We are operating in the digital industrial era where technologies are evolving rapidly to create a more efficient, more productive, and more agile factory. With the advent of the IoT and the complexities involved in operating a global supply network, today’s manufacturing companies are more interested than ever before to get a sense of the future. After all, it is important to know how certain events could impact operations in the future and what companies can do to mitigate unforeseen risks due to volatile production conditions to efficiently manage warehouse operations. 

While predicting the future may not be completely possible, what companies do have are a bunch of intelligent and powerful solutions that can forecast supply chain situations and the elements that influence their production cycles. Thanks to advances in technology, including Machine Learning, Artificial Intelligence (AI), Robotics, etc., companies are able to orchestrate continuous collaboration between supply and demand, planning and fulfillment, expectations, and customer satisfaction.

What is Supply Chain Forecasting?

Supply chain forecasting is a great way to help manufacturers, operations managers, and production planners make educated predictions about factors influencing warehouse operations. This includes production elements such as material sourcing, job allocation, transport logistics, production scheduling, supply chain inventory optimization, and more. 

In fact, a recent study revealed that accurate forecasting was one of the topmost priorities for manufacturing companies as modern manufacturing continues to develop, diversify, and expand to new regions of the world.

While supply chain forecasting methods positively impacts the manufacturing organization at several levels, it particularly sees some great benefits in the warehouse. After all, the competitive advantage it gives to managers of manufacturing operations management with effective production capacity planning and control is unbeatable.

How AI can enable supply chain forecasting?

Here are the 5 ways how AI-driven operations enable plant managers to make accurate supply chain forecasting for efficient warehouse management and better shop floor control.


When we talk about streamlining operations, the no. 1 prerequisite is usually having effective production scheduling. Achieving the desired sequence of production depends largely on a streamlined sequence of inflow and outflow of materials, at the right time + quantity + place + adequate manner. Accurate demand-supply forecasting can easily contribute to achieving the most optimum production scheduling on the factory floor. This helps plant managers effectively run schedules and align with the availability of raw materials and component parts. 

Forecasting helps by giving accurate indications needed to plan production cycles and helps managers operate with more agility, transparency, and flexibility and the readiness to adapt to changing production environments as well.


Inventory levels are like the building blocks of manufacturing set up. A shift in any direction, whether high levels or low levels can shake the basic foundation of the operations cycle. From warehousing strategies, transportation costs, or even yard and container management, effective inventory control can impact the core operational aspects of a factory’s production stream. Supply chain planning and forecasting types can equip operations managers to accurately predict demand or orders for certain products. This helps them work more effectively with suppliers to attain adequate inventory levels which greatly reduces the risks of parts overstocking or shortages. 

Accurate supply chain forecasting gives plant managers the necessary clarity of demand and supply situations. This helps easily evaluate the market demand forecasting versus the actual volume of inventory necessary to successfully fill orders and ensure scheduled delivery windows. 


From an operational angle, usually, the asset, machine, or resource that takes the longest amount of time is characterized as the bottleneck. Bottlenecks are the biggest cause for industrial waste with millions of dollars being spent in operational delays, inefficiencies & lost revenue. Therefore, plant managers 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 data from equipment, processes, and products can help determine where bottleneck operation occurs and eventually boost safety and throughput while extending the life of key processing and material handling equipment.

Accurate supply chain forecasting helps gain visibility and analyze existing data with valuable insights. This can give operations managers the necessary functional intelligence for bottleneck elimination at the source. 


With rising complexities in industrial operations, plant managers have to constantly deal with mounting operations costs. Unplanned equipment downtime, increasing quality expectations, inventory lockouts, and managing unused industrial material or parts are just some of the reasons contributing to high operational costs. Accurate supply chain forecasting comes as a boon here as it helps managers with the much-needed foresight to not order more stock than necessary to fulfill customer orders. In addition, forecasting techniques also reduces costs associated with other critical operational tasks such as job allocation and management, sourcing raw materials, process optimization, and product obsolescence costs. 

Supply chain forecasting has a long term impact on operational cycles, touching each and every aspect of the value chain. Therefore, accurate forecasting can be highly cost-effective at every step leading to a higher throughput.  


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.

Effective forecasting models can contribute to improved distribution planning and logistical control to help balance various aspects like order management, packaging, distribution, and outbound logistics. 

AI-driven Supply Chain Intelligence – Now achieve operational efficiencies across the shop floor 

When supply chain forecasting happens through the powerful realms of AI, this can result in superior operational outcomes. All of this with minimal effort of time, resources, and human involvement. AI in supply chain provides it with contextual intelligence which can be used to reduce the operating costs, eliminate wasteful processes and bottlenecks and accelerate decision making. With the introduction of AI, manufacturing companies can work on enhancing factory scheduling and production planning, by analyzing different issues at several levels and then optimize them.

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Guest Post was written by Anita Raj, Industry 4.0 and AI Evangelist

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