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





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



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



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


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.



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


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




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.







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


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 MixOnce 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


  • FuelIn 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 & RepairAchieving 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