Cloud analytics: supply chain’s critical link
julio 1, 2021 / Unisys Corporation
Short on time? Read the key takeaways:
- Manufacturers must fine-tune their supply chains using the elasticity of the cloud to handle enormous data sets.
- The scalability of cloud analytics allows manufacturers to expand into new markets, create new revenue streams, manage supply chains on a global level, and much more.
- Manufacturers increasingly depend on seasoned partners to contribute their knowledge to overcome challenges.
- Partners can deliver breakthroughs with advanced cloud analytics.
Manufacturers’ adoption of cloud analytics has been invigorated by the disruption of supply chains worldwide over the past few years.
Yet, never was it more obvious how vulnerable commerce is to sustained uncertainty, shortages, gluts and broken links in a globalized system. Now, the march is on for manufacturers to fine-tune their supply chains dynamically, in real-time, by harnessing cloud elasticity to use valuable, massive data sets.
This was always the promise of digital transformation – that intelligent, integrated supply chain solutions, free of manual input or human error (the cause of nearly a quarter of all downtime in manufacturing), would enable manufacturers to create efficient planning and production programs, better manage inventory, ship and track material and manage personnel.
The reality is even more promising, thanks to cloud analytics, which offers the scalability needed for high-compute workloads. Virtually any kind of file can be a target of analytics, meaning the rivers of incoming data are often heavily unstructured and massive, so traditional analytics can’t crunch them effectively.
Gartner predicts that by 2022, public cloud services will be essential for 90% of data and analytics innovation. 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% of these organizations reported being entirely data-driven.
Before exploring the major benefits of cloud analytics, let’s look at a few challenges to overcome first.
Obstacles to using cloud analytics
Due to scalability, cloud analytics can help manufacturers find and serve new markets, develop new revenue streams, improve customer service and optimize supply chains on a global scale. Benefitting from this shift to performing data analytics in the cloud means first overcoming a few obstacles:
- The shortage of people with data analytics skills is one challenge. It takes a broad and varied skillset to derive information from fast-flowing data streams and use artificial intelligence and machine learning capabilities to extract additional value.
- Data security is a vital consideration for manufacturers’ data and IT leaders, understandably focused on protecting their data assets. The shift to the cloud is intended to permit exponentially greater access to data by multiple parties, internal and external, in the extended supply chain. Not only are the insights derived from analytics competitively sensitive, but the potential exposure of customer information is a genuine concern.
- Making the actual journey is also challenging – starting with planning the implementation, executing it, managing it and continuously optimizing it– all without interruption in service. Instead of the traditional “lift and shift” approach of retooling applications and systems to rehost them on the cloud, manufacturers are rearchitecting applications to live natively in the cloud and maximize cost efficiency
To meet these challenges, manufacturers increasingly rely on experienced partners to provide expertise in data security, cloud migration and analytics at each stage of their cloud migration. Throughout the journey, they can develop their in-house knowledge and capabilities to optimize their cloud analytics in the future.
Opportunities to benefit from cloud analytics
Through this effort, partners can deliver breakthroughs like these as a result of advanced cloud analytics:
Value/Revenue – A major airport gathered extensive data about routine weekly flights to specific locations. It combined that with the buying habits of the typical passengers on those flights at airport shops during flight delays.
Among other insights, the data revealed that passengers to London seek different products than passengers to Rio de Janeiro. At the same time, a flight to Japan might suggest a different set of buying habits. Cloud analytics detected patterns, so the airport knew 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 fishing practices in reserved areas and the protection of vulnerable species. A cloud analytics proof of concept enabled the company to track the fishing boats using GPS and speed data and identify where the boats stopped to catch fish. Overlaying that data with marine reserve maps documented whether the catch was made in a reserve or non-reserve area.
Certifying the catch in this way, and recording the certification on the blockchain so that it can follow the catch throughout the entire supply chain, allowed the company to demonstrate its sustainable practices without exposing prime fishing areas to competitors. In addition, customers could be provided with a QR code that would let them see precisely where the fish was caught. This demonstrated the viability of capitalizing on integrated supply chain analytics and blockchain to drive sustainability.
Productivity – Traditionally, factory processes are optimized according to the experience and wisdom of their operators. When manufacturing companies expand via acquisition, they end up with factories running entirely different systems, perhaps in other countries. These systems often use innovative machinery tagged with sensors that measure inputs like flow, temperature and milling processes and produce extensive information.
Using cloud analytics and artificial intelligence, they can create true digital twin copies of live operations to benchmark and compare factories, identify productivity improvements, test new processes, create solutions, and prevent outages.
Efficiency –Manufacturers have had to confront fractured, unreliable transportation systems to receive their raw materials and ship goods. Airlines, railways and trucking companies have 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.
Security – Shipments of high-value merchandise are prime targets for thievery and counterfeiting. Pharmaceutical manufacturers, for example, are fighting back with sensors on shipments. These sensors provide them with extensive data that will identify patterns when algorithms are applied and results analyzed on the cloud, including types of shipments, particular trucking companies, targeted routes and predominant methods.
Speed the supply chain with cloud analytics
The world is ever more digital and complicated. Pandemics, hostilities and natural disasters complicate it further. These unpredictable and uncontrolled events demand rapid and highly informed adjustments from manufacturers and their supply chains.
Manufacturers need answers in hours, minutes – even seconds, not weeks or months. That is now eminently possible because of advances in cloud analytics. By collecting vast amounts of data along the chain and creating algorithms to make the data useful, data scientists can envision ways to add business value and fulfill critical missions.
