Transporting analytics to the Internet of Things

By IDB Guest Blogger: Lee Ann Dietz, SAS

Why are so many companies across a diverse set of industries investing in and around the Internet of Things? Everywhere I go, every blog I read … I sound like my favorite band from the 80s: the Internet of Things is watching me.

In reality, it’s the reverse: I’m seeing the Internet of Things (Iot) everywhere: companies investing in sensors, networking and applications with the expectation that this investment will increase revenues, lower costs and improve profitability over the short and long term.

While the term the Internet of Things was coined in 1999 by Kevin Ashton at Procter & Gamble, the mainstream application of IoT is just getting started. As the trend has heightened, I’ve been evaluating the potential for IoT to support better decision making in travel and transportation.

My experience in the travel and transportation industry has always been about using analytics to support decision making. In fact, I started my career as a pricing strategy analyst at American Airlines. And now I’m fully converted. You can call me an evangelist for the IoT in transportation, especially because the potential to take data coming from the IoT, incorporate purposeful analytics and reach better decisions quickly, is significant.

This week, I am presenting as a guest of the Institute for Defense & Business at the 2015 NDTA-USTRANSCOM Fall Meeting on the convergence of IoT and analytics in the transportation industry.

I am speaking directly about why I have become so enamored with the ability of the IoT to deliver business value, especially in transportation.  It’s really quite simple: the IoT delivers value through the data that the things provide for decision-making.

This data is collected by sensors and devices on railcar components, semi-truck engines, or other elements within the transportation value chain. And it is now available on a real-time, or streaming, basis.  This provides the ability to learn from trends in that data quickly and act upon those trends within seconds.

Gone are the days where data is collected over the span of weeks or months, sent to an analyst for review, and – after another week or two of data crunching – the analyst presents a report or PowerPoint to her boss with recommendations for changes.

As Tom Davenport said:

To make the Internet of Things useful, we need an Analytics of Things.  This will mean new data management and integration approaches, and new ways to analyze streaming data continuously.

So, in order to take advantage of the streaming big data (and it is big data by every definition of the phrase) coming from the sensors, we must reconsider how we use analytics.  Remember, that I didn’t say that the analytics themselves must change.  In most cases, we can use the same analytics applied to streaming data as we used in a batch model.

What we need is a good understanding of where we apply the analytics: on the edge, at rest or in the middle. Let me explain:

  • Analytics on the edge means analysis at the specific device or sensor.
  • Analytics at rest means data pulled out of the stream and used for high-performance analytic model development
  • Analytics in the middle takes place on data as it’s streaming. Some analysts have called this middle ground “the fog,” and it’s relevant because it can be a combination of the streaming data itself enriched with sitting data such that we can detect more complex events sooner.

We have now arrived at a different place in the analytic continuum.  The optimal analytics experience is a multi-stage analytics experience.  It includes continuous queries on data in motion and at the edge, with incrementally updated results.  This new process moves analytics from centralized data warehouses to edge analytics, which are closer to the occurrence of the events.

What does multi-stage analytics of IoT data look like? It happens fast (seriously, we are speaking about microseconds or msecs at this point) and at very high volumes.  It requires specific business rules that give instructions on whether to save, discard, aggregate, transform or enrich the streaming data without overloading the entire system or network.  Multi-stage analytics includes pre-determined data mining, decision making, alerting, scoring, and profiling of the data to exploit the value of the streaming data.  And, it might also include managing the data differently – creating “out of order” handling to make the data source streams understandable to the analytics and the decision-makers.

We have all the building blocks in place to exploit the value of the Internet of Things and the Analytics of Things: sensors, or assets, creating data, the communications network connecting the data and the analytic and computing applications that make use of the data flowing to and from the things.

The Internet of Things can be transformative in transportation operations: in maintenance and engineering, you will have more information sooner, which means you can predict the maintenance needs of individual assets before failures occur and proactively service assets at an opportune time when your asset is near a repair facility.  This reduces costs across your operations.  In supply chain situations, you can monitor inventory levels on a near real-time basis, develop better forecasting models and optimize this inventory, when and where you need, lowering supply costs, increasing efficiency and enhancing revenue opportunities. On the customer side of transportation, you can enhance the customer’s experience by providing real-time forecasts of arrivals and notifying them sooner if delays occur.  And, happier customers are loyal customers.

The Internet of Things opens up tremendous opportunities for transportation companies, generating significant streaming data which can be relevant for decision-making.  However, it is critical to apply the appropriate analytics to streaming data in order to derive value from that data.

Multi-stage analytics is not rocket science; it’s simply the judicious application of the right analytics at the right time in the right place to the right data, which is what you need to exploit the value of the Internet of Things.  That is why I’ve become an Internet of Things and Analytics of Things evangelist.

 

This content is reposted with permission from SAS Voices, where the original post appeared.

How DoD Supply Chains Can (and Should) Learn from Private Sector Mistakes:

Using a “Value Added” Approach in Supply Chain Forecasting

By IDB Guest Blogger: Michael Gilliland, SAS

 

Materiel and supplies are critical to the men and women of the U.S. Armed Forces, at home and abroad. Whether it’s ammunition, weapons, IT, or toilet paper, if they need it, someone is in charge of getting it and making sure there is sufficient inventory. The Government Accountability Office has identified this mission-critical role – supply chain management – as an area for improvement for the U.S. Department of Defense (DoD), particularly inventory management through better forecasting. As DoD works to improve its supply chain forecasting capabilities, this is an area where DoD can learn from the mistakes of the private sector.

The reality is that forecasting can be a huge waste of management time. This is not an indictment of the practice of forecasting as a whole, but rather of how organizations usually approach and apply forecasting incorrectly.

The problem is not that forecasting is pointless, irrelevant, or unnecessary. Rather, the problem is supply chain leaders squandering too much time and too many resources on forecasting with a myriad of bad practices.

Tools and Methods

In some cases, the technologies organizations use for demand planning and forecasting are outdated or simply misapplied. By relying on outdated tools or methodologies, these forecasters miss the progress made in recent years to improve accuracy, reduce bias, and minimize the cost of forecasting through large-scale automation. ​ Even heroic efforts on their part are likely to deliver underwhelming results, whereas an unpoliticized and unbiased forecast can lead to cost savings and, more important, can save lives.

Expectation Management

The goal of forecasting is to obtain an objective, dispassionate number that is as accurate as can reasonably be expected given the nature of whatever you happen to be forecasting. Rather than working from this perspective, however, many managers and forecasters have unrealistic expectations for the level of accuracy achievable. They rely too heavily on the current “fit” of models to history when their job is to forecast the future. Almost invariably, the forecast will be less (often much less) accurate than the fit to history. (It is always much easier to explain the past than to predict the future.)

 Don’t Trust the Process

Perhaps the most blatant example of waste in the forecasting process is “forecasting by committee.” This is where a forecast is passed through so many different stages of approval and has been tweaked by so many collaborators that its integrity is actually degraded. It is easy to see how this approach could occur in any bureaucratic environment – including the military – where each participant has a personal agenda they express with their adjustment.

The problem is that this kind of elaborate review process ends up being extremely costly – in two ways. First, because the process is unnecessarily consuming everyone’s time. And second, because your outfit may actually be in a worse position than if you had not attempted to incorporate so much “management intelligence” into the forecast in the first place. In a study of eight commercial supply chain companies, Steve Morlidge (author of the book Future Ready) found less than half their forecasts were more accurate than the “naïve forecast” – i.e., the forecast you get by doing nothing and simply using the last available data point as your future prediction.

Know Your Limits

Even if you use the proper tools and methods to forecast future supply chain needs, it is important to realize that overall forecastability still limits the maximum possible accuracy of forecasts. Supply chain leaders need to avoid demanding a level of forecast accuracy that is simply impossible to obtain because they have not considered the nature of what they are trying to forecast.

To illustrate this concept, suppose your job is to forecast Heads or Tails each day in the tossing of a fair coin. While you may have some lucky streaks and forecast correctly several days in a row, over the long haul your forecast will be correct just 50% of the time. It doesn’t matter if your ranking officer demands 60 percent accuracy or higher – you are limited to a 50 percent accuracy ceiling by the nature of the behavior you are trying to forecast. Not even bigger computers and more sophisticated software will help – there is nothing anyone can do to achieve 60% accuracy. Unachievable objectives motivate forecasters to simply give up, or find a way to cheat and game the system.

supply chain forecasting scatterplot

Volatility and Forecast Accuracy. Scatterplot: Mike Gilliland.

As seen in this scatterplot of 5000 items being forecast by a consumer goods manufacturer, the variability or “volatility” of a demand pattern has a big impact on how well we can expect to forecast it. Smooth, stable, repeating patterns can be forecast quite accurately with simple methods. But wild, volatile, erratic patterns may never be forecast accurately, no matter how many resources we commit to forecast them. To the extent that we can control and limit volatility, we are likely to achieve more accurate forecasts.

Solution: Knowledge is Power

Private sector companies are increasingly using a method called “Forecast Value Added (FVA) Analysis” to improve performance of their forecasting processes. FVA is the application of basic science to evaluate a forecasting process – measuring each step in the process, and determining whether it is “adding value” by making the forecast more accurate and less biased. Here is an example of an FVA report (in this case, the Analyst Override step is just making the forecast worse!):

supply chain forecasting: FVA example table

Example: Forecast Value Added (FVA) Analysis. Table: Mike Gilliland.

Forecast Value Added analysis is about rooting out the waste and inefficiency from forecasting efforts (so is consistent with a lean approach to supply chain management). It allows organizations to streamline their process and redirect the non-value-adding efforts into more productive activities that will be more beneficial to the mission at hand. Understanding cautionary tales of corporate sector mistakes and challenges can help the DoD create real improvements in defense supply chain management. By avoiding wasteful steps and procedures, defense organizations have the opportunity to achieve better forecasts with less effort, and less cost.

Gilliland, Mike headshotMichael Gilliland is an IDB guest blogger with more than 15 years of forecasting and supply chain management experience in the food, apparel, and consumer electronics industries. Gilliland has been featured in the LOGTECH Advanced Program in Logistics and Technology. He is author of The Business Forecasting Deal, and has published articles in Supply Chain Management Review, Foresight: The International Journal of Applied Forecasting, Journal of Business Forecasting, Analytics, and APICS magazine. He is also an editor for Foresight. Gilliland holds a BA in philosophy from Michigan State University and master’s degrees in philosophy and mathematical sciences from Johns Hopkins University. He writes The Business Forecasting Deal Blog at blogs.sas.com/content/forecasting. For more than a decade, SAS and the IDB have worked collaboratively to raise the level of awareness of the value analytics to defense leaders.

Exploring the Army Operating Concept

As the U.S. Army draws down after nearly fifteen years of war, the service is reflecting on lessons learned as well as looking ahead to future conflicts and challenges. A central product of this analysis is the new Army Operating Concept called “Win in a Complex World.” Published October 2014, this document from Army Training and Doctrine Command seeks to characterize the nature of the world we now live in (unpredictable, quickly evolving, and increasingly complex) and shift attention away from weapons, technologies and systems in favor of a focus on the capabilities at all levels (tactical, operational, and strategic) that will be needed to win future conflicts.

The Army Operating Concept took center stage in Huntsville, Alabama at the Association of the U.S. Army’s Global Force Symposium. Serving as the keynote speaker for the symposium, General Ray Odierno, Chief of Staff of the Army, highlighted the challenges the United States is facing around the world; from West Africa to North Korea, the U.S. is confronted with a broad range of enemies and enemy capabilities. Many of these threats are long-term in nature and will require sustained operations to bring them to an end. At the same time, the Army and all other services are entering an era of tightened budgets which, according to the service chiefs, including GEN Odierno, severely threatens the Army’s readiness and modernization efforts. It is these modernization efforts that are needed to achieve the capabilities needed to win in a complex world.

Given the lessons learned from the past fifteen years and the budgetary difficulties that lie ahead, the Army is indeed at what GEN Odierno described as “a strategic reflection point.” Tremendous challenges await the service and its major commands. However, GEN Odierno reminded the audience that “it’s people who win wars,” and that the U.S. Army remains the world’s best land force because of its men and women in uniform who are out in the world making a difference.

Discussion:

Almost every session at AUSA ILW Global Force Symposium centered on the Army Operating Concept.  Do you think the AOC will drive real change in the Army, or will it fall victim to various constraints such as contracting, personnel systems, and the ever-changing budget situation? Share your thoughts in the comments below.

You can view GEN Odierno’s full remarks via the AUSA YouTube Channel:

IDB Featured in Military Logistics Forum “Educating the Logistician”

militarylogisticsforum.2014.07In their July 2014 issue, Military Logistics Forum took a look at what it takes to prepare 21st century logicians. IDB was mentioned alongside other prestigious organizations offering logistics education which understand what it takes to be masters of the supply chain–regardless of which sector one is in.

The article begins on page 13

You can also find the article here.

Big Data for National Security

A few weeks ago, during the Special Operations Forces Industry Conference, the Institute for Defense & Business in conjunction with SAS Institute sponsored a luncheon panel discussion about Analytics and the Future of SOF. They offered me the opportunity to partner with Ambassador (retired) David Litt and Major General (retired) Kevin Leonard, U.S. Army to discuss how analytics can be used as an enabler for SOF operations in austere areas like Africa.

We’ve all heard the hype about “Big Data.” But SO WHAT? It’s generally discussed in the context of marketing, with one of the favored examples being Target identifying which of their customers are pregnant through their buying habits so that they can then send them coupons for diapers. But if you are not trying to sell diapers, you have probably missed out on the conversation, since selling diapers is a far cry from national security.

So instead of discussing “big data,” why don’t we discuss “Predictive Battlespace Awareness” or “Intelligence Preparation of the Operational Space”? These are two topics that are highly relevant to National Security. Funnily enough, both of these discussions lead us right back to big data, analytics, and the subject of last week’s conference, how can analytics be an enabler for SOF in the future.

Predictive Battlespace Awareness is the concept that you can develop a situational awareness picture so rich it enables you to anticipate what will happen so that you can develop course of action to thwart your adversary before they can act. A recent blog on my website discusses agribusiness in Africa as a sample use case. Achieving situational awareness is one element of the decision cycle: Observe, Orient, Decide, Act, also known as the OODA-loop.

For years, in all industries, the defense community not withstanding, we’ve been making huge investments into sensor technology to enable the observation phase of our OODA loop. And now we find ourselves overwhelmed with that very same data. We are no longer looking for a needle in a haystack, we are looking for a specific needle in a needlestack, and, as one might expect, it’s pretty painful.

Analytics technologies allow us to explore that data in a way that empowers us to advance our decision cycle through the orientation and decision phases. This means we can act more quickly when opportunities present themselves, respond more rapidly to thwart our adversaries, and anticipate where problems might develop so we can put remedial measures in place ahead of time.

Further, big data analytics capabilities can act as an enabler to collaboration. In an age of complexity, the next operation may require cooperation across multiple nations, non-governmental organizations, and both civilian and military authorities. Big data can facilitate this, even despite the fact that each organization runs on their own siloed system and processes.

In each of these cases, Special Forces will be able to leverage huge amounts of data to develop an intelligence picture of their operating space, collaborate with a coalition of any size and shape, and build the network they need to manage the complexity that is a part of normal operations.

This essentially recaps the discussion between David, Kevin at the discussion panel. Once again, I’d like to thank SAS and the IDB for the opportunity to share ideas. Big data may have us swimming in the ocean, but analytics capabilities offer a huge return on investment, not just for people looking to sell diapers, but for those of you who work in the national security community—in fact, the return on investment from a national security perspective is infinite!


Lt Col Tammy Schwartz, USAF (Ret.)Lt Col Tammy Schwartz, USAF (Ret.) is an IDB guest blogger and recognized innovator with more than 20 years of national security experience. Schwartz is a former Chief Technology Officer for Air Force Enterprise Networking and is currently a Consultant for SAS and the Owner of Llamrai Enterprises. You can read more from Schwartz on her blog.