FEATURE Logistics
THE PREDICTIVE POWER OF MACHINE
Chris Butcher, Data & AI Presales Solution Architect at Columbus, explains how Machine Learning (ML) technology can take the guesswork out of advanced planning, helping manufacturers decide where and when to use it to get most success
E
very manufacturing company has one main goal – to produce exactly the amount of product to meet demand. No more. No less. This
requires manufacturers to maintain proper stock levels, address seasonality sales, all the while ensuring available equipment and appropriate personnel. It’s a tall order but AI- powered demand forecasting brings it down to size and delivers the results. Yet there remains a notable gap in the adoption of AI technologies such as machine learning (ML) within the manufacturing industry. McKinsey reports that 73% of enterprises continue to rely on manual or outdated forecasting methods. But when the cost is high, manufacturers can no longer forecasting.
Everything from optimised production and and hiring rely on accurate decision-making. But factors such as capacity, demand and cost aren’t always known parameters, especially geopolitical tensions. Variations in supplies, transportation and lead times only add to these uncertainties, which can greatly and inventory planning. Here’s where real-time data integration can enable manufacturers to gather and analyse data for more precise forecasting that can better handle the “unknowns”. ML plays a crucial role in this by improving the accuracy of demand forecasting, especially when it comes to avoiding the traditional challenges associated with planning such as long delivery times, high transport costs, and high inventory and waste levels. With assistance from ML technologies, manufacturers can increase their value generation, heighten customer satisfaction and sustain a competitive edge.
The common pitfalls of traditional demand forecasting:
22 February 2026 | Automation
THE PREDICTIVE POWER OF MACHINE
• Time-consuming forecasts that limit quick adjustments • Inaccurate forecasts that cause costly overstocking or understocking • Failure to factor in external events and market changes, which reduces the ability to adapt to unforeseen circumstances • High costs of maintaining a demand planning team and expensive forecasting tools
ML, when integrated into supply chain management systems, addresses all of these issues by leveraging advanced algorithms, data analytics and pattern recognition to provide more precise and actionable insights for manufacturers to navigate uncertainties. ML-based forecasting can substantially
reduce errors by up to 50%, allowing manufacturers to do the impossible – anticipate demand with enough time to manufacture the right stock and get as close as possible to producing the exact amount needed to meet future demand.
So how can manufacturers get started on their AI journey?
1) Build resiliency into supply chains with strategic partnerships
procurement and supplier management. For instance, manufacturers can use ML- generated insights on future demand patterns to collaborate closely with suppliers and ensure the timely availability of raw materials and components.
This minimises lead times, reduces the risk
of production delays due to shortages and allows for negotiation of favourable terms
with suppliers. This can help manufacturers, especially those looking to regionalise their operations, create more resilient supply chains. 2) Trim waste to boost savings allocation, meaning raw materials, labour and minimise waste and allow manufacturers to According to McKinsey, applying intelligent forecasting to supply chain management can reduce errors by between 20% and 50% – and translate into a reduction in lost sales and product unavailability of up to 65%. Warehousing costs can also fall by 5 to 10%, and administration costs by 25% to 40%. 3) Don’t forget the untapped revenue opportunities
ML-based demand forecasting can be a vital way for manufacturers to grow revenue. By aligning production with anticipated demand, manufacturers can strike a balance between holding enough stock and meeting customer requirements. It also ensures products are available when customers are ready to purchase and allows manufacturers to plan for peak demand periods, optimising sales even during seasonal trends and holidays. Before AI can deliver results, manufacturers need to get themselves data-ready. Many organisations believe their processes are in order. In reality, shortcuts and workarounds
automationmagazine.co.uk
Page 1 |
Page 2 |
Page 3 |
Page 4 |
Page 5 |
Page 6 |
Page 7 |
Page 8 |
Page 9 |
Page 10 |
Page 11 |
Page 12 |
Page 13 |
Page 14 |
Page 15 |
Page 16 |
Page 17 |
Page 18 |
Page 19 |
Page 20 |
Page 21 |
Page 22 |
Page 23 |
Page 24 |
Page 25 |
Page 26 |
Page 27 |
Page 28 |
Page 29 |
Page 30 |
Page 31 |
Page 32 |
Page 33 |
Page 34 |
Page 35 |
Page 36 |
Page 37 |
Page 38 |
Page 39 |
Page 40
