On the battlefield the challenges the military faces change very, very quickly. During the wars in Iraq and Afghanistan there was a push from the Pentagon to digitize the battlefield, including using real time analytics. Dr. Noel Greis, Director at the Center for Logistics and Digital Strategy at University of North Carolina, explains how an innovative collaboration involving Saffron was able to help Boeing manage the battlefield supply chain using real time data analytics.
The supply chain is an essential, complex and very costly element of any war zone. Loss or delay of materials or assets could seriously impact a conflict situation. Nevertheless, in the past, supply chains have been managed in a relatively straightforward manner. Soldiers monitor inventory, and request resupply when levels run low.
The advent of the digital, sensored battlefield presented a new opportunity to manage the supply chain more efficiently, using real time analytics to help improve decision making.
Boeing has a very large, defense systems division, and works with the Department of Defense. Boeing contacted Dr Greis at the Center to see if she could help them develop some new tools that will enable them to manage the battlefield supply chain, in real time. A collaboration was put together including a company that did battlefield sensors, and the idea was to sensor up the supply chain.
The supply chain included platforms in the battlefield (e.g. tanks or supply trucks). So think of the battlefield as a huge sensor field with all the vehicles sensored.
Inventories are also sensored.
Then you can now take all of that information and bring it into an information space.
Saffron was then used as a data analytics tool to make sense out of that information, making recommendations about how to allocate the inventories.
One asset which is invaluable is fuel. With the sensors able to track when fuel was needed, and the delivery location, Saffron’s real time analytics are able to make more effective recommendations.
Although the system was designed to be autonomous, there are options for a human to be in the loop, so that if Saffron recommended re-supply on a certain date, of a certain amount, at a certain time to a certain set of vehicles, the commander could okay it or not okay it.
The commander would still be part of the decision making process, but it made fuel delivery more efficient, more accurate, and involving less guess work.
The implications in a commercial sense are obvious.
With greater information awareness along the supply chain about inventories, companies in the logistics sector could be more cost effective.
Anything which currently relies on human judgement for determining when something is needed could be supported by real time analytics, which would verify when materials, goods, and assets are actually required.
The same efficiencies Boeing sought on the battlefield with Saffron will have commercial implications globally, as business embraces Big Data.
Dr. Noel Greis, is the Director of the Center for Logistics and Digital Strategy, University of North Carolina, and is an executive advisor to Saffron.
When we think about how doctors and engineers work, it is reasoning by experience and by similarity. Now the arrival of ‘big data’ is driving the need for big individualized memories… so we can leverage all that experience.
The human capacity to remember the complete history of something and to bring all that knowledge and experience to bear, for any one vehicle or one patient is very limited.
Nevertheless, doctors and engineers reason by experience and by similarity.
Even if this is not the same vehicle or patient, they ask themselves what has been seen before.
What did they learn over the years that is like this?
Have they seen this problem on a different machine? How about a similar type of problem or a similar factual background?
This was an approach that worked while the sum of experience was learnable, or at least manageable via books, records and computers.
Yet healthcare is a great example of an industry where big data is now prevalent everywhere you look.
There are lots and lots of patients, and lots of different, complex, fast changing situations for the practitioners, physicians and others to make sense of.
Similarly manufacturing, and more specifically maintenance repairs, is becoming increasingly complex.
Each vehicle, facility, engine and other machinery has its own history. These consist of millions of parts, all the individuals involved in manufacture and maintenance, plus all the use since then. A single vehicle can last decades, which goes beyond human capacity.
So how do we capture all that data and experience and bring it to bear in a new situation at high speed and with intelligence – so that the new / current problem can be diagnosed and dealt with.
We need a way to understand each vehicle and each person, with its own history and own specific and individual problems.
This complete history, is in fact a memory – a computer based memory containing everything single thing we have learned.
Every person, place and thing.
Such memories are now feasible – we call them a memory base.
And just like a human memory, we can ‘recall’ what is relevant and what makes sense in our current situation.
Data is coming at us at an ever-increasing rate. Fundamental patterns in the data are changing, or need to be adaptive. From the business perspective, it is the pace of business that drives a requirement of being able to deal with that.
If we think of more democratized operational business intelligence rather than traditional strategic business intelligence, in manufacturing. When a vehicle breaks the maintenance repair could be an aeroplane, a tractor trailer, or any farm vehicle, these are very large and complex vehicles. If people are faced with that situation, time is money. For instance, an aeroplane is down, money is being lost by not having that plane in service. There is often service level agreements where very explicit money is exchanged given the downtime of these vehicles and the contracts that are in place to service them.
The ability to be faced with a situation or problem and to know what had been seen and done before and how it was dealt with is integral. This is called ‘the Corporate Memory’. Being able to answer questions like:
What have we done?
Was it successful?
How did we replace that part?
If we didn’t have a replacement part, what vendor is the best?
Where do I go given the part, volume and geography?
How to rapidly exploit the experience on the front line?
Essentially, in the business case Saffron can save a lot of money. It has been proven in business cases with ROI being 10 times the return on the cost of ownership.
Our customers apply the Saffron Natural Intelligence Platform in a variety of ways. If your data is complex and large, if you have disparate data sources within which your experience resides, or if your questions don’t organize themselves in neat, orderly, unchanging “rules,” Saffron can help.
“Saffron is enterprise ready.”
– Global Manufacturing Company
Following are examples of how Saffron is helping companies serve customers while saving them time, money and achieving better business results.
Industry: Global Manufacturing
Business Challenge: Supply chain management – machine-parts failure analysis
Customer had been trying for over a year to identify why they couldn’t trace the connections among work orders, supplier orders, engineering orders, and the like, to identify root cause of a significant parts defect. Data was stored in some 40 disparate databases, and text was inaccessible within transactional type records.
Customer applied Saffron’s “Connections” Reasoning Method to find in the Experience the root cause of the problem in a matter of days after the data had been ingested into SaffronMemoryBase.
Quantitative metrics are customer confidential. Time to discovery and action was accomplished by customer’s subject matter expert at the start of the sense making process using SaffronAnalyst.
Business Challenge: Supply Chain Optimization – Replacement components inventory management.
A critical product asset required an inventory of certain made-to-order components, rapid replacement of which was time-sensitive in order to assure end-user satisfaction and stem financial losses for extended product downtime. Company maintained many disparate data files across multiple divisions but could not discern best method for replacing a certain critical component in a time efficient manner nor expose their prior experience with related situations across the enterprise.
Using the “Analogies” Reasoning Method, customer isolated the Experience within the disparate data sets to identify how the component was replaced in the past. By answering questions such as who, what, when, where and why with accurate outcomes, Saffron improved the time to identify and find replacement parts from hours down to minutes.
Quantitative metrics are customer confidential
Corporate purchasing cards were being misused all across a large, multinational organization. Rogue spending and potential spend aggregation were hard for management to pinpoint using traditional spend management tools then in use.
Using the power of the Connections and Analogies Reasoning Methods, customer was able to identify from their Experience purchasing card spend aggregation for even “non-obvious” transactions across multiple cards. This enabled the customer to pinpoint the errant individuals and their departments.
The solution resulted in significant savings across multiple spend categories.