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.
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.
Imagine a Decision Support tool where you have, at-the-ready, your entire organization’s Experience about all prior decisions your company has ever made – and their outcomes – good, bad or ugly.
With Saffron … you do.
Saffron helps you squeeze every ounce of prior Experience possible from your data – across your enterprise knowledge base, or residing in your business processes – and considers it on your behalf so you can make quicker, more informed decisions today.
Saffron Natural Intelligence Platform
The Saffron Natural Intelligence Platform operates in a dimension beyond the limiting rules and models of traditional business intelligence or data analytics tools. What’s different is Saffron works by memory based reasoning. That means it identifies all the attributes for a specific decision case, and quickly identifies other similar cases, so you can quickly know as much as possible – and use your prior experience – on the matter about which you’re making a decision.
Saffron’s history with Decision Support applications is extensive. In National Security work we’re involved with Intelligence, Surveillance and Reconnaissance operations. In the Manufacturing sector, we’re helping generate replacement optimization recommendations for non-repairable parts; and in Banking & Finance, we’re supporting deep analysis of suspended mortgage loans to identify best actions to shorten approval cycles, to name just a few.
How it Works
It’s very straightforward.
We start by understanding what you want to make decisions about, and the data sources you want included in the analysis. By identifying the known possible outcomes, such as “go/no go,” “approve/deny,” or for more complex decisions, multiple choices. By computing all the likely outcomes, showing similarities among prior cases, and identifying key factors, Saffron presents you with just the data you need to make more informed decisions. And as you make them, the system learns and remembers, enriching its capabilities for future use.
Here’s an example of how Saffron helped a customer company in the mortgage loans industry to apply Experience Management by Decision Support. The customer tried for two years, but failed to create a new suspended loans decision support tool using traditional rules-based and statistical methods. They then approached Saffron to discuss applying our associative memory technology to the problem. Their story follows.
Industry Use Case
Mortgage Loans Processing / Suspended Loans
In mortgage loans processing, suspended customer-loan applications can grow into lost revenue, costing banks a lot of money. Some customer-loan applications are suspended due to incomplete or inconsistent information; others because of problems with the property itself. But still others are suspended for reasons amounting to a “mismatch” with the approval rules, as pre-defined by the company’s loan processing software.
Mortgage bankers want suspended loans to re-enter the approval process as quickly as possible, both to insure good customer service and to achieve optimum revenue for the mortgage loan company. To do this, suspended loans must be carefully examined to determine how best to correct these inconsistencies.
“We tried for two years to solve this problem–and failed. Until Saffron.”
— Financial Services Company
Decision Support Objective
Use the collective Experience of mortgage bankers, to reduce the volume of loans suspended and not closed in the mortgage loan approval process, empowering individual bankers to make faster revenue-impacting decisions.
A successful, well-respected global mortgage loan company processes a high volume of mortgage loans daily. Their goal is to ensure every applicant is correctly evaluated in their pursuit of a mortgage loan. All loans are subject to strict rules in a well-defined loan approval processing system.
Many mortgage loan applications fall out of the approval process for failure to meet the “rules’ of the existing loan approval management system. When this happens a customer’s loan application is suspended. Thousands of loans can be in a suspended mode at any one time for a variety of reasons, e.g., applicants may forget to report all their income, or leave important information off their applications. Any combination of such things can cause a loan application to be suspended, and potentially rejected.
With thousands of unique loan applicants, it is challenging to quickly evaluate each loan, and know what requirements must be met to return the loan into the approval process. Each unapproved loan represents disappointed customers and significant dollars in lost revenue to the mortgage company. Plus, every additional day a loan remains suspended, the likelihood it will fail to close increases.
Saffron worked with the customer to create a new Experienced-based decision support tool using SaffronMemoryBase and Saffron REST APIs. The solution helps company mortgage bankers rank suspended loans according to how likely they are to close, recalling similar loans successfully closed in the past to suggest modifications for the bankers’ consideration, and highlighting the shared characteristics of these similar prior loans.
Saffron developed the Reasoning Methods REST API for decision support, which includes rank- orderering loans most likely to close; finding similar loans; recommending actions; explaining how/why those actions will work; and automatically learning from actions and outcomes. The customer developed the end-user interface for the mortgage bankers, leveraging Saffron’s REST API for ease of integration.
As the banker works with a suspended loan, SaffronMemoryBase incrementally learns the outcomes – the successes and failures – of each of the banker’s modifications and incorporates these outcomes into SaffronMemoryBase for immediate use by all other bankers working on new and existing suspended loans.
Using Saffron now enables this banking customer to apply the mortgage department’s sum-total Experience across all loan decision makers, giving each the Experience of many others. Saffron also defined an extensive new “set” of more than 125 unique loan-status attributes as decision criteria, all unconstrained by rules-based or statistical business intelligence models.
The implementation is underway. Our goal is to improve suspended loan conversion rates by 3 – 5% annually. The net result is satisfied customers supported by better, faster loan decisions.
Making decisions is about weighing the likelihood of outcomes to select the most favorable action. To enable a better decision, a Decision Support tool needs to compute likelihood across scores of possible outcomes, and make recommendations based on past cases.
The basic premise of Saffron REST APIs is to add easy-to-configure Decision Support capabilities to SaffronMemoryBase.
Saffron models the essence of your Decision Support process with three simple concepts:
1. Cases – A case is an application entity upon which decisions will be made. In the suspended loan example, a loan is a case. The characteristics of a case are represented as a set of properties called attributes.
2. Attributes – An attribute is a name-value pair representing a property. The possible results of acting upon a case based on a decision are represented by a set of labels called outcomes. In the suspended loan example, the properties of the loan are mapped as the attributes of the case.
3. Outcomes – In the suspended loan example, the final loan status of approved or denied is modeled as outcomes.
The key functions for Saffron’s Decision Support REST API include:
Triage – The rank ordering of events/transactions according to their likelihood of outcomes. Rank ordering will be based upon the Customer’s defined factors which may be numerous and large in scale.
Recommend – Saffron will recommend the actions most likely to result in favorable outcome based on the case’s similarity to other case with favorable outcomes.
Nearest Neighbor – Saffron will identify existing cases within SaffronMemoryBase that are most similar to the case under examination. Similarity analysis is based on the distance between the attributes of a case at a given point in time or status and their values at an outcome.
Explain – Saffron will support the rank ordering and recommendations with two forms of explanation. First, prior similar cases and actions will be presented as the basis of prior experience. Second, the most discriminant factors for the rank and recommendation will highlight the most important aspects of similarity and difference.
Adapt – Saffron will adapt and learn over time by considering additional case experiences that occur when cases are resolved. Additional case experiences will be captured on the fly for continuous learning and improvement of the rankings and recommendations, providing a dynamic experience-based decision support capability
In Saffron’s world, Experience Management by Decision Support focuses primarily on Reasoning by Classifications and Temporal Analysis REST APIs. These allow you to reuse the Decision Support capabilities built into SaffronMemoryBase across the enterprise. You can also support a variety of operations and easily integrate Saffron into existing and new end-user interfaces.
By mapping the entities for a given situation into these simple concepts, and ingesting the data into SaffronMemoryBase, powerful Decision Support functions result, and they help you and your company make better decisions.
For more information about Experience Management by Decision Support, contact us.
While applicable in virtually any business environment, Sense Making is well known for its value in military Intelligence analysis. Saffron has first-hand experience applying this dimension of our robust data analytics solution to the problem of IED (Improvised Explosive Device) defeat in Iraq.
Sense Making Objective
Draw on Experience to save lives by finding enemy insurgents and stopping random bombings before they occur.
The U.S. Army and its coalition partner war-fighters in Iraq needed the ability to decide and act at the tactical level to eliminate, capture or exploit insurgent targets directly or indirectly involved in IED activity. To be effective, this targeting had to be done prior to actual IED attacks (and at the time of our forces’ choosing) in order to prevent loss of life for both civilians and war-fighters.
Saffron Technology played a lead role on the team formed to enable and enhance the analytic workflow of war-fighting intelligence analysts. SaffronAnalyst was developed by Saffron to provide an analyst user interface consistent with the workflow and thought processes of an actual Intelligence analyst. Although the system was already capable of identifying associations and their frequencies between basic entities like people, places and things — Saffron was asked to provide this analysis within the existing analyst workflow by integrating Saffron’s Sense Making capabilities with other third- party tools to provide an end-to-end analytic workbench.
The Customer-Saffron Solution
We worked closely with military Intelligence analysts, and Saffron’s engineers were trained in all the current methods of analysis used by the customer. The customer’s existing processes involved manually extracting entities and building a co-occurrence matrix. Saffron identified immediate opportunities where the Saffron Natural Intelligence Platform could improve the “time and speed to results” over their current manual process.
Saffron engineers observed Intelligence analysts in action and spent time on-site witnessing their analytical process in real time, against real targets. This made it easier to identify where SaffronAnalyst should integrate into the analysts’ workflow, so as to enhance — but not disrupt — current procedures.
The solution called for adding new features and capabilities to Saffron’s existing product to incorporate the desired components of the current manual process. At the same time, the customer created new approaches to parsing unstructured data. To ensure a streamlined and accelerated data-analysis process for the customer’s smaller operational units, Saffron and the customer teamed with a third-party ETL (data Extract, Transform and Load ) vendor.
Integration of the two products was developed, tested and implemented in under four weeks. Intelligence analysts reduced the time for creating their Critical Entity Link Charts by 80% in the final benchmark test. And Saffron further enhanced its Sense Making tool.
Many organizations face a similar challenge of quickly analyzing large volumes of intelligence data with a small team of people. Saffron Natural Intelligence Platform’s Sense Making capabilities make it ideal for helping organizations and their workgroups analyze large volumes of such data in a short amount of time.
Instead of two-dimensional models and manually designed tables, Saffron uses dynamic, multi-dimensional memories to quickly identify connections and their frequencies of occurrence within massively dense and “noisy” data sets.
In this particular case, Saffron was integrated with entity extraction tools including SRI, Inc.’s NetOwl and Basis Technology, Inc.’s Rosette, and ingested varied unstructured data sources into SaffronMemory Base. Saffron’s analytic results were then integrated with i2, Inc.’s Analyst Notebook to provide complete transportability of the Connections, Analogies and other supporting evidence from Saffron directly to the Intelligence analyst’s desktops. In addition, new visualization capabilities for SaffronAnalyst were added, including new link analysis capabilities, entity management, export to ESRI’s ArcGIS product, analyst interest collaboration, and more. Throughout, Saffron’s Connections, Analogies and Network Reasoning Methods were used in this Sense Making implementation.
The value emerges in the extraordinary speed and accuracy with which the Saffron Natural Intelligence Platform returned its data analysis, giving Intelligence analysts enough confidence to act, and enough time to save lives.
Experience Management by Sense Making is Saffron’s proven, sophisticated method of fast, comprehensive and pertinent data analysis.
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.