VIN Profitability Margin Analysis · VIN ProfItabIlIty MargIN aNalysIs ... customers to increase...

1

Automotive

09.12 eb 7333

VIN ProfItabIlIty MargIN aNalysIs


Creating a more Precise vision of the Profitability for each and every vehicle

http://www.teradata.com

2

Automotive

09.12 eb 7333


table of CoNteNts

2 executive Summary

3 it’s 2012. Can You trace the margins for each and every Car You Produce?

3 the Challenges of today’s Automotive market

4 traditional Approaches Are Not enough

5 the benefits

8 How to Create big Data-Driven vPmA

9 Which Data and From Where?

9 A Customizable Roadmap

11 the teradata Analytic environment for vPmA

12 About the Author

exeCutIVe suMMary

in 2012, successful automobile manufacturers should be able to walk into a parking lot, scan a viN, and immedi-ately understand the margin on that vehicle through its entire lifecycle.

the reasons for this are clear: manufacturers are under enormous pressure to create innovative, customer-pleasing cars with strong warranties at reasonable price points – and to do so efficiently enough to maintain or grow profits. this is no easy task, which is why under-standing profitability at the viN level is so critical. Such understanding demands that companies have extremely detailed and quantifiable visibility into the role of every component part and process, how they interact, and their affect on profits and customer satisfaction.

most manufacturers have understood this for years, but the advent of big data analytics has changed the game. today companies gather data from every conceivable source system – from research and development, supply chains, and the manufacturing floor to individual dealer-ships customer call centers and web site visits – and in every conceivable structural format. to take advantage of this, manufacturers must be able to gather and integrate that data quickly and easily and apply sophisticated analytics that allocate costs in the most granular detail so they can make faster, better business decisions all along the value chain. the analytics that are generated with viN Profitability margin Analysis (vPmA) far exceed the value of existing cost allocation techniques, such as zero-based budgeting, activity-based costing, and rolling budgets.

the benefits of vPmA extend throughout every aspect of a company’s operation and mean:

~ Decisions are timelier and incorporate deeper, richer insights into how each component and step in a product lifecycle contributes to profitability.

~ Companies understand customer needs in more detail and have more opportunities to fully test ideas before investing in new product designs, operational models or services.

~ Data driven, customer-centric innovation points the way to new, strategically savvy product launches.

~ Working from a single source of truth, the entire organization can work together to ensure that all reasonable customers’ needs and wants are meet.


3

Automotive

09.12 eb 7333


the good news is that the technology finally exists – in the form of a robust, teradata solution architecture – to make these benefits a reality. And companies can start small and build on initial successes until big data vPmA becomes an enterprise-wide commitment.

When that happens, auto manufacturers will have a way to integrate and transform heterogeneous data sources to present margin analysis views that drastically reduce the time to detection, diagnosis, and remediation of cost variances – and enable a new era of data driven innova-tion tailored to verifiable customer needs and desires.

It’s 2012. CaN you traCe the MargINs for eaCh aNd eVery Car you ProduCe?

Faced with a series of challenges that are shrinking profit margins, automobile manufacturers are looking to more precisely design, source, build, sell, and service their products. Central to that goal is the ability to understand in extremely granular detail how component parts and processes contribute to the profitability of each and every vehicle.

this is not some abstract idea. in this day and age, suc-cessful companies should ultimately be able to walk into a parking lot, scan a car’s vehicle identification number (viN) and know within minutes how much margin they have secured from that vehicle’s entire lifecycle to date.

to do that, companies need a sophisticated form of viN Profitability margin Analysis (vPmA), which tracks all costs, revenues and associated margins that a product incurs as it moves through the various stages of its life-cycle. the concept has been around for some time, but until now it has never truly reached its promise. With the advent of big data analytics, the goal is now in reach.

big data analytics quickly and cost-effectively merge unconventional data structures with Structured Query Language (SQL) data, thus enabling companies to quickly integrate massive volumes of rich and disparate data to produce timely analyses and game-changing insights that foster greater profitability. in the case of the automotive industry, big data means gathering data from every conceivable source system – from research and development, to production and supply chains; from manufacturing and warranty to individual dealerships and customers, including call centers and web site visits.

this paper discusses how automotive manufacturers can use big data analytics to create strategically informed vPmA that builds on existing processes and enables companies to track financial margins for every unique product they manufacture, sell and service.

the ChalleNges of today’s autoMotIVe Market

moving any product from design through manufacturing, shipping, sales and service has always offered countless opportunities for margins to erode. today, that concern is heightened because numerous factors are forcing auto-motive manufacturers to do more with fewer resources.

~ the rise of the Customer: Fierce competition and access to customer-specific insights are driving companies to invest in tailored products and services to meet individual needs.

– Shorter Product Lifecycles: Rapidly changing cus-tomer needs have accelerated product lifecycles and created a demand for new designs that must arrive to market faster. to cut time and costs, many companies are adopting global platforms – a shared set of com-mon design, engineering, and production efforts from which multiple vehicle variants can emerge. these platforms can save companies as much as $750 per vehicle, or millions of dollars per year, in part by dra-matically reducing production time. there are risks, however, tied to the need for companies to coordi-nate multiple efforts and ensure features are tailored appropriately to specific markets.

– Designing for Value: Global economic doldrums and rising gas prices have shifted customer prefer-ences to smaller vehicles and greater value at lower price points. Automotive companies have responded with better features, options and amenities, but companies must truly understand the entire prod-uct lifecycle to offset the costs of these additional features.

– Service/Warranty Policy as Competitive Advantage: Confident that their investments in quality are paying off, many automotive companies offer ever more generous product warranty agreements. While these draw customers and could be profitable, they also run the risk of eroding initial profits if too many repairs become necessary.


4

Automotive

09.12 eb 7333


~ telematics and the Connected Vehicle: internet connectivity means companies can access important information directly from the product, such as real time diagnostic and parametric data. this adds to the potential for precise analyses and even individualized maintenance contracts, but because outside appli- cations typically sit on top of the manufacturer’s platform, a large part of the value proposition moves to third parties.

~ global supply Chains: the inherent complexity of global supply chains means that transportation and part sourcing operations have grown in both scale and complexity, resulting in higher supply chain management costs. volatility – be it from natural disasters, a debt crisis or fluctuations in fuel and raw materials prices – has made an already difficult task much harder. Perhaps the biggest challenge to planners is that the traditional role of spend analysis has moved far beyond tracking costs to understanding quality and value. Planners now have to incorporate information from all lines of business so that they can get insights into how their decisions affect the profitability of the final products – not just at the time of the sale but over the entire product lifecycle.

~ the end of Incentives: traditionally, automotive manufacturers have used a variety of sales and marketing programs targeted at both dealers and customers to increase sales. but changes in customer behavior and profitability have forced companies to scale back the number and scope of these programs. to properly calibrate incentives and sustain profitability through a product’s entire lifecycle, manufacturers need greater insights into the margin of each vehicle. these insights will drive choices not just about incentive programs, but about which product lines to invest in, as well as new design options.

tradItIoNal aPProaChes are Not eNough

manufacturers have long sought to glean insights into and maintain viN level profitability through a number of processes that include activity-based costing, zero-based budgeting and rolling budgets and forecasts.

these techniques all have value, but too often suffer from a myriad of flaws, including a reliance on averages for allocations rather than using detailed allocations,

limited granularity, and assumptions based on history – not on up-to-date product and customer behavior – all of which can lead to poor decisions based on unsupported assumptions.

in addition, today’s margin analyses typically reinforce departmental barriers that foil knowledge sharing and the critical concept of having a single source of truth from which all key decision makers can work. Companies considering adoption of new budgeting and forecast-ing systems to better respond to customer needs must break down those silos – something many of the exist-ing processes simply do not do. All of the functions and departments in the product lifecycle have to work together to ensure that only the features, activities and standards that deliver the maximum value to the individual customer are considered when designing, building, selling and servicing products.

Yet in many cases the various departments have their own financial systems, processes and classifications and choose different ways to manipulate, analyze and report on the data. the result is inconsistent financial data across all functional groups.

So in the case of zero-based budgeting, the re-evaluating of assumptions without up-to-date, cross-department data does not yield many new insights. Rolling budgets hamstrung by the lack of a single source of truth can actually add to costs, due to this being a laborious and resource intensive process. And all three approaches struggle to achieve reliable results because they focus more on planning than on addressing and informing strategic goals.

the bottom line is that companies need more than a score or value for each customer or account; they need detailed data and allocations. they need to be able to reconcile all financial systems and be able to drill into detailed results to understand the behaviors that are driving costs. they need more sophisticated financial modeling capabilities that include capital allocation, risk and amortization. they need to be able to assign rev-enues and costs based on transactional drivers, such as call detail records, care calls and collections. And they must be able use the output of all this modeling for other processes, including management reporting, costing and operational root cause analysis. that’s where the real advantages begin to kick in.


5

Automotive

09.12 eb 7333


For example, one approach that companies are using to replace activity-based costing is detailed cost alloca-tion. this is a tough hurdle – getting the analysis down to the individual vehicle, process or components – but it is something that vPmA supported by big data analytics can provide in a way that previous systems could not.

Similarly, bill of materials (bom) is a list of all parts needed for the assemblies, sub-assemblies, components, and sub-components required to build a vehicle. Again here, bom data gets very complex and crosses organiza-tional boundaries as the products go through the phases of design, manufacturing and finally service. Keeping the changes that occur and the different cycles synchronized often becomes a major challenge, which if not handled properly can lead to devastating inaccuracies that can undermine quality, reduce part reuse, increase costs, delay time to market and, ultimately, erode margins over the product’s lifecycle.

As companies get more detailed and sophisticated in their cost allocations procedures, another important process is being able to analyze margin forecasts at a particular state and time and benchmark those assump-tions in real or near real time. this can significantly reduce forecast errors – another advantage that sophisticated vPmA offers.

Finally, successful analyses of future life cycle costs often depend on simulation and forecasting that use much larger data sets than traditional methods. typically most companies employ one or a combination of four groups of cost estimation techniques to do such forecasts:

~ analogy based techniques (Qualitative methods) – these techniques start with trying to identify a product that is similar to the new product the manufacturer intends to build so they can use the past history to estimate what the cost of the new product will be. one challenge is getting agreement on the “degree of similarity.”

~ Parametric Models (Quantitative Methods) – Here, companies evaluate future costs based on a series of variables that are supposed to influence the final cost of the product, such as performance and the materials used. Statistical techniques that take into account how all these features relate to one another to forecast the final cost of the product.

~ engineering approaches – these are detailed analyses of the manufacturing processes and the features of the new product that sum up the total cost of the elementary options and production costs to get a final cost number. Detailed knowledge of all the inputs is, of course, critical.

~ artificial Neural Networks – many manufacturers have begun to use a new approach that uses artificial neural networks (ANN) to estimate the entire product life cycle cost more accurately. ANN mimics the working of the human brain and to solve complex calculations like forecasting the margin of the entire product life cycle at the viN level.

Again, all of the above analyses demand more and more detailed data – and the ability to quickly integrate and analyze that data

in short, successful vPmA depends on all groups involved making their decisions based on timely, complete, identi-cal and reliable data and interpretation – in brief, big data analytics. that is the path to game-changing insights.

the beNefIts

As defined above, big data analytics quickly and cost-effectively merge unconventional data structures with SQL data, thus enabling companies to quickly integrate massive volumes of rich and disparate data to produce timely analyses and insights. A recent study in the mit Sloan management Review found that 58% of the over 4,500 managers and Analysts interviewed from around the globe, rated as crucial the ability to use big data analytics to create competitive advantage.

When big data analytics are the platform behind vPmA, companies can expect to lower costs and improve profit-ability in a variety of ways.

Decisions are timelier and incorporate deeper, richer insights into how each component and step in a product lifecycle contributes to profitability. Companies under-stand customer needs in more detail and have more opportunities to fully test ideas before investing in new product designs, operational models or services.


6

Automotive

09.12 eb 7333


this leads to data driven, customer-centric innovation. Companies can make fully informed decisions about new strategic directions and products, because vPmA forces the entire organization to use detailed, quantifiable assumptions drawn from clear visibility into how prod-ucts and components meet customer needs. the entire organization, working from a single source of truth, can work together to ensure that all reasonable customers’ needs and wants are met.

this plays out in every aspect of a company’s business.

finance and budgeting: Perhaps the most straightfor-ward way that vPmA increases profitability is by using a single source of truth to reduce the costs of the bud-geting and financial forecasting. A major American manufacturer expects the enhanced speed and accuracy enabled by its new vPmA system will reduce the cost of its finance and budgeting function from 0.7% of total revenue to 0.4%, which translates into $500 million in savings over several years.

Product design and development: vPmA using big data analytics shortens the time and improves the reliabil-ity involved in gathering, integrating and analyzing the data required to understand what customers want and whether or not the company can profitability build it. it allows companies to quantify assumptions using detailed visibility into projected performance – and the clear insights into customer needs and product costs are what enable profitable development of innovative products. Detailed customer margin segmentation down to the individual vehicle helps move companies closer to their strategic goals of profitable mass customization.

in short, the visibility and traceability that vPmA provides translates into an important set of insights, including:

~ Return-on-investment for new products measured over the entire product lifecycle.

~ the portfolio value of new products in development or under consideration in net present value.

~ Whether new products offer competitive differentiation from a price, lifecycle quality and reliability perspective for both the manufacturer and the customer.

~ understanding of the cost and need for downstream engineering change orders.

~ Ways to improve design and development activities by proving key performance indicators in such areas as new project costs, revenues, resource allocation and utilization.

Procurements and Purchasing: When purchasing depart-ments view procurement in a siloed manner and cost cutting becomes the primary focus, the result tends to be that products meet initial cost cutting goals but fall short on delivering the original design and product offerings’ value and long-term profits. vPmA provides enterprise-wide access to spend data, as well as to the effects that procurement and purchasing activities have on lifecycle costs of each and every customer and vehicle. Specifi-cally, vPmA enables manufacturers to:

~ identify and forecast saving opportunities that do not hurt customer value and expectations.

~ identify procurement and purchasing activities that are not under spend management.

~ identify and prioritize the top spend categories throughout the entire product life cycle.

~ Provide measurable insight into cost management programs, especially their effect on customer satisfaction and value creation – and whether those activities result in improved profits.

~ Provide the data required to improve supplier contracts and provide insight into whether to contract or expand supplier networks to mitigate supply chain risk.

supply Chain Management: Supply chain manage-ment has become more complicated. Globalization is the primary factor, but in addition many companies are decentralizing, outsourcing design and develop-ment functions to suppliers and forming new alliances, most notably for green technologies. vPmA can help companies build stronger, more cooperative and more profitable partnerships by enabling companies to track the effects of their actions. For example, indisputable insight into the effect of cheaper and less durable parts on the long-term profitability of both partners enables mutually beneficial decisions for each stage of the pro-cess. vPmA – and the ability to drill down to the part/component level makes this level of strategic coopera-tion possible.


7

Automotive

09.12 eb 7333


it is difficult to overstate the importance of supplier relationships to auto manufacturers, yet many such relationships continue to be very strained. because of the enhanced transparency and traceability for all parties involved, vPmA helps companies build stronger, coop-erative, less adversarial partnerships.

Manufacturing: vPmA augments the strategies and met-rics most automotive companies use – including Kaban, lean manufacturing, and theory of constraints – by going beyond detailed viN Level activity data to provide insight into the margins associated with these activities and component parts. vPmA enables companies to allocate costs in extremely granular detail to all manufacturing activities, which helps the companies reduce costs while maintaining and creating customer value and overall profitability.

this is critical, because historically the manufacturing stage of the automotive product lifecycle has carried the highest costs – and has been the focus of countless effi-ciency campaigns. manufacturing cost analyses benefit from having access to the true costs, including adjusting for foreign exchange rates and the part price on the day of manufacturing.

vPmA enables auto manufacturers to:

~ Analyze available manufacturing options and their effect on meeting increased customer demands for lower prices, shorter lead times and product customization.

~ Analyze available manufacturing options and their effect on meeting increased pressure to do more with the same assets and people.

Distribution and Logistics: Globalization has dramati-cally heightened the challenge of tracking the costs associated with distribution and logistics. issues such as delivery delays and the volatility of fuel costs have made rapid response times an absolute essential. vPmA enables companies to modify existing logistics on the fly and develop new delivery options that ensure efficient distribution at the lowest possible cost.

it allows manufacturers to weigh all the countermeasures they have at their disposal for both long-term and short-term strategic planning because they can:

~ Determine the optimal route, carrier and contract required to create value for every customer.

~ Provide a data and fact-driven framework for cooperating with carriers and suppliers.

~ Provide the data and analytical framework for carrier and compliance auditing.

sales and Marketing: A proliferation of digital sales and marketing channels – along with shrinking sales and mar-keting budgets – mean it’s never been more important or more challenging to make the right choice about how, when, what and where to market. Strategies evolve rap-idly. through iterative analyses and detailed product and market segmentation, vPmA captures in exquisite detail today’s rich mix of marketing activities and channels, as well as the dizzying array of organizations – both internal and external – involved in executing the activities. this enables small, effective trials that reduce investment risk as well as detailed visibility into the efficacy and return-on-investment of your marketing spend. timely insights make it possible to quickly fine-tune initiatives.

by enabling companies to allocate sales and marketing expenditure to an individual serial number or vehicle and the associated margins, vPmA delivers insights into:

~ knowledge of spend – the iterative process of vPmA gives manufacturers the ability to capture over time the total marketing spend and margins of diverse channels and third-party groups. understanding this in greater detail is the first step in optimizing marketing programs.

~ Marketing risk – marketing risk increases when companies struggle to understand where and how much to invest. For example: How much should the company invest in a new digital channel at the expense of traditional ones? vPmA provides the detailed customer and product segmentation data – and associated financial margins – that give manufacturers the ability to run smaller and more cost effective trials that reduce the risk of over- or under-investing in a particular channel.

~ Marketing spend return on Investment (roI) – understanding true marketing spend and associated risks makes it easier for manufacturers to calculate the Roi of marketing spend and better forecast future Roi. the ability to add real time data to the analysis allows companies to adjust to the digital speed at which new challenges operate.


8

Automotive

09.12 eb 7333


Figure 1.

Warranty and Maintenance services: As noted earlier, while warranty and maintenance offers can be wildly successful in the sales and marketing stage, they carry significant risk in the latter stages of the product life-cycle. Competition around these packages has become fierce, only increasing the risks. by gathering detailed data on the quality and reliability of the various parts and components early, companies get a true picture of product quality through the entire lifecycle so warranty policies are informed by facts. in addition, the same data – and additional analyses – help companies determine how much money to set aside to meet long-term war-ranty obligations.

vPmA can provide the framework for strategic planning in two key areas:

~ Competitive Warranty Policy – Strategists have to balance the need to gain market share against the need to ensure profits. the success of 10-year or lifetime warranty depends on the quality of the product. therefore, policy designers need extremely detailed data on the quality and reliability of the various parts and components – and, in fact, data from the entire product lifecycle along with associated margins.

~ Warranty reserve forecasting – vPmA – because of the detailed traceability it provides – also helps determine how much reserve is needed to meet warranty obligations. improved forecasts mean companies can set aside as much reserves as needed, but no more, thus confidently freeing up money for investment in other key areas of the company.

hoW to Create bIg data-drIVeN VPMa

As with any major strategic initiative, vPmA demands executive and organizational commitment. in this case, a commitment to the idea that detailed, data driven insights can improve decision-making throughout the enterprise – from strategic decisions in the executive suite to day-to-day tactical decisions on the front line. the insights require technology that can reliably clean and integrate data from disparate sources within and outside the organization, as well as a powerful, scalable analytical platform that can handle workloads of any size.

implementation can be intimidating, until it becomes clear that this can be a staged process, with each initial

Vin A Vin b

Revenue Variance (AbS)*

New Sale – List Price 23500 23300 200

Servicing & Parts – List Price 300 300 0

Less Discounts/Rebates -1500 -600 900

Customer Revenue 22300 23000 700

Less Sales Commissions -3545 -3660 115

Total net Revenue 18755 19340 585

Costs

R&D 1000 1000 0

Manufacturing Costs

materials 9000 8700 300

Labour 4200 4300 100

Rework 100 0 100

Distribution

Freight 500 500 0

Repairs 1000 0 1000

Preparation 350 200 150

Warranty

materials 150 150

Labour 150 150

other Costs 300 0 300

Total Costs 16450 14700 1750

Vin Profit 2305 4640 2335

Which of these areas

have greatest Variability, has biggest impact on profitability and is most in

need of analysis?

SALeS vARiAbiLitY

R&D, mANuFACtuRiNG CoSt vARiAbiLitY

DiStRibutioN CoSt vARiAbiLitY

SeRviCe CoSt vARiAbiLitY


9

Automotive

09.12 eb 7333


success justifying next steps. Companies can and often should start small, typically in the stage of the product lifecycle that has the greatest margin variability from viN to viN (the squeaky wheel), and/or where data is easiest to obtain and has the broadest application.

So, for example, sales is often a smart area in which to start because all of today’s incentives can create remark-able variability from car to car, location to location. in addition, sales data is often readily available and can be applied to many different aspects of the operation, such as warranty and services agreements and fraud preven-tion efforts. As companies begin the process, they can look as well at sub-segments, departments, and process areas within sales. this can continue in a serial fashion or in parallel depending on the need and resources available to tackle the issues.

WhICh data aNd froM Where?

one of the first critical questions that companies con-front is what data to use and how and where they should obtain it? General ledger often is among the first to pop-ulate a warehouse, because it tends to be a fairly simple data set and, at an aggregate level, includes revenues, costs, assets and liabilities. beyond that, companies can use a series of “screens” to decide. the screens include:

~ level of allocation – While overly simple allocations can create misleading “insights,” in the initial stages, it’s better to limit the number of allocations and become more sophisticated as time goes on, looking for the value drivers behind costs, and revenue. Key questions include: How much viN level allocation of revenue and costs needs to be calculated to get the needed answers? Can you get it directly from source systems? Do you need detailed data right away or will high-level allocations do?

~ data reusability – is the data useful in its own right and can it be easily repurposed for other reporting and analytical purposes?

~ Variability – is there enough variability from one serial number to another to be useful for increasing margins at the viN level?

~ data availability – How hard is the data to get and how easy is it to source unchanged from the source systems? in the initial stage, the easier the better.

a CustoMIzable roadMaP

though every company must tailor its implementation of vPmA to its own needs, there are certainly common threads that run across all automotive manufacturers. typically, the area with the greatest variability is sales and marketing and so it represents a logical first step and a good opportunity for seeing quick results. From there, most companies would move on to Service, Distri-bution, manufacturing, Purchasing and Design. below is a high-level look at the viN-level data needed for each of these areas.

Figure 2. vPmA Customizable Road map.

1. Sales Variability

2. Service Variability

3. Distribution Variability

4. Manufacturing Variability

5. Sourcing Variability

6. DesignVariability

OEM Supplier

Program,Platform,

Model (VIN), ProductVariances

VariancesUtilities

VariancesMaterials

VariancesLabor

VariancesLogistics

VariancesIndirect

VariancesDirect

VariancesPurchase Price

VariancesSupplier


10

Automotive

09.12 eb 7333


DAtA NeeDeD otHeR uSeS

sales ~ Detailed Sales Data – Wholesale and selling price ~ incentives Data ~ vehicle master ~ Sales Channel master ~ Customer master ~ bill of materials (bom) integration data

Global sales dashboard Global sales forecasting

service ~ Warranty Claims at viN level ~ Replacement Parts Detail ~ Labor Costs Detail ~ Parts master ~ Service technician Data (Name, Certification etc.) ~ bom integration, as serviced for each viN at the part level

early warning system Supplier cost recovery Fraud reduction Reserve forecasting

distribution ~ vehicle Shipments at viN – Ship/ocean, truck, Railroad Shipments – Shipment allocations to viN level ~ transportation Supplier master ~ bom integration - Port installed options at viN level

early warning system Supplier cost recovery Fraud reduction Reserve forecastingService cost optimization

Manufacturing ~ manufacturing Costs – Labor – materials – Fixed Costs ~ viN detail data (build data, Shift, Plant, Line etc.) ~ manufacturing Cost allocations to viN level ~ bom integration - As built bom created for each viN at the part level

manufacturing cost analysismaterial planning analysisvehicle quality traceability to manufacturing manufacturing process analysis and optimization

Purchase/ design

~ Development Costs ~ Development allocation costs for viN Nomenclature (part level) ~ viN Level Profitability Analysis can include:

– viN Level Design variability on viN Level Profitability – vehicle Product offering analysis – Accounting for Simulated vehicles

– Analysis of Costs and margins of all proposed vehicle configurations using complete design and engineering boms

Product development cost analysis


11

Automotive

09.12 eb 7333


the teradata aNalytIC eNVIroNMeNt for VPMa

to create the necessary analytic environment, a number of factors must be in place: automated data collection, centralized and sophisticated data warehousing, collab-orative information sharing, and the ability to deploy a wide range of analytics right within the warehouse.

~ teradata value Analyzer (tvA) improves profitability analytics by calculating a behavioral-based, enterprise-wide view of value — by customer, product, sales channel or organization. instead of traditional value averages and summary information, tvA uses detailed activity data to create more accurate measurements of profitability and help you make better strategic and tactical decisions for all critical enterprise functions.

~ the teradata High Performance Hardware/Database Platform automatically distributes data and balances mixed workloads in the most complex environments while eliminating data latency. Scalable to 186 petabytes, the symmetric multiprocessing and massive parallel processing – unique teradata features – mean that multiple queries race efficiently through mountains of integrated data to deliver a single, consistent and trustworthy view of the product lifecycle. because teradata embeds all analytics within the database, it can accommodate any and all queries easily, so companies can quickly identify problems or areas for improvement within their supply chain.

~ teradata’s master Data management (mDm) provides a unified view of data across multiple systems to meet the analytic needs of a global business. mDm creates

singular views of master and reference data, whether it describes customers, products, suppliers, locations, or any other important attribute.

~ the teradata manufacturing Logical Data model (mLDm) provides an industry template and data relationships for defining the company data model. the template defines direct traceability data and other lifecycle data associated with traceable components (assembly processes, manufacturing equipment, operators, time stamps, test results, shipping/transportation, service/repair, and warranty claim information). As such, the mLDm provides a comprehensive and flexible blueprint of how data is organized within a teradata Database, diagramming the relationships of data extracted from disparate sources to provide an industry-specific, enterprise view of the value chain that enables companies to achieve the deepest answers as quickly as possible. the mLDm includes general ledger and financial sub-ledger (e.g. Accounts Receivable, Accounts Payable, Fixed Assets, Purchasing, etc.) subject areas, which help speed firms’ ability to link profitability measures back to official financial statements – and ensure accuracy and consistency of results.

~ more than 3,000 Professional Services consultants – all of whom bring industry expertise and top-level experience with data warehousing and business intelligence – enable a uniquely tailored solution. teradata consultants are expert in creating a comprehensive analytical environment that makes a vPmA effort – in fact, the entire enterprise – more responsive to customer needs and changing regulatory requirements.


Automotive

09.12 eb 7333


10000 innovation Drive Dayton, oH 45342 teradata.com

the best Decision Possible is a trademark, and teradata and the teradata logo are registered trademarks of teradata Corporation and/or its affiliates in the u.S. and worldwide. teradata continually improves products as new technologies and components become available. teradata, therefore, reserves the right to change specifications without prior notice.

All features, functions, and operations described herein may not be marketed in all parts of the world. Consult your teradata representative or teradata.com for more information.

Copyright © 2012 by teradata Corporation All Rights Reserved. Produced in u.S.A.

real ChaNge, real MargIN IMProVeMeNt

increasingly, profit margins in the auto industry rely on extremely efficient operations, especially since small improvements can add millions of dollars to the bottom line. being able to fully leverage and rigorously analyze the massive volumes of viN level profitability data that accumulate every day could help companies make those improvements.

the trick is being able to conduct precision analyses of program, platform, and or viN/product profitability data that lead to top-down/middle-out/bottom-up

understanding of cost and profit variances and their root causes. the right solution can deliver reliable insights into complex variables that include component purchase price, production labor and materials, transportation, logistics, incentives, rebates, post-sales support and other attributes that influence costs, pricing and, ulti-mately, profits.

in short, with a robust solution architecture in place, vPmA integrates and transforms disparate data sources to present margin analysis views that drastically reduce the time to detection, diagnosis, and remediation of cost variances.



VIN Profitability Margin Analysis · VIN ProfItabIlIty MargIN aNalysIs ... customers to increase...

Documents

Transcript of VIN Profitability Margin Analysis · VIN ProfItabIlIty MargIN aNalysIs ... customers to increase...