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A Sailor takes inventory aboard USS Porter in the Mediterranean Sea. Image: US Navy

The wave of Big Data analytics continues to sweep across all areas of business, and defence is no exception. The US Navy, for instance, is relying on it to optimise how it issues contracts, provides accurate figures for billing and maintenance of naval equipment, and deploys personnel across naval operations.

Data analytics firm Saalex Solutions is working with the US Naval Surface Warfare Center – Corona (NSWC) in California, US, to put all the data the US Navy has collected over the past twenty years into practice, helping to save time and money and making life easier for military personnel.

“The end objective is to really serve the warfighters and make sure the warfighter has the tools and assets at their disposal,” says Arthur Glaab, chief strategy officer at Saalex. “In the past, we always made technologies conform to our behaviours. In this case, technology is driving behavioural changes and as a result you are seeing a higher level of readiness at a lower cost and the speed of efficiency has increased tremendously.”

When highlighting the benefits of data analytics, many have referred to the three Vs – volume, velocity and variety – which succinctly sum up Saalex’s services to the Navy.

Volume refers to the overwhelming mass of data that has been collected, which requires high levels of computational power to digest.

“Going back 20 years or so, many organisations including those in the military, have been collecting lots of data, but it was hard to get your head around what that data is telling you,” says Glaab. “So the ability with advanced computing technologies is, they capture the data and do analytics on it to look for trending histories, and then act on it appropriately. It is the same thing that any organisation is engaged with right now.”

“It seems that the US Navy has been more reluctant to implement data analytics than other organisations.”

Velocity refers to the speed at which data that has been collected is fed into the analytics in real time or near-real time, without long periods of delay or the risk of human error when collecting the data.

Glaab notes: “If you’re under constantly changing dynamics of operations, getting data right away and being able to act on it in a real-time fashion, it gives you a predictive capability that would have otherwise had to be manually calculated.”

The final V, variety, refers to how the extensive range of data can be presented for different military personnel to make decisions based on efficiency. For the US Navy this applies to a diverse range of operations, from contract billing and maintenance, to equipment and weapon procurement, to the deployment of general and specialised staff, on duty and at home.

It seems that the US Navy has been more reluctant to implement data analytics than other organisations. Glaab disagrees, saying that the organisational constraints of the government branch may have resulted in the slower delivery of technological advances.

Glaab explains: “When a CEO decides to make an investment he makes an investment, and he isn't tied down by the planning and budgeting policies that are in use by the US Government. I don't think the Navy is reluctant; it is the operational construct in which they operate.”

Saalex, since being founded in 1999, has almost exclusively provided its services to the defence and aerospace industry. It aims to support the military in the areas of technology and organisational performance.

Glaab explains that this includes consolidating bases, training ranges, and test ranges, for example. “We have multiple users using the same infrastructure and so from the taxpayers’ perspective that's good because it lowers the cost. But one of the things that has been a challenge is trying to figure out who is using the equipment and when, where and how much and how do you appropriately bill for that?”

Saalex can, for instance, fix the cost of using certain types of assets on a “use basis” rather than a “subscripted basis”. Under subscripted basis, a company would bill the Navy for a proportion of time, regardless of whether the asset was used in that time or not, which is inefficient.

“One challenge is trying to figure out who is using the equipment and when, where and how much and how do you appropriately bill for that?”

Using Saalex’s data analytics, Glaab says, “we are able to see a better use of resources and assets, and are able to bill accordingly by the user of those assets”. But there are plenty of other examples, one of which is the optimisation of staffing of naval operations.

“When I first got into the business of defence and aerospace several decades ago, [for] man power and forecasting networks - we used to literally call it a 'standboarding basis' or a 'manning the ship basis' - you would put X number of people on the contract, and there would be downtimes when people were not quite as busy,” says Glaab.

“With data analytics, and looking at which job and where they are located, what resources they are using, you can go in to change your staffing profile accordingly, and make sure you are allocating the resources appropriately.”

Another area where Big Data is providing efficiency improvements, according to Saalex, is inventory control.

“In the past, if you ever walked onto a facility or asset like the Kennedy Space Center, for example, they used to do a lot of in-mass type inventory ordering. You literally had parts in various bins and you would say ‘okay we've hit this minimum level so order up to the maximum level’. And it was just visual inspections,” says Glaab.

“But with data analytics, and with coding of the parts and use and tooling, we can actually do the analytics on the level of demand for certain parts and how much tooling is being used. Based on that, you can order appropriately to make sure that you are carrying only the inventories that you need and when you need, thereby lowering the overall costs of operations.”

“By doing data analytics on tooling and resource usage, the team found that restructuring its process flow could create better efficiencies.”

Glaab experienced this case first-hand working on the Delta II Launch vehicle programme.

“What we used to do is that when someone would send a payload for launch, we would expect the payload to arrive roughly 45 days prior to launch,” says Glaab. “The fuelling and testing would take 45 days. And the reason why was because the way the schedule was built you would rather take on a task beginning to completion and then start the next task beginning to completion.”

After a while, the users of the Delta II Launch vehicle asked Glaab’s team why their crew would be needed for 45 days, which came at a considerable cost to them.

By doing data analytics on tooling and resource usage, the team found that restructuring its process flow could create better efficiencies. Instead of working one task from beginning to completion, they would run other tasks that required the same tooling on the launch pad concurrently. By doing this, they were able to reduce the dwell time on the launch pad from 45 to 22 days – a significant time and cost saving.

“Through data analytics and tracking the kind of resources we used, we were able to do that,” says Glaab.