Unlocking the value of cross-facility data sets

For the manufacturing industry, the benefits of migrating to cloud computing have been heavily publicised, but in an industry that has been slow to embrace new technology, a mass move to the cloud can feel like a leap in the unknown. Despite an increased adoption of smart manufacturing technologies, some companies may still feel hesitant. Instead, many decide to test the water by implementing a cloud storage model in just one production site. However, this implementation model can only provide limited benefits in comparison to a mass, multi-site migration to the cloud.

So what should companies expect to undertake during their cloud migration?

Define business objectives

Before migrating to the cloud, companies should first consider how it can help them achieve -and in some cases refine - their business objectives and plan their migration with these objectives in mind. For businesses that want to improve collaboration and benchmarking across multiple locations, for example, the cloud plays a significant role.

A company with multiple production sites operating in several locations will be familiar with the complications of cross-facility benchmarking. Often, business objectives or Key Performance Indicators (KPIs) are only set for single site locations. In an 'ideal' situation, the business objectives have to be coordinated across all locations to offer a clear, company-wide mission.

To achieve better collaboration and transparency across sites, companies can resort to using a cloud storage and computing application that gathers all available production data (from multiple production sites) in one place. Certain individuals or teams in the company can be granted access to relevant data sets and reports, depending on their responsibilities within the organisation.

Determine the suitable status

Once a business objective is clear, companies should identify what the ideal status of each process is. By using production data and energy information stored and analysed in the cloud, a company can gain insight on productivity, Overall Equipment Effectiveness (OEE), energy usage and more. This insight helps companies make changes that will bring the existing production environment closer to the suitable status.

Combined with the right SCADA software, the cloud unlocks rich company-wide data sets. By bridging information from different facilities in real-time, the software generates a bird’s eye view of company-wide operations and detailed analysis of energy consumption, productivity and other operational KPIs. This makes it easier for a company to monitor progress against the original business objectives and scale up or down when necessary.

Already, a large number of manufacturers are using industrial automation to speed up production and increase efficiency. With the large scale adoption of intelligent machinery, cloud computing is poised to become the obvious solution to store and manage the complexity of data this industry connectivity creates.

Unlike the restrictions associated with on-premises storage, cloud based models provide unlimited scalability, allowing companies to store both real-time and historical data from all production their sites and integrate any production lines or sites to their cloud solution in a seamless manner. When accompanied with data analytics software, like zenon Analyzer, cloud computing can help companies prevent potential problems in production and even ignite entirely new business models.

Continuous improvement

For manufacturers with strict energy efficiency and productivity targets, easy access to company-wide data is invaluable. However, the knowledge provided by the cloud does not end with past and present data, but also gives manufacturers a glimpse into the future of their facilities.

By using the cloud, companies can implement a long-term continuous improvement strategy. Often, continuous improvement will follow the simple Plan-Do-Check-Act (PDCA) model often used in energy management applications. This allows companies to make decisions based on data analytics and to evaluate the effectiveness of those decisions in the short and medium run.

Using data collected from industrial machinery, companies can also employ predictive analytics technology to forecast why and when industrial machinery is likely to fail, which also means they can minimise costly downtime.

Predictive analytics allows manufacturers to identify potential problems with machinery before breakdowns occur. Avoiding expensive overheads for production downtime and costly fines for unfulfilled orders, the priceless insights predictive analytics can provide is the obvious solution to such costly problems.

Converting from the safe familiarities of on-premises storage to an advanced cloud model may seem risky. As with any major business transition, there is bound to be hesitation surrounding the potential problems the changeover could bring. Before making a decision, companies should closely assess three things: business objectives, how the cloud can help them achieve the ideal status across one or multiple production sites and how it can help them continuously improve in the long run.