FREE ESSAY ON ACCOUNTING ERP

ACCOUNTING ERP

Enterprise Resource Planning (ERP)- These systems break from the Assets=L+E scheme. ERP
systems do not have the preparation of financial statements as their primary goal. Many
ERP vendors stress an objective of inputting data only once and using it to generate
various views. ERP vendors stress the process focus of their products. The software can
span across functional borders, enabling integration of data and information flows. ERP
systems can also support a variety of tasks including supply chain management, inventory
management, logistics, human resource manganement, finance, accounting, manufacturing
planning, sales, and distribution. However, these systems are often inflexible and impose
certain rules and processes on the organization. Successfully implementing these systems
is often difficult and costly. 
Many approaches have been advanced to solve business problems with only limited success.
ERP provides some benefit, but has only provided marginal success. As a result, many
people are less eager to try something new. They seem convinced that nothing can fix
their problems and they resist future changes to protect themselves from further
frustration and exhaustion. 
The basic question in designing ERP is should the organization change the software or the
process to match the software? ERP advantages- one vendor solution, viability, broader
offering. Disadvantages- time and diversity. One-vendor solution- this is viewed as a one
stop shop approach. It typically allows the user to contact and communicate with just one
source. Viability- the traditional ERP firms are more established and score higher on the
viability scales. When an anticipated market consoldation occurs, these companies are
more likely to bode well through this consolidation. Broader offering- Many organizations
that are buying Ginancials solutions today may have the need for additional products (HR,
Procurement, Order Entry, Manufacturing) down the line. Because mergers and acquisitions
continue to occur throughout all industries, these companies may not be able to identify
all their needs right now. Disadvantages...Time- An integrated ERP approach has
traditionally taken longer and creates many "Catch 22" situations. THere are integral
decisions in implementing any component of an ERP system that impact other parts of that
same system. Diversity- As ERP vendors are broadening their offerings to reflect the
needs of the marketplace, they are involved in many strategic directions that are playing
out in parallel. Vendors need to determine where future R&D investments will be applied.
The concern is that some of these vendors are trying to be "everything" to "everybody."
At some point, there will be a threshold that will cause a breakpoint. 
A more significant benefit for most companies is the improved use of their information
technology. ERP systems present better information in a more timely manner to the people
who need it. Sales reps can check inventory levels and prices before committing to
deadlines; managers can check margins before offering special deals. 
ERP systems can also make employees, whether in customer service or production, more
productive. ERP systems can reduce turnaround or order fulfillment time and increase
accuracy in fulfilling customer orders. For cash savings, ERP systems can help reduce
inventory costs through improved stock tracking. 
Last year, Walbar, a manufacturer of turbine blades and other parts for airplane engines,
replaced its business application system with an ERP system from JD Edwards. 
The total cost for the implementation at the Mississauga plant, including the IBM AS/400
computer, servers, new IBM workstations, software, installation and training was about US
$1 million. Mammone sees real improvements in improved customer service; quicker and more
accurate delivery of customer orders through better materials planning and order
tracking. In Mammone's industry, mistakes are very costly, as there are only about a
dozen manufacturers of airplane engines in the world. He says you can't afford to annoy
any of them. 
One key benefit of ERP systems is the way it integrates a company's flow of information.
Using an ERP system, the sales, purchasing, production, inventory control and accounting
departments all use the same information. One set of data is used throughout the company
to make sure customers get what they want when they want it, and that the whole thing is
profitable for the company. 
ERP systems do just what you'd expect a business application to do: record customer
orders and purchase orders, keep track of inventory, create invoices, handle all the
accounting including sales, accounts payable and receivable, and budgeting. Another
important advantage of ERP is that it provides information to support the information
manager's need to manage: things such as performance indicators, and alerts to situations
such as shortages or shortfalls from quotas, and bottlenecks. 
An ERP system is not something that you can pull out of a box or install from a CD-ROM;
every implementation is customized to fit the needs of the enterprise. Any company
installing a new ERP system can pick and choose modules, or can phase in the
implementation over months or years. For instance, you might want to start with only the
core ERP back office functions such as accounting, payments, inventory and sales. Then
add human resources planning, strategic procurement or e-commerce as you become used to
the new system. You could phase in your ERP solution as your older systems become
obsolete, one at a time. Naturally, this approach requires a lot of programming to
integrate the new system into the old one. 
THE PAYOFFS OF EPR
Manufacturers with fully functional ERP systems report the following benefits:
Reduced inventories 50%
Reduced order-cycle times 43%
Increased production capacity 36%
Lower total logistics costs 32%
Decreased procurement costs 29%
Reduced manufacturing waste 29%
Lower distribution costs 14%
REASONS FOR IMPLEMENTING ERP
Manufacturers implement ERP systems primarily to:
Get a competitive advantage 71%
Help service major customers 71%
Replace an older system or eliminate the Y2K problem 57%
The system should be complete enough to support both Discrete as well as Process
manufacturing scenario's. The efficiency of an enterprise depends on the quick flow of
information across the complete supply chain i.e. from the customer to manufacturers to
supplier. This places demands on the ERP system to have rich functionality across all
areas like sales, accounts receivable, engineering, planning, Inventory Management,
Production, Purchase, accounts payable, quality management, production, distribution
planning and external transportation. EDI (Electronic Data Interchange) is an important
tool in speeding up communications with trading partners. 
The amount of inventory required to run a business effectively is always a concern. If
you have too much cash flow problems can result, too little and you run the risk of poor
customer service. How can you run your business effectively and still maintain a
reasonable amount of inventory? The cost of carrying inventory can run 30% or more of the
value of the inventory per year. $10,000,000 of inventory can cost you $3,000,000 per
year for the privilege of carrying it.
7 ways to reduce inventory
1. Improve your data accuracy - If you don't know how much you have or where it is, it's
as if it doesn't exist. The question What is your inventory accuracy? often gets the
answer, I don't know, Lousy or some low percentage. Its difficult to maintain inventory
accuracy without a well designed cycle counting system. Our experience has been that a
well designed and implemented cycle counting system pays for itself in a very short time.
This is not merely counting things from time to time. It is a system designed to identify
and solve inventory system problems.
2. Reduce your lead time - The longer your lead time, the more inventory you have in your
system. A client with a 22 week customer lead time could produce a rush order in one
week. The manufacturing process was the same. The difference was that the rush order
didn't sit on the shop floor in long lines of WIP inventory waiting for something to
happen. Don't put it on the floor unless you intend to do something with it. 
3. Increase the velocity of your operation - The amount of inventory you have has little
to do with your level of customer service. It has more to do with how fast you can
replace it. If it takes six weeks to replace an item, you must reorder with at least six
weeks (plus safety stock and Just in Case inventory) supply or you risk a stock out. If
you can replace the same item in one day, a two day supply will give you more than enough
to fill any order and a stock out is only for one day, not until the next batch is
produced.
4. Eliminate misalignment from your process - Many companies buy raw material in
thousands, produce product in hundreds and sell in units. These misalignments create
large quantities of inventory that run the risk of slow movement, obsolescence and
damage, not to mention tying up valuable cash. Most companies justify this behavior based
on economies of scale. Careful analysis shows that this should be called false economy of
scale. Buy just what you need, produce at the rate of customer consumption. Refine your
material acquisition process and change your manufacturing process to produce in smaller
batches. Just in Time techniques are targeted at eliminating misalignment. 
5. Clean your attic - Many companies want to be all things to all people. I've had
clients tell me, If we don't carry that item (typically ordered once a year if that
often), our customer won't buy from us. My response is Where else would they go to buy
it, no one else carries it! I've also heard, Someone will buy it some day, We spent too
much money on it to throw it away, and, the best one of all We've written it off, so it
doesn't cost us anything. Turn all those mistakes into whatever cash you can. Liquidate,
donate, have a sale. Set an inventory turns target and increase an item's turns by
increasing its velocity or get rid of it. If your customers leave you because you don't
carry some obsolete inventory item, you've got bigger problems than this paper can
address.
6. Eliminate variation - Erratic vendors, yield problems on the shop floor and other
quality problems cause unneeded inventory to pile up because the response is order early,
order more than we need, start more than the forecast and increase safety stocks
throughout the system. If a product has an 80% yield and you need 100 units, on average
you need to start 125 units to average 100 units completed. The trouble is you will only
get 100 units 50% of the time! So one quickly learns to start 140, 150 or more to insure
a yield of 100 every time! Sometimes this results in 120 units completed and the extras
go into inventory, not to mention the extra raw material and capacity required. 
7. Replenish based on market demand - Forecasts are great and necessary but they are no
more than educated guesses. And the farther out into the future the forecast the higher
the probability that the guess will be very wrong. To use market demand to replenish
finished goods keeps the inventory level aligned with what customers are actually buying.
Of course you will have to do all of the above six things well to do this effectively,
but it's possible. So there it is. In these days of ERP systems, information technology
and other high tech systems, it almost seems too simple. I can assure you that these
things are easier to talk about than to execute, but the payoff is worth it. 
Part B of question 2.)
What Is a Data Warehouse?
A data warehouse is a decision support database that is maintained separately from an
organization's operational databases and it usually resides on a dedicated server. This
database is designed based on what kind of information a company is seeking (e.g., sales
marketing, healthcare membership and providers, etc.) and it adopts a STAR (or SNOWFLAKE)
schema design for maximum efficiency in performance. Extracting appropriate data from
existing operational database(s), cleansing or scrubbing the data, denormalizing the
data, and then loading the data into the database populate the database. (This data
population process is also known as the data transformation process.) This database is
then the place for top executives, managers, analysts, and other end-users to mine a rich
source of company information. They can ask compelling business questions and find
answers in their data so they can make key and timely business decisions from their
desktops using GUI On-line Analysis Processing (OLAP) tools. 
Attributes Of A Data Warehouse 
According to W.H. Inmon, who is considered the father of data warehousing, A Data
Warehouse is a subject-oriented, integrated, time variant, nonvolatile collection of data
in support of management's decision-making process. These fundamental attributes of a
data warehouse are further explained below: 
Subject Oriented 
Operational data, such as order processing and manufacturing databases, are organized
around business activities or functional areas. They are typically optimized to serve a
single, static, application. The functional separation of applications causes companies
to store identical information in multiple locations. The duplicated information's format
and currency are usually inconsistent. For example, in a delivery database, the customer
list will have very detailed information on customer addresses and is typically indexed
by customer number concatenated with a zip code. The same customer list in the invoicing
system will contain a potentially different billing address and be indexed by an
accounting Customer Account Number. In both instances the customer name is the same, but
is identified and stored differently. Deriving any correlation between data extracted
from those two databases presents a challenge. In contrast, a data warehouse is organized
around subjects. Subject orientation presents the data in a format that is consistent and
much clearer for end users to understand. For example subjects could be Product,
Customers, Orders as opposed to Purchasing, Payroll. 
Integrated 
Integration of data within a warehouse is accomplished by dictating consistency in
format, naming, etc. Operational databases, for historic reasons, often have major
inconsistencies in data representation. For example, a set of operational databases may
represent male and female by m and f, by 1 and 2, by x and y. Frequently the
inconsistencies are more complex and subtle. By definition, data is always maintained in
a consistent fashion in a data warehouse. 
Time variant 
Data warehouses are time variant in the sense that they maintain both historical and
(nearly) current data. Operational databases, in contrast, contain only the most current,
up-to-date data values. Furthermore, they generally maintain this information for no more
than a year (and often much less). By comparison, data warehouses contain data that is
generally loaded from the operational databases daily, weekly, or monthly and then
typically maintained for a period of 3 to 5 years. This aspect marks a major difference
between the two types of environments. Historical information is of high importance to
decision-makers. They often want to understand trends and relationships between data. For
example, the product manager for a soft drink maker may want to see the relationship
between coupon promotions and sales. This type of information is typically impossible to
determine with an operational database that contains only current data. 
Nonvolatile 
Nonvolatility, another primary aspect of data warehouses, means that after the
informational data is loaded into the warehouse, changes, inserts, or deletes are rarely
performed. The loaded data is transformed data that originated in the operational
databases. The data warehouse is subsequently reloaded or, more likely, appended on a
periodic basis with new, transformed or summarized data from the operational databases.
Apart from this loading process, the information contained in the data warehouse
generally remains static. The property of nonvolatility permits a data warehouse to be
heavily optimized for query processing. 
Built From Scratch 
Because each company has its own business needs and business queries, a data warehouse
database is normally built from scratch utilizing the available data warehousing enabling
tools. Determining what kind of questions or queries that end-users need is the first
step, though, a time consuming one. Data modeling for such a customized data warehouse
database can then be developed. Identifying what data is needed from the operational
database(s) and then populating the data warehouse would be the subsequent steps. The
entire process can then be repeated as additional refinement is needed over time. 
From the attributes described above, it is apparent that the purpose and usage of an
operational database and a data warehouse vary greatly. The chart below summarizes these
differences: 
Category Operational Database Data Warehouse
Function Data processing, support of business operations Decision support
Data Process oriented, current values, highly detailed Subject oriented, current and
historical values, summarized and sometimes detailed 
Usage Structured, repetitive Ad-hoc, some repetitive reports and structured applications

Processing Data entry, batch, OLTP End-user initiated queries 
Figure 1: Operational Databases vs. Data Warehouses 
Deviation from the Traditional Data Warehouse Attributes 
As the data warehouse technology becomes a mainstream technology, some traditional
attributes are being deviated from in order to meet users' increasing demands. The most
noticeable ones are timing variant, nonvolatile, and built from scratch. 
Deviation from time variant & nonvolatile 
As the size of the data warehouses becomes larger and larger (e.g., in terabytes), the
amount of time to reload or append data can become very tedious and time consuming.
Furthermore, users are demanding more up-to-date data to be included in the data
warehouse. Instead of adhering to the traditional data warehouse attributes of keeping
the data nonvolatile and time variant, new data is being added to the data warehouse on a
daily basis, if not on a real-time basis. Thus, new approaches are being made to tackle
this task. Two possible methods are: 
? Perform hourly/daily batch updates from shadowed log files. Transformation rules are
executed in this process. Thus, when the data reaches the target data warehouse database,
it is already transformed and summarized. 
? Perform real-time updates from shadowed log files. Again, transformation rules are
executed in this process. Instead of batch updates, this takes place on a per transaction
basis that meets certain business selection criteria.
Deviation from built from scratch 
For customers that are in the horizontal industry, meaning their applications are unique
to their own businesses, it is essential to build a data warehouse from scratch. However,
for customers that are in a vertical industry, meaning their applications are either
coming from the same vendor or the functionality of those applications from various
vendors are similar in nature, it is possible to leverage an off-the-shelf pre-packaged
MART. The MART is a data-modeling template that is designed with a certain set of queries
in mind for that specific vertical industry. Instead of designing data models from
scratch, leveraging these MARTs can reduce the development time and cost. According to
Frederick Rook's prediction, (a Senior VP of Platinum Technology Inc.,) approximately 80%
of the data warehouses or data marts for the vertical industries will be pre-packaged in
the next two years or so. This approach definitely deviates from the traditional one. 
What Is a Star/Snowflake Schema? 
As mentioned earlier, the data warehouse database adopts a star or snowflake schema to
maximize performance. A star or snowflake schema design is very different from that of an
operational database schema design. In an operational database design, the data is highly
normalized to support consistent updates and to maintain referential integrity. In a data
warehouse design, the data is highly denormalized to provide instant access without
having to perform a large number of joins. A star or snowflake schema design represents
data as an array in which each dimension is a subject around which analysis is performed.

As the name implies, the star schema is a modeling paradigm that has a single object in
the middle radially connected to other surrounding objects like a star. The star schema
mirrors the end user's view of a business query such as a sales fact that is qualified by
one or more dimensions (e.g., product, store, time, region, etc.). The object in the
center of the star is called the fact table. This fact table contains the basic business
measurements and can consist of millions of rows. The objects surrounding the fact table
(which appear as the points of the star) are called the dimension tables. These dimension
tables contain business attributes that can be used as SQL search criteria, and they are
relatively small. The star schema itself can be simple or complex. A simple star schema
consists of one fact table and several dimension tables. A complex star schema can have
more than one fact table and hundreds of dimension tables. Figure 2 depicts a simple star
schema. 
images/starp1.gif Star Schema 
The snowflake schema is an extension of the star schema where each point of the star
explodes into more points. In this schema, the star schema dimension tables are more
normalized. The advantages provided by the snowflake schema are improvements in query
performance due to minimized disk storage for the data and improved performance by
joining smaller normalized tables, rather than large denormalized ones. The snowflake
schema also increases the flexibility of the application because of the normalization
that lowers the granularity of the dimensions. However, since the snowflake schema has
more tables, it also increases the complexities of some of the queries that need to be
mapped. Figure 3 below depicts a snowflake schema. 
Query optimization 
Performance in data retrieval can be greatly enhanced through the use of multidimensional
and aggregation indexes in a star or snowflake environment. Over 90% of data warehousing
queries are multidimensional in nature using multiple criteria against multiple columns.
For example, end-users rarely want to access data by only one column or dimension, such
as finding the number of customers in the state of CA. They more commonly want to ask
complex questions such as how many customers in the state of CA have purchased product B
and C in the last year, and how does that compare to the year before. 
To optimize the query, an index can be put on each column that end-users want to query in
the dimension tables. When an end-user issues a query, a qualifying count based on index
access only can be returned without touching the actual data. According to Bill Inmon, it
is much more efficient to service a query request by simply looking in an index or
indexes instead of going to the primary source of data. In addition to multidimensional
queries, end-users often want to see the data aggregated. A data aggregation is usually a
COUNT or SUM, but can be an AVERAGE, MINIMUM, or MAXIMUM, such as number of customers,
total sales dollars or average quantity. An aggregation is typically organized or grouped
by another column, such as sum of sales by region, or average quantity of product line B
sold by sales rep. By placing an index on aggregated values, performance can be enhanced.

Summary 
Data is the building block for useful information. With access to accurate and timely
information, appropriate business decisions can be made to maximize profit and gain
competitive advantage over other competitors. Most companies today have no shortage of
data; however, the data exists in the form that is difficult for human access or
interpretation. The challenge lies in transforming the data into useful information. With
the data warehousing technology, the means of achieving this is possible.