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.
In part 2 of her article, Dr Noel Greis describes how a Saffron application to help make real time decisions and manage transactions in the military supply chain can apply to the business world.
All across the supply chain there are lots of transactions, and the biggest problem when you’re managing supply chains is that you don’t want a ‘stock out’. You don’t want to be at the bottom of the chain, standing in front of the customer and there’s nothing on the shelf.
It’s probably the worst thing that could happen to a retailer because in today’s environment, people have so many other choices, so they can readily choose another product, or choose another vendor.
We’ve looked at complex military supply chains supporting critical missions.
But imagine very complex product supply chains today that traverse the entire globe from emerging markets to developed markets.
You have complicated products, and individual parts may be sourced all over the world. The parts have to come together at a certain point and time for assembly and then distribution to the customer.
The supply chain is only as strong as the weakest link.
So if, across the supply chain there’s one little link where something is missing, your entire supply chain can fail.
So we can support commercial supply chains with the Saffron engine just like we did for the battlefield
We can get information across the entire supply chain, both inventory information and demand information, that enable us to balance supply and demand.
We can gather all of this information and use our Saffron engine to make recommendations about where we need to build up inventory for example, so that we don’t have a stock out at the very end of the process when the product is supposed to get to the customer.
It’s the situation that occurs in the battlefield. We’re getting lots of information in real time, using this information to better understand the dynamics of that process in order to avoid a stock out . The Saffron engine lets us make better decisions to manage our inventories more efficiently and effectively.
For example, the supply chain might experience a variety of potential disruptions, like a plain old quality problem.
A shipment of parts comes in from a supplier and it’s no good. So now you don’t have any parts and you have to hold up the supply chain. That supply chain disruption affects another node downstream in the supply chain and so on.
All of these disruptions are happening along the supply chain but the Saffron engine enables us to manage them in real-time and then to make the decisions that keep the parts flowing.
So before, where our solider or retailer might have gotten on the phone, to expedite an order, now they have the opportunity to let the application manage the risk.
Technology can take on some of these tasks and in the process, keep the soldiers on the battlefield better prepared to complete their missions.
Or keep retail customers happy.
Dr. Noel Greis, Director, Center for Logistics and Digital Strategy, UNC
In this two part article, Dr Noel Greis describes an application using the Saffron Sensemaking Engine and battlefield sensors to help make real-time decisions and manage transactions in the military supply chain.
In the battlefield it’s very important to have what the US military calls Situational Awareness.
When you’re aware of the situation, you’re aware of the resources that you have, the resources that are needed and anything that would describe the current situation or affect the need for resources.
Around the time of the Iraq war the Bush administration placed a very strong emphasis on bringing technology onto the battlefield. The US sought a more networked, information-rich battlefield environment.
A key aspect of that effort was having the right resources in the right place at the right time.
Making sense of an organic, ad hoc, real time process.
“Johnny, I’m really running low… you’ve got to get X to me and you’ve got to get it to me in the next three hours!”
This is a typical supply chain conversation. Reactive, and dependant on people interpreting events as they notice them. A management approach that gets less effective as the complexity of the supply chain increases and likelihood of unforeseen events goes up.
In our application Boeing had a classic resupply mission – whether it was resupply of water, of gasoline, of ammunition, anything that might be needed.
The idea was to gather information in real time from the battlefield and use Saffron to make decisions to do with managing the transactions in the supply chain.
The world is becoming sensored.
Whether it’s from vehicles that are in the battlefield, from aircraft above the battlefield, or from the actual humans that are in the field, we can bring all of that information together and make decisions about how to respond.
The battlefield is an increasingly sensored environment. You have sensors on all the vehicles. You may have sensors on the soldiers. You have sensors everywhere. The application gathered information from all of the platforms and all the individuals.
We created a system where we had situational awareness of everything that was happening in the battlefield as well as along with the supply chain.
We pushed all this data into our Saffron engine within a decision support environment to make recommendations about how to launch a resupply mission, when to launch it and what route to take.
You’re using Saffron to not only observe but to make decisions. In this case decisions about how to resupply from forward positioning stations or from a port or from any place where you would draw assets.
Many technologies came together in this particular application. Saffron was the core engine that we used to interpret the information. But we had to have other technologies that would that would bring data from the various sources – from the vehicles, from the individuals, networking software, and then software that enabled information-sharing across all these elements.
Dr. Greis is recognized for her work in connecting enterprises with the latest digital and BI technology to create data-driven businesses.
CARY, N.C., February 14 /PRNewsWire — — Saffron Technology, Inc., a privately held Big Data software analytics firm, announced the appointment of Dr. Noel Greis to its Executive Advisory Board with immediate effect.
As the director of the Center for Logistics and Digital Strategy at University of North Carolina, Dr. Greis has been helping industry leverage digital technology, especially business intelligence engines, to shift decision towards the use of experience-based analytics in operational decision making. A producer of award-winning research on logistics, global operations strategy, technology management and technology commercialization, Dr. Greis works with commercial and government – both defense and civilian – organizations.
The appointment builds on several innovation projects already completed between Dr. Greis, Saffron Technology and market leading USA brands.
“We have field tested Saffron Associative Memory technology via successful research collaborations with organizations such as Boeing Research and Technology,” says Dr. Greis. “Now we are seeing a wave of interest in Saffron that is driven by changing market dynamics. In particular, Saffron’s associative memory approach has that rare capability to anticipate future events using all the data available, without a-priori models. This is critical for Big Data analytics. It is also what companies are looking for right now. So it is an exciting time to come on board, and work more closely with Saffron on market applications”.
Dr. Greis’ initial research projects included an innovative battlefield supply chain management solution for Boeing, an analytic tool to detect transactional anomalies for SAP, a solution to detect failure trends in the space shuttle orbiter for NASA, as well as predictive models to anticipate engine failure in vehicles.
Saffron continues to add talent in 2012. Dr. Greis’ appointment comes just weeks after the appointment of Dr. Paul Hofmann from SAP.
According to Co-Founder and CEO, Dr. Manny Aparicio, this is a reflection of heightened market interest in Associative Memory technology. “Saffron’s adoption continues togrow as companies struggle with the volatility and complexity associated with Big Data. Supply chain and logistics is one of the most complex areas for Big Data Analytics, and we are delighted to have Noel Greis join our Advisory team. We have a successful track record of partnering together on complex analytic solutions. We know she has the right experience and network to accelerate the connection of our technologies with private enterprise.”
About Saffron Technology, Inc.
Saffron, founded in 1999, is the world leader in Associative Memory technology. Saffron’s Natural Intelligence Platform, including SaffronMemoryBase(TM), is a market-proven cross-industry intelligence platform that supports real-time sense-making, decision support, and experience-based prediction. Customers apply Saffron to operational decision support, global risk management, market intelligence and more. Saffron is designed to work with “big data” – high velocity, complex, unstructured data sources while providing the agility needed for continuous adaptation. Saffron combines semantics, statistics and context into a single representation of knowledge. Saffron customers are innovators, in academia, corporations, and government. They are engaged in high-stakes research, financial services, risk management, supply chain operations, national security and business development.
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.
Decision-making is based on a history of business intelligence that has been built up over time and is in a company’s data warehouse. Donald Feinberg of Gartner has concluded that the decision-makers aren’t using 80% of the information out there and this is profound.
Decision-makers aren’t using 80% of the data available
In any industry where there are customers, and where customers are interacting with a brand, information on a particular person is being created. An example would be manufacturing, there are many databases in a manufacturing line; quality reports, problem determinations and fixes. Whilst some of that data will be partially structured, the real content is usually only expressed by a human. The data within these databases is not exploited from all the unstructured elements that are even in structured data.
Natural approach sees 10 to 20 times return on mission.
When people make decisions, we recall our past experiences to find the analogies and similarities. What was like this situation? What was different? What did I do?
How did it turn out?
This is the way the world works… except in business where increasing focus on data has meant more rule based decision-making.
“Less than 20% of information we need to make decisions is stored in the company’s data warehouses. A new approach is needed… DBMS are not capable of handling and searching through all the types of data we have – email, text, voice, video… ” – Donald Feinberg, Gartner VP
High Velocity, High Complexity, High Diversity
A fuzzy world of diverse, complex and disorganized data sources can’t accurately be explained via tables and SQL queries. Indeed, 80% of the answers can’t even be found within your business.
Big Data is not just big in volume but big in velocity as intelligence goes real time.
According to Forrester – current BI technologies are “falling short in the ever-faster race” to meet business needs. (Download report free)
Unlocking experience unlocks ROI
Like natural human intelligence, Saffron focuses on how People, Places and Things fit together.
Like a true corporate memory, it doesn’t require rules, models or statisticians. You don’t even have to decide what to include or exclude. Everything you can know is included.
One global manufacturer has experienced a 10x return on cost of ownership versus savings in year 1.
And because there are always new problems to solve, this is a recurring business case. In year 2, ROI is expected to grow to 20x.
Democratized and operationalized.
BI is no longer just for forecasting, or just for management. Democratized data means real time intelligence within your company. Available to anyone and everyone as needed to get the job done.
Saffron Advantage: experience intelligence without rules.
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.