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 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:
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".
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.
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.
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.
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.
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.
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