In the next five to 10 years, smart supply chains will exert a strong pull on the global economy. The paradigm shift is already underway, as linear and sequential supply chain operations move to a digitized, open framework — part of Industry 4.0 — that maximizes machine capabilities with high throughput and limited resources.

The scope for supply chain management is immense — it has the potential to enhance every supply chain function, from inventory forecasting to demand and supply management. Applied correctly, smart supply chain management could revolutionize strategic decision-making, creating a truly agile and optimized ecosystem. Even just a 1% increase in efficiency is worth the effort, netting a company as much as $276 billion in 15 years. Plus, research has shown that nearly 80% of companies with leading supply chain operations reach above-average revenue growth.

Enter the Machines

But it’s not as easy as companies simply snapping their fingers and updating their supply chains. One major problem is the massive amount of data needed to optimize supply chain systems. A survey by McKinsey found that while 90% of manufacturers believe Industry 4.0 will change their operational landscape, less than half feel prepared to take steps to implement it, citing concerns over processes and the massive amounts of data required.

By using existing data sets, businesses can realize radical efficiencies. Artificial intelligence and machine learning will bridge the gap, helping companies analyze and understand mountains of data in order to maximize efficiency, solve complex operational problems, and ultimately make the switch from a reactive and preventative approach to a predictive and prescriptive one.

Machine learning capabilities also free leaders from handling daily mundane issues, letting them focus instead on planning and strategizing. According to McKinsey, machine learning can also improve forecasting accuracy by up to 20%, potentially leading to a 5% decrease in inventory costs and revenue increases of up to 3%.

In other words, machine learning will help companies move toward operational perfection by granting them end-to-end supply chain visibility that intercepts so-called “unpredictable” issues. This visibility puts an end to costly snafus such as dead ends and bottlenecks and results in smooth operational cycles and patterns.

The Future of Smart Supply Chains

To survive in the global economy, companies will have to invest in smart supply chain management. As they do, the following shifts will take place:

1. Digital transformation efforts will increase.

Top organizations already budget an average of $17 million for artificial intelligence for supply chain management operations. That amount will increase and more businesses will get on board as business leaders learn to trust digital technology and understand the value it brings. End-to-end automation and value stream optimization will become essential for business survival. As a result, complex operations will become increasingly digitized and functional aspects of organizations will become more robotics-driven.

2. Data quality will win over quantity.

Businesses will learn that smart supply chain management is about having the right data, not having all the data. Thus, organizations will begin investing in technologies that streamline and unify data improvement efforts. They’ll learn to rely on filtering mechanisms and how to join existing, traditional, and future data sources to build a cohesive, holistic picture of their supply chain. As the cloud computing market reaches $410 billion by 2020, the supply chain management industry will also experience further consolidation of cloud-based data lakes, further enabling streamlining analytics with data-at-rest and data-in-motion concepts and powering innovation and improved infrastructure parameters.

3. Bottleneck management will improve.

As predictive analytics take root and enable end-to-end visibility, companies will be able to anticipate and fix problems before they occur. What’s more, systems to detect, rank, and eliminate issues will be automated, effective, and reliable — putting an end to human error. Empowered by machine learning, supply chain management companies can predict and be prepared for nearly any disaster, whether it occurs in the industry (such as a bottleneck) or the natural world (such as an earthquake). This is especially important, as the global supply chain risk is currently the highest it’s ever been, according to the CIPS Risk Index. Simply put, predictive analytics helps companies predict the unpredictable.

4. Sales and operation planning will become more effective.

Connected devices collecting real-time data can provide a more accurate picture of demand than was ever possible before. This enhances sales and operation planning by ensuring existing plans are viable and giving companies the information needed to realign instantly if conditions such as warehouse and transport capacity change.

Although some companies are already transitioning to a smart supply chain management system, many more will jump on board in the next few years. The investment is well worth the time and funds required, as even small increases in efficiency provide a huge return on investment.

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