus on more strategic activities.
Bottlenecks are the biggest cause for industrial waste with millions of dollars being spent in operational delays, inefficiencies & lost revenue. Not surprisingly, you are always looking at eliminating potential bottlenecks that can affect a supply chain’s throughput, operational efficiency, overall productivity, and profitability.
Adequate visibility into the supply chain using AI helps determine where bottlenecks can occur and eventually boost safety and throughput while extending the life of key processing and material handling equipment.
AI brings in the much needed agility in supply chain forecasting. AI systems can continuously update forecasts in real-time based on changing market conditions, enabling proactive inventory replenishment and preventing stock outs.
This dynamic approach consequently minimizes time loss, improves customer satisfaction, and optimizes inventory management to avoid costly stock outs.
A clear visibility into demand can help you manage your capacity more effectively. You can line up your production smartly and not end up unsure about your capacity. Such visibility will also help you produce just right, by which way you can also make room for new products’ production.
Operational success finally depends on whether the product/ service was delivered on time to the customer. After all, nothing is more annoying than having 99% of operational processes in place and finally not having the shipment done as per committed schedules.
This can create a huge dent on the bottom line. Having a robust smart supply chain forecasting mechanism and artificial intelligence in logistics can contribute to effective shipment control.
If operations managers get on-time visibility into the delivery schedules and are alerted about any discrepancies ahead of time, unnecessary delays and costs can be avoided.
In today’s challenging business environment, embracing the power of AI-based forecasting can help you with what you need most right now: reduce costs and build agile supply chains.
As organizations continue to navigate uncertainties and seek competitive advantages, investing in robust forecasting capabilities will be a key differentiator in building resilient, cost-effective, and customer-centric supply chains.