AI/IoT/Analytics

Business Metamorphosis Exercise

Bill Schmarzo By Bill Schmarzo September 16, 2014

I am teaching an MBA course at the University of San Francisco with Professor Mouwafac Sidaoui titled “Turning Big Data into Business Power.” It’s a wonderful experience, as I’ve always dreamed of being a college professor. Maybe this is my second career once I retire from flying around the country on unnecessarily tight airplanes helping clients on their big data journeys.

The MBA course has provided me the opportunity to utilize the students as guinea pigs for exercises that I’ve always wanted to test, but never could find the right client engagement.  Fortunately, grad students are used to being guinea pigs, and that opportunity presented itself in week 3 of the class.

In working with organizations to help them measure how effectively they leverage data and analytics within their key business processes using the Big Data Business Model Maturity Index (BDBMI) in Figure 1, I wanted to create an exercise to project what the Business Metamorphosis (Phase 5 of the BDBMI) might look like.   While it’s not possible to start your Big Data journey at this stage, I was curious if we could create an exercise that would help clients use the Business Metamorphosis vision creation process to identify and prioritize their strategic big data use cases.

Big Data Business Model Maturity Index

So I tested a Business Metamorphosis Vision exercise with the class, and I am excited to say that it achieved impressive results!  So I am now going to share the process with you, my readers, so that you have the power to determine how Big Data can help improve (optimize) your key business processes.

Boeing: Transforming From Selling Planes to Selling Air Miles

I asked the students to pretend that they were management consultants that had been asked by Boeing to contemplate how big data could metamorphosize Boeing’s future business model.  The Boeing executives had studied examples of how companies such as a jet engine manufacturer (transforming from selling jet engines to selling “thrust”), a farm machinery manufacturer (transforming from selling farm equipment to selling “farming yield”), and an energy producer (transforming from selling kilowatts to selling “energy efficiency”) were leveraging big data – coupling new sources of customer, product and operational data with advanced analytics – to transform or metamorphosize their business models.  The Boeing executives wanted to understand how they could leverage the same approach to transform or metamorphosize their business model.

Image of Boeing plane

Step 1: Articulating the Metamorphosis Vision

The first step in the exercise was to articulate and understand the business ramifications of Boeing’s new business model vision.  We used the following as our starting point:

Boeing wants to transform their business model by transitioning from selling airplanes to selling air miles to airline companies.

This would mean the following to Boeing and their business model:

  • Instead of airline customers (e.g., United, American, Delta) buying or leasing airplanes, Boeing would instead sell these airlines “air miles” (e.g., transporting 200 to 250 customers 2,200 air miles from SFO to JFK) as needed.
  • Boeing would enjoy a huge competitive advantage over Airbus and other airplane manufacturers by dramatically improving the cash flow, eliminating maintenance and inventory costs, and reducing airport delay risks for their airline customers.
  • Boeing would be responsible for owning and managing the fleets of airplanes (likely under the Boeing label), and that the airlines would contract with Boeing to acquire (provision?) the air miles to transport the airline’s passengers from one location to another as needed.
  • Boeing would assume all responsibilities for ensuring that planes are up and running; that if the planes were not flying, then Boeing was not being paid

Step 2:  Articulating the Customer Value Proposition

The next step was to brainstorm what this metamorphosis would mean to Boeing’s airline customers (United, American, Delta).  This could include the following:

  • Significantly improve airline cash flow by converting the fixed monthly airplane lease payments to a variable cost.  This gives the airlines significant flexibility in defining, scheduling and managing passengers and routes.
  • Dramatic reduction in maintenance costs including spare parts inventory and maintenance personnel (including hiring, training and managing) as all of these costs would be covered by Boeing); costs associated with flight delays due to mechanical issues would now become Boeing’s responsibility.
  • Airlines would have to differentiate in areas other than airplane configuration (because the same planes would likely be used to serve multiple airlines) such as: on-plane customer service and amenities, on-board meals (yea, right), gate area customer service and amenities, frequent flyer reward programs, club locations and amenities, ticket pricing, convenience, and trip duration times (e.g., reduce number of connections), etc.

Step 3:  Defining Your Data and Analytic Requirements

The next step in the process was to brainstorm Boeing’s big data requirements.  We did this by exploring their big data requirements from three perspectives:  1) data, 2) analytics and insights (e.g., predict, forecast, score, recommend, optimize), and 3) operational and business questions.  While these three perspectives heavily overlap (and there may be other perspectives to consider), they enabled the class to organize the brainstorming process into three distinct steps.

 

Perspective #1:  What data would Boeing need to acquire in order to make sales, marketing, pricing, maintenance and other operational decisions?

  • Airline routes including departure, destination, and miles
  • History of # of passengers flown by route, day of week, holiday and seasonality
  • Age of the airplanes
  • Airplane capacity
  • Airplane weight
  • Cargo weight
  • Baggage weight
  • Traffic/route patterns
  • Airplane flight speed
  • Maintenance history of the airplanes
  • Fuel consumption and efficiency of airplanes
  • Air flight turn around time by flight and airport
  • Airplane configuration
  • History of Fuel costs
  • Weather
  • Wind patterns
  • Cost of maintenance and spare parts
  • Cost of maintenance crew
  • % of empty seats per flight

 

 

Perspective #2:  What analytics and insights would Boeing need to create order to make sales, marketing, pricing, maintenance and other operational decisions?

  • Forecast # of passengers by route
  • Forecast # of passengers by holiday and seasonality
  • Forecast # of passengers by time of day and day of week
  • Forecast fuel costs / fuel price index
  • Forecast weather patterns
  • Predict airline maintenance needs
  • Predict amount of time and replacement parts necessary to fix certain maintenance problems
  • Optimal plane configurations
  • Average weight per customer

 

 

Perspective #3:  What are the key operational and business questions that Boeing needs to contemplate in order to make sales, marketing, pricing, maintenance and other operational decisions?

  • How can I speed airplane turnaround at the gate, because planes that aren’t flying aren’t making Boeing any money?
  • How can I configure the airplanes to get passengers on and off the plane more quickly?
  • How can I design/build airplanes to get passengers on and off the plane more quickly?
  • How can I speed loading and unloading baggage?
  • Are there certain airplane configurations that make getting passengers on and off the planes faster?
  • Can I incent more passengers to check bags so that less time is spent trying to fit luggage into the overhead bins?
  • Should Boeing offer “ramp service-as-a-service” where Boeing takes responsibility for loading and unloading the airplane baggage (to speed airplane turnaround)?
  • How do I get the FAA to modernize their flight departure, landings and flight paths in order to reduce flight times?
  • How do I select which airplanes and/or jet engines to replace with more efficient models?
  • How do I balance the jet engine fuel efficiency versus jet engine maintenance?
  • How can I reduce spare parts and maintenance costs?
  • What is the optimal number and type of airplane configurations in order to reduce spare part and inventory costs?
  • How can I leverage low-cost, centralized locations to support the emergency plane and inventory needs of the high-volume airports (e.g., Cedar Rapids, IA servicing ORD, MSP and STL)?

Using this approach, students were able to quickly generate insightful data and analytic requirements without having any working experience with either Boeing or any airline company. I think they impressed themselves!

Transformation to an As-A-Service Business Model

The one thing that these visions have in common is the metamorphosis from a product-based business model to an “as-a-service” business model, such as:

  • Jet-engine-thrust-as-a-service
  • Farming-yield-as-a-service
  • Energy-efficiency-as-a-service
  • Air-Miles-as-a-service

One way for businesses to gain insights about how they can embrace this new “as-a-service” business model is to see what’s happening with “Information Technology-as-a-service” or ITaaS.  A good blog by Scott Bils (ITaaS: It’s About More Than Just Cloud) lays out many critical “as a service” issues.

“ITaaS is in fact a new IT business and operating model.  ITaaS is basically about IT becoming a service provider that offers and orchestrates IT services instead of organizing around traditional technology silos. The model provides users self-service access to internal and 3rd party services through an integrated service catalog that supports consumption-based chargeback and billing.

The important point is that the service is as standardized as possible, with a clear SLA and cost data.”

ITaaS providers are wrestling with many of the same operational questions that organizations must consider as they look at how big data could metamorphosize them into an “as-a-Service” business model, including:

  • How do customers order services?
  • How do customers provision services?
  • How do I charge (tiered, volume discounts, surge pricing, etc.)?
  • How are services supported?
  • Are custom services (consulting) available and at what prices?
  • Is there a self-service portal (that works like a traditional on-line storefront)?
  • Is there a catalog of services to see what’s available?
  • How do I monitor capacity utilization (to determine how much service I have left)?

Conclusion

If the business is ready to create this “as-a-Service” Business Metamorphosis vision, Big Data is going to play a critical role enabling it.  The Big Data Vision Workshop offered by EMC (to further identify and prioritize the data, analytic and user experience requirements) and the Proof of Value Lab (to flesh out the business case, calculate ROI, demonstrate analytic capabilities, and build more detailed UEX mockups) are good next steps to further build out that vision.

Big Data/ Data Science Engagement Process

Figure 1: Vision Workshop and POV Lab Process

No matter what your organization’s ultimate business vision, going through the Business Metamorphosis Vision exercise can yield some valuable insights into how big data can be leveraged to uncover new monetization opportunities.  And it’s easier to do than one might think, as the students in my class discovered (see Figure 2).

University of San Francisco Executive MBA Class

Bill Schmarzo

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