Business Analytics Solution for Airline Industry

 

If the airline industry could be described in two words, it would be "intensely competitive". The airline industry generates billions of dollars every year and still has a cumulative profit margin of less than 1%.  The reason for this lies in this industry’s vast complexity. Airlines have a multitude of different business issues that need to be solved at once, such as globally uneven playing field, revenue vulnerability, an extremely variable planning horizon, high cyclicality and seasonality, fierce competition, excessive government intervention and high fixed and low marginal cost. To ensure the best chance for full economic recovery, airlines should fully leverage their most prolific asset - data.  Data used in conjunction with innovative technologies that would allow the creation of an Business Analytics Solution, will provide the capabilities for a comprehensive intelligent management and decision-making system throughout the enterprise.  The ultimate benefits of implementing and using an enterprise wide intelligence platform, together with airline business acumen and experience would include timely responses to current and future market demands, better planning and strategically aligned decision making, and clear understanding and monitoring of all key performance drivers relevant to the airline industry.

Achieving these benefits in a timely and intelligent manner will ultimately result in lower operating costs, better customer service, market leading competitiveness and increased profit margin and shareholder value. 

Airlines throughout the world are currently facing an unprecedented financial crisis. Factors contributing to this crisis are low customer satisfaction, overtraded markets, insufficient and under utilization of aircraft capacity, poor labor relations, excessive government intervention, high labor costs, ever increasing oil prices resulting in spiraling fuel costs, and generally  high operational costs. The low profit to turnover ratio of airlines have been further exacerbated by growing low-fare competition, increasing security costs, and frequent dynamic shifts in air travel consumer behavior. The historical business model of many network airlines now appears to be unable to support sustained profitability under any but the most favorable economic conditions. The industry is at a turning point.  The market dictates an “adapt or die” policy, and the airlines that whish to survive will face the challenge of having to make significant changes to their current archaic business model. To do this requires far more allowance for innovative technologies that would allow airlines to build an end-to-end Business Analytics Solution. The core capabilities of these technologies will ensure the flow of consistent, repeatable and reliable enterprise wide intelligence needed to tackle all the challenges the industry is facing.

Purpose of Business Analytics Solution for Airlines 

Purpose of an Business Analytics Solution for airlines is to bridge what is called the Information-to-intelligence gap.  The disparity between what an airlines has – which is prolific amounts of data from disparate source systems – and what an airline wants – which is to achieve strategy alignment for a competitive edge; whether it be through compliance, increased profitability, decreased risk, or to better manage performance, planning, etc.

 

  Addressing the Business issues 

Some of the challenges that can be successfully addressed by Business Analytics Solution are:

• The need for accurate daily and weekly performance measurement reports (e.g. “flash/estimated” revenue, operating costs and net contribution reports for every aircraft’s actual flight per sector/route).
• The Need to better manage all aspects of risk.
• The Need for better impact analysis and more effective optimization of all resources as well as being able to produce accurate passenger-revenue forecasts, 
• The Need for a holistic, 360 degrees view of the airline industries customers, suppliers, service providers and distributors.
• The Need for expense verification models in order to better control all industry cost aspects.

 
Issues related to Performance Management 

Airlines usually operate in a globally competitive environment and therefore require prompt and accurate enterprise performance measurements. Furthermore, airlines are volume driven and small variations (passengers flown, fuel spent/bought, load carried) can multiply into major effects – therefore appropriate and timely action is critical. Airlines suffer substantial difficulties to produce daily/weekly reliable performance measurements. Current airlines “legacy” IT systems such as Revenue Accounting, require several weeks after a month end to generate revenue results for every flight per sector/route.  Business Analytics Solution for Airlines can automate production of daily activity reports such as number of passenger flown per flight/sector, distance flown, etc which can be used to provide estimated performance measurements such as daily or weekly revenues for specific routes or sectors.

Issues related to Risk Management

The global airline industry has been subjected to major catastrophes over the past years.  It is accordingly imperative for airlines to develop various risk management models and strategies to protect themselves from negative impact of these types of events. Furthermore, due to the global playing field, airlines often earn its revenues and pay its costs in different baskets of currencies (e.g. USD, Euro, GBP etc). As a result there is frequently a mismatch between the flow of revenue receipts and expenses of each basket of currency - creating risk exposure. Using Business Analytics Solution for Airlines Infrastructure, relevant data can be gathered, consolidated and cleaned, risk can be modeled, and risk exposure can be measured and presented on “as and when” basis, as requested by business user. 

Issues related to Control and Verification

Airline carriers require a number of control and verification models to be able to control costs arising from its various operational activities. To enable this, airlines have a pressing need for a complete and integrated repository of flight information data gathered from all its disparate business units. This will enable computation of various efficiency analytics - e.g. planed fuel usage compared with actual fuel usage per aircraft, crew utilization (roster optimization). These issues could also be fully addressed by the Business Analytics Solution for Airlines, which will access, consolidate and analyze relevant flight and aircraft data. In turn this would help to create a 360 ° view of each flight and aircraft, allowing the business users to dramatically improve their control and verification systems. 

 

 Issues related to be able to better forecast

Airlines require the development of an effective and holistic forecasting model to regularly asses the impact of options and alternatives such as increasing aircraft seats available, adjusting fares, introducing new routes etc. Forecasts should also take account of actual statistical trends and results e.g. actual passengers carried and actual average fares earned. Such forecasts should then be compared against budgets and prior year performance. Business Analytics Solution for Airlines  has a market leading and powerful forecasting engine capable of generating large number of forecasts automatically and making them available to the people who would used them for sound decision making. 

        
 Issues related to a lack of a holistic view of core business components. 

Airlines would greatly benefit from knowing and understanding its business environment along some of the key business issues, such as performance, behavior, risk, profitability, etc. Using customers as an example - the main objective would be to enrich the knowledge about individual customers leading to new strategic customer segments. This intelligence would allow airlines to reap the host of benefits such as successful, targeted customer promotions, cross-selling and up-selling campaigns for different flights and booking classes leading to improved yield and revenue. For example, it would give airlines the power of knowing to limit discounts on flight routes which are usually over-booked, allowing the large number of passengers to compete for high profit seats immediately prior to departure. Such multidimensional views of the business can help the airline to better serve its customers through more effective, efficient and personalized service, receiving in return customer loyalty, support and market share, all leading to higher profitability.

 Conclusion

The Business Analytics Solution for Airlines is designed on Usable, Interoperable, Scalable, and Manageable technology, and encompasses all aspects of turning information into strategically aligned, powerful and accurate intelligence and empowering the business user into intelligent action by ensuring the delivery of the right intelligence to the right business user in the right format in a timely manner.  Solution is built on core technological components of Data Integration, Data Management, Data Analysis and Information Deployment, all of these components being fed by centrally shared enterprise wide metadata.  Built into these core technology components are airline specific data models, statistical and analytical models, pre-written reports and all necessary training and methodologies for a successful and sustainable solution for airlines implementation.  All of these items collectively give the  the capability and capacity to address the host of the burning issues prevalent in the airline industry.

 

Goran Dragosavac

Police using ‘predictive analytics’ to prevent crimes before they happen

 

By Agence France-Presse

Crime fighters have long used brains and brawn, but now a new kind of technology known as “predictive policing” promises to make them more efficient. A growing number of law enforcement agencies, in the US and elsewhere, have been adopting software tools with predictive analytics, based on algorithms that aim to predict crimes before they happen. The concept sounds like something out of science fiction and the thriller “Minority Report” based on a Philip K. Dick story.

 

Without some of the sci-fi gimmickry, police departments from Santa Cruz, California, to Memphis, Tennessee, and law enforcement agencies from Poland to Britain have adopted these new techniques.

The premise is simple: criminals follow patterns, and with software — the same kind that retailers like Wal-Mart and Amazon use to determine consumer purchasing trends — police can determine where the next crime will occur and sometimes prevent it.

 

Colleen McCue, a behavioral scientist at GeoEye, a firm that works with US Homeland Security and local law enforcement on predictive analytics, said studying criminal behavior was not that different from examining other types of behavior like shopping. “People are creatures of habit,” she said. “When you go shopping you go to a place where they have the things you’re looking for… the criminal wants to go where he will be successful also.” She said the technology could help in cities where tight budgets were forcing patrol reductions.

“When police departments are laying more sworn personnel, they can do more with less,” she said.

The key to success in predictive policing is getting as much data as possible to determine patterns. This can be especially useful in property crimes like auto theft and burglary, where patterns can be detected. “You can build a model that factors in attributes like the time of year, whether it is hot and humid or cold and snowy, if it is a payday when people are carrying a lot of cash,” says Mark Cleverly, who heads the analytical unit for predictive crime analytics. “It’s not saying a crime will occur at a particular time and place, no one can do that. But it can say you can expect a wave of vehicle thefts based one everything we know.”

 

In Memphis, officials said serious crimes fell 30 percent and violent crimes declined 15 percent since implementing predictive analytics in a program with IBM and the University of Memphis in 2006.

The program known as CRUSH — Criminal Reduction Utilizing Statistical History — targeted certain “hot spots” to allow police to deploy more efficiently. John Williams, crime analysis manager for the city’s police, said the system has had a dramatic impact, allowing Memphis to get off the list of worst US cities for crime. “If the data is indicating a hot spot, we are able to immediately deploy resources there. And in a lot of instances we are able to make quality arrests because we’re in the right area at the right time,” he told AFP.

 

Although beat officers can use their instincts for similar results, Williams said the software could be far more precise, such as predicting burglaries in a small geographic area between 10 pm and 2 am.

In one case, the software was able to help police break up a group that was committing armed robberies on the city’s Hispanic population. “There were 84 robberies, but we had no idea it was so organized,” Williams said. By crunching the numbers, police were able to pinpoint the zone and time of likely holdups: “We caught a group of robbers in progress, we had leads on additional robberies,” he said.

 

Williams said police officials from as far away as Hong Kong, Rio de Janeiro and Estonia have come to review the experience in Memphis. In Los Angeles, another program developed by scientists at the University of California-Los Angeles and Santa Clara University was tested in a single precinct, and resulted in a 12 percent drop in crime while the rest of the city saw a 0.2 percent increase. That test and others led to the creation of a company called PredPol. And Los Angeles will expand its use of the program under contract with PredPol, said CEO Caleb Baskin.

 

Baskin said the system is based on a model from mathematician George Mohler which “is very effective in predicting the time and location for crimes that have not yet taken place.” PredPol had begun working with other cities in California and “we’ve had inquiries from a lot of places in the US and international locations,” Baskin said. “The science that underlies the tool will work anywhere. The question is does the agency maintain a database that we can plug into.” While use of such analytics generally wins plaudits for helping “smarter” policing, it does raise concerns about Big Brother-like snooping.

 

Andrew Guthrie Ferguson, a law professor at the University of the District of Columbia, said the use of technology could be positive but that it could lower the threshold for constitutional protections on “unreasonable” searches. “To stop you and frisk you and search you, a police officer needs easonable suspicion, so my question is how will this affect reasonable suspicion?” he said. If the search is based on a computer algorithm, Ferguson said, and the case comes to court, “How do you cross-examine a computer?” IBM’s Cleverly said the technology can in many cases improve privacy.

“You can pinpoint the record of who has access to information, you have a solid history of what’s going on, so if someone is using the system for ill you have an audit trail,” he said. As for “The Minority Report” and its predictive software, Cleverly said, “It was a great film and great short story, but it’s science fiction and will remain science fiction. That’s not what this is about.”

What to do with False Positives?

 I often hear complaints from business  folk that their models need improvement because there are too many false positives. Just for clarity - in a case of fraud transactions – false positives are related to those transactions which were assigned to be fraudulent when in reality they weren’t.  Sure, one needs to always minimize occurrence of false positives as much as possible, but it is not always the model’s fault. Sometimes what looks  like a clear cut fraud – just isn’t. It is a fuzzy area where the difference between patterns of your event and non-event are completely blurred. Kind of – it could go either way!

Some implementations of analytics have been built on false positives. These are the people who look like buyers of particular brand – and yet they are not. Well, the logical assumption is that if some marketing stimuli is sent to these people – they are more likely to become buyers of that brand, due to its high-degree of look-alike-ness than randomly selected folk. I have completed several successful projects geared solely on acting on these ‘so called’ modeling mistakes.

 Another example is building a model capable of predicting who will be dormant customers within a period of time.  After building the model we score it on some existing base comprising of known (historical) dormant  customers as well as of those who are not. Then, we focus on false positives and compare them to one’s that are correctly predicted.  Often the difference is so small between the two groups in terms of their usage patterns – that we may as well call them all dormant customers. Even though false positives are technically not dormant yet –  for all intents and purposes they really are. So, we go back to the business definition of what constitutes dormant customer and we look at the whole phenomenon with a  new fresh angle. Thanks to comparative studies between accurate predictions and false positives.

So what I am trying to say in this article is that what appears to be modeling “mistake” can be turned into the value from more than one different angle. There is always a reason why models make mistakes – and tiredness is never one of them.     

 Goran Dragosavac