Though the COVID-19 pandemic has rattled supply chains around the world, it has also been a boon for the adoption of cloud analytics. 

The old model saw a supply chain system that dealt with a constant spate of uncertainty, shortages and broken links. However, the elasticity of cloud technology allows for rapid collection, interpretation and value proposition of supply chain data sets. 

Research firm Gartner Inc. predicts that by 2022, public cloud services will be essential for 90% of data and analytics innovation. Meanwhile, an IDC survey of CEOs found that 70% want their organizations to be more data-driven but see significant room for improvement, as only 27% reported being completely data-driven. 

Enter cloud analytics, which offers the scalability needed for high-compute workloads. Today, virtually any kind of file can be a target of analytics, which means the rivers of incoming data are often heavily unstructured and massive, such that traditional analytics simply can’t crunch them effectively. 

However, the shift to performing data analytics in the cloud is not without its challenges. There is a shortage of individuals with data analytics skills, and it takes the proper training and background to use AI and machine learning effectively to derive the right information from fast-flowing data streams. Data security is a vital consideration for manufacturers’ data and IT leaders, rightfully focused on protecting their data assets. And making the actual journey is a challenge — starting with planning the implementation, executing it, managing it and then ultimately optimizing it in an ongoing fashion — all without any interruption in service. 

To meet these challenges, manufacturers are increasingly relying on experienced partners to provide their expertise in data security, cloud migration and analytics at each stage of their cloud migration. Throughout the journey, they can develop their own in-house knowledge and capabilities for optimizing their cloud analytics going forward. Through this effort, partners can deliver breakthroughs across several critical areas, including: 

• Value/revenue. A major airport recently gathered extensive data surrounding its weekly flights to certain locations and combined that with the buying habits of typical passengers on those flights at airport shops during flight delays. The data revealed that passengers to London, for example, seek different products than passengers to Rio de Janeiro.

Cloud analytics detected patterns that revealed what products to promote to appeal to passengers and when. The result? A 20% sales uplift for airport shop sales. 

• Sustainability. A commercial fishing organization wanted to address consumer perceptions around the sustainability of its practices and the protection of vulnerable species. A cloud analytics proof-of-concept enabled the company to track its fishing boats using GPS technology. Overlaying that data with marine reserve maps verified whether catches were made in protected vs. non-protected areas.

Certifying catches in this way — and recording the certification via blockchain in order to follow the data throughout the supply chain — allowed the company to demonstrate its sustainable practices without exposing prime fishing areas to competitors. 

• Productivity. Traditionally, factory processes have been optimized according to the experience and wisdom of their operators. Today, as manufacturing companies expand via acquisition, they find themselves with factories that are running entirely different systems, perhaps in different countries, using innovative machinery that produces and measures extensive system data.

Using cloud analytics and AI, manufacturers can now create true digital twin copies of live operations to benchmark and compare factories, identify productivity improvements, test new processes and prevent outages.

Other areas of opportunity also exist, including supply chain efficiency and security. The pandemic confronted manufacturers with fractured, unreliable transportation systems for receiving their raw material and shipping their goods, as airlines, railways and trucking companies struggled to align capacity with uncertain demand. That experience has led many to invest in cloud-based analytics that use historical data to predict the least expensive or most reliable transporter. 

As for security, shipments of high-value merchandise are prime targets for thievery and counterfeiting. Pharmaceutical manufacturers, for example, are fighting back with sensors on shipments that provide them with extensive data that, when algorithms are applied and results analyzed on the cloud, will identify patterns that can better determine types of shipments, particular trucking companies and targeted routes. 

Today, the world is increasingly digital, complicated and subject to disruptions that demand rapid and highly informed adjustments from manufacturers to optimize and protect their supply chains. They need answers in hours, minutes — even seconds. 

Thanks to cloud analytics, that is now a reality. By collecting vast amounts of data along the chain and creating algorithms to make the data useful, data scientists can identify ways to add business value and fulfill critical missions. More importantly, cloud analytics can help manufacturers deliver on the promise of digital transformation to bring supply chains into the future: intelligent, integrated supply chains that can help manufacturers better manage and track inventory, manage personnel, develop new revenue streams and improve customer service. In short, it is time manufacturers put their heads in the cloud.

Stijn Van Impe is leader of EMEA advisory services at Unisys.

Though the COVID-19 pandemic has rattled supply chains around the world, it has also been a boon for the adoption of cloud analytics. 

The old model saw a supply chain system that dealt with a constant spate of uncertainty, shortages and broken links. However, the elasticity of cloud technology allows for rapid collection, interpretation and value proposition of supply chain data sets. 

Research firm Gartner Inc. predicts that by 2022, public cloud services will be essential for 90% of data and analytics innovation. Meanwhile, an IDC survey of CEOs found that 70% want their organizations to be more data-driven but see significant room for improvement, as only 27% reported being completely data-driven. 

Enter cloud analytics, which offers the scalability needed for high-compute workloads. Today, virtually any kind of file can be a target of analytics, which means the rivers of incoming data are often heavily unstructured and massive, such that traditional analytics simply can’t crunch them effectively. 

However, the shift to performing data analytics in the cloud is not without its challenges. There is a shortage of individuals with data analytics skills, and it takes the proper training and background to use AI and machine learning effectively to derive the right information from fast-flowing data streams. Data security is a vital consideration for manufacturers’ data and IT leaders, rightfully focused on protecting their data assets. And making the actual journey is a challenge — starting with planning the implementation, executing it, managing it and then ultimately optimizing it in an ongoing fashion — all without any interruption in service. 

To meet these challenges, manufacturers are increasingly relying on experienced partners to provide their expertise in data security, cloud migration and analytics at each stage of their cloud migration. Throughout the journey, they can develop their own in-house knowledge and capabilities for optimizing their cloud analytics going forward. Through this effort, partners can deliver breakthroughs across several critical areas, including: 

• Value/revenue. A major airport recently gathered extensive data surrounding its weekly flights to certain locations and combined that with the buying habits of typical passengers on those flights at airport shops during flight delays. The data revealed that passengers to London, for example, seek different products than passengers to Rio de Janeiro.

Cloud analytics detected patterns that revealed what products to promote to appeal to passengers and when. The result? A 20% sales uplift for airport shop sales. 

• Sustainability. A commercial fishing organization wanted to address consumer perceptions around the sustainability of its practices and the protection of vulnerable species. A cloud analytics proof-of-concept enabled the company to track its fishing boats using GPS technology. Overlaying that data with marine reserve maps verified whether catches were made in protected vs. non-protected areas.

Certifying catches in this way — and recording the certification via blockchain in order to follow the data throughout the supply chain — allowed the company to demonstrate its sustainable practices without exposing prime fishing areas to competitors. 

• Productivity. Traditionally, factory processes have been optimized according to the experience and wisdom of their operators. Today, as manufacturing companies expand via acquisition, they find themselves with factories that are running entirely different systems, perhaps in different countries, using innovative machinery that produces and measures extensive system data.

Using cloud analytics and AI, manufacturers can now create true digital twin copies of live operations to benchmark and compare factories, identify productivity improvements, test new processes and prevent outages.

Other areas of opportunity also exist, including supply chain efficiency and security. The pandemic confronted manufacturers with fractured, unreliable transportation systems for receiving their raw material and shipping their goods, as airlines, railways and trucking companies struggled to align capacity with uncertain demand. That experience has led many to invest in cloud-based analytics that use historical data to predict the least expensive or most reliable transporter. 

As for security, shipments of high-value merchandise are prime targets for thievery and counterfeiting. Pharmaceutical manufacturers, for example, are fighting back with sensors on shipments that provide them with extensive data that, when algorithms are applied and results analyzed on the cloud, will identify patterns that can better determine types of shipments, particular trucking companies and targeted routes. 

Today, the world is increasingly digital, complicated and subject to disruptions that demand rapid and highly informed adjustments from manufacturers to optimize and protect their supply chains. They need answers in hours, minutes — even seconds. 

Thanks to cloud analytics, that is now a reality. By collecting vast amounts of data along the chain and creating algorithms to make the data useful, data scientists can identify ways to add business value and fulfill critical missions. More importantly, cloud analytics can help manufacturers deliver on the promise of digital transformation to bring supply chains into the future: intelligent, integrated supply chains that can help manufacturers better manage and track inventory, manage personnel, develop new revenue streams and improve customer service. In short, it is time manufacturers put their heads in the cloud.

Stijn Van Impe is leader of EMEA advisory services at Unisys.