Ashu's Blog — left, right, and all in between

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Recent trends in Big Data Analytics

I was reading this book Big Data by Victor Schonberger and it had some great patterns in the way Big Data Analytics is evolving:


  1. Datafication: Information about all things will be captured. With the true IoT, there will be more data for everything. Think of models that can be transferred. If I were to put sensors in seats and have cars manufactures predict driver fatigue. On a long drive, if the driver slumps into a certain range of positions, I can predict a car accident will take place in next 5 seconds. I can use the same models and sell to AMC entertainment to use the seats to see movie focus groups/pilots and see if they are truly interesting to audience. The business models for this data monetization are: sell raw information, delver analytics, foster marketplaces, etc.


  1. Exactitude: Sampling is expensive and not really needed for directionally correct decisions. Exactness requires carefully curated data - “Small Data”.  This was the DW world and is still important for financial transactions, launching a rocket. But for trending decisions like marketing campaigns, big data helps and is Good EnoughIt’s like these 3 famous self-portraits by Rembrandt, Van Gogh, and Picasso. All 3 masterpieces and yet the level of details dwindled down as can be.



  1. Causation: Big data discovers patterns and correlations. So more data trumps any algorithms one can write. It is like the human brain. It takes in a lot of inputs and goes by experience to process and learn from new data points. Think about a day in your life - What is the best road to take? Would there be any bad weather? How to invest my money? How is my health? There are many decisions that you can do better if only you can access the data and process them. The idea is to know, understand and then predict. We are always trying to move from hindsight to insight to foresight.





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House of cards - Predictive Analytics

Some of my friends were amazed how Netflix used all the data of pauses, plays, rewinds of their 40M subscribers  (now 67 M) on their platform to produce House of Cards:


Also in the B2C and B2B worlds, everyone is talking of Internet of Things, Big Data, and Analytics everywhere. I think these 3 trends sum it all up.


  1. Information about all things can be captured – location, stress on a bridge, vibrations of an engine, web clicks, Twitter feeds, etc.
  2. Sampling is expensive and not really needed for directionally correct decisions. The size of data and the processing power therein helps remove this need.
  3. Exactness requires carefully curated data - “Small Data”.  This was the DW world and is still important for financial transactions, launching a rocket. But for trending decisions like marketing campaigns, Big Data helps and is Good Enough (reference Big Data by Victor Schonberger)
  4. Big data discovers patterns and correlations. So more data trumps any algorithms one can write. It is like the human brain. It takes in a lot of inputs and goes by experience to process and learn from new datapoint.
  5. Think about a day in your life - What is the best road to take? Would there be any bad weather? How to invest my money? How is my health? There are many decisions that you can do better if only you can access the data and process them. The idea is to know, understand and then predict. We are always tring to move from hindsight to insight to foresight

·         To know what happened? (hindsight + oversight)

o    Basic analytics + visualizations

o    Interactive drill down

·         To explain why?(Insight)

o    Data mining, classifications, building models, clustering  

·         To forecast (Foresight)

o    Neural networks, decision models


In framing the Analytics problem – we need to balance data, SME knowledge, and performance. One of the things I have noticed in my work is when the analysts build models the real skill in creating effective analytic model is knowing which models and algorithms to use. They can use different techniques: neural networks, decision trees, linear regression, naïve Bayes, etc. But these days many analytic workbenches now automatically apply multiple models to a problem to find the combination that works best. ( ) One needs to explore different paths – they look at the problem from different perspectives. When these algorithms are combined there is resulting synergy. Once the modeling data sets were finalized, the largest incremental gain was not achieved by fine tuning the training parameters of an individual algorithm, but by combining predictions from multiple algorithms.


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Entertain me...

According to World Travel & Tourism Council (WTTC) the Entertainment and Hospitality Industry is a $8 Trillion industry today and will grow to $12 Trillion by 2020. It is also a very accurate indicator of the global economy – the canary in the coal mine. If global economy is down due to any reason, E&H falls steeper and deeper and almost real time – a true barometer of the global economy.   It’s typically broken into 3 sub-segments:
  1. In Travel & Leisure parks and resorts, cruise lines, car rentals, airlines and mediators in between
  2. In Lodging vacation clubs, hotel chains, etc.
  3. In Media & Entertainment Content producers, Content aggregators, Digital right management companies, TV distributors, etc.
  All of them are changing really fast – with the onslaught of Social Media and a truly Digital Lifestyle, this industry is seeing some paradigm shifts. Every customer touch point is evolving so fast in this changing world. Let’s look at some examples of this
  • You look at a poster of Bahamas. Right there is a QR reader for you to see the top 3 hotels, car services and sea excursions.
  • A website like TripIt – once your itinerary is up, they will send you emails 1 day before to checkin. If there is a volcanic eruption in Iceland, they will show you 2 hours before flight that it is cancelled. Options for another flight right there. No running to gates with 300 other passengers and trying to use your Elite status to get another seat home.
  • They offer Travel Diary or “Traviary’ -  for before, during and  after pictures/comments
  • When you land after a certain flight – Hertz sends you a message/ email that your car is in a certain lane.
  • No looking at online behavior itself, 80% business can be lost in less than 5-7 seconds. If your mobile or website waits more than 5 seconds, typically people hit BACK button. After 2 more attempts you have typically lost the business.
  So all these customer touch points are dictated by only one word – EXPERIENCE. That is what the process and technology platforms have to deliver and enable the human interacting with the customer. And that EXPERIENCE starts much BEFORE traveler walks into the property.  
cust exp  
As an example take Cruise Lines customer touch points, looking at this from a customer’s glasses:
Segmenting the customers - Seniors, Boomers, Families, Couples, Singles, etc. – is key and the unique experience they seek. E.g. Singles can find information on print media - men's magazines (GQ, Cosmo, etc.), Travel Mags (CondeNast, etc.).     The consumer is
  1. more knowledgeable with internet and mobile accessibility, online consumer-created content and social networks. Everyone who books a vacation, looks at TripAdvisor, Expedia, etc. for ideas, feedback and reviews. Price and convenience are essential differentiators for most consumers.
  2. more empowered and demands unique requirements and they want more self-service and multi-channel options. You go online the moment your friend tells you something. This speed, volume, and transparency associated with internet travel distribution has put new pressure on most companies to re-calibrate their value chains.
  3. more diverse since there is a lot of global and local influence and the consumer is very individualistic and demanding more convenience. Everyone looks for specific personalization and destination flexibility - such data-driven is key for companies to compete.
    Hospitality 3

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Sentimental Customer – Analyze This!

I was talking to an executive of a famous cruise ship company. He was flying from S. Florida to Seattle and was of course dreading the flight as it is. On top of that his flight was cancelled, and he got bumped down to a middle seat on to a much later flight. At that point he started tweeting some of his friends about his horrible experience. In some time the stewardess came up to him and apologized and offered a free ticket for another trip. He naturally appreciated this gesture and that set him thinking if it was his tweets that were being tracked. He is now doing a whole new CRM effort within his own company  - he knows the cruise customer is there for  4-7 days and can offer more upselling opportunities if they can listen and act better.
  Sentiment Analyses 1        
At the core of any sales and marketing effort, there only really 3 pieces:
  • Customer acquisition - Increase revenue and market share
  • Customer Insight & Innovation - Create, improve and differentiate products and services
  • Customer Experience & Service - Improve customer loyalty and reduce or avoid costs
  In today’s world, with the advent of connected consumers, the Customer Insight discipline is so important, especially in a B2C world. This is done through focus groups, buzz monitoring, community tracking, sentiment analyses and market research or pure competitive intelligence.  
The social media phenomenon has surely led to a new way of business engagement is revolutionary set of constituent interaction channels. This technology platform (Twitter, Facebook, etc.) gives everyone expanded ‘voice of the customer’ ability to exercise influence on the interaction with companies and peers.
  • User-generated content is often triggered by emotion
  • This can be amplified via the “viral effect”
  • Impact cannot be stopped or undone
  • It can forces companies to act in shorter cycle times
  • The scale can be fast and truly global
  Earlier this is what happened:
  • You talked to your customers, told them what to do
  • 95% happy customers is good
  • You wanted them to come to you
  Now this is what happens and companies need to adjust ASAP:
  • Consumer are talking among themselves, it’s time to listen
  • 5% unhappy customers is bad
  • You go to where they hang out
  It’s like the sewing circle of colleagues or clique of friends that you tell about a good doctor or a great sale has expanded to be truly global with platforms like Twitter, Facebook, etc.  
1.      LISTEN   Companies need to be listening on platforms like Blogs, Open Micro-Blog (Twitter), Closed Micro-Blogs (Yammer), Open Social Network (MySpace, WhatsApp), Closed Social Network (Facebook), Commentable User Generated Content (YouTube), Discussion Forums (Ning, TripAdvisor), etc.  
The listening part has many technologies like claraBridge, Attensity, Radian 6, Overtone, SAS, etc. Some companies are using Natural Language Processing (NLP) to understand syntax & context. As an example please see sentence below posted somewhere:
  Sentiment Analyses 3  
One can use advanced linguistics to understand topics and sentiment: “The EFTPS enrollment for my tax return was easy, but it took too long to get the confirmation package So the Category Sentiment is:
= Positive for “enrollment”
= Negative for “timeliness”
One customer verbatim from the customer can result in multiple categories within multiple taxonomies
E.g. “After being a retired Army General and a FSCO member for 38 years, I am quite upset that service representative would treat me so rudely over the phone when I called in to complain about the fact that you guys unexpectedly raised my auto premiums at the same time Geico is calling me offering me a major discount.” Brand-Related
  • Core Values > Service
  • Core Values > Loyalty
  • Core Competency >  Deliver Exceptional Customer Experiences
  • Eligibility > Retiree
  • Rank > Officer > General
  • Service > Army
  • Tenure> Greater than 30 years
  • Premium Increase
  • Products > P&C > Auto-Insurance
  Customer Experience-Related
  • Moments of Truth > Rate Increase Notification
  • P&C/Auto Insurance / Geico
    2.      ACT   The tough part of all this is to separate the signal from the noise and do something when its really important. The prioritization criteria for such an action is typically based on:  
  • # of customers impacted
  • Severity of impact to customer experience (i.e. moment of truth)
  • Severity of potential impact (e.g. legal liability)
  • Level of control to resolve
      Sentiment Analyses 2

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Show me the Money - Business Value Realization

When it comes to Information Technology (IT) everyone knows the different questions the executives are interested in:  
  • How can I better use IT to deliver shareholder value?
  • How can I use IT to deliver on our business strategy?
  • Am I spending the right amount on IT?
  • How can IT reduce the costs of operation?
  • Are there better ways to use technology to become more effective?
  • Where should I focus my attention for new capabilities?
  • How do we get a return on our invested IT capital?
  • How do I measure the value and results from IT?
  • What IT assets do I need to own?
  The term “Value” is used so much in business and never completely understood. What is value anyway. Value at the highest level has 3 components:
  • How much cash will the company generate? How much cash injection will it need?
  • How certain is the cash generation and realization of the investment ?
  • When will cash be generated? When will value be harvested??
Show me the Mullah 1  
Measuring and communicating the business value of IT remains one of the biggest challenges for CIOs. The major challenges in assessing business value of IT are:  
  1. Difficult to separate value achieved from other improvements (organizational, process redesign, etc.)
  2. Measuring value is often subjective
  3. Practices for monitoring/inspecting and communicating value are seldom underappreciated
    To really understand what a project’s value is to the firm and then did it ever deliver that value, there is some focus needed on:  
  • Value Identification - Identifies what the sources of value are from IT, that can be used to deliver business benefit
  • Value Capturing - Identifies to what extent new IT initiatives are explicitly tracked and benefit realization for the initiatives
  • Value Delivery - Formalizes the expectations set between the business and IT, both in terms of value and relationship
  • Value Measurement - This capability defines how the success of an IT organization will be measured
  Value should be measured against business capability measures, those that can be directly quantified into financial measures. Measures should be developed jointly by the business and IT, then aligned with business capabilities. For business cases, benefits should be quantified and aligned with the capability measures for the impacted capabilities.Value Management is a journey and becomes the generation of Change Management. The components to do this are:  
  1. A business case framework to define and quantify benefits captured from investments (Templates/ Model)
  2. A methodology to measure benefit realization against management expectations for investments and report performance (Process)
  3. A sustainable discipline to enable better decisions in terms of future project prioritization and investments (Governance)
    Show me the Mullah 2          
Some of the best practices:  
  • Think of the Shareholder Value Tree for your project- always try to get a helicopter view of the situation
  • Value should be measured against business capability measures, those that can be directly quantified into financial measures. For business cases, benefits should be quantified and aligned with the capability measures for the impacted capabilities
  • Incorporate Benefits Realization as a work stream. Include checkpoints in project lifecycle for monitoring value during implementation
  • Partner with the business to discuss value in the language that stakeholders want and then drive business results
  • Define the right metrics you need to track for
    • Behavior Modifier — Aligns employees with the IT organization’s goals and objectives in a manner that motivates employees and influences desired behaviors
    • Accountability for Results — Holds managers accountable for results and forces them to direct value to the business
    • Performance Orientation — Shows what the performance of the IT organization “has been” and “where it is headed to” – and where to focus management attention. Shifts focus from reactive to proactive management`
    • Vision Connected — Quantifiable statement of the IT organization’s “to-be” state or vision

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Have you ever sold an old belonging - basics of Revenue Management

Have you ever sold anything? Old books, your bike car or even your house?? Whether you did that over a garage sale, on eBay or even more advanced technology like Zillow, etc. what did you feel right after the sale? - “I could have raised the price another 10% and the dude would have still bought it”. “Dang! Why did I throw in my comics for free with the sale”. Or “oh my god I didn’t sell this bike just because I didn’t reduce my price by $10. It’d have been good riddance”.   Well, that’s just the nature of sales and pricing. Now imagine the plethora of players in the value chain of any industry like Entertainment & Hospitality
  • Distributors like Kayak, Expedient, etc.
  • GDS’s like Worldspan, Sabre, Amadeus
  • Operators like Contiki, Pleasant Holidays, etc.
  • Consolidators like Pegasus, Trust, etc.
  The consumer is willing to pay only so much depending on the product, the season, her expectations, different bundling of products. But all these players lead to so many types of rates for the same room: Published, Negotiated, Packaged (Expedia), Opaque (e.g. LastMinute, PriceLine), and  Restricted. Of course like any pricing strategy there is a stark dependency on so variables: demand patterns, channels, competition, etc. Imagine a hotel or vacation club that offers rooms – different destinations, different experiences (Jacuzzi, etc.); Services (jet ski tour thrown in), Security, additional room types, etc.  
The basic problem is as depicted below:
REv Mngt 5  
The only options management has are:
  • reject demand
  • inventory excess demand (queueing)
  • modulate capacity (add facilities, scheduling, resource allocation)
  • modulate demand (pricing, yield management)
  In the Hospitality industry there is a detailed science behind this pricing and demand fulfillment. This is to really be able to maximize the revenue. It’s called Revenue Management or Yield Management.   The idea is two-fold: Have a market segmentation strategy (capture consumer surplus) and match price to demand (peak-load pricing). So airlines sell First class, Business class, economy class etc. Hotels create suites, single rooms, double rooms, etc. and price the products differently. Intelligently allocate fixed capacity to products. Also then allocate more capacity to low price points if demand is weak; allocate more capacity to high price points if demand is strong. Ever wondered why flights are always over booked and over sold.   Hotels, travel agencies, airlines, vacation clubs, car rentals, theatres, sporting venues, and other industry players are even unlocking the power of Big Data to enhance revenue management. That’s why this is a cycle of these activities and combines a lot of science with the art of pricing. Providers recognize that data analytics are helpful in establishing the optimal price for their products – the right price to the right customer at the right time. The basic algorithms are as below:  
1. Segmentation/product design – discriminate (sort) customers based on their actual willingness-to-pay (reservation price). Since the willingness to pay is tough to find for all customers, they try to find a variable that is correlated with willingness-to-pay (a “sorting mechanism”). Advance Purchases are encouraged.  
2. Forecasting – factor in seasonality, trends, truncation (reservations accepted vs. potential demand), special events, promotions, etc.
  • perfect forecasts (deterministic)
  • uncertain forecasts (stochastic)
3. Capacity Allocation - evaluate the opportunity cost (displacement cost/bid prices) of using resources required to meet current demand. So accept current request if Revenue is greater than Displacement Cost. Mostly companies have to rank demand from highest revenue to lowest  
4. Control – Try to control demand and prices by ad hoc negotiation, “posting” remaining availability (reservation system booking limits), open/closed status indicators, bid prices/hurdle rates, etc. Ultimately, it boils down to an accept/deny decision for each service request.   This is where statistical tools and big data help. An example of a tree function for marginal revenue analyses for overbooking a flight for example is as below:
 REv Mngt 4  
C          capacity of flight
p          probability that the reservation shows up
r           revenue from booking seat
s          cost (free flight, goodwill) of denied boarding
Rev Mngt 2  
In a nutshell the business can be described as:
Demand Creation is           …     Marketing
Demand Capture is            …     Sales
Demand Management is   …    Revenue Management  

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CRM basics from Caribbean street performers

Happy New Year to all my readers!
As all northern folks who look to get a winter break in the warm waters of the Caribbean, we just returned after savoring our piece of heaven. There were hordes and hordes of tourists – from cruise ships, flocking from airports, from all parts of the world, etc.
On one of the beaches they had street performers doing all sorts of things – swallowing swords, eating fire, other pyrotechnics, etc. The way some of the performers conducted themselves to get the spectators to throw in money into their TIPS jar was nothing short of CRM basics 101.
One of the street performer – a man juggling balling pins on a unicycle, also balance some pyrotechnic with his mouth – was amazing. He focused on the Core CRM processes:
  1. Generate demand – He stood adjacent to the arrival of cruise locations and had flames lit up in the sunset-laden beach area to get everyone to be curious enough to swing by
  2. Engage Customer – When he started his juggling act with bowling pins, he invited a front row standing  kid to come up and throw to him one by one the bowling pin while he balanced on a unicycle> he talked to the kid quite a bit and complemented him on his throws.
  3. Acquire new customers – He would yell at oncoming folks and passersby. He chose a location which was conducive to folks stop by as they enjoyed their ice creams or lemonades.
  4. Service customers – He brought stickers for kids and lulled them to watch his flame and juggling
  5. Deal with Globalization – He spoke a few Mandarin words to the Chinese travelers – “Ni hao”. Asked a kid and heard he was Italian and mentioned greetings in Italian.
  6. Embrace the Viral Effect of Social CRM –  He yelled at people making his video and taking pix to send to fried through Facebook or even YouTube.
As someone said, “Customer service is not a department, it’s an attitude”. The science behind itthe framework for such capabilities has three main components:
1.     Insight-driven Marketing
  Esther Dyson summed it up in the magazine strategy+business (December 2009): “People spend a lot of time online not looking for something, or at least not for something that can be bought or sold. Marketers need to understand that the Web is not about them; it’s about us. Marketers and media sites keep thinking, ‘Well, if we can only tweak our banner ads right, we can get the same success rate as Google.’ But they can’t, because a banner ad is usually shown to someone who is not looking for the item advertised.”
cr0 3

2.     Customer Segmentation and Targeting  
This the ability to classify or cluster customers / prospects based on certain business rules or inherent customer data behaviors and buying patterns. This is made possible through customer insight, data mining, segmentation, and prognosis. The key to creating customer segmentation and to targeting the right customers is to have adequate insight and to drive interactions with customers as per that insight.  
3.     Customer Touch Point Transformation
  These days customers interact with companies at many touch points—call centers, online, mobile apps, point of sales in the case of the retail industry, etc. In order to offer a complete and holistic experience for the customer, the company should look at contact touch point transformation at every level and have a decent integrated contact-management system
Contact center
With this basic framework in mind, one has to recognize that some of the recent world phenomenon makes this even a bigger mandate:  
  • Exponential expansion of media options makes targeting consumers far more complex
  • “Cash-rich, time-poor” consumers are demanding more relevant offerings, experiences and communication
  • Consumers are far more technology-savvy and more active in controlling the consumption cycle
  • Demographic and social changes are creating a more diverse, fragmented consumer base and buyer values
  • Products/services, stores and messages are proliferating and becoming increasingly commoditized

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Master Data Management

Master Data are the fundamental business data in the company, typically long-lived and used across multiple applications, inherently including the subset of Master Reference Data.   Master Data, including Reference Data, is not to be considered “Metadata”. Metadata is data about data. It describes data content but it is NOT the content.  There is no formal and universal definition of how deep to take the definition from a content perspective. Only those metadata whose management will bring more value to the enterprise than the cost of the labor needed to create and maintain it should be managed and integrated formally. For example, “Master Data” definitions (customers, suppliers, products, organization, etc.) which will be the most ever-present and shared data across an enterprise will be most critical.
  MDM 4  
    Master Reference Data are used to understand, navigate and query information based on or related to the Core Master Data from various business level and/or user level perspectives.  
  MDM 2  
      Value Proposition of MDM:  
  • Enables 360º view of the enterprise and corporate performance management improvement.
  • Provides competitive advantage by enabling customer behavior insight & predictive modeling.
  • Improves the forecast management through more effective logistics and inventory control.
  • Reduces cost of manual master data reconciliation & alignment efforts, error fixing, etc.
  • Reduces the data redundancy cost by consolidating & eliminating duplicate masters (DB/apps).
  • Reduces application development & maintenance costs, by having clear master data interfaces.
  • Improves the decision quality by securing reliable, high quality master data “just in time”.
  • Improves the availability of key data – speed of access, data timeliness, common (user) support.
  • Eliminates/prevents redundant & non-coordinated master data activities within the company.
Executing components of the MDM Strategy
MDM 3        
  • Data Governance
    • Key roles
    • Desire to roll-out data governance to other areas of the business
    • Data Quality
      • Incorporating DQ approach into EDW instance consolidation project
      • Process Improvement

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Competitive Intelligence using Analytics

Having spent so much time in the Entertainment and Hospitality industry, I see everywhere the key systems being CRM and Revenue Management systems. Yield Management is a market segmentation strategy to capture the consumer surplus, widely used in the Entertainment and Hospitality industry. Airlines, hotels, theme parks, car rentals, cruise ships, broadcasting (TV, radio), and Utilities (telecom, electricity), etc. always used segmentation and peak-load pricing. But with the advent in computing power of systems this technique is now able to do excessive and data intensive calculations with linear programming to optimize revenue. Just like airlines realized long ago that a flight flying with an empty seat is forgone revenue, many companies use historical data to optimize differential revenue gains. American Airlines had added $1.4B additional revenue over three-year period in the early 1990s. Hertz added 1-5% revenue annually and so did companies like Marriott Hotels, Royal Caribbean Cruise Line, etc.  Computer algorithms can use variables like time of purchase/usage (advance/spot purchase, day-of-week/season), purchase restrictions (cancellation options, minimum term, Saturday night stay), purchase volume (individual vs. group), and duration of usage (single night/weekly rate) to get the right price to the right customer. Next time you are stuck an airport and an airlines mentioned over-booked flight, know that some computer algorithm was doing some marginal analysis based on capacity of flight, typical cancellation rate, revenue from booking seat and cost of denied boarding (these days with the pressure airline are also adding costs like loss of goodwill along with the free flights they give you). Other analytic systems within an organization depending upon what the goals of a business or its units are.
  • Monte Carlo Simulation – This technique uses data to establish a pattern between a domain of possible inputs. The calculations generate inputs randomly from a probability distribution over the domain, perform computations on these and aggregate the results. This tries to minimize reinventing the wheel by reuse of research results. In the Pharmaceutical industry, for example, during the Discovery phase of their value chain, data mining is used to search contextual information based on secondary relationships,
  • Regression Analysis – This is a set of techniques used for modeling and analyzing several variables and establish a relationship between a dependent variable and one or more independent variables. It is widely used in Marketing and Sales part of a company’s value chain since it can provide deep insight on customer behavior and provide an enhanced decision-making for future customer interactions. Companies are using industrialized analytics which uses closed-loop promotion and data mining to optimize marketing budget allocation (i.e. optimizes marketing channel and product mix). The closed-loop-promotion ensures feedback of mission-critical data to marketing and provides on-demand access to marketing decision-support. This helps creates an optimal marketing mix (as Jerome McCarthy called this the 4P’s – product, price, place, and promotion). It started with improvements in systems for statistical analysis that helped understand why certain things were happening in the business environment. These were heavily used to do web traffic analyses during the emergence of eCommerce. Then these systems were extended to forecasting and extrapolation to see what would happen if the trends continued. Then the era of predictive modeling built systems what could try to predict what will happen next, based on empirical data and heuristics. Of course all these systems are used to help with optimization of spend and maximize the revenue. As an example, within the financial services industry, specifically Banks, such models are used to predict Retention & Loyalty, perform Portfolio Analytics, help with Fraud (Transaction/ Payment) and Anti Money Laundering efforts. They help answer questions like Bank Servicing - Which customers/ segments are at risk? Which ones are profitable? How do I retain them? How do I win back customers? They can also help with Loyalty Programs - What drives loyalty for my customer base? How do I design an effective loyalty program? They are used for Cross Selling and Upselling - Which deposit account holders would be interested in an auto loan? Can we sell insurance along with auto loan? Which products can be sold on credit card welcome call? Which ‘Gold’ customers would increase their spend if we upgrade them to ‘Platinum’? Can I identify customer requiring short term loan? It helps with Campaign Management with segmentation modeling, profitability analysis, retention campaigns, win-back campaigns, etc.
  • Neural Network Analysis  – Apart from the obvious impactions in the study of real biological neurons, this technique is used in areas like distribution & logistics, sociology, economics, etc. Any company that produces a product is chartered with getting that product to its customers (whether that’s the final customer, retailer, wholesaler, etc.) in the least expensive manner. They are constantly trying to reduce total inventory while maintaining service levels of supply at each warehouse or distribution center location. Neural network models help with optimizing with the variables involved: number of warehouses to have, location of each warehouse, size of each warehouse, allocation of products to the different warehouses, allocation of customers to each warehouse, etc.  The objective is to balance service level of supply against production/ purchasing costs, inventory carrying costs, facility costs (storage, handling and fixed costs), and transportation costs.
Comp ANalytics

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Data Visualization

Sure a picture is worth a thousand words. But didn’t the dude in Matrix see numbers and know the stories behind this? Well, only in movies. According to research, Data visualization is so powerful because the human visual cortex converts objects into information quickly. As we continue the journey of Data – Information – Knowledge – Wisdom, the feedback loop of models and visualization to see patterns is key.
Data Visualiaztion 1  
As Big Data grows, it’s clear that the technology to gather and store data far EXCEEDS the ability to Analyze it. However, not all visualizations are actually that helpful. You may be all too familiar with lifeless bar graphs, or line graphs made with software defaults and couched in a slideshow presentation or lengthy document. The best data visualizations are ones that expose something new about the underlying patterns and relationships contained within the data. Understanding those relationships — and being able to observe them — is key to good decision making.  
  • Pizza and Cola sell together more often than any other combo – is there a cross-marketing opportunity?
  • Does Plant and Clay Pot sales IMPLY sales of Soil?
  • Milk sells well with everything – people probably come here specifically to buy it. Should we raise prices since less price elasticity?
  • What is the one item you want to have in your store in case of a hurricane?
  • Does buying any kind of pepper also denote sales of  banana?.
  • Does buying any kind of pepper also denote sales of  banana?.
  • Which customers are most likely not to have an accident?
  An important distinction lies between visualization for exploring and visualization for explaining. Exploring data is all about statistical acumen and understanding the nature of what the data represents in your enterprise. Visualization tools are an aid but they have been around for eons. Once you have explored, you will almost always find less than a handful of factors stand out and need explanation. Your presentation should not be about fancy graphs but the right power point / keynote /video storyline for your audience. It seldom needs voluptuous graphs ... if you are trying to describe more than this handful of points, then you are already lost in your quest.  
The key is use the right Visualization for the right Data at the right Time. I found this chart very helpful to decide the decision tree for which types of visualizations to use for different scenarios:
There are so many tools to do this kind of analyzes:
  • Qlik, SAP, SAS, and Tableau Software deliver the latest table stakes in visual discovery: storyboard capabilities.
  • Google Fusion Tables: Bust your data out of its silo and combine it with other data on the web. Collaborate, visualize and share
  • Datawrapper: An open source tool helping anyone to create simple, correct and embeddable charts in minutes
  • Infogram: is user-friendly interface to help develop creative, interactive infographics
  • Piktochart: Piktochart is a simple WYSIWYG editor to help develop and design charts and infographics
    Visualization for explaining is best when it is cleanest. Here, the ability to pare down the information to its simplest form — to strip away the noise entirely — will increase the efficiency with which a decision maker can understand it. As big data becomes bigger, and more companies deal with complex datasets with dozens of variables, data visualization will become even more important.  
Data Visualiaztion 2        

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Techniques in Predictive Analytics

So last week when I wrote about Predictive Analytics, I got responses from folks saying, “The value from such areas is clearly there. But the challenge is which technique to use and the ever-sliding sword of showing ROI so there is buy in for these analyses”.  
In framing the Analytics problem – we need to balance data, SME knowledge, and performance. One of the things I have noticed in my work is when the analysts build models the real skill in creating effective analytic model is knowing which models and algorithms to use. They can use different techniques: neural networks, decision trees, linear regression, naïve Bayes, etc. But these days many analytic workbenches now automatically apply multiple models to a problem to find the combination that works best. One needs to explore different paths – they look at the problem from different perspectives. When these algorithms are combined there is resulting synergy. Once the modeling data sets were finalized, the largest incremental gain was not achieved by fine tuning the training parameters of an individual algorithm, but by combining predictions from multiple algorithms.  
So with the myriad tools and techniques that exist, the way to approach this is to ask the questions that are really important for what the company is trying to solve:  
  • Strategic Customer Questions
    • Who are the most/least profitable customers?
    • Who are the most/least satisfied customers?
    • What is fastest/slowest customer segment?
    • What are the reasons for customer attrition?
    • What are the costs of customer transactions?
  • Strategic Product Questions
    • What are our most/least profitable products?
    • What are our production costs & how can we lower them?
    • What is our cycle time & how can we lower it?
  • Strategic Employee Questions
    • Who are the most productive salespeople, employee?
    • Which managers have the highest retention rates? What do they do?
    • What is the cost of turnover?
  • Strategic Financial Questions
    • How accurate are the financial forecasts?
    • How much financial data is used to answer business decisions?
    • What impacts the demand of our product?
    • What items are affecting our margins the most?
  Based on this one has to look at some of the following techniques:  
  • Classification – predicting an item class, “Decision Tree”
  • Association – what occurs together, “Market Basket”
  • Estimation and Time Series – predicting a continuous value
  • Web and Text Mining – extracting information from unstructured data
  • Clustering – finding natural clusters or groups in data
  • Deviation Detection – finding changes or outliers
  • Link Analysis – finding relationships

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Predictive Analytics

These days it’s tough to walk out of any meeting with Business or IT organizations without touching the topic of Big Data or Analytics. Lot of people struggle with what this is – everyone BELIEVES that it can help if done rightly. But what is it?
The way as I look it: As organizations mature on their Business Intelligence capability, the questions they ask mature too. It’s not about only looking at what the data tells about problems you need to solve. But can data tell you to THINK OF NEW PROBLEMS that you can solve. Things you didn’t know. THINK of something different. Organizations are faced with ever increasing business challenges: Driving new sources of growth, Cost management and cash conservation, Increased business complexity and the need for operational excellence, or Business restructuring in an increasingly global business environment. Ubiquitous computing and technology capabilities have increased dramatically the volume of data at companies’ disposal, yet there remains little in the way of actionable insights (Big Data). Companies need timely, in-depth actionable insights if they are to remain competitive globally to effect a “whole business” approach to big data analytics to deliver business results. Analytics-driven optimization of key business processes
  • Staking out distinctive market strategy (CRM Strategy and Loyalty programs)
  • Finding the best customers, and charging them the right price (Revenue Management )
  • Minimizing inventory and maximizing availability in supply chains (Inventory Optimization)
  • Understanding and managing financial performance (Forecasting)

Predictive Analytics

    Business Intelligence technologies are deductive in nature validating the hypotheses of the business problems you want to solve. Examples:
  • Product shortage by market
  • Vendor spend by category
  • Brand health by market
  • Periodic trend analysis
  • Periodic P&L and Financial      Reports
  Predictive Analytics is Inductive in nature - pull out meaningful relationships and patterns and tells you of different things that might be addressing the same or new problems. Example:
  • Business Mix Optimization      (Product, Geography, etc.)
  • Price sensitivity by consumer      segment
  • Customer Behavior Modeling
  • Performance/profitability      analysis
  As an example, NETFLIX, a US movie delivery company, asked engineers and scientists around the world to solve what might have seemed like a simple problem: improve Netflix's ability to predict what movies users would like by a modest 10%. From $5 million revenue in 1999 reached $4.3 billion revenue in 2013 as a result of becoming an analytics competitor. By analyzing customer behavior and buying patterns created a recommendation engine which optimizes both customer tastes and inventory condition.
As another example, Analyzing Love: Data Mining on in AllAnalytics,, online dating service, tries to predict the likelihood of attractions between people.  95% of relationship can be predicted by analyzing as few as 10 characteristics in each profile. The find things like:
  • Members with accounts on Twitter, which only allows for messages of no more than 140 characters, have shorter relationships.
  • People identifying themselves as Republicans are more willing to connect with Democrats than the reverse
  Predictive Analytics 2 Predictive Analytics 3    

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Global Sourcing

In this day and age there is an assumed maturity in the way initiatives within a business are sourced and out-sourced. When it comes to IT applications and their development and maintenance, there are 4 possible scenarios that companies deal with:
  • Insource  - Maintain control internally (usually for reasons of intellectual property, privacy, or strategic responsiveness)
  • Staff Augmentation - Save money while maintaining responsibility for application support and maintenance activities
  • Co-source- Leverage external cost structure benefits and expertise while maintaining an appropriate level of control
  • Outsource - Delegate IT (or selected functions therein) to an external organization for which it is a core competency
With this industry evolved over the years, the rationale for IT outsourcing decisions has shifted from cost being the sole consideration to include a number of strategic factors. No doubt cost is still top of the mind, especially with this economy. But a lot of other considerations are in play:
  • Strategic Importance
    • Relative impact of a service area on the company’s revenues and overall profitability
    • How strategic is the function to my organization today? How does it fit into our future plans?
  • Current Capability
    • Relative strength of a service area's technical & business know-how, processes, and tools
    • What are the capabilities of the function?  How do those capabilities compare to our requirements, and to our peers?
  • Perceived Value / Cost
    • Perceived value of a service area relative to the costs incurred
    • What is the function’s capacity to adapt and change?
  • Ownership Preference
    • Relative preference of management to own, share, or transfer out IT assets based on company beliefs, values, and sourcing experience
    • How easily can the function be transitioned to another sourcing strategy?
    Business Quarterly indicates 75% of US executives considered financial motivations as secondary to other strategic objectives when outsourcing. Business Week reports, “The really smart business owners have figured out how to use outsourcing as a strategic tool instead of simply looking for savings.” CIO magazine reveals strategic value rivals cost reductions for outsourcing motivations.   Based on some reports by The Outsourcing Institute the top reasons for outsourcing look as below:


  No matter what the goals, the key success factors of outsourcing are always:
  • Be clear about objectives-- cost, process improvement, and the ability to focus on the core business are the most common
  • Incorporate business outcomes as a performance measure from the outset of the arrangement
  • Look beyond price and promises of cost reductions for an outsourcing provider that brings a wide set of skills and strengths, and a long-term track record of delivering results
  • Give as much attention to performance measurement and the quality of your relationship with your provider as you do to the contract
  • Use active governance to manage the outsourcing relationship for maximum performance
  • Task talented executives with optimizing outsourcing arrangements

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Business Cases – Show me the Money !

Ever since Jerry Maguire blurted this out, people have been using this as a corporate euphemism for ROI/ Business case.
One of the critical roles for any organization is to manage the value achievement of the initiatives they pursue. They need to ensure sponsor and executive ownership of the business case. The business case allows the stakeholders in IT projects to jointly address their key concerns with project investments:
  Business cases highlight the initiatives that create the greatest value, support decision- making, and help track program performance. It is good to define the business case early and plan on many iterations since it:
  • Demonstrates how a major investment creates value
  • Includes both quantitative and qualitative rationale
  • Supports business decisions by weighing choices or options
  • Creates a way to track performance and measure success after a decision has been made
  • Gains alignment and management consensus for a project
  In some organizations, the term ‘Business case’ may also be referred to as
  • Cost/benefit analysis
  • ROI analysis
  • Feasibility study
  • Capital funding request
  • Case for action
  • Once the team has understood the importance of having a business case to guide the investment decisions of the initiatives, there is debate on what level of detail should it have. There are many approaches to building out a business case and the main elements are
    • Benefit models
    • Cost models
    • Cash flow models
    • Assumptions (timing, dependencies)
    • Sensitivity Analysis
    • Qualitative Factors Analysis (non-financial benefits, risks)
  The financial models can be Top-Down (more high level and helps form an initial hypothesis wider ranges to reflect uncertainty) or Bottoms-Up (more quantitative and time spent on thorough data collection and analyses). But the key point is that you need to build the business case with ranges and confidence levels. Once the numbers were compelling, the ranges could change but they would not change the decision.    

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IT Service Management

At a BPM event recently in Orlando, I was chatting with a colleague about IT and the BPM responsibility. This guy is the SVP of IT operations and handles Infrastructure for his company. When someone asked him who from the business was responsible for the BPM aspects in his firm from the business side, his response was “We in IT are actually responsible for the BPM aspects and optimization therein.” Another guys goes, “The only real applications the business is concerned about is e-mail”   That set me thinking about IT Service Management, etc. Having spent some time doing ITIL work, I am familiar with the concept of IT service management, which involves moving:
  • Multiple points of contact with the business
  • Service defined and measured in technical terms (if at all)
  • Work driven by technology
  • Organized to support systems
  • Managed relationships established with customers
  • Service defined, measured and reported on in business terms
  • Work driven by service requirement
  • Organized to deliver service
So ITSM is all about better service at lower cost. But the challenges with a full blown ITIL deployment is that ITIL is far too generic for an organization to implement at a fast pace, in totality. Process reengineering and change management are always required and are rarely considered. Some practitioners have said that it complements other IT management methodologies like CMMI, etc. But the way I look at this is that CMM focuses on improving and appraises the maturity of application development.  ITIL is focused on best practices around IT Operations and Services. This kind of demarcation:  


  The ITIL v2 broke these Operations into Service Support (ensuring that the customer has access to appropriate services to support business functions) and Service Delivery (IT services are provided as agreed between the Service Provider and the Customer).   But the key to achieving good IT service management even at a small scale is by using the following guiding principles:
  • Business Relationship Management: Ongoing liaison and relationship building with Client community.  Maintain an understanding of the business and IT requirements.
  • Service Delivery Management: Understand the IT Services provided and the businesses reliance on these Services.  Carry out the appropriate business liaison and escalation for Service issues.
  • Service Performance Review: Formally review service performance against agreed upon SLAs. And good luck with that J
  • Service Level Agreement Management: Maintain service definitions and assess implications of any changes
  • Service Enhancement Request: Receive and shape requests for new/enhanced services

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Supply Chain Excellence

Achieving supply chain excellence is complex and challenging, but success in achieving supply-chain driven competitive advantage enables superior customer service, profitable revenue for growth and significant increase in shareholder value. Inventory Management is the conductor of the symphony for Retail Supply Chain execution. It is critical for customer service since Inventory management is what initiates all merchandise movement and controls the timing within the supply chain
  • Supply chain assets and inventory usually comprise at least half of all non-store based assets
  • Supply chain activities typically account for as much as 40 – 70% of operating costs (including procurement  and markdowns)
  Some of the statements from retailers across all kinds of products:
  • “Assisted Inventory Management (AIM) helped us exceed our inventory-turn goal, making us the leader among national drugstore chains in this important productivity measure. We achieved inventory turns of 5.0 times for the year, up from 4.6 times in earlier years.” – CVS
  • “Positioned among the best in retail, our supply chain helps drive sales, reduce costs and ensure the availability of products our guests most want and need.” – Target
  • “We completed the conversion of each of our operating divisions to a common technology platform with greatly enhanced inventory management tools, permitting more sophisticated inventory planning and more precise by-store inventory allocation.” – Saks
  The three main components of the Inventory Optimization program address both the process and physical infrastructure of the supply chain.  
  1. Inventory Management Process  - this addresses end-to-end inventory management built on two core processes:
  • Foundational for continually replenished basic merchandise. Periodic automatic replenishment, long life, stable supply, short lead time to continually meet normal demand
  • Highly Variable which is typical of merchandise with high demand spikes due to promotions, fashion, short life and seasonal demand
  1. Network and Flow Strategy - Network Optimization starts with establishing a vision of alternative flow paths and ends with a full evaluation of end-to-end physical supply chain and a recommended distribution network strategy. One  has to assess merchandise flow paths to provide revenue growth, minimize supply chain costs and support overall inventory strategies.  Then one has to determine alternative distribution      strategies including buildings size and location, transportation strategies, inventory deployment strategies, and benefit based business cases.
  1. Store Operations – Design and implement a well-defined process for store operations related to receiving, shelf stocking, perpetual inventory accuracy and plan-o-gram maintenance.
  • Organization & Labor Planning
  • Life Cycle Management
  • Shelf Replenishment
  • Data Integrity Maintenance
  The idea is to push operations from
  • Stores Ordering for basic merchandise to Automatic Replenishment Approach which is centrally  maintained and helps with enhanced High Performance forecasting and allocation abilities
  • Store Reviews ( All replenishment orders to supplement simple forecasting & ordering logic) to Exception Only Reviews. No store review for standard items and examples of exception reviews: items with high inventories, poor service levels etc.
  • Limited Standards & Policies (In-stock policies and Service levels) to Standard Policies Across the Supply Chain. This is through reliable & repeatable inventory management processes and uniform service standards based on merchandise goals and category/SKU profitability
Forecasting SCM 2  

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RFID in the age of Mobility

Having done a lot of work in the supply chain industry, I am so intrigued by RFID and its potential once the costs go further down. Radio frequency identification (RFID) is a generic term for technologies that use radio waves to automatically identify individual items. RFID technology is not new or complex; it has been around since the early radar systems in the 1940’s. What is new is how manufacturing advancements have reduced costs of implementing RFID systems (particularly tags). These silicon-based electronic identification tags, consisting of a tiny processor, memory, antenna and can be read and written wirelessly and can be made cheap, without a battery. The main components of this technology are:
  • Device made up of an electronic circuit and an integrated antenna
  • Radio frequency used to transfer data between the tag and the antenna
  • Read-only or read / write
  • Receives and transmits the  electromagnetic waves
  • Wireless data transfer
  • Receives commands from application software
  • Interprets radio waves into digital information
  • Provides power supply to passive tags
  IT Infrastructure
  • Reads / writes data from / to the tags through the reader
  • Stores and evaluates obtained data
  • Links the transceiver to an applications, e.g. ERP
Of course there has been a major drag in the adoption of this technology. The key challenges have been:
  • Not only costs of tags and readers, but the costs of integration of the RFID technology into the IT technology stack - e.g. ERP, etc.
  • Lack of worldwide data standards
  • Country-specific frequencies allocation
  • Vendors are very fragmented
  • Tag and data overload – How do we handle the data?
  • Read-rate accuracy
  • Tag and reader collision – Signals can interfere with each other
  • Privacy fears from the tracking provided by this technology
But more and more this technology is coming into mainstream. Especially after Walmart mandating the use of RFIDs in their supply chain management. Walmart believes that they can cut out costs and make their supply chain even more lean with this deployment.  
The uses of this technology are of course endless. I was recently reading about the CyberTM Tire from Pirelli Tire Systems that transmits information on road conditions and friction coefficients to the car's computer. Already some hospitals are using RFIDs to tag patients with wristbands to scan by hospital staff using PDAs or tablet PCs connecting to patients' data using a WLAN.  
And as this become more prevalent there are other uses that are surely ridden with privacy issues. There is much research where people are looking at ways to monitor real time health in individuals. There is a RFID implanted in the human wrist that send signals to the health insurance company at all times. When you wake up in the morning and go for a jog; you arrive at work and an email from the company (always monitoring your vital stats) sits in you inbox, proclaiming a reduced premium for the day. You have breakfast at McDonalds over the weekend. Lo and behold, your premium just went up.    

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IT Spend Analyses

A few days ago I was in a CIO roundtable in Atlanta and one of the CIOs mentioned that despite the state of the economy their IT Organization was thinking of spending some if their budget on some innovative initiatives so that when we get to the bottom of the J-curve in the economy, they’d be ready to win over strategic goals. Really set me thinking – how are companies dividing their IT spend on keep-the-lights-on operations and strategic or innovative investment. Top executive management these days has two main questions:
  1. How can the IT organization be transformed to be an enabler of creating  business value rather than just being a cost of doing business?
  2. How can we achieve better results at a lower cost?
  I guess it’s always important for the IT organization to evaluate internally how IT’s value contribution to the business should be planned, managed, and assessed. Unfortunately, the link between business value and IT is often not understood by executives and especially in times like these IT spending levels are overly-squeezed. The common issues that we have seen:
  • Typically, IT spending level is based on historical or competitive benchmark levels
  • Lack of recognition for IT contribution on business side
  • Short term, simple IT cost cutting drives down value adding and innovative IT initiatives first
  • As a result, IT capabilities deteriorate and mid-term IT operating costs rise
  • Eventually, higher IT operating costs eat away funds for innovations and this furthers the overall IT budget explosion. A big vicious circle!
  Of course a company’s position on its spending is dependent upon many macro factors:
  • Number and size of competitors
  • Industry growth rate and rate of change
  • Industry margins/pricing
  • Product differentiation factors – physical products or knowledge assets
  Mandatory or non-discretionary IT investments are for keep-the-lights-on functions - IT Operations, regulatory, etc. Things like technical support, IT infrastructure management, technical upgrades to infrastructure components, required maintenance, enterprise-wide project support fall in this category.
  Discretionary spending, which is about IT investments that are Strategic, Enabling, and Sustaining, are on things like R&D (focus on future technologies), etc. These investments should create a strategic or economic advantage in the market, create barriers to entry, etc.   As written by Michael Treacy and Fred Wiersema in their classic book, Discipline of Market Leaders, there are three basic "value disciplines" for a company to pursue – operational excellence, customer intimacy, and /or product leadership. If the direction of the company is clear, well-communicated, and well-understood, then some strategic IT investments are driven from the same:  
  • If there is a product/service innovation  focus, then the company needs to focus on increasing value to existing customers, developing new markets and channels, etc. Examples of initiatives are eInnovation, eDesign Collaboration, PLM, etc.
  • If the company is focusing on Customer Intimacy, then the company needs to improve understanding of its customer needs, increase customer insight, etc. The initiatives fall in realms like Customer Insight (Inbound Marketing), Integrated View of Customer (DW, Analytics), etc.
  • If the company is trying to create new scales and reduce interaction costs between partners and customers, it needs to invest in increasing service levels at lower costs, concepts like “Super” Distributor, Supplier Collaboration, etc.
IT Spend  

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Fleet Management

Fleet Management, of which Strategic Sourcing is a core part, is an integrated set of actions, which occur in a rational and logical manner, with the overall objective of attaining lowest Total Cost of Ownership (TCO). Key issues in fleet management involve capital commitments and management, as well as operating effectiveness and cost.  Fleet asset utilization is not typically tracked or measured, which leads to unwanted outcomes, such as having more vehicles than necessary, additional operating and maintenance costs and not always having the right vehicles for the jobs they are needed to do.  Additionally, fleet costs are usually   fragmented and a rarely captured in total, which leads to problems in trying to adequately and accurately assess operating efficiency and evaluate out-sourcing opportunities.   The first step for true optimization is getting a good handle on the existing fleet in terms of its make-up, utilization and operating cost, reviewing the administrative and operating practices related to procurement, operations, maintenance and disposition, as well as determining replacement scheduling. The foundation is based upon the following three areas:
  • Strategy (replacement scheduling, outsourcing/insourcing and fleet organization)
  • Operations (vehicle pooling, maintenance & repair, inventory management, fuel management)
  • Administration (Standards & specifications, fleet utilization, budget & cost reporting)
  The areas to explore the fleet management practices:
  • Fleet inventory (including but not limited to manufacturer and model year, type, location, VIN #, GVWR, acquisition price, options purchased, lease payment, annual operating and maintenance costs, sale price if retired, auction fees and class – how it’s used)
  • Equipment Utilization – Miles, hours or both on equipment where there may be two measures of utilization
  • Fleet “spend” at invoice level and at options level if available.
  • Current agreements and in progress negotiations
  • Current leases, short term rentals, and ownership models

Fleet Rationalization, Utilization and Fleet Mix - Once the standards and specifications process has taken place, putting rigor and focus in the area of rationalization and utilization brings value and savings to the company and fleet. The goal of this component of the process is multi-dimensional:
  • Ensuring that the proper utilization targets by class and location (e.g.,: metro v. rural) are set and used to reduce the number of low-use vehicles in the field
  • Rationalizing the fleet based on job function and job assignment.
  • Developing a fleet policy that optimizes the use of pooling vehicles, how and when to use short-term rentals and take home vehicles.
  • Identify fleet operating needs that may include needs for surplus vehicles including seasonal work requirements, construction projects, regulatory mandates, etc.
Focus on the 80/20 rule when it comes to prioritizing fleet opportunities.  Develop standards and specifications for the portion of the fleet that can be standardized and will provide the highest value/impact, such as passenger vehicles, SUVs, LD and MD trucks aerial and digger derricks.  Utility and construction equipment is often overlooked  
  • Fuel - In most cases, not incorporating the sourcing of bulk fuel (v. fuel management services) as a part of any fleet sourcing engagement. Past   experience has shown that this exercise returns almost no incremental  value and usually devolves into an exercise around sourcing transportation from supplier fuel racks to client bulk tank facilities.
  • Maintenance & Repair - Achieving the lowest TCO for fleet, maintenance and repair is an integral component of the equation. Inherently  maintenance and repair costs will decrease as an output of developing the standards and specifications and replacement schedule process. Other areas should also be evaluated, such as opportunities for network consolidations of maintenance and repair shops, etc.
  • Determining a “Levelized” Replacement Schedule - Developing a “Levelized” Replacement Schedule is a key concept in improving fleet management and obtaining benefits from strategic sourcing. Sharing the information with both internal finance and external vendors and suppliers is instrumental in planning for future fleet acquisitions and capital needs as well as structuring multi-year deals.
   Fleet TCO  
In summary, maximizing fleet effectiveness depends on managing it like a business, in an integrated and holistic fashion, across two major dimensions.
  • FLEET OPERATIONS – Operating revenue, Operating costs, Contribution margin, Productivity metrics and measures, Performance metrics and measures
  • FLEET ASSET MANAGEMENT – Fleet sizing, Standards and specs, Strategic sourcing, Life-cycle management, Maintenance and repair, Disposition management
Fleet Mngt 2      

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