Tag Archives: SAP SOA ERP ECC Netweaver

ECC 6.0 Release Notes

Release Note link for ECC 6.0 – Updated 032708

https://websmp107.sap-ag.de/~form/sapnet?_SHORTKEY=01100035870000429688&

What Data Mining Can and Can’t Do

What Data Mining Can and Can’t Do

Peter Fader, professor of marketing at University of Pennsylvania’s Wharton School, is the ultimate marketing quant—a world-class, award-winning expert on using behavioral data in sales forecasting and customer relationship management. He’s perhaps best known for his July 2000 (PDF) expert witness testimony before the U.S. District Court in San Francisco that Napster actually boosted music sales. (Napster was then the subject of an injunction for copyright infringement and other allegations brought against it by several major music companies.)

The energetic and engaging marketing professor has a pet peeve: He hates to see companies waste time and money collecting terabytes of customer data in attempts to make conclusions and predictions that simply can’t be made. Fader has come up with an alternative, which he is researching and teaching: Complement data mining with probability models, which, he says, can be surprisingly simple to create. The following is an edited version of his conversation with CIO Insight Executive Editor Allan Alter.

CIO INSIGHT: What are the strengths and weaknesses of data mining and business intelligence tools?

FADER: Data mining tools are very good for classification purposes, for trying to understand why one group of people is different from another. What makes some people good credit risks or bad credit risks? What makes people Republicans or Democrats? To do that kind of task, I can’t think of anything better than data mining techniques, and I think it justifies some of the money that’s spent on it. Another question that’s really important isn’t which bucket people fall into, but when will things occur? How long will it be until this prospect becomes a customer? How long until this customer makes the next purchase? So many of the questions we ask have a longitudinal nature, and I think in that area data mining is quite weak. Data mining is good at saying, will it happen or not, but it’s not particularly good at saying when things will happen.

Data mining can be good for certain time-sensitive things, like is this retailer the kind that would probably order a particular product during the Christmas season. But when you want to make specific forecasts about what particular customers are likely to do in the future, not just which brand they’re likely to buy next, you need different sets of tools. There’s a tremendous amount of intractable randomness to people’s behavior that can’t be captured simply by collecting 600 different explanatory variables about the customer, which is what data mining is all about.

People keep thinking that if we collect more data, if we just understand more about customers, we can resolve all the uncertainty. It will never, ever work that way. The reasons people, say, drop one cell phone provider and switch to another are pretty much random. It happens for reasons that can’t be captured in a data warehouse. It could be an argument with a spouse, it could be that a kid hurt his ankle in a ballgame so he needs to do something, it could be that he saw something on TV. Rather than trying to expand data warehouses, in some sense my view is to wave the white flag and say let’s not even bother trying.

Do you think people understand the limitations of data mining?

They don’t. And this has nothing to do with data mining or marketing, but it has a lot to do with human nature. We’re seeing the same issues arising in every area of science. As data collection technology and model-building capabilities get better, people keep thinking they can answer the previously unknowable questions. But whether it’s the causes of diseases or mechanical failure, there’s only so much we can pin down by capturing data.

Do people who use data mining packages understand enough about how to use them?

I can’t make generalizations that are too broad, but there are some people who are hammers looking for nails. They think they can answer any problem using one set of procedures, and that’s a big mistake. When you go into other domains, you need to pull out different tools. One of the things that just makes me crazy is when people misuse the kinds of statistics that are associated with data mining. A lift curve will show us how well our predicted rank order of customer propensities corresponded to their actual behavior. That’s a fine thing to do in a classification setting, but it’s not particularly diagnostic in a longitudinal setting. We want ‘when’-type diagnostics to answer ‘when’-type questions. People just aren’t looking in the right places to see whether their model’s working.

Exactly what do you mean by a propensity as opposed to a behavior?

The difference is that just because people have a tendency to do things doesn’t mean that they will. You might be someone who buys from Amazon once a month on average. Does that mean over the next 10 years, over the next 120 months, you’ll buy 120 items? No. You could go two years without buying, or you might buy five items in a given month. The amount of variability around your propensity is huge. That’s where all this randomness comes in.

Have companies hurt themselves by misusing data mining tools?

Let me start with a positive example. I have tremendous admiration for what actuaries do, and therefore for the way insurance companies deal with their customers. Actuaries will not look at all your characteristics and say when you will die. They’ll simply come up with a probabilistic statement about the likelihood that someone with your characteristics will die, or what percent of people who share characteristics will live to be 70. They understand that it’s pretty much impossible to make statements about each and every policyholder.

Now, carry that over to the marketing world. Lots of firms talk about one-to-one marketing. I think that’s a real disservice to most industries. One-to-one marketing only works when you have a very deep relationship with every customer. So one-to-one marketing works great in private wealth management, or in a business-to-business setting where you meet with the client at least once a month, and understand not just their business needs but what’s going on in their life. But in areas approaching a mass market, where you can’t truly distinguish each individual, you just have a bunch of people and a bunch of characteristics that describe them. Then the notion of one-to-one marketing is terrible. It will do more harm than good, because the customers will act more randomly than you expect, and the cost of trying to figure out what specific customers will do far outweighs the benefits you could get from that level of detail.

It’s very hard to say who’s going to buy this thing and when. To take that uncertainty and square it by looking across two products, or to raise it to the nth power by looking across a large portfolio of products, and say “these two go together,” and make deterministic statements as opposed to talking about tendencies and probabilities, can be very, very harmful. It’s much more important for companies to come up with appropriate groupings of similar people, and make statements about them as a group.

I don’t want to pick on Amazon in particular; they really tout the capabilities of their recommendations systems. But maybe this customer was going to buy book B anyway, and therefore all the recommendations were irrelevant. Or maybe they were going to buy book C, which would have been a higher-margin item, so getting them to buy book B was a mistake. Or maybe they’re becoming so upset by irrelevant recommendations that they’re going away entirely. I don’t want in any way to suggest that cross-selling shouldn’t be done, but what I’m suggesting is that the net gains from it are less than people might think. It often can’t justify the kinds of investments that firms are making in it.

You’ve been championing the use of probability models as an alternative to data mining tools. What do you mean by a probability model?

Probability models are a class of models that people used back in the old days when data weren’t abundantly available. These modeling procedures are based on a few premises: People do things in a random manner; the randomness can be characterized by simple probability distributions; and the propensities for people to do things vary-over time, across people, across circumstances. Probably the best known example is survival analysis, which stems largely from the actuary sciences. It’s also used in manufacturing. You put a bunch of lightbulbs on a testing board and see how long they last. In many ways, that’s what I suggest we do with customers. We’re not going to make statements about any one lightbulb, just like we shouldn’t make statements about any one customer. We’ll make collective statements about how many of these bulbs will last for 1,000 hours. It turns out that the analogy of survival analysis in manufacturing and actuarial and life sciences carries over amazingly well to customers. A lot of managers would bristle at the idea, but I think that metaphor is far better than all this excessive customization and personalization that’s been going on. Customers are different from each other just as lightbulbs are, but for reasons that we can’t detect, and reasons that we’ll have a very hard time taking advantage of.

What kind of problems can probability models solve?

Probability models have three basic building blocks: One is timing-how long until something happens. One is counting-how many arrivals, how many purchases or whatever will we see over a given period of time. And choice-given an opportunity to do something, how many people will choose to do it. That’s it. Most real-world business problems are just some combination of those building blocks jammed together. For instance, if you’re modeling the total time someone spends at a Web site during a given month, you might model it as counting-timing: a count model for the number of visits and a timing model for the duration of each one. My view is that we can very easily build simple models in Excel for each of those three things. A lot of people have built this kind of model over the years, and have tested them very carefully, in some cases putting them directly up against data mining procedures. They have found that their capabilities are not only astonishing, but far better than data mining. If you think about all the different ways you can combine timing, counting and choice, you can tell all kinds of interesting stories about different business situations.

How would you use these models to identify the most profitable customers or calculate customer lifetime value?

This is where probability models can come together beautifully with data mining. We can use these models to come up with very accurate forecasts about how long this customer will stay with us or how many purchases they’ll make over the next year. So use the basic probability model to capture the basic behavior and then bring in data mining to understand why groups of customers with different behavioral tendencies are different from each other. You see, behavior itself is not perfectly indicative of the true underlying propensities, which is what managers really want to know. And so we build a probability model that helps us uncover the propensities, and then we can take those propensities-the customer’s tendency to do something quickly or slowly or to stay online a long time or not-and throw those into the data mining engine to explain those as a function of the 600 variables. You’ll find a much more satisfying and fruitful explanation in terms of being able to profile new customers and understand the likely actions of current ones. When it comes to taking the outputs of the probability model and understanding them, data mining procedures are the best way to go.

Can probability models capture longitudinal or predictive information?

Very, very well. In fact, one of my favorite examples is looking at customer retention and return. You can do it simply without any explanatory variables at all. The irony is that if you bring in explanatory variables, in many cases the model will do worse. This makes managers crazy. They need to know why these people are different. But if you’re bringing in explanatory variables that aren’t really capturing the true underlying reasons for the differences, then you’re just adding noise to the system. Your ability to come up with an accurate forecast for each group might actually be worse.

So you use data mining to help you figure out why those propensities exist.

That’s right. The key is to explain the propensities-the tendency to do things-as opposed to the behavior itself.

You said these models can be built in a spreadsheet. It doesn’t sound like you have to be a high-powered Ph.D. to create them.

Of course, that never hurts. But yes, these models are far more transparent to managers because the stories they tell are simpler, the demands on the data are far simpler, and the implementation is much easier. So what I like to do is to start people out with some of the really simple models and get people hooked. Show me how many customers we’ve had in year one, two, three, four, five, and I’ll tell you how many we’ll have in year nine and ten before we even bring in all the explanatory variables that data miners want to do.

If companies move to using models more, what data can they stop collecting and what data will they still need to collect?

Ultimately, what matters most is behavior. That shouldn’t be a controversial statement, but a tremendous amount of the data that’s being collected is nonbehavioral. Data on demographics, psychographics, socioeconomics and even consumer attitudes can not only waste servers and storage space but can actually make the models perform worse. I have lots of examples of data that leads to tremendously misleading inferences about what really matters.

So behavior’s what matters most, and even then you can often summarize behavior in very simple ways. For instance, in many cases we find that you don’t even need to know exactly when each transaction occurred to make forecasts. Simply give me summary statistics, such as frequency. Just tell me when was the last time they made a purchase and how many purchases they made over the last year, and that will explain pretty much everything worth explaining. You mentioned that a CIO Insight survey found that the amount of customer data companies are collecting is increasing at an annual rate of about 50 percent. I would claim that most of that 50 percent is completely wasted. It’s one thing to have 50 percent more data, but you’re certainly not getting 50 percent more knowledge or insight. In fact, you could be doing more harm than good, because you’re crowding out the few variables that really do matter.

What companies have done a good job of using models this way?

I wish I could put some companies on a pedestal, but I’ve never seen a firm really embrace this stuff as fully as I’d like. And I’ll tell you why: It’s really my fault. It’s the fault of academics who spend almost no time teaching these procedures. Most firms just aren’t getting exposed to this stuff.

What should CIOs do to help their companies use analytical and modeling tools appropriately?

For one thing, remember that more is not necessarily better. CIOs often push back on analytics because of cost, but if someone could give them all this additional data for free, they’d take it. That’s often wrong. Additional data can actually harm you because you’re going to start capturing random, quirky, idiosyncratic things that aren’t related to the true underlying propensities. The flipside is that a few simple measures that have been around forever, like recency and frequency, are all you need. If you can use data collection technology to get those measures more accurately or on a timelier basis, then maybe it’s worth the investment. Second, remember that some surprisingly simple models can take you incredibly far if you’re willing to not worry so much about drivers. Don’t bother looking for the drivers; first, capture the behavior. So start simple; that often means start in Excel. You’d be amazed at how much you can accomplish without even having to leave the spreadsheet environment.

Copyright (c) 2007 Ziff Davis Media Inc. All Rights Reserved.



http://www.sapdevelopment.co.uk

This site has a useful compilation of SAP information for ABAP and BW developers.


Gueldenpfennig’s SAP PDFs

Volker ‘s Excellent repository of SAP pdf’s, principally for R/3. Volker is an SAP hardware guru… and has previously consulted for my prior employer http://bradycorp.com. Last year, he explained to me how to pronounce his name… instantly forgotten. Now, close your eyes and spell out Gueldenpfennig…

30 Things You Didn’t Know You Could Do on the Web

This PC World article gives a number of interesting findings, such as web-based book publishing for $8.50 (see below), even how to charter your own jet. Some of it is actually quite interesting and useful, giving a sense of the breadth and creativity of web-based solutions that have appeared in recently on the web.

Trend Buzz, Reality TV, and more…
Free Tech Help, Parenting Skills, and more…
Charter Jet, Get Surreal, and more…
Desktop Info: Webify Your Desktop
Web APIs: Make the Big Sites Work for You
Be Your Own Shock Jock
Make $$ From Your Site

Publish Your Masterpiece
So you’ve completed your 1000-page opus but can’t find a publisher? Do it yourself on Lulu.com. Unlike most self-publishing sites, Lulu charges no up-front fees and requires no minimum orders. Just upload a word processing document and follow a wizard to choose the book’s size, format, cover art, and price or commission. Lulu takes 20 percent of the cover price. You can sell your book via Lulu, Amazon.com, Barnes & Noble, or your own Web site. If you order copies for yourself, you pay only binding and printing costs–around $8.50 for a standard 200-page paperback.

Listing of All SAP .pdf Files Relating to R/3 System Config

llent Resoure – listing of all the .PDCF files on that help.sap.com. PDF Index
To find a specific PDF file on the CD, choose Edit -> Find (on This Page)… in the menu bar of the Internet Explorer.

Path
ABAP/4 OLE Automation Controller
data/pdf/BCFESDE6/BCFESDE6.pdf
ABAP Programming (BC-ABA)
data/pdf/BCABA/BCABA.pdf
Activity-Based Costing
data/pdf/COALE/COALE_ALE_171.pdf
Activity-Based Costing (CO-OM-ABC)
data/pdf/COOMABC/COOMABC.pdf
Actual Costing / Material Ledger
data/pdf/COPCACT/COPCACT.pdf
Advance Payments
data/pdf/PYINT/PYSOME_ADVANCE.pdf
ALE in Profit Center Accounting (EC-PCA-TL)
data/pdf/ECPCA/ECPCA_ALE_154.pdf
ALE Introduction and Administration
data/pdf/BCMIDALEIO/BCMIDALEIO.pdf
ALE Programming Guide
data/pdf/BCMIDALEPRO/BCMIDALEPRO.pdf
ALE Quick Start
data/pdf/CABFAALEQS/CABFAALEQS.pdf
ALE QuickStart for Distributed HR
data/pdf/CABFAALEHR/CABFAALEHR_ALE_QS.pdf
ALE Scenarios in Asset Accounting
data/pdf/FIAA/FI-AA-ALE.pdf
ALE Scenarios in Personnel Cost Planning
data/pdf/PACMCP/PACMCP_ALE_015.pdf
ALV Gird Control (BC-SRV-ALE)
data/pdf/BCSRVALV/BCSRVALV.pdf
APIs for Logistics
data/pdf/LOAPI/LOAPI.pdf
Archiving Application Data (CA-ARC)
data/pdf/CAARC/CAARC.pdf
Archiving Application Data (FI)
data/pdf/BCBMTWFMFI/BCBMTWFMFI.pdf
Argentina
data/pdf/CAINTAR/CAINTAR.pdf
Arrears Processing for Deductions
data/pdf/PYINT/PYSOME_ARREARS.pdf
Assembly-to-order (LO-ASM)
data/pdf/LOASM/LOASM.pdf
Asset Accounting (FI-AA)
data/pdf/FIAA/FIAA.pdf
Asset Information for Intranets (FI-AA)
data/pdf/FIAA/FI-AA-IAC.pdf
Availability Check and Requirements in Sales and Distribution
data/pdf/SDBFAC/SDBFAC.pdf
Balance Sheet Valuation (MM-IM-VP)
data/pdf/MMIVMBVAL/MMIVMBVAL.pdf
Bank Accounting (FI-BL)
data/pdf/FIBL/Bankbuchhaltung.pdf
Banks (PA-PA)
data/pdf/PAPA/PAPA_ALE_048.pdf
BAPI ActiveX Control
data/pdf/BCFESDE8/BCFESDE8.pdf
BAPI Programming Guide (CA-BFA)
data/pdf/CABFABAPIREF/CABFABAPIPG.pdf
BAPI User Guide (CA-BFA)
data/pdf/BCMIDAPII/CABFAAPIINTRO.pdf
Basic Data (QM-PT-BD)
data/pdf/QMPTBD/QMPTBD.pdf
Basic Functions (SD-FT-PRO)
data/pdf/SDFTPRO/SDFTPRO.pdf
Basic Functions
data/pdf/TRTMBF/TRTMBF.pdf
Basic Functions and Master Data in SD Processing (SD-BF)
data/pdf/SDBF/SDBF.pdf
Basis (BC)
data/pdf/CAARCBC/CAARCBC.pdf
Basis
data/pdf/MYSAP/SR_BC.pdf
Batch Management (LO-BM)
data/pdf/LOBM/LOBM.pdf
BC – ABAP Dictionary
data/pdf/BCDWBDIC/BCDWBDIC.pdf
BC ABAP Workbench Tools
data/pdf/BCDWBTOO/BCDWBTOO.pdf
BC ABAP Workbench Tutorial
data/pdf/BCDWBTUT/BCDWBTUT.pdf
BC Basis Programming Interfaces
data/pdf/BCDWBLIB/BCDWBLIB.pdf
BC – Business Workplace
data/pdf/BCSRVOFC/BCSRVOFC.pdf
BC – Central Maintenance and Transport Objects
data/pdf/BCDWBCUSCMTO/BCDWBCUSCMTO.pdf
BC Data Modeler
data/pdf/BCDWBTOODMO/BCDWBTOODMO.pdf
BC Documentation of System Objects
data/pdf/BCDOCDTL/CADOCDT1.pdf
BC Extended Applications Function Library
data/pdf/BCDWBLIB2/BCDWBLIB2.pdf
BC – Namespaces and Naming Conventions (BC-CTS-NAM)
data/pdf/BCCTSNAME/BCCTSNAME.pdf
BC R/3 Database Guide:DB2/400
data/pdf/BCDBDB4DBA/BCDBDB4DBA.pdf
BC R/3 Database Guide: DB2 Universal Database for UNIX & Windows
data/pdf/BCDBDB6DBA/BCDBDB6DBA.pdf
BC R/3 Database Guide: Informix
data/pdf/BCDBINFDBA/BCDBINFDBA.pdf
BC SAPconnect
data/pdf/BCSRVCOM/BCSRVCOM.pdf
BC SAP Graphics: Programming Interfaces
data/pdf/BCFESGRA2/BCFESGRA2.pdf
BC SAP Graphics: User’s Guide
data/pdf/BCFESGRA1/BCFESGRA1.pdf
BC – SAP Printing Guide
data/pdf/BCCCMPRN/BCCCMPRN.pdf
BC SAPscript Raw Data Interface
data/pdf/BCSRVSCRRDI/BCSRVSCRRDI.pdf
BC SAPscript: Printing with Forms
data/pdf/BCSRVSCRPROG/BCSRVSCRPROG.pdf
BC – SAP Style Guide
data/pdf/BCDWBERG/BCDWBERG.pdf
BC Style and Form Maintenance
data/pdf/BCSRVSCRFORM/BCSRVSCRFORM.pdf
BC System Services
data/pdf/BCCSTADM/BCCSTADM.pdf
BC The SAP Communications Server
data/pdf/BCSRVCS/BCSRVCS.pdf
BC – Word-Processing in the SAPscript Editor
data/pdf/BCSRVSCREDIT/BCSRVSCREDIT.pdf
Benefits (PA-BN)
data/pdf/PABN/PABNXX.pdf
Billing (SD-BIL)
data/pdf/SDBIL/SDBIL.pdf
Billing Plan (SD-BIL-IV)
data/pdf/SDBIL/SDBIL2.pdf
Brazil
data/pdf/CAINTBR/CAINTBR.pdf
Business Area (FI)
data/pdf/FIBUSI/FIBUSI.pdf
Business Configuration Sets (BC-CUS)
data/pdf/BCCUSBCS/BCCUSBCS.pdf
Business Document Service (BC-SRV-BDS)
data/pdf/BCSRVBDS/BDS_STRUCTURE.pdf
Business Partner Master Data (LO-MD-BP)
data/pdf/LOBP/LOBP.pdf
CA – Cross-Application Components: Workflow Scenarios
data/pdf/BCBMTWFMCA/BCBMTWFMCA.pdf
CA – Cross-Application Functions
data/pdf/CAGTFADM/CAGTFADM-CA.pdf
CAD Interface (CA-CAD)
data/pdf/CACAD/CACAD.pdf
CA – Drilldown Reporting
data/pdf/CAREP/CAREP.pdf
CA Extended Table Maintenance
data/pdf/BCDWBCUSTMECA/BCDWBCUSTME.pdf
CA – Message Control (CA-GTF-BS)
data/pdf/CAGTFBSMC/CAGTFBSMC.pdf
Capacity Evaluation (PP-CRP-ALY)
data/pdf/PPCRPALY/PPCRP_ALY.pdf
Capacity Leveling (PP-CRP-LVL)
data/pdf/PPCRPLVL/PPCRP_LVL.pdf
Capacity Leveling in PP-SOP and LO-LIS-PLN
data/pdf/PPCRPSOP/PPCRP_SOP.pdf
Capacity Planning in Customer Service and Plant Maintenance
data/pdf/PPCRPPMSM/PPCRP_PMSM.pdf
Capacity Planning in Long-term Planning
data/pdf/PPCRPLTP/PPCRP_LTP.pdf
Capacity Planning in MPS and MRP
data/pdf/PPCRPMRPMPS/PPCRP_MRPMPS.pdf
Capacity Planning in Process Industries
data/pdf/PPCRPPPPI/PPCRP_PPPI.pdf
Capacity Planning in Repetitive Manufacturing
data/pdf/PPCRPREM/PPCRP_REM.pdf
Capacity Planning in Sales and Distribution
data/pdf/PPCRPSD/PPCRP_SD.pdf
Capacity Planning in Shop Floor Control (PP-SFC)
data/pdf/PPCRPSFC/PPCRP_SFC.pdf
Capacity Planning in the Project System
data/pdf/PPCRPPS/PPCRP_PS.pdf
Cash Management (TR)
data/pdf/TRCM/TRCM.pdf
CATT: Computer Aided Test Tool (BC-CAT-TOL)
data/pdf/BCCATTOL/BCCATTOL.pdf
CATT: Enhanced Mode (BC-CAT-TOL)
data/pdf/BCCATTOL/CACATTOL.pdf
CBI Question & Answer Database
data/pdf/SVASAQADBCBI/QADBCBI.pdf
Central Adress Management (BC-SRV-ADR)
data/pdf/BCSRVADGUID/BCSRVADGUID.pdf
Change and Transport System – Overview (BC-CST)
data/pdf/BCCTS/BCCTS.pdf
Changing the SAP Standard (BC)
data/pdf/BCDWBCEX/BCDWBCEX.pdf
Characteristics (CA-CL-CHR)
data/pdf/CACLCHR/CACLCHR.pdf
Check Management
data/pdf/FIBP/FI-BL-BM-CM.pdf
Chile
data/pdf/CAINTCL/CAINTCL.pdf
China
data/pdf/CAINTCN/CAINTCN.pdf
Claim Management
data/pdf/PSCLM/PSCLM.pdf
Classification System (CA-CL)
data/pdf/CACL/CACL.pdf
Client Copy and Support
data/pdf/BCCTSCCO/BCCTSCCO.pdf
Closing and Reporting (FI)
data/pdf/FIGLCR/FIGLCR.pdf
Collaborative Engineering & Project Management
data/pdf/PSCOLL/PSCOLL.pdf
Computing Center Management System (BC-CCM)
data/pdf/BCCCM/BCCCM.pdf
Concept Check Tool
data/pdf/SVASACCT/SVASACCT.pdf
Coding Block
data/pdf/BCBMTOM/AC_COB_ALE_016.pdf
CO External Data Transfer
data/pdf/CO/EXTERNALDATA.pdf
Colombia
data/pdf/CAINTCO/CAINTCO.pdf
Commitments Management (CO)
data/pdf/COKAO/COKAO.pdf
Communication / Printing (SD-FT-COM)
data/pdf/SDFTCOM/SDFTCOM.pdf
Compensation Management (PA-CM)
data/pdf/PACM/PACM.pdf
Components of the Logistics Information System (LIS)
data/pdf/LOLIS/LOLIS_KOMPONENTEN DES LIS.pdf
Consolidation (EC-CS)
data/pdf/ECCS/ECCS.pdf
Controlling (CO)
data/pdf/CAARCCO/CAARCCO.pdf
Controlling (CO)
data/pdf/MYSAP/SR_CO.pdf
Controls Tutorial (BC-CI)
data/pdf/BCCICTUT/BCCICTUT.pdf
Country Versions
data/pdf/MYSAP/SR_CACSR.pdf
Consolidation (FI-LC)
data/pdf/FILC/FILC.pdf
Configuration Management (LO-CM)
data/pdf/LOCM/LOCM.pdf
Confirmation
data/pdf/PSCON/PSCON.pdf
Connecting to External Time Management Systems
data/pdf/PT-BFA/PT-BFA_ALE_168.pdf
Connecting to SAP CAMPBELL Personnel Administration
data/pdf/PAPAXXET/PAPAXXET_ALE_088.pdf
Connection with External Time Recording Systems
data/pdf/PT-BFA/PT-BFA_ALE_034.pdf
Consumption-Based Planning (MM-CBP)
data/pdf/MMCBPCBP/MMCBPCBP.pdf
Controlling (CO)
data/pdf/CO/CO.pdf
Conversion to the Euro in Human Resources
data/pdf/HREURO/HR-EURO.pdf
Cost Center Accounting (CO-OM-CCA)
data/pdf/COALE/COALE_ALE_060.pdf
Cost Center Accounting (CO-OM-CCA)
data/pdf/COOMCCA/COOMCCA.pdf
Costs
data/pdf/PSCOS/PSCOS.pdf
Country Versions
data/pdf/CAINT/CAINT.pdf
Credit and Risk Management (SD-BF-CM)
data/pdf/SDBFCM/SDBFCM.pdf
Cross-Application Components (CA)
data/pdf/CAARCCA/CAARCCA.pdf
Cross-Application Mass Maintenance (CA-GTF-MS)
data/pdf/CAGTFMS/CAGTFMS.pdf
Cross-System Planning Situation (CA-BFA)
data/pdf/CA-BFA/CA-BFA-IS-028.pdf
Customer Service (CS)
data/pdf/CS/PMSMASC.pdf
Customer Service (CS)
data/pdf/MYSAP/SR_CS.pdf
Customer Service Processing (BC-SLS-OA)
data/pdf/SDSLSOASCO/SDSLSOASCO.pdf
Customizing (BC-CUS)
data/pdf/BCBECUSIMG/BCBECUSIMG.pdf
Customizing System Settings (BC-CUS)
data/pdf/BCBECUSIMG/BCBECUSIMG-SYSTEMEINSTELLUNG.pdf
Customizing Cross-System Tools
data/pdf/BCDWBCUSCST/BCDWBCUSCST.pdf
Cross-Application Time Sheet (CA-TS)
data/pdf/CATS/CATS.pdf
Dangerous Goods Management (EHS-DGP)
data/pdf/EHSDGP/LOEHSDGP.pdf
Data Retention Tool (DART)(CA-GTF-DRT)
data/pdf/CAGTFDART/CAGTFDART.pdf
Database Administration (Oracle) with SAPDBA
data/pdf/BCDBORADBA/BCDBORADBA.pdf
Dates
data/pdf/PSDAT/PSDAT.pdf
DCOM Connector Logon Component
data/pdf/BCFESLOG/BCFESLOG.pdf
Decentralized Warehouse Management (LE-IDW)
data/pdf/LEIDW/LEIDW.pdf
Defects Recording (QM-IM-RR-DEM)
data/pdf/QMIMDEF/QMIMDEF.pdf
Demand Management (PP-MP-DEM)
data/pdf/PPMPDEM/PPMPDEM.pdf
Derivatives
data/pdf/TRTMDE/TRTMDE.pdf
Desktop Office Integration (BC-CI)
data/pdf/BCCIOFFI/BCCIOFFI.pdf
Developing an Infotype (Planning)
data/pdf/PAXX/PYINT_INFOTYP_PD.pdf
Developing an Infotype in Personnel Administration
data/pdf/PAXX/PYINT_INFOTYP.pdf
Direct and Indirect Quotation for Exchange Rates
data/pdf/CAMENG/PREIS-UND MENGENNOTIERUNG.pdf
Distributed Contracts (MM-PUR, MM-SRV)
data/pdf/CABFAISMM/CABFAISMM.pdf
Distributed Profitability Analysis (CO-PA)
data/pdf/COPA/COPA_ALE_064.pdf
Distribution Resource Planning (PP-SOP-DRP)
data/pdf/PPSOPDRP/PPSOPDRP.pdf
Documents
data/pdf/PSDOC/PSDOC.pdf
Document Management
data/pdf/CADMS/CADMS.pdf
Documentary Payments (SD-FT-LOC)
data/pdf/SDFTLOC/SDFTLOC.pdf
Documentation Maintenance
data/pdf/PAXX/PYINT_PDSY.pdf
Dynamic Modification of the Inspection Scope
data/pdf/QMQCDYN/QMQCDYN.pdf
Early Warning System: Overview
data/pdf/LOLIS/LOLIS_FRÜHWARNSYSTEM.pdf
Editor for Functions and Operations (PY-XX-TL)
data/pdf/PAXX/PYINT_FUNKTION.pdf
Editor for Personnel Calculation Rules (PY-XX-TL)
data/pdf/PAXX/PYINT_REGEL.pdf
Editor for Personnel Calculation Schemas (PY-XX-TL)
data/pdf/PAXX/PYINT_SCHEMA.pdf
EH&S Environment, Health & Safety
data/pdf/EHSSAF/CAGTFADM-EHS.pdf
Electronic Account Statement (FI-BL)
data/pdf/FIBLEBS/FIBLEBS.pdf
Electronic Data Interchange / IDoc Interface (SD-EDI)
data/pdf/SDEDI/SDEDI.pdf
Employee Self-Service
data/pdf/CAESS/ESSIAC.pdf
Employment and Salary Verification in the Internet (PA-PA-US)
data/pdf/PYUS/PAPAUS_WS01000045.pdf
Enhancements, Modifications… (CA-BFA)
data/pdf/CABFABAPIREF/CABFABAPIMOD.pdf
Engineering Change Management
data/pdf/LOECH/LOECH.pdf
Engineering Workbench (PP-BD)
data/pdf/PPBDEWB/PPBDEWB.pdf
Entering Measurement and Counter Readings in the Internet
data/pdf/PMEQMEQ/PMEQMEQ-SF-IAC.pdf
Entering Planning Data in the Workflow (CO-PA)
data/pdf/COPA/WFT200_000.pdf
Enterprise Controlling (EC)
data/pdf/CAARCEC/CAARCEC.pdf
Enterprise Controlling (EC)
data/pdf/MYSAP/SR_EC.pdf
Enterprise Organization (CO)
data/pdf/CO/COUORG.pdf
Enterprise Modelling – Consultant’s Handbook
data/pdf/SVASAORG/SVASAORG.pdf
Environment, Health & Safety
data/pdf/MYSAP/SR_EHS.pdf
European Monetary Union: Euro (CA-EUR)
data/pdf/CAEUR/CAEUR.pdf
Evaluating the Payroll Results using Infotypes or the Logical Database
data/pdf/PYINT/PYINT_REPORTING.pdf
Executive Information System and Business Planning
data/pdf/ECEIS/ECEIS.pdf
Expert Mode
data/pdf/BCBMTOMEXP/BCBMTOMEXP.pdf
External Data Transfer
data/pdf/CADATA/CADATA.pdf
External Services Management (MM-SRV)
data/pdf/MMSRV/MMSRV.pdf
External Supply of the Time Sheet
data/pdf/PT-BFA/PT-BFA_ALE_169.pdf
Features Editor
data/pdf/PAXX/PYINT_MERKMAL.pdf
FI Accounts Receivable and Accounts Payable
data/pdf/FIBP/FIBP.pdf
Financial Accounting (FI)
data/pdf/CAARCFI/CAARCFI.pdf
Financial Accounting (FI)
data/pdf/MYSAP/SR-FI.pdf
Financial Accounting – General Topics
data/pdf/FITX/FITX.pdf
FI Financial Accounting: Data Transfer Workbench
data/pdf/CAGTFADM/CAGTFADM-FI.pdf
Financial Information System (FI)
data/pdf/FIGLIS/FIGLIS.pdf
FI/SD – Credit Management/Risk Management
data/pdf/FIARCR/FIARCR.pdf
FI – Special Purpose Ledger
data/pdf/FISL/FISL.pdf
Flexible General Ledger (FI-GL)
data/pdf/FIGLMD/FIGLMD.pdf
Forecasting (LO-PR)
data/pdf/LOPR/LOPR.pdf
Foreign Exchange
data/pdf/TRTMFX/TRTMFX.pdf
Foreign Trade / Customs (SD-FT)
data/pdf/SDFT/SDFT.pdf
Funds Management
data/pdf/FIFM/FIFM.pdf
General Ledger Accounting (FI-GL)
data/pdf/FIGL/FIGL.pdf
General Report Selection
data/pdf/BCSRVREP/BCSRVREP.pdf
Generic Business Tools for Application Developers (BC-SRV-GBT)
data/pdf/BCSRVGBT/BCSRVGBT_STRUCTURE.pdf
Generic Object Service (BC-SRV-GBT)
data/pdf/BCSRVOBS/BCSRVOBS.pdf
Getting Started
data/pdf/BCDOCGETTING/BCDOCGETTING.pdf
Goods Receipt Process for Inbound Deliveries
data/pdf/LEWE/LEWE.pdf
GR/IR Account Maintenace (MM-IM-VP)
data/pdf/MMIVWERE/MMIVWERE.pdf
Handeling Unit Management (LO-HU)
data/pdf/LOHU/LOHU.pdf
How do I use the Reuse Library?
data/pdf/BCDWBUTLRELIB/BCDWBUTLRELIB.pdf
Human Resources (HR)
data/pdf/CAARCHR/CAARCHR.pdf
Human Resources (HR)
data/pdf/MYSAP/SR_HR.pdf
HR Form Editor (PY-XX-TL)
data/pdf/PAXX/PYINT_FORMS.pdf
HR Forms Workplace (PY-XX-FO)
data/pdf/PYXXFORM/PYINT_FORMBUILDER.pdf
HR Funds and Position Management (PA-PM)
data/pdf/PAPM/HRPOS.pdf
HR – Human Resource Management
data/pdf/CAGTFADM-PA/CAGTFADM-PA.pdf
HR Infotypes
data/pdf/HRINF/HRINF.pdf
HR Tools (PY-XX-TL)
data/pdf/PAXX/PAXX.pdf
HTMLBusiness Language Reference
data/pdf/BCFESITSHBLR/BCFESITSHBLR.pdf
Hypertext Structure Maintenance (BC-DOC-DTL)
data/pdf/BCDOCDT2/BCDOCDT2.pdf
IACs for External Services Management (MM-SRV)
data/pdf/MMSRV/MMSRV_IAC.pdf
IACs in Foreign Trade
data/pdf/SDFTIMP/SDFTIAC.pdf
IDoc Class Library (BC-FES-AIT)
data/pdf/BCFESDED/BCFESDED.pdf
IDoc Connector for XML Component (BC-FES-AIT)
data/pdf/BCFESIDOCXML/BCFESIDOCXMLSTR.pdf
IDoc Interface: EDI
data/pdf/BCSRVEDISC/CAEDISCAP_STC.pdf
IDoc Interface / Electronic Data Interchange (BC-SRV-EDI)
data/pdf/BCSRVEDI/CAEDI.pdf
IM – Investment Management
data/pdf/CAARCIM/CAARCIM.pdf
Implementation Assistant
data/pdf/SVASAIA/SVASAIA_STRUKTUR.pdf
Import Basis Module (SD-FT-IMP)
data/pdf/SDFTIMP/SDFTIMP.pdf
Incentive Wages: Overview
data/pdf/PTRCIW/PYSOME_INCENTIVE_WAGES.pdf
Inflation Accounting
data/pdf/CAINTINFLATION/CAINTINFLATION.pdf
Information System
data/pdf/TRTMIS/TRTMIS.pdf
Inspection Data Interface (QM-IDI)
data/pdf/QMIFIDI/QMIFIDI.pdf
Inspection Lot Completion (QM-IM-UD)
data/pdf/QMIMUD/QMIMUD.pdf
Inspection Lot Creation (QM-IM-IL)
data/pdf/QMIMIL/QMIMIL.pdf
Inspection Planning (QM-PT-IP)
data/pdf/QMPTIP/QMPTIP.pdf
Inspection Planning with the Engineering Workbench
data/pdf/QMPTWB/QMPTWB.pdf
Integration with SAP Business Workflow
data/pdf/BCBMTOM99/BCBMTOM99.pdf
Interfaces to Accounting (AC)
data/pdf/CAGTFACINT/CAGTFACINT.pdf
Interface Toolbox in Human Resources (PX-XX-TL)
data/pdf/PAXX/PYSOME_INTERFACE.pdf
Interfaces to the Project System
data/pdf/PSST/PSSTINT.pdf
Internal Orders (CO-OM-OPA)
data/pdf/COOMOPA/COOMOPA.pdf
Internal Service Request
data/pdf/COOMOPA/INTERNALREQUEST.pdf
Internet Application Development with Flow Flies: Reference
data/pdf/BCFESITSFLOW/BCFESITSINTPLAT_DESC.pdf
Internet Application Development with Flow Flies: Tutorial
data/pdf/BCFESITSFLOW/BCFESITSINTPLAT_TUTORIAL.pdf
Internet Time Sheet
data/pdf/CATS/CA-TS-IAC-001.pdf
Introduction to BPML
data/pdf/SVASABPML/SVASABPML-STRUKTUR.pdf
Introduction: Overview
data/pdf/LOLIS/LOLIS_LISBIBLIOTHEK.pdf
Introduction to Data Archiving (CA-ARC)
data/pdf/CAARC/CAARC_INTRO.pdf
Inventory Management and Physical Inventory (MM-IM)
data/pdf/MMIM/MMIM.pdf
Inventory Sampling (MM-IM-PI)
data/pdf/MMIM/MMIM_IS.pdf
Investment Management (IM)
data/pdf/IM/IM.pdf
Investment Management (IM)
data/pdf/MYSAP/SR_IM.pdf
ITS Administration Guide
data/pdf/BCFESITSADMIN/CABFAWEBADMIN.pdf
ITS Implementation Models
data/pdf/BCFESITSIACPROG/BCFESITSIACPROG.pdf
ITS System Templates
data/pdf/BCFESITSTEMP/CABFAWEBITSTEMP.pdf
ITS User Management
data/pdf/BCFESITSUM/BCFESITSUM.pdf
Job Search
data/pdf/BCCCM/WA_JOBSEARCH.pdf
KANBAN on the Internet (PP-KAB-CRL)
data/pdf/PPKAB/PPKAB_IAC.pdf
Language Transport (BC-CTS-LAN)
data/pdf/BCCTSLAN/LAN_COMP.pdf
Legal Control (SD-FT-CON)
data/pdf/SDFTCON/SDFTCON.pdf
LE – Logistics Execution
data/pdf/CAARCLE/CAARCLE.pdf
Library of ALE Business Processes
data/pdf/CABFAIS/CABFAIS.pdf
Line Design
data/pdf/PPFLW/PPFLW.pdf
Loans
data/pdf/PYINT/PYSOME_LOAN.pdf
Loans Management (TR-LO)
data/pdf/TRLO/TRLO.pdf
LO – General Logistics Workflow Scenarios
data/pdf/BCBMTWFMLO/BCBMTWFMLO.pdf
Logistics Execution (LE)
data/pdf/MYSAP/SR_LE.pdf
Logistics – General (LO)
data/pdf/CAARCLO/CAARCLO.pdf
Logistics General (LO)
data/pdf/MYSAP/SR_LO.pdf
Logistics Information System (LO-LIS)
data/pdf/LOLIS/LOLIS.pdf
Logistics Invoice Verification (MM-IV-LIV)
data/pdf/MMIVLIV/MMIVLIV.pdf
LO Logistics General
data/pdf/CAGTFADMLO/CAGTFADMLO.pdf
Long-Term Planning (PP-MP-LTP)
data/pdf/PPMPLTP/PPMPLTP.pdf
Maintenance Bills of Material (PM-EQM-BM)
data/pdf/PMEQMBM/PMEQMBM.pdf
Maintenance Planning
data/pdf/PMPRMMP/PMPRMMP.pdf
Maintenance Task Lists
data/pdf/PMPRMTL/PMPRMTL.pdf
Manager’s Desktop (PA-MA)
data/pdf/PAMA/PAMA.pdf
Managing Special Stocks (MM-IM)
data/pdf/MMIM/MMIM_ST.pdf
Market Risk Management
data/pdf/TRMRM/TRMRM.pdf
Master Data Distribution (Human Resources)
data/pdf/CABFAALEHR/CABFAALEHR_ALE_VERT.pdf
Material
data/pdf/PSMAT/PSMAT.pdf
Materials Management (MM)
data/pdf/CAARCMM/CAARCMM.pdf
Materials Management (MM)
data/pdf/MYSAP/SR_MM.pdf
Material Master
data/pdf/LOMDMM/LOMDMM.pdf
Material Requirements Planning
data/pdf/PPMRP/PPMRP.pdf
Memory Management (BC-CST-MM)
data/pdf/BCCSTMM/BCCSTMM.pdf
Messages 6.2 (BC)
data/pdf/BCDBADAMELDUNGdata/BCDBADAMELDUNGEN_62.pdf
Messages 7.2 (BC)
data/pdf/BCDBADAMELDUNGdata/BCDBADAMELDUNGEN_72.pdf
Mexico
data/pdf/CAINTMX/CAINTMX.pdf
Migration/Upgrading to Oracle Version 8.1.5: UNIX
data/pdf/BCDBORA/BCDBORA_STR.pdf
MM Component Short Description
data/pdf/CACOMPMM/CACOMPMM.pdf
MM Materials Management
data/pdf/CAGTFADM-MM/CAGTFADM-MM.pdf
MM – Materials Management: Workflow Scenarios
data/pdf/BCBMTWFMMM/BCBMTWFTMMM.pdf
MM – Material Price Change (MM-IV-MP)
data/pdf/MMIVMVAL/MMIVMVAL.pdf
MM MM-MOB and WM-LSR Interfaces
data/pdf/MMWMLVS/MMWMLVS.pdf
MM Vendor Evaluation
data/pdf/MMISVE/MMISVE.pdf
Model Company Great Britain (PY-GB)
data/pdf/PYGBMC/PYGBMC.pdf
Money Market
data/pdf/TRTMMM/TRTMMM.pdf
Network Integration Guide (BC-NET)
data/pdf/BCNET/BCNETNIG.pdf
Notifications (CA-NO)
data/pdf/CACAN/CACAN.pdf
Objects on Loan/Internal Control (PA-PA)
data/pdf/PAPA/PAPA_ALE_166.pdf
Off-Cycle Activities
data/pdf/PYXXGROC/PYSOME_OFFCYCLE.pdf
Old and New Processing of Averages
data/pdf/PYINT/PYSOME_AVERTECH.pdf
Order BOMs
data/pdf/PPOBM/PPOBM.pdf
Organizational Plan Mode
data/pdf/BCBMTOMOM99/BCBMTOMOM99.pdf
Other Countries (PY-XX)
data/pdf/PYINT/PYINT_STANDARD.pdf
Other Single Roles
data/pdf/MYSAP/SR_MISC.pdf
Output Determination (SD)
data/pdf/SDBFOC/SDBFOC.pdf
PA – Personnel Management: Workflow Scenarios
data/pdf/BCBMTWFMPA/BCBMTWFMPA.pdf
Partial Period Remuneration (Factoring)
data/pdf/PYINT/PYINT_FACTORING.pdf
Payments
data/pdf/FIBP/FI-AP-AP-PT.pdf
Payments
data/pdf/PSCAF/PSCAF.pdf
Payments
data/pdf/PYINT/PYINT_PAYMENTS.pdf
Payment Card Processing
data/pdf/SDBILIVPC/SDBILIVPC.pdf
Payment Program for Payment Requests (FI-BL)
data/pdf/TRZP/TRZP.pdf
Payment Release
data/pdf/FIBP/FI-AP-AP-RP.pdf
Payroll Account (Report RPCKTOx0;HxxCKTO0)
data/pdf/PYINT/PYSOME_PAYROLL_ACCOUNT.pdf
Payroll Argentina (PY-AR)
data/pdf/PYAR/PYAR.pdf
Payroll Australia (PY-AU)
data/pdf/PYAU/PYAU.pdf
Payroll Austria (PY-AT)
data/pdf/PYAT/PYAT_ÖSTERREICH.pdf
Payroll Basics (PY-XX-BS)
data/pdf/PYINT/PYINT_TECH_BASICS.pdf
Payroll Belgium (PY-BE)
data/pdf/PYBE/PYBE.pdf
Payroll Brazil (PY-BR)
data/pdf/PYBR/PYBR.pdf
Payroll Canada(PY-CA)
data/pdf/PYCA/PYCA.pdf
Payroll Denmark (PY-DK)
data/pdf/PYDK/PYDK.pdf
Payroll France (PY-FR)
data/pdf/PYFR/PYFR.pdf
Payroll Germany (PY-DE)
data/pdf/PYDE/PYDE.pdf
Payroll Great Britain (PY-GB)
data/pdf/PYGB/PYGB.pdf
Payroll Hong Kong (PY-HK)
data/pdf/PYHK/PYHK.pdf
Payroll in a Background Operation
data/pdf/PYINT/PYINT_BATCH.pdf
Payroll Indonesia (PY-ID)
data/pdf/PYID/PYID.pdf
Payroll in the SAP System
data/pdf/PYINT/PYINT_BASICS.pdf
Payroll Ireland (PY-IE)
data/pdf/PYIE/PYIE.pdf
Payroll Italy (PY-IT)
data/pdf/PYIT/PYIT.pdf
Payroll Japan (PY-JP)
data/pdf/PYJP/PYJP.pdf
Payroll Journal (Report RPCLJNx0;HxxCLJN0)
data/pdf/PYINT/PYSOME_PAYROLL_JOURNAL.pdf
Payroll Malaysia (PY-MY)
data/pdf/PYMY/PYMY.pdf
Payroll Mexico (PY-MX)
data/pdf/PYMX/PYMX.pdf
Payroll New Zealand (PY-NZ)
data/pdf/PYNZ/PYNZ.pdf
Payroll Philippines (PY-PH)
data/pdf/PYPH/PYPH.pdf
Payroll Portugal (PY-PT)
data/pdf/PYPT/PYPT.pdf
Payroll Singapore (PY-SG)
data/pdf/PYSG/PYSG.pdf
Payroll Spain (PY-ES)
data/pdf/PYES/PYES.pdf
Payroll Sweden (PY-SE)
data/pdf/PYSE/PYSE.pdf
Payroll Taiwan (PY-TW)
data/pdf/PYTW/PYTW.pdf
Payroll Thailand (PY-TH)
data/pdf/PYTH/PYTH.pdf
Payroll United States (PY-US)
data/pdf/PYUS/PYUS.pdf
Payroll Venezuela (PY-VE)
data/pdf/PYVE/PYVE.pdf
Payroll South Africa (PY-ZA)
data/pdf/PYZA/PYZA.pdf
Payroll Switzerland (PY-CH)
data/pdf/PYCH/PYCH.pdf
Pension Fund CH: Reference Guide
data/pdf/PYCHNTPF/PYCHNTPF2.pdf
Pension Fund CH: Technical User Handbook
data/pdf/PYCHNTPF/PYCHNTPF3.pdf
Pension Fund CH: User Hand Book
data/pdf/PYCHNTPF/PYCHNTPF1.pdf
Performance Monitor
data/pdf/BCCCM/WA_PERFMONITOR.pdf
Periodic Declarations
data/pdf/SDFTGOV/SDFTGOV.pdf
Personnel Administration
data/pdf/PAPA/PAPA.pdf
Personnel Cost Planning
data/pdf/PACMCP/PACMCP.pdf
Personnel Development
data/pdf/PAPD/PAPD.pdf
Personnel Time Management (PT)
data/pdf/PT/PT.pdf
Peru
data/pdf/CAINTPE/CAINTPE.pdf
Philippines
data/pdf/CAINTPH/CAINTPH.pdf
Plant Maintenance
data/pdf/MYSAP/SR_PM.pdf
Plant Maintenance and Customer Service (PM/CS)
data/pdf/CAARCPM/CAARCPM.pdf
PM/CS – Data Transfer in Plant Maintenance and Customer Service
data/pdf/CAGTFADMPM/CAGTFADMPM.pdf
PM/CS – Plant Maintenance & Customer Service: Workflow Scenarios
data/pdf/BCBMTWFMPM/BCBMTWFMPM.pdf
Posting (FI)
data/pdf/FIDC/FIDC.pdf
Posting to Accounting (PY-XX-DT)
data/pdf/PYINT/PYINT_POSTING.pdf
PP Bills of Material Guide
data/pdf/PPBDBOM/PPBDBOM.pdf
PP – Capacity Planning
data/pdf/PPCRP/PPCRP.pdf
PP Component Short Description
data/pdf/CACOMPPP/CACOMPPP.pdf
PP PDC Interface
data/pdf/PPPDC/PPPDC.pdf
PP KANBAN
data/pdf/PPKAB/PPKAB.pdf
PP – PI-PCS Interface: Linking of Process Control
data/pdf/PPPIPCS/PPPIPCS.pdf
PP – Production Orders
data/pdf/PPSFC/PPSFC.pdf
PP – Work Centers
data/pdf/PPBDWKC/PPBDWKC.pdf
Preference
data/pdf/SDFTPRE/SDFTPRE.pdf
Preparations for Consolidation (FI)
data/pdf/FIGLCP/FIGLCP.pdf
Pricing and Conditions
data/pdf/SDBFPR/SDBFPR.pdf
Process Flow Hierarchy
data/pdf/SVASAPROZ/SVASAPROZ.pdf
Processing a Calibration Inspection
data/pdf/QMITCV/QMITCV.pdf
Product Catalog and Online Store on the Internet (LO-MD-AM)
data/pdf/ISR/LO-MD-AM.pdf
Product Cost Controlling Information System (CO-PC-IS)
data/pdf/COPCIS/COPCIS.pdf
Product Cost Planning
data/pdf/COPCPCP/COPCPCP.pdf
Production lot planning / individual project planning
data/pdf/PPSEIBAN/PPSEIBAN.pdf
Production Planning and Control (PP)
data/pdf/MYSAP/SR_PP.pdf
Production Planning and Control (PP)
data/pdf/CAARCPP/CAARCPP.pdf
Production Planning and Control (PP)
data/pdf/CAGTFADMPP/CAGTFAMDPP.pdf
Production Planning & Control Workflow Scenarios
data/pdf/BCBMTWFMPP/BCBMTWFMPP.pdf
Production Planning – Process Industries (PP-PI)
data/pdf/PPPI/PPPI.pdf
Production Resources/Tools (PRT)
data/pdf/PSPRT/PSPRT.pdf
Product Lifecycle Management (PLM)
data/pdf/LOPLM/PLM_OVER.pdf
Product Safety (EHS-SAF)
data/pdf/EHSSAF/LOSEC.pdf
Product Structure Browser
data/pdf/CADMS/CADMS_BROWSER.pdf
Profitability Analysis (COPA)
data/pdf/COPA/COPA.pdf
Profit Center Accounting (EC-PCA)
data/pdf/ECPCA/ECPCA.pdf
Programming Utilities for the Logical Databases PNP and PAP
data/pdf/PAXX/PYINT_RMAC.pdf
Project Estimator
data/pdf/SVASAPE/SVASAPE_01.pdf
Project Information System
data/pdf/PSIS/PSIS.pdf
Project Progress
data/pdf/PSPRG/PSPRG.pdf
Project System (PS)
data/pdf/MYSAP/SR_PS.pdf
Project System (PS)
data/pdf/CAARCPS/CAARCPS.pdf
Project System (PS)
data/pdf/PS/PS.pdf
Public-Key Technology
data/pdf/BCSEC_PUB_KEY/BCSEC_PUB_KEY.pdf
Purchasing (MM-PUR)
data/pdf/MMPUR/MMPUR.pdf
Quality Certificates
data/pdf/QMCA/QMCA.pdf
Quality Management
data/pdf/QMPTBD/ALE_QM.pdf
Quality Management (QM)
data/pdf/CAARCQM/CAARCQM.pdf
Quality Management (QM)
data/pdf/MYSAP/SR_QM.pdf
Quality Notifications
data/pdf/QMQN/QMQN.pdf
Quality-Related Costs (QM-IM-IC)
data/pdf/QMCOST/QMCOST.pdf
Question & Answer Database
data/pdf/SVASQADBS/Q&ADBSTRUCSTAN.pdf
QM in Procurement (QM-PT-RP-PR)
data/pdf/QMPROC/QMPROC.pdf
QM in Production
data/pdf/QMPROD/QMPROD.pdf
QM in Sales and Distribution (QM-PT-RP-SD)
data/pdf/QMSD/QMSD.pdf
QM/PM Partner Roles
data/pdf/BCBMTOM/CS_BD_BP_ALE_5.1.pdf
QM – Quality Management: Data Transfer
data/pdf/CAGTFADM/CAGTFADM_QM.pdf
QM – Quality Management: Workflow Scenarios
data/pdf/BCBMTWFMQM/BCBMTWFMQM.pdf
R/3 Database Management CONTROL (BC)
data/pdf/BCDBADADBA/BCDBADADBA_CONTROL.pdf
R/3 Database Manager DBMCLI (BC)
data/pdf/BCDBADADBA/BCDBADADBMCLI.pdf
R/3 Database Manager DBMGUI (BC)
data/pdf/BCDBADADBA/BCDBADADBMGUI_72.pdf
R/3 on IBM AS/400
data/pdf/BCOPAS4/BCOPAS4.pdf
Real Estate: Data Transfer (RE)
data/pdf/CAGTFADMRE/CAGTFADMRE.pdf
Real Estate Management
data/pdf/ISRE/ISRE.pdf
Real Estate Management (RE)
data/pdf/MYSAP/ROLES_S_REM.pdf
Recruitment
data/pdf/PARC/PARC.pdf
Reference Manual 7.2 (BC)
data/pdf/BCDBADAREFERENZ_73/BCDBADAREFERENZ_73.pdf
Remote Communications
data/pdf/BCFESDEI/BCFESDEI.pdf
Remuneration Statement (Report RPCEDTx0;HxxCEDTO)
data/pdf/PYINT/PYINT_REMUNERATION_STATEMENT.pdf
Repetitive Manufacturing (PP-REM)
data/pdf/PPREM/PPREM.pdf
Replication Manager
data/pdf/BCDBADREPMGR/BCDBADREPMGR.pdf
Reporting in Human Resource Management
data/pdf/HRREPORTING/HRREPORTING.pdf
Report Programming in HR
data/pdf/PAXX/PYINT_PROGRAMM.pdf
Reports and Analyses (SD)
data/pdf/SDISREP/SDISREP.pdf
Repository Service Component (BC-FES-AIT)
data/pdf/BCFESDEH/BCFESDEH.pdf
RE Real Estate Management Archiving
data/pdf/CAARCISRE/ISRE_CAGTFARC.pdf
RE Real Estate Management: Workflow Scenarios
data/pdf/BCBMTWFMISRE/BCBMTWFMISRE.pdf
Resources
data/pdf/PSCRP/PSCRP.pdf
Results Recording
data/pdf/QMIMRR/QMIMRR.pdf
Retroactive Billing
data/pdf/SDBILRB/SDBILRB.pdf
Revenues and Earnings
data/pdf/PSREV/PSREV.pdf
Reverse Buisness Engineer
data/pdf/SVASARBE/RBE.pdf
RFC C++ Class Library (BC-FES-AIT)
data/pdf/BCFESDEA/BCFESDEA.pdf
RFC Java Class Library (BC-FES-AIT)
data/pdf/BCFESDEG/BCFESDEG.pdf
RFC Programming in ABAP
data/pdf/BCFESDE2/BCFESDE2.pdf
Room Reservation Management (PE-RPL)
data/pdf/BCSRVOFCRPL/PERPL.pdf
Routings (PP-BD-RTG)
data/pdf/PPBDRTG/PPBDRTG.pdf
Salary Packaging
data/pdf/PYZASP/PYZASP.pdf
Sales (SD-SLS)
data/pdf/SDSLS/SDSLS.pdf
Sales and Distribution (SD)
data/pdf/SD/SD.pdf
Sales and Distribution (SD)
data/pdf/MYSAP/SR_SD.pdf
Sales and Distribution
data/pdf/CAARCSD/CAARCSD.pdf
Sales and Distribution (SD) Workflow Scenarios
data/pdf/BCBMTWFMSD/BCBMTWFMSD.pdf
Sales and Operations Planning (LO-LIS-PLN)
data/pdf/LOLISPLN/LOLISPLN.pdf
Sales Employee (PA-PA)
data/pdf/PAPA/PAPA_ALE_054.pdf
Sales Support: Computer-Aided Selling (CAS)
data/pdf/SDCAS/SDCAS.pdf
Sample Management
data/pdf/QMIMSM/QMIMSM.pdf
SAP ArchiveLink (BC-SRV-ARL)
data/pdf/BCSRVARL/BCSRVARL.pdf
SAP ArchiveLink – Scenarios in Applications (BC-SRV-ARL)
data/pdf/BCSRVARLSC/BCSRVARLSC.pdf
SAP Automation
data/pdf/BCFESRFC/BCFESRFC.pdf
SAP Automation GUI Code Generator (BC-FES-AIT)
data/pdf/BCFESDEB/BCFESDEB.pdf
SAP Automation GUI Interfaces (BC-FES-AIT)
data/pdf/BCFESDE9/BCFESDE9.pdf
SAP Automation RFC and BAPI Interfaces (BC-FES-AIT)
data/pdf/BCFESDE5/BCFESDE5.pdf
SAP@Web Studio
data/pdf/BCFESITSWEBSTUD/BCFESITSSTUDIO.pdf
SAP Business Partner (SAP BP)
data/pdf/CABP/CABP.pdf
SAP Business Workflow (BC-BMT-WFM)
data/pdf/BCBMTWFMSTART/BCBMTWFMSTART.pdf
SAP Business Workflow Demo Examples
data/pdf/BCBMTWFMDEMO/BCBMTWFMDEMO.pdf
SAP Communication: CPI-C Programming (BC-CST-GW)
data/pdf/BCSRVSKPR/BCSRVSKPR.pdf
SAP Communication: Configuration (BC-SRV)
data/pdf/BCSRVSKCF/BCSRVSKCF.pdf
SAP Container
data/pdf/BCCIDOCK/BCCIDOCK.pdf
SAP Control Framework
data/pdf/BCCIGOF/BCCIGOF.pdf
SAP Data Provider
data/pdf/BCCIDATAPROV/BCCIDATAPROV.pdf
SAP Exchange Connector (BC-SRV-COM)
data/pdf/BCSRVCOMMSX/BCSRVCOMMSX.pdf
SAP Graphics
data/pdf/BCFESGRA/BCFESGRA.pdf
SAP GUI for HTML
data/pdf/BCFESITSSAPGUIHTML/BCFESITSSAPGUIHTML.pdf
SAP High Availability (BC-CCM-HAV)
data/pdf/BCCCMHAV/BCCCMHAV.pdf
SAP HTML Viewer
data/pdf/BCCIHTML/BCCIHTML.pdf
SAP Internet Mail Gateway (BC-SRV-COM)
data/pdf/BCSRVCOMINT/BCSRVCOMINT.pdf
SAP Knowledge Provider (BC-SRV-KPR)
data/pdf/BCSRVKPR/DMB_STRUCTURE.pdf
SAP License (BC-CST-SL)
data/pdf/BCCSTSL/BCCSTSL.pdf
SAP List Viewer (ALV): Classic
data/pdf/CAGTFLV/CAGTFLV.pdf
SAP MAPI Service Provider (BC-SRV-GBT)
data/pdf/BCSRVOFCMAP/BCSRVOFCMAP.pdf
SAP/MS SQL Server DBA in CCMS (BC-DB-MSS-DBA)
data/pdf/BCDBMSSDBA/BCDBMSSDBA.pdf
SAP Open Information Warehouse
data/pdf/CAOIW/CAOIW.pdf
SAP Patch Assembly/Distribution Engine (SPADE)
data/pdf/BCUPGOCSSPADE/BCUPGOCSSPADE.pdf
SAP Patch Manager (SPAM)
data/pdf/BCUPGOCSSPAM/BCUPGOCSSPAM.pdf
SAPphone (BC-SRV-COM)
data/pdf/BCSRVCOMTEL/BCSRVCOMTEL_STRUC.pdf
SAP Picture (BC-CI)
data/pdf/BCCIIMAGE/BCCIIMAGE.pdf
SAP Query (BC-SRV-QUE)
data/pdf/BCSRVQUE/BCSRVQUE.pdf
SAP Retail
data/pdf/MYSAP/SR_ISR.pdf
SAProuter (BC-CST-NI)
data/pdf/BCCSTROUT/BCCSTROUT.pdf
SAP Session Manager
data/pdf/BCFESSEM/BCFESSEM.pdf
SAP Smart Forms (BC-SRV-SCR)
data/pdf/BCSRVSCRSF/BCSRVSCRSF.pdf
SAP Textedit
data/pdf/BCCITEXTEDIT/BCCITEXTEDIT.pdf
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data/pdf/CACOMPPT/CACOMPPT.pdf
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data/pdf/PT-BFA/PT-IAC_001.pdf
SAP Toolbar (BC-CI)
data/pdf/BCCITOOLBAR/BCCITOOLBAR.pdf
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data/pdf/BCCITREE/BCCITREE.pdf
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data/pdf/COACCSCHEDMAN/COSCHEDMAN.pdf
Scheduling Agreements for Component Suppliers
data/pdf/SDSLSOASCH/SDSLSOASCH.pdf
SD Partner Functions
data/pdf/BCBMTOM/SD_BF_ALE_5.2.pdf
SD – Sales and Distribution
data/pdf/CAGTFADMSD/CAGTFADMSD.pdf
Secure Network Communications (BC-SEC-SNC)
data/pdf/BCSECSNC/BCSECSNC.pdf
Secure Store & Forward / Digital Signatures (BC-SEC-SSF)
data/pdf/BCSECDISI/BCSECDISI.pdf
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data/pdf/TRTMSE/TRTMSE.pdf
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data/pdf/BCCSTADM/BCCSTSAL.pdf
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data/pdf/BCSECAUDLOG/BCSECSAL.pdf
Serial Number Management (LO-MD-SN)
data/pdf/LOMDSN/LOMDSN.pdf
Service: Feedback Notifications (SV-FDB)
data/pdf/MYSAP/SR_SVFDB.pdf
Setting Up Activity Allocation in R/3 Time Management
data/pdf/PT-BFA/PT-BFA_ALE_043.pdf
Settlement
data/pdf/COABR/COABR.pdf
Shipping
data/pdf/LESHP/LESHP.pdf
Simulation
data/pdf/PSSIM/PSSIM.pdf
Singapore
data/pdf/CAINTSG/CAINTSG.pdf
South African Model Company (PY-ZA)
data/pdf/PYZAMC/PYZAMC.pdf
South Korea
data/pdf/CAINTKR/CAINTKR.pdf
Space Management Interface (LO-MD-PL)
data/pdf/ISR/ISR_ALE_001.pdf
Spain
data/pdf/CAINTES/CAINTES.pdf
Special G/L Transactions: Bills of exchange
data/pdf/FIBP/FI-AP-AP-BE.pdf
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data/pdf/LOSS/LOSS_SPECSYST.pdf
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data/pdf/BCDBADASQL/SQL_72.pdf
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data/pdf/PPBDCAP/PPBDCAP.pdf
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data/pdf/QMIFSTI/QMIFSTI.pdf
Statistical Process Control (SPC)
data/pdf/QMQCSPC/QMQCSPC.pdf
Structures
data/pdf/PSST/PSST.pdf
Supplier Workplace
data/pdf/MMPURSWP/MMPUR_SWP.pdf
Supply Chain Planning Interfaces (LO-SCI)
data/pdf/LOSCI/LOSCI.pdf
Support Line Feedback
data/pdf/SVSLF/SVSLF.pdf
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data/pdf/BCRRR/BCRRRSAA.pdf
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data/pdf/BCDOCTER/CADOCTER.pdf
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data/pdf/QMITCP/QMITCP.pdf
The Appointment Calendar (BC-SRV-GBT)
data/pdf/BCSRVOFCCAL/BCSRVOFC.pdf
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data/pdf/BCFESBGW/BCFESBGW.pdf
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data/pdf/BCMIDDCOM/BCMIDDCOM.pdf
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data/pdf/PT/PTPE50.pdf
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data/pdf/PYINT/PYINT_DIALOG.pdf
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data/pdf/BCFESDE4/BCFESDE4.pdf
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data/pdf/BCFESDE3/BCFESDE3.pdf
The SAP Lock Concept (BC-CST-EQ)
data/pdf/BCCSTEQ/BCCSTEQ_PT.pdf
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data/pdf/BCCATPLN_2/BCCACATPLN_2.pdf
Time Management Aspects in Payroll
data/pdf/PYINT/PYINT_TIME_DATA.pdf
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data/pdf/PT-BFA/PT-BFA_ALE_167.pdf
Time Zones
data/pdf/CAGTFTIM/CAGTFTIM.pdf
Training and Event Management
data/pdf/PE/PE.pdf
Transfering Trip Costs to Accounting
data/pdf/FITV/FITV_ALE_100.pdf
Transfering Wage Components to Payroll (PY-XX-TL)
data/pdf/PAXX/PYINT_PCIF.pdf
Transfer of Legacy Assets to the R/3 System
data/pdf/FIAA/FIAA_CAGTFADM.pdf
Transfer of PRICAT Messages (SD-MD-PL)
data/pdf/ISR/ISR_ALE_002.pdf
Translation Tools for Coordinators
data/pdf/BCDOCTTC/CADOCTTC.pdf
Translation Tools for Translators
data/pdf/BCDOCTTL/CADOCTTL.pdf
Transportation
data/pdf/LETRA/SDTRA.pdf
Transport Management System (BC-CTS-TMS)
data/pdf/BCCTSTMS/BCCTSTMS.pdf
Transport Organizer (BC-CTS-ORG)
data/pdf/BCCTSORG/BCCTSORG.pdf
Transport Tools (BC-CTS-TLS)
data/pdf/BCCTSTLS/BCCTSTLS.pdf
Travel Management (FI-TV)
data/pdf/FITVPLAN/FITVGENERIC.pdf
TR – Cash Budget Management
data/pdf/TRCB/TRCB.pdf
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data/pdf/CAARCTR/CAARCTR.pdf
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data/pdf/MYSAP/SR_TR.pdf
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data/pdf/TREU/TREU.pdf
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data/pdf/TRTMALE/TRTM_ALE.pdf
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data/pdf/BCBMTWFMTR/BCBMTWFMTR.pdf
Turkey
data/pdf/CAINTTR/CAINTTR.pdf
United States
data/pdf/CAINTUS/CAINTUS.pdf
Unqualified Advance Payments
data/pdf/PYINT/PYSOME_UNQUAL_ADVANCE.pdf
Updates in the R/3 System (BC-CST-UP)
data/pdf/BCCSTUP/BCCSTUP_PT.pdf
Users and Roles (BC-CCM-USR)
data/pdf/BCCCMUSR/BCCCMUSR.pdf
Using Evaluation Schemas
data/pdf/PYINT/PYSOME_EVALUATION.pdf
ValueSAP
data/pdf/SVASAP/SVASAP.pdf
Variant Configuration (LO-VC)
data/pdf/LOVC/LOVC.pdf
Venezuela
data/pdf/CAINTVE/CAINTVE.pdf
Versions
data/pdf/PSVER/PSVER.pdf
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data/pdf/PYINT/PYINT_DATAEX.pdf
Wage Type Distribution
data/pdf/PYINT/PYINT_WAGE_TYPE_DISTRIBUTION.pdf
Wage Type Reporter (H99CWTR0)
data/pdf/PYXXFOLGE/PYINT_WAGE_TYPE_REPORTER.pdf
Wage Type Statement
data/pdf/PYINT/PYINT_WAGE_TYPE_STATEMENT.pdf
Wage Type Valuation
data/pdf/PYINT/PYINT_WT_VALUATION.pdf
Wage Types
data/pdf/PYINT/PYINT_WAGETYPES.pdf
Warehouse Management Guide
data/pdf/LEWM/MMWM.pdf
Web Transaction API
data/pdf/BCFESITSTRANAPI/BCFESITSTRANAPI.pdf
Web Transaction Programming
data/pdf/BCFESITSTRANPROG/CABFAWEBPWT.pdf
Web Transaction Tutorial
data/pdf/BCFESITSTRANTUT/BCFESITSTRANTUT.pdf
WebRFC Programming
data/pdf/BCFESITSWEBRFC/CABFAWEBRFC.pdf
Workflow
data/pdf/PSWFL/PSWFL.pdf
Working with Parameter Effectivity
data/pdf/LOECH/LOECH_EFF.pdf
Worklist
data/pdf/QMWORK/QMWORK.pdf

Larry Ellison Interview: SAP’s Bill McDermott Market Share Claims are "Ridiculous"

This detailed interview with Ellison by the San Francisco Chronicle gives the expected trenchant jabs at SAP, and gives a good sense at how Larry sees the industry at the moment.

ORACLE CORP.
ON THE RECORD: Larry Ellison

Sunday, May 8, 2005

The last time Oracle Corp. CEO Larry Ellison visited The Chronicle was in late 2003, when the Redwood City software giant was waging a bitter takeover campaign against rival PeopleSoft Inc.

Oracle prevailed in that controversial battle, which led to two trials, the firing of PeopleSoft Chief Executive Officer Craig Conway and the loss of 5,000 jobs. The merger also turned Oracle into an even more formidable technology behemoth.

Ellison returned to The Chronicle recently to reflect on the battle over PeopleSoft and its meaning for Oracle and the tech industry.

He also talked extensively about his personal life, including his marriage to novelist Melanie Craft and turning 60 in August. He shared his insights into friendship, his legacy and reaffirmed one of his unfulfilled dreams: to own a sports franchise.

Below are excerpts of the conversation:.

Q: It’s been an eventful year. You’ve had a lot of personal changes, changes in personal life and also in business. You also turned 60. What does Larry Ellison want to be known for?

A: Gee. (Laughs.) You had to mention (my age)? That’s a brutal way to start.

Q: You must be thinking of what you want to be known for. What do you want people to remember you for?

A: Want to be known for? That’s an interesting question. In terms of a legacy, I don’t think I’ve ever thought about it that way.

Q: Well, you’ve thought about your business, obviously.

A: Yeah, I certainly like to be successful in my business. I’d like to be able to leave the business in good shape. I think one of the measures of a business is in how well it does after you leave.

I think Jack Welch did a great job building General Electric. That’s quite a high standard. Jeff Immelt took over, and they haven’t missed a beat. So I certainly would like to do that.

But I don’t know… I mean it’s such an interesting question… I have to think about it for the first time. What I want to be known as? Or known for? (Laughs.)

Q: What do you feel you’ve done at Oracle in terms of the culture that you’re talking about?

A: I think I’m very goal-oriented. I’d like to win the America’s Cup. I’d like Oracle to be the No. 1 software company in the world. I still think it’s possible to beat Microsoft, believe it or not, for us to be a more important company than Microsoft.

Q: And personally?

A: To have a good relationship with my family and friends. I think it’s all you really can do.

Q: What constitutes a good relationship with family and friends?

A: Freud said there are two things that make up life: love and work, not necessarily in that order. I think one of the components is a certain number of accomplishments that you can measure in your work, and having some number of close relationships with a handful of people inside of your family and your friends, where you can tell the truth and they find out who you really are and you are not afraid of disclosing who you really are.

Q: Is it hard for someone like you to choose your friends, to be friends with anybody?

A: This sounds funny, but in general I like people. I find people interesting. But there are only a handful of people that you spend enough time with to really get to know them well and they get to know you well and that you rely on. That’s a fairly small group of people.

One of the best things about my life is I get to meet all sorts of interesting people. I got to spend a lot of time with Bill Clinton, who I think is just a fascinating guy. It made my life more interesting to meet him in person.

Q: Who are your closest friends?

A: Oh, Steve Jobs, David Geffen are a couple. I assume you meant outside of my family. My nephew’s a judge, and I’m very close friends with him.

Q: How about philanthropy?

A: I have a medical foundation. There’s going to be a big announcement with Harvard very soon about a large database and journal we’re starting, basically assessing how government and private foundations do in measuring improvements to world health.

I had a big interview a long time ago where I said we measure philanthropy the wrong way. We measure the input: how much someone gives. It’s better to measure how many lives we save. So those are all goals I have for myself.

But I’m not sure it’s realistic to set a goal how you want to be thought of. I know how I want my kids to think about me. I think (public perception) ebbs and flows. During the bubble, CEOs were heroes.

During the aftermath, what did the Economist say? That the last 50 U.S. CEOs were seen headed for the border swearing they would not be taken alive. So a lot of how you are thought of is based on the fashion of the time.

Q: What will you do with your fortune? Are you giving it to your kids? Are you going to give it to your charity? I assume you’re not going to try to take it with you.

A: (Laughs.) Well, no one has figured out how to do that yet. I think my kids will get some. Certainly, a majority of it will go to different charities.

Q: Any particular charity?

A: I have a very large medical foundation. I’m expanding it. I spent time with (Harvard President) Larry Summers, another fascinating guy, the day after the faculty voted to censure him. (Summers was censured for comments he made about women and science.) The day after was better than the day he had before. (Laughs.)Q: What is it about your friends? Do they have anything common? Are they like you or unlike you? Do you see any commonality there?

A: These are all very hard questions. This is not what I expected to talk about. (Laughs.) Meaning of life questions. Yeah, I think some of my friends live in a similar world that I do. I think Steve Jobs does. David Geffen does. Some of them not at all. A very good friend of mine — I won’t mention his name — is a local FBI agent in San Francisco. I’ve known him since he was a little kid. I knew his dad. He and I are good friends and play basketball. So not all of my friends are billionaires.

Q: You mentioned Steve Jobs as one of your friends. In an interesting coincidence, you’re both adopted. Did you ever discuss what role that has played in your lives?

A: It’s funny you said that because we don’t discuss it. I know it’s something Steve and I have in common, but no. We go on hikes and chat about a lot of different things, about our families and about business in the valley.

I ask him for advice. He never asks anyone for advice, but I give it to him anyway. Even without him asking, I volunteer it. (Laughs.) He doesn’t need any advice.

It’s very hard to know what the impact of being adopted is on your life. I had a reasonably happy childhood, so it’s hard to talk about, “Gee, this is really awful or that was awful.” Neither one of us suffered terribly when we were young.

So no, we don’t talk about it. But maybe its just repression. Maybe it’s so painful we can’t go there, but it’s impossible to know that.

Q: You were married last year. Can you discuss how that changed your life?

A: We were living together for years. I think we were together for 6 1/2 years before we got married. We knew each other well. We’re close friends … and I think that’s a great foundation for marriage.

She works hard at her job. She’s a novelist. She’s writing her fourth novel right now. She used to be a romance novelist. She swears she’d rather be dead than write another romance novel. She’s writing a different kind of book now. I guess I shouldn’t say anything.

Q: One of her books was about marrying a rich executive. Did she talk to you about that?

A: I read her books. I get them a chapter a time. I do early editing. And she says, “Thanks for sharing,” and throws my comments away. Once in a while, she’ll take a single word to make me feel better. The theme or the plot of bright, spunky girl meets rich playboy and tames him, it’s a classic romance novel plot. She wrote the same exact plot before.

Q: Does that story line speak to your relationship?

A: I don’t think so. (Laughs.) Not at all.

Q: Thanks for sharing such personal information. Now let’s get to some business news. (German software giant) SAP reported recently that its share of the market share has grown to 41 percent in the United States, and Bill McDermott, CEO of SAP America, predicted it would be 50 percent by the end of the year. What is your reaction to that?

A: Well, they clearly don’t know how to calculate market share. I mean that’s just the most ridiculous thing. If you sold one license yesterday and we sold zero yesterday, did you achieve 100 percent market share yesterday?

That’s how they do their calculation. They look at the last three months of sales, the last 12 months of sales. They didn’t count the PeopleSoft sales during that period, so they just threw those out. We have about twice as many customers as they do in the United States. They can’t move up nine points in market share in one year.

Q: There’s been a lot of talk about your acquisition strategy. You’ve acquired two companies (PeopleSoft and Retek) in the past three months. Is there any concern about indigestion?

A: No. I think we know how to do this. I think you just measure us by our numbers. I don’t ask you believe anything I say, but we report, on penalty of jail, numbers every three months. Our growth is spectacular. Our profit growth is spectacular, and our profits are growing much faster than SAP’s. Our license revenues are growing much faster than SAP’s.

I think we’re doing very well. That’s not a sign of indigestion. I think that’s a sign of a successful acquisition, first of PeopleSoft and then of Retek. Our profits are up 30 percent. SAP’s profits are up 11 percent.

Q: One analyst has described your acquisition strategy as too reactive. For instance, you made a bid for PeopleSoft a few days after it acquired J.D. Edwards.

A: Is reactive a compliment or a criticism?

Q: Criticism. That you’re paying too much.

A: We’re reacting, and we paid too much. Well, our profits are up 30 percent. That’s all I can say — I’ll just keep repeating our profits are up 30 percent. What we paid we have to write off.

So after all the accounting for the acquisition, our profits are up 30 percent. SAP’s are up 11 percent. We are doing a lot better than they are, partially because of the acquisition. We don’t think we paid too much. And the proof is our profits are up 30 percent. That’s just a fact.

Q: How do you see this competition with SAP playing out?

A: I think there are some industries where they are strong like oil and gas, and there are other industries where we are strong. We have all the banks in North America. They have half a bank in North America right now. There are some industries where we just utterly dominate them. Financial services, for instance.

It’s not really us versus SAP. It’s us versus SAP in the oil and gas business. If you’re an Exxon or a big gas company or a big oil company, you probably have an SAP implementation. If you’re a big bank, you probably have an Oracle implementation.

Q: What industries are growing, and where will you two be going head to head?

A: We bought Retek because retail is up for grabs. SAP is not strong in retail. We are not strong in retail. Retail is a relatively green field. In banking, it’s pretty wrapped up by us. Oil and gas is pretty much wrapped by them. Telecommunications is pretty much wrapped up by us.

So I think you have to look at industry by industry to understand who is going to be the eventual winner in the applications space. In the database space, which is very different than applications, everyone uses the same Oracle database. And there, I think, we are overwhelmingly the dominant company.

Q: Across the board, IT spending in general, where is it right now?

A: Up a little. Up maybe 4, 5 percent, something like that, which is not bad. I think the days of people talking about IT spending up 20 percent or more are over.

Q: Does that lead to a consolidation period?

A: The industry is maturing. It is going to consolidate. That’s exactly what happened to every other industry. I didn’t invent the idea of consolidation. What happens during a period of consolidation is (the company in) second place can suddenly move and become the winner.

One of the interesting things is (that) when you look at when the automobile industry, the clear winner in the early phase of the automobile industry was Henry Ford. Ford was the No. 1 car company in the world by far. Then a guy by the name of Alfred Sloan decided to get all of the good losers together — Chevrolet and Buick and all those guys — and built General Motors. Then General Motors passed Ford during the consolidation phase in the automobile industry.

So during the entrepreneurial growth phase, Ford won. During the consolidation phase, Ford lost and found itself in second place.

Q: So you don’t want to be Henry Ford.

A: It’s too late for anyone to be Henry Ford. That phase is over. We did pretty well in the database business. We have the No. 1 database in the world, and I’ll argue that, at the dawn of the information age, that’s not a bad position to have.

Q: Let’s talk about the PeopleSoft acquisition. Did that play out the way you thought it would?

A: In some ways, hats off to Craig Conway. He did a very good job, I think, of trying to keep PeopleSoft independent. Now, he did the wrong thing. He did something that was very self-serving, but he was good at it.

Keep in mind that Conway was the one who had the idea of merging the two companies in the first place. Craig approached me about the merger, except his idea was that he would run the merged company. He loved the idea of the merger with him running it. He hated the idea of the merger with me running it.

In fact, if you actually just removed him and put me in his place, the merger was suddenly an antitrust problem. So it was a little bit wild.

Q: Is that why you thought you could beat the Justice Department?

A: We thought we could beat the Justice Department because they had no case. They had no case at all.

Q: What would be wrong with Conway running the merged firm?

A: Since we own the merged company, I would have had to believe that Craig would do a better job than I would, and I don’t believe that.

Q: How long are you going to stay at Oracle? Is there a succession plan in place?

A: I’ll say that Oracle must have a great succession plan because virtually every software company in the Bay Area is run by an ex-Oracle (employee.) The CEOs worked for Oracle. We must have a lot of talent. (Laughs.)

We have some very talented people. Charles Phillips certainly could be CEO. Safra Catz certainly could be CEO. We have a couple of senior engineering managers who are candidates. But for me to pick one of them now and say this is the anointed successor, I think, is a huge mistake. Jack Welch didn’t do that.

Q: Where is the mistake in that? Intel, for instance, makes its line of succession very clear.

A: Why would you decide in advance? It’s a little bit like saying, “I’m certain this guy is going to win the marathon,” 10 miles into the marathon. Why do that?

Q: You just keep growing with more mergers. Who’s next?

A: I think you’ll see us growing much deeper into banking. You might see us acquiring companies in the banking area. You might see us acquiring in the retail area. I think you might see us acquiring companies in telecommunications. I think you’ll see us getting stronger in business intelligence.

Q: Why do you want to be bigger than Microsoft?

A: Because software is all about scale. The larger you are, the more profitable you are. If we sell twice as much as software, it doesn’t cost us twice as much to build that software. So the more customers you have, the more scale you have. The larger you are, the more profitable you are.

Q: That’s the technical answer. But what’s really driving you? Don’t you have enough money? Is it about winning?

A: That’s one of the unknowables. Am I doing it for purposes of vanity or because of my obligation to the shareholders? That’s my job. I’ll bring up Barry Bonds. What’s he trying to do? Is he trying to hit those home runs to help the team win, or is he trying to hit those home runs for vanity?

Q: Could it be both of them?

A: Yeah. It’s hard to figure out. The honest answer is probably a little of each. That’s what you do. If someone said, “Ah, we won the World Series last year. Let someone else win this year. I don’t need to try as hard because it’s just not fair.” I’m not sure you want that guy on the team.

Q: At what point did you realize that you had won in the PeopleSoft battle?

A: When in the $24 a share tender offer, 60 percent of the shareholders voted “yes.” I think at that point, the board of directors’ position of “just say no” was completely untenable. And it was over.

Q: So why did you agree to sell at $26.50 if you knew that there was chance you get it for $24?

A: Because there’s also a chance we wouldn’t. There’s also a chance the judge wouldn’t pull the pill. And we thought it was worth $26.50. Our calculations were worth more than that. I mean, pay $26.50 and it’s accretive to profit.

Q: So how much did they leave on the table?

A: Well, who knows? It’s like the question: How much do we overspend? If some things are worth more to you, if you really covet your neighbor’s house for some reason, because you want to put in a swimming pool right next door, then that house is worth more to you than anybody else.

Q: Does it bother you that PeoleSoft employees resisted your takeover bid?

A: I think engineers are very funny. Different groups reacted differently. Some of the marketing people were very emotional and set up little — I don’t know — shrines to the company. (Laughs.) Peculiar thing.

The engineers were saying, “Well, I just hope I get a parking space closer to my office.” (Laughs.) The engineers were very funny. You can decide which is better. It’s up to you.

Q: Well, those two groups had very different job prospects.

A: Yeah. And we offered 90 percent of the engineers that were from PeopleSoft jobs. And 99 percent that we offered jobs, took the job.

Q: What percentage was it for marketing?

A: Less. There are a lot fewer people in marketing communications.

Q: So the shrines didn’t work out.

A: The shrines prayed to the wrong god, obviously.

Q: Can you talk about having to let go a large number of people?

What was it like for you? Do you stay up at night? Do you see these people at the supermarket?

A: I have a really tiny supermarket near my house. Well, no, listen. The first layoff I did at Oracle — the only one I’ve ever done at Oracle till this one pretty much — was 500 people in 1991 when we badly missed one of our quarters. Worst day I ever had at work.

Q: You mentioned the bubble a couple of times. How goofy was that? How illusory was it? Did you know?

A: Did I know? I certainly would walk around Oracle saying, “Maybe I’m the one’s that crazy.” Ariba is worth more than Daimler-Benz, the largest industrial corporation in Europe? These guys make Internet procurement software that my cat could have written on a free weekend. What is going on?

Q: But you’re not questioning the Internet as a viable business technology. You’re questioning the business models that were created.

A: Exactly. I had an argument with somebody over WebVan. Someone said, “Larry, I love WebVan.” “Of course, you love WebVan. They give you $20 worth of meat for $10, and they deliver it to your home. Everyone would love that.”

Q: Google is at $200 right now. Is that a fair value?

A: It’s high-ish, but Google is an unusual company. I think in the long term it’s very hard to figure out where their competition will come from and if there’s such a thing as a global Yellow Pages and if we all use Goggle for comparison shopping when we buy tennis shoes or tennis rackets or cars or whatever. If you spend a lot of time at Google, what’s that all worth? It could be worth a lot of money.

Q: We noticed you have a security detail with you today. Is that a regular thing?

A: Depends where I am. Sometimes I have security with me. Whenever I’m with the media, I always have guys with guns. (Laughs.)

Q: Is that a recent addition for you?

A: I’ve had some strange (incidents). I finally got security when someone climbed over my fence at midnight on a Saturday night and knocked on my door.

Q: What did they want, sugar? Options?

A: She just said she was wondering what I was doing. (Laughs.) A very attractive young lady, but it’s midnight, and she’s knocking. No thank you. It’s not a good thing.

Q: Let’s finish on the sports page.

A: Yes! (Laughs.) Sailing or …?

Q: Do you still have an interest in buying the 49ers or another NFL franchise? How close did you come with (49ers owner) Dr. (John) York?

A: I never talked to him. I’ve got friends who found out for me whether he wanted to sell. He doesn’t want to sell. I did talk to Chris Cohan and tried to buy the Warriors. He didn’t want to sell either. No way to make a hostile takeover. (Laughs.) He was the only shareholder.

So there’s going to be a franchise in Los Angeles that I’m interested in, and there are some other NBA teams that might be available, or Major League Baseball in Los Angeles is a possibility.

Q: Which sport do you like the best?

A: I play a lot of basketball so I suppose. … It’s funny. Playing? Watching? I think football is a great show. I think baseball has gotten very good. I like sports.

I sail competitively. I drive my America’s Cup boat a lot. Sailing, playing tennis, basketball.

In terms of owning a team? I think an L.A. franchise and building a really amazing stadium for football is something that quite interests me. And enhanced television for football interests me.

Q: Would you want to become an owner for the vanity part of it or for business purposes?

A: Oh, it’s for fun. I’m not sure that it’s the vanity part that I’m interested in. Competing. The fun of competing. It’s going there with your family and friends and having a great time.

Q: You also mentioned the Warriors.

A: I tried.

Q: What would you do to make them more competitive?

A: It’s pretty easy to make them more competitive. I would go with better players and coaches and front office. Uh, I get into all sorts of trouble when I criticize different teams. I just think they’ve got the worst record in basketball over the last 10 years. They’re the worst-managed team in basketball.

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ON HIS FRIENDSHIP WITH STEVE JOBS:
I ask him for advice. He never asks anyone for advice, but I give it to him anyway..

ON LAYOFFS AFTER THE PEOPLESOFT MERGER:
When you’re merging and consolidating functions, jobs are lost. That is a fact … . It’s certainly not fun..

ON BUYING A PROFESSIONAL SPORTS TEAM:
There’s going to be (an NFL) franchise in Los Angeles that I’m interested in, and … some NBA teams … might be available.

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BRIEFCASE
Name: Larry Ellison

Age: 60

Job: Chief executive officer and co-founder, Oracle Corp.

Family: Married to novelist Melanie Craft; two children from a previous marriage

Residence: Woodside

Interests: Sailing, basketball, playing guitar

Participating in this interview were Chronicle Executive Vice President and Editor Phil Bronstein; Business Editor Ken Howe; Deputy Business Editor Alan T. Saracevic; Assistant Business Editors Marcus Chan and Sam Zuckerman; Insight Editor James Finefrock; reporters Benjamin Pimentel, David Baker, Todd Wallack, George Raine, Benny Evangelista, Ning Yu and Kathleen Pender; and editorial assistants Steve Corder and Colleen Benson.

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URL: http://sfgate.com/cgi-bin/article.cgi?file=/c/a/2005/05/08/BUGP7CKKEJ1.DTL

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