When will Computers become truly Intelligent?

To date, all the traits of human intelligence have not been captured and applied together to spawn an intelligent artificial creature. Currently, Artificial Intelligence rather seems to focus on lucrative domain specific applications, which do not necessarily require the full extent of AI capabilities. This limit of machine intelligence is known to researchers as narrow intelligence.

There is little doubt among the community that artificial machines will be capable of intelligent thought in the near future. It's just a question of what and when... The machines may be pure silicon, quantum computers or hybrid combinations of manufactured components and neural tissue. As for the date, expect great things to happen within this century!

Definition...

Many branches of Artificial Intelligence today set out to solve domain specific problems, by using algorithms that display single characteristics of intelligence, if any at all. Some applications, in fact only show a remote emergent possibility of intelligence, a fact covered up by the use of numerous marketing buzz-words. Admittedly, A.I. is a hot topic that has increasingly picked up popularity and interest, and is becoming an umbrella of coolness targeted by advertising.None the less, the many branches and applications of A.I. remain fascinating, and we'll try to bring some of them together to analyse their common characteristics.

The first step towards this goal will be taken in the next page of this essay by discussing the importance of the representation of a problem. Then we'll consider the two major approaches to solving problems, namely the classical approach and the statistical approach.

It seems rather ironical for a site dedicated to the field of Artificial Intelligence not to have even the simplest definition. So here it is, and as a consequence, it is also a partial list of the content you can expect from this site.

Let's get things started by stating a very important fact: This is not a definition of intelligence, human or otherwise, nor of the process of simulating it artificially. This essay discusses and describes the field of Artificial Intelligence, its branches, research openings and applications. There is an considerable difference between the two, as you will quickly notice. Indeed, the field of A.I. has grown to be so much more than attempts to simulate (human) intelligenceThe following section will show how these concepts are split into various branches, of which we'll describe a few. With the theoretical background covered, we'll finally mention some practical applications of artificial intelligence

Artificial Intelligence is a branch of Science which deals with helping machines find solutions to complex problems in a more human-like fashion. This generally involves borrowing characteristics from human intelligence, and applying them as algorithms in a computer friendly way. A more or less flexible or efficient approach can be taken depending on the requirements established, which influences how artificial the intelligent behaviour appears.

AI is generally associated with Computer Science, but it has many important links with other fields such as Maths, Psychology, Cognition, Biology and Philosophy, among many others. Our ability to combine knowledge from all these fields will ultimately benefit our progress in the quest of creating an intelligent artificial being.

Motivation...

Computers are fundamentally well suited to performing mechanical computations, using fixed programmed rules. This allows artificial machines to perform simple monotonous tasks efficiently and reliably, which humans are ill-suited to. For more complex problems, things get more difficult... Unlike humans, computers have trouble understanding specific situations, and adapting to new situations. Artificial Intelligence aims to improve machine behaviour in tackling such complex tasks.

Together with this, much of AI research is allowing us to understand our intelligent behaviour. Humans have an interesting approach to problem-solving, based on abstract thought, high-level deliberative reasoning and pattern recognition. Artificial Intelligence can help us understand this process by recreating it, then potentially enabling us to enhance it beyond our current capabilities.

Key Success Factors

Airline Industry

The ability for airlines to succeed today is measured according to several key success factors.

Often key success factors next appear as elements of a competitive strength assessment in examining the relative strength of the business unit compared to its rivals in the industry.
When a strategic management control system is designed to ensure achievement of the business unit's strategic objectives, key success factors may suggest either strategic objectives themselves or measures for strategic objectives for that business unit—or both.
ey success factors have several direct and several possible uses for any business unit whether it is for-profit or not-for-profit, large or small, domestic or foreign. In strategic analysis of a business unit, key success factors often initially appear as analytical tools for examining the character of the industry in which the business unit competes?
he key point of this examination for those in other industries is that practitioners of strategic management should look closely at the number of key success factors appropriate for the industry being examined at the time of the examination

An industry's key success factors (KSF's) are those competitive factors that most affect industry members' ability to prosper in the marketplace... KSF's by their very nature are so important to future competitive success that all firms in the industry must be competent at performing or achieving them or risk becoming an industry also-ran. Crafting and Executing Strategy

The Key Success Factors

The origin of the key success factors focused on in this article dates from an earlier study by this author regarding the success or failure of new U.S. interstate airlines after deregulation in 1978.

That study explained according to 12 key success factors the success or failure of eight airlines that began interstate service between 1978 and 1995. A computer model of an airline was constructed and then simulated the operations of the eight airlines examined over the time periods studied, most of the airlines for a five-year period. Those same 12 factors can be used with today's eight leading (by service volume) U.S. airlines to explain their respective situations and to suggest changes that each airline must make to survive in the long run.

Successful airlines must do many things well. Not doing well in any one area may not result in failure as we define it. However, performing very poorly in any one area, or poorly in two or more areas, appears to make success elusive.

Airlines are in part service businesses. To be successful, an airline must be effective in four general areas: 1) attracting customers; 2) managing its fleet; 3) managing its people, and 4) managing its finances

Attracting Customers

In this article, we use two factors of measurement with regard to customers: 1) the attractiveness of the airline's service and 2) the effectiveness of the airline's promotional expenditures. In the original research we used a rather complex model of an airline's "attractiveness" relative to that of its competitors, for example including infrastructure convenience, and scope of service. The base was the attractiveness of the price of tickets. In this analysis only the relative price of tickets has been used because ticket price was by far the most significant factor in attractiveness. A lower relative price would generally be more attractive to most travelers.

Similarly, the derivation of the promotional effectiveness in the current analysis has been simplified to that of the base used in the original study model. A measure of ticket sales per dollar of promotion expense is used in this study, with higher sales per promotion dollar being advantageous. Except where otherwise noted, the data for the analysis are taken from the U.S. Department of Transportation databases.

Managing the Fleet

In the area of fleet management, the same factors are used for this analysis as in the earlier study. Airplane utilization in hours per day deals with how well the companies' major assets (airplanes) are used as a group. The load factor relative to the industry average indicates how well the average individual airplane is used. Simply stated, the load factor is that proportion of an airplane's seats that are sold and actually filled at departure.

Managing People

We use two factors with respect to how well the airline manages its people. Productivity, in airline capacity per employee, is a measure of how effectively the employees work together in providing the physical service of getting passengers from one place to another. Morale is a measure of how committed employees are to providing good service to the airline's customers. As in the original study, productivity is measured in available seat miles per employee. Morale is measured using proxies since the original morale model is complex and requires information not currently available for the airlines being examined. In this case, lost bags per 1000 passengers and complaints per 100,000 enplanements derived from the Air Travel Consumer Reports are used as indicators of how committed airline employees are to serving their customers. The activities that result in lost bags or in poor enough treatment of passengers that they file complaints are indicative of the morale of the airline employees. Labor-management relations (including strikes and threatened strikes) are one example of a driver of these effects.

Managing Finances

The last of the four areas is financial management, for which six factors are used. Unit revenue and unit cost are important by themselves, but their relationship is also important. Therefore, we have compared both unit revenue and unit cost as well as the unit margins among the airlines. A measure of capacity to normalize these factors is used since the airlines fly all their available seats, not just those that are occupied. Better unit revenue may not be an advantage for an airline whose unit costs are out of line.

In addition to unit revenues and unit costs, funding for growth is an important factor for an organization's long-term success. Most successful organizations choose to grow over time. In the case of the airlines, growth is measured in terms of capacity growth. Furthermore, in order to grow, an airline needs adequate funds. To be attractive for most equity investors, an airline must grow its equity over time. Moreover, to be attractive to most debt investors, a reasonable debt-to-assets ratio is desirable. In this realm of funding, this study is less precise. However, in light of this study's prior research, the measures in this case appear to indicate the likelihood of enduring success for the airlines.

The Analysis

The second direct use of key success factors often occurs in the construction of a competitive strength assessment of the business units to be compared, the rivals within the industry. The first step in this assessment is analyzing the data. The second step is presenting the analysis in a comparative manner.

This article limits its analysis to U.S. airlines because the U.S. Department of Transportation maintains a database of information which U.S. airlines are required by law to provide to the government. Some of the same key success factors apply to airlines of other countries, but to the author's knowledge, no other country has a similar consistent source of airline information, nor does the International Air Transport Association (the international airline organization).

Conclusions

With regard to the key success factors in the U.S. airline industry, one question comes to mind: Why is the airline industry able to continue to attract enough investors to keep all these airlines in business?

US Airways' merger with America West may be an indicator of a consolidation trend. However, something else may be involved. A 1995 Fortune assessment concluded, "Chaos may just be in the nature of this crazy business." The possible explanation that followed dealt with an economic phenomenon known as "core theory." Fundamentally, core theory is a mathematical formulation of the competitive environment of an industry. As in many mathematical models, there may be many, one, or no solutions to the equations of the model. According to this theory, the model for the airline industry has no solution, therefore it is an "empty core." A lot of things have changed in the ensuing decade, but the industry still seems to be just as chaotic as before. However, Lester Telser, the University of Chicago economist who is the proponent of core theory, is still exploring that theory with respect to the airline industry.

While the economists pursue their theories, it would seem to be appropriate for more airlines to emulate Southwest and its few other successful rivals—if not literally, at least in terms of the competitive strengths of the key success factors.

As explained in the introduction, an increase in the number of key success factors for a maturing industry is supported by this example. It is impossible to choose only five or six of these 12 and have confidence that successful performance will follow. In fact, as the industry further matures and more competitors are able to imitate the practices of the successful airlines, even more key success factors may be needed.

And more key success factors may be needed as more international expansion is pursued, as indicated in the introduction. Perhaps a risk assessment factor that includes such risks as currency risk or political risk (perhaps "Managing the Public Policy Environment") will be appropriate as large organizations evolve to have no dominant national home.

Role of Short-Term Correlation & Portfolio Diversification

From the third quarter of 2008 to the present, the financial markets have “gone to one,” meaning that all investment options have become highly correlated. They have all gone down (with the notable exceptions of cash and government bonds).

Study shows that the nearer and shorter the timeframe, the greater the likelihood an investment will move from uncorrelated to correlated.

The benefit of holding uncorrelated assets is that they should not all move in lock step, so that while one goes down, hopefully another will increase. The question that this article attempts to answer is whether the long-term correlations that sales and marketing materials often quote are similar in the short term as well.

Typically, correlation between investment assets and asset classes is calculated over extended time periods, such as 5, 10, or 15 years. But what is of greater concern to the investor is what the correlation will be next month. The use of a low 15-year correlation might obscure more recent data due to the length of time over which the correlation was calculated. Could it be, for example, that the last 12 months would show a much higher correlation between assets than the figure contained in the marketing literature?

What is Correlation?

Most investors have the singular goal of maximizing investment return given a certain level of risk tolerance. Modern portfolio theory holds that returns are maximized in the long run when they are held in a diversified portfolio. A statistical measure of diversification is “correlation," which is measured on a scale that runs from -1.0 to +1.0. A correlation coefficient of -1.0 or +1.0 is considered perfect correlation—knowing how one series of data moves provides perfect information on how the second series will move.

A negative correlation coefficient signifies that the two series move in opposite directions, for example, as one series increases, the other decreases. This is also known as an inverse correlation. A positive or direct correlation indicates that the series move together—as one increases, the other also increases. It is rare that one comes across perfect correlation, that is, a correlation coefficient of exactly -1.0 or +1.0.

The plus or minus sign indicates whether the relationship is direct or inverse, whereas the calculated value indicates the strength of the relationship. As the correlation coefficient moves from zero toward +1.0, there is an increasingly direct statistical relationship. Conversely, as the correlation coefficient moves from zero to -1.0, there is an increasingly inverse statistical relationship. In addition, a correlation of -0.7, then, is exactly as significant as a correlation of +0.7. A correlation coefficient of zero indicates that there is no statistical relationship between the two series of numbers—the series behave randomly with respect to one another. This is also called “non-correlation,” or, sometimes, the two series are said to be “uncorrelated.”

One important point about correlation is that it does not represent causality. For instance, in school-age children, shoe size is a great predictor of reading ability, not because shoe size has anything to do with reading, but because it is a proxy for age—older children tend to read better.

This issue note looks at the near-term issues regarding correlation. Using two series of random numbers (180 observations to simulate 15 years of monthly returns) and running a short (100-trial) Monte Carlo simulation (a process that repeats the same trial), these uncorrelated random series showed significant 36-, 24-, and 12-month correlations. This suggests that investors should also consider short-term correlations between assets when attempting to diversify their portfolios. In addition, correlations should be rebalanced as often as asset allocations because investment strategies, personnel, and so forth change over time.

Correlation and Investing

Some investors believe that they make only three investment decisions: asset allocation, manager selection, and vehicle choice. Asset allocation is important because it is widely held that diversification is a cornerstone of investing theory. Diversification follows the logic of not putting all of your eggs in one basket. If an investor invests in a single stock, then the portfolio will do as well or as poorly as that single stock. If the investors select two stocks, they would appear to have achieved some level of diversification, but this is only at a company level. If both companies are engaged in the same industry—like Pepsi and Coca-Cola, or American Airlines and Delta, or Ford and GM—then the stock price movements that affect an industry segment will affect both stocks—that is, 100 percent of their portfolio. So, the investors might want also to diversify along company, industry, or geographical lines.

Diversification is usually quantified by correlation, that is, the degree to which the movement of one investment or asset class allows for inferences about how another investment or asset class will move. This is not indicative of causality, but simply a statistical relationship that may include causality and that can also occur simply by chance. A portfolio is not diversified if all of its

holdings are correlated with one another, meaning that if one holding moves a certain way, we can predict how the other holdings will move. Brokers of commodity-based products (whether futures contracts or hard-asset ownership), infrastructure investments, and real estate funds often cite "uncorrelated with existing asset classes" as a major selling point of their products.

The Results

The test revealed that, overall, the two series were uncorrelated. In the 100 trials, the overall correlation of .20 was only obtained once. When we reviewed the correlations of the last 36, 24, and 12 months, some startling results were evident. In the last 36 months of each trial, the correlation was 0.2 or more 27 percent (27/100) of the time, 0.3 or more 9 percent of the time, and 0.4 or more 2 percent of the time.

For the last 24 months, a correlation of 0.2 or more occurred 33 percent of the time, a correlation of 0.3 or more resulted 18 percent of the time, a correlation of 0.4 or more was obtained 6 percent of the time, and a correlation of 0.5 or more occurred 2 percent of the time.

The last 12 months, however, may be the most relevant period because this timeframe is the most likely to impact a portfolio. A correlation of 0.2 or more occurred 61 percent of the time, a correlation of 0.3 or more was evident 39 percent of the time, a correlation of 0.4 or more occurred 21 percent of the time, and a correlation of 0.5 or more was found 10 percent of the time. An investor adding an investment and expecting it to be uncorrelated (based on 15 years worth of data) could very well be surprised at the resultant effect.

Conclusion: Do Your Short-Term Correlation Home-Work

Our findings suggest that if an investor is adding an investment to his or her portfolio with the goal of aiding diversification, he or she should parse the long-term correlation into shorter-term metrics. The nearer and the shorter the timeframe, the greater the likelihood that the investment will move from uncorrelated to correlated. As the correlation that will be added to the portfolio is more reflective of the 180th month than the first month of the series, the additional calculation of a near-term 36-, 24-, and 12-month correlation could prove useful. Perhaps there is an investment that can be added to the portfolio that, over the long term, will provide uncorrelated returns and, therefore, aid in diversification. However, if the return stream is presently correlated to the portfolio, the investor should wait a couple of periods before adding the investment, thereby mitigating the short-term effects of correlation.

Review Investments Periodically for Correlation Shifts

An additional implication from this study concerns investments that are already in the portfolio. Once an investment is added, there is usually no further attention devoted to the correlation. This study suggests, however, that the correlations of the existing investments should also be reviewed periodically. Manager changes, style drift, and so forth may mean that the original correlation that made the investment attractive is no longer accurate.

Businesses Win and Which Lose?What Determines ?

How do you get people to question their assumptions?

IF I ask some one questions, like, "In the past 90 days, what were your three most important strategic accomplishments?"

I do not simply accept answers like, “We met our revenue budget.” A strategic accomplishment is one which changes the field of play in a company’s favor.

Another question

I ask is, “In the past 90 days, what were the three most important ways you fell short of your potential?”

The answers give insight into what people think the company should be emphasizing, but isn’t.

I also ask, “In the past 90 days, what are the three most important things you have learned about your strategy.”

This is a tough question because it asks people to learn and adapt the strategy and tactics of the company.

At Pepper dine we give a lot of attention to teams. What do you think about teaming in the workplace?

When companies is to be helped for them set strategy, I begin by creating what I call a Mind Bank. For groups to be effective, they need to have a method of aggregating the opinions and insights of individual members in a way that doesn't undermine their diversity and independence. I always send 30 to 50 people a "strategy sketch" document, and ask them to return it to me with their reflections on what they consider the most important issues facing the business. If you're looking for a way to make better decisions in your company, you will find that the more minds you get working on the problem, the better the solution.
Are you working on another book?

Yes, it is titled Bounce: The Art of Turning Tough Times into Triumph. It will be published by Random House on September 15 [2009].
What inspires you?

My six- and nine-year-olds! They remind me to see life through their eyes, with the future being one big shiny opportunity. Children are born with a transparent or clear way of seeing things. They respond to the world as it is. They are transparent. What you see, is what you get?

Business Forecasts with Artificial Intelligence

Today's business world is driven by customer demand. Unfortunately, the patterns of demand vary considerably from period to period. This is why it can be so challenging to develop accurate forecasts. Forecasting is the process of estimating future events, and it is fundamental to all aspects of management. The goals of forecasting are to reduce uncertainty and to provide benchmarks for monitoring actual performance. Emerging information technologies and artificial intelligence (AI) techniques are being used to improve the accuracy of forecasts and thus making a positive contribution to enhancing the bottom line.

A new generation of artificial intelligence technologies have emerged that hold considerable promise in helping improve the forecasting process including such applications as product demand, employee turnover, cash flow, distribution requirements, manpower forecasting, and inventory. These AI based systems are designed to bridge the gap between the two traditional forecasting approaches: managerial and quantitative.

Forecasting Approaches

Generally speaking, forecasts are based on quantitative analysis, qualitative analysis or a combination of both. Often quantitative forecasting is referred to as objective analysis while qualitative forecasting is called managerial or judgmental analysis. Typically, there is tension between these two approaches. Quantitative forecasts, which are often favored by operations, tend to be developed using a bottom up approach while managerial-based forecasts, usually preferred by the marketing group, are approached from a top down perspective. For example, a primary marketing goal is to insure adequate supply while operation's focus is on minimizing inventory. The resolution of these two approaches is how forecasting errors occur and presents an opportunity for using artificial intelligence methods. Quantitative forecasting can be characterized by one of the two basic techniques:

• Time Series - The future will tend to look and behave like the past. For example, gasoline prices for the next six months will continue along the same lines as they have over the past six months.
• Relational - The future is dependent on the direction of a variety of factors. For example, new housing starts might be a function of interest rates and local weather conditions.

A time series is a set of data points recorded over successive time periods. Examples include monthly billables, weekly unit product demand and quarterly inventory levels and stock prices. A relational database consists of the recording of several variables for a number of observations. For example, a financial relational database could consist of revenues, earnings and assets for the Fortune 500.

The following graphic highlights the typical forecasting process. The resultant forecasts are evaluated by comparing predictions with actual results. This assessment is accomplished by examining the error terms. An error term is the difference between the prediction and the actual outcome. Based on an error assessment, the forecasting process is continually updated through the adjustment of model inputs.

Forecasting Approaches

Generally speaking, forecasts are based on quantitative analysis, qualitative analysis or a combination of both. Often quantitative forecasting is referred to as objective analysis while qualitative forecasting is called managerial or judgmental analysis. Typically, there is tension between these two approaches. Quantitative forecasts, which are often favored by operations, tend to be developed using a bottom up approach while managerial-based forecasts, usually preferred by the marketing group, are approached from a top down perspective. For example, a primary marketing goal is to insure adequate supply while operation's focus is on minimizing inventory. The resolution of these two approaches is how forecasting errors occur and presents an opportunity for using artificial intelligence methods. Quantitative forecasting can be characterized by one of the two basic techniques:

• Time Series - The future will tend to look and behave like the past. For example, gasoline prices for the next six months will continue along the same lines as they have over the past six months.
• Relational - The future is dependent on the direction of a variety of factors. For example, new housing starts might be a function of interest rates and local weather conditions.
• The following process outlines a plan for improving forecast accuracy using artificial intelligence support systems:

  1. Evaluate and characterize the current forecasting system.

  2. Measure the current level of error.

  3. Compare error levels with industry norms.

  4. Specify new requirements.

  5. Characterize the economic impact of improved forecasts.

  6. Identify alternative AI forecasting options.

  7. Select best approach(s).

  8. Develop implementation schedule.

  9. Identify potential bottlenecks and problem areas.

  10. Implement new system and monitor performance.

Future of Business intelligence in Retail:

The Future of BI in Retail:

BI will be defined by the retailers that have figured out how to maximize customer satisfaction and profitability with the right combination of quality products, friendly and efficient service, unique value, a differentiated shopping experience, and a business model that truly serves its community -- locally and globally. How will this be accomplished? It starts with understanding the customer and then linking that insight into every decision that is made, from merchandising to marketing to distribution to store operations to finance, so that retailers can predict how to best serve their customers' ever-changing needs and desires.

Our vision for the future of retail BI provides for that very scenario, through our intelligence platform and our solutions for customer, merchandise, operations, and performance intelligence that are combined in a suite designed to equip retailers to become truly innovative.

A solution seeking to use customer behavioral data to make better merchandising or marketing decisions needs to interface with sales transaction systems, loyalty systems, in-house credit systems, coupon redemption systems, catalog and Internet customer data systems, and so forth. A system that recommends optimized price changes should interface with the price management system, the item master, the system that generates labels, etc.

t is important to note that a good BI solution will be able to integrate with any other system or platform. That said different BI solutions need to interface with different operational systems for different purposes.

A solution seeking to use customer behavioral data to make better merchandising or marketing decisions needs to interface with sales transaction systems, loyalty systems, in-house credit systems, coupon redemption systems, catalog and Internet customer data systems, and so forth. A system that recommends optimized price changes should interface with the price management system, the item master, the system that generates labels, etc.

There must be a closed-loop interface between the operational systems that retailers rely upon to conduct day-to-day business and the BI systems that help them conduct that business more efficiently and profitably.

Marketing -- By understanding customers better -- whether by profiling, segmenting, gauging propensity to respond, or using market basket analysis -- retailers can create better-defined targeted campaigns, reducing expenses (printing, paper, postage) while increasing response rates, revenues, and gross margins. Also, as retailers gain a better understanding of their customers' buying behavior, this analysis can then be used to create more effective merchandising plans for the next season.

Operations -- Understanding and predicting changes in demand -- by hour, by day, by location, by promotion, by price change -- mean that the store floors, the catalog call centers, and the fleet crews delivering replenishment orders from the DC to the store are all appropriately staffed. This understanding also leads to optimal productivity since store-level human capital costs can be scheduled better and managed more efficiently.

Specific Areas in Business intelligence solutions

Operational and transactional systems such as merchandise management, ERP (enterprise resource planning), and POS, are very good at what they do -- organizing huge amounts of operational data and transactions. These systems can tell retailers what has happened in their business and what their customers have done -- last week, last month, and last year.

It's critical, however, for retailers to understand what will happen: what the demand will be for a select assortment of merchandise, what impact an incremental price change will have on demand, which floor plan will sell more designer shoes, which customers will respond to a direct mail or catalog offer.

Real value comes from systems that go beyond the limitations of operational software alone, systems that can take operational data and create enterprise intelligence and predictive insights.

These BI systems must combine data management (consolidating, organizing, and cleansing huge amounts of disparate data from varying systems and platforms) with predictive analytics (data mining, forecasting, optimization). When they do, retailers can make sense of customer, product, supplier, and operational data and draw insights that will help them run their businesses better and more profitably.

Leading retailers around the globe -- like Wal-Mart, Foot Locker, Staples, Williams-Sonoma, and Amazon.com and many others -- have begun using BI and analytics to make an array of strategic decisions. These include where to place retail outlets, how many of each size or color of an item to put in each store, and when and how much to discount. The effects of these decisions can save or generate millions of dollars for retailers.

The Strength of the Market for BI in Retail Today:

The market is very strong and getting stronger. While it is difficult to find a comprehensive suite of retail-specific BI offerings that spans the spectrum from competitive intelligence to merchandise planning and optimization (product, price, promotion, and placement) based on customer insight, to knowing how to maximize the ROI on the next marketing campaign, to understanding where to build the next store, to reducing supply chain costs. Retailers are telling us over and over that they are seeking a single, stable, reliable, and proven provider of superior BI solutions. They are implementing projects that span multiple years and will deliver value for years to come.

The Retailers that are Realizing the Most Benefits from BI:

We find that the retailers that are realizing the most significant returns on their investments are those that take a purposeful, pragmatic approach to establishing an intelligence platform upon which to base all other BI solutions. A single, reliable demand forecast, for instance, can also be used in merchandising, marketing, logistics, store operations, call center staffing, etc., for operational benefit. BI that remains segmented by functional area can provide some value, but retailers can realize a much larger return by building the foundation upon which the rest of the house will stand. This is true of both top-tier and midmarket retailers, regardless of segment.

Specific Areas in Which Retailers can Benefit Most Include:

Merchandising -- This is clearly the most important area of a retailer's business and an area where retailers are beginning to exploit the full value of BI. Analysis of past performance, combined with plans and forecasts of future customer behavior, leads to more accurate initial allocations of merchandise across channels and stores. Assortment and size optimization that are based on customer demand patterns ensure that the correct assortments, size, and case-pack distributions get sent to the correct stores. Daily price, promotion, and markdown optimization ensures that items are priced for optimal profitability, both preseason and in season. Space automation and optimization ensure that departmental sales and profit per square foot are maximized, and products are given the correct inventory and space on the shelf or on the rack. Optimized fulfillment ensures that products are allocated or replenished based on demand. Accurate analysis also results in a more efficient use of manpower in picking, packing, and shipping the first wave of product, while minimizing additional, costly payroll expenses to facilitate transfers between stores, vendor returns, changing signage and labels for markdowns, and otherwise correcting mistakes.

Business intelligence solutions for the retail industry

Traditionally, the retail industry has lagged behind other industries in adopting new technologies, and this holds true in its acceptance of BI technology. Some industries, such as financial services, have become very sophisticated in using BI software for financial reporting and consolidation, customer intelligence, regulatory compliance, and risk management. However, retailers are quickly catching up and beginning to recognize the many areas of BI that can be applied specifically to their businesses.

The Strength of the Market for BI in Retail Today:

The market is very strong and getting stronger. While it is difficult to find a comprehensive suite of retail-specific BI offerings that spans the spectrum from competitive intelligence to merchandise planning and optimization (product, price, promotion, and placement) based on customer insight, to knowing how to maximize the ROI on the next marketing campaign, to understanding where to build the next store, to reducing supply chain costs. Retailers are telling us over and over that they are seeking a single, stable, reliable, and proven provider of superior BI solutions. They are implementing projects that span multiple years and will deliver value for years to come.

The competitive game is changing for retail. As the industry continues to consolidate, retailers have begun to realize that using technology to better understand customer buying behavior, to drive sales and profitability, and to reduce operational costs is a necessity for long-term survival.

Retailers are now paying significant attention to BI software, specifically in the areas of merchandise intelligence (including merchandise planning, assortment, size, space, price, promotion, and markdown optimization), customer intelligence (including marketing automation, marketing optimization, and market basket analysis), operational intelligence (including IT portfolio management, labor optimization, and real estate site selection), and competitive intelligence. There are many factors that have led retailers to adopt BI software: increased competition, the need to squeeze more profitability out of less space, prevalent credit card usage, the Internet's role as an alternative sales channel, the popularity of loyalty cards, and soon, RFID (radio frequency identification). These milestones have created a wealth of data that retailers are now beginning to appreciate and use.

Within individual companies, we view the history of BI in retail through a method that we devised to describe the status of any company's evolution toward becoming an intelligent enterprise. We believe that organizations pass through five fundamental stages as they advance in their use of BI as a competitive differentiator:

Operate -- At this most basic level are the companies rife with information mavericks: the guys in basement offices hammering away on desktop spreadsheets. If they go, the knowledge goes with them. There are no processes, and each request becomes an ad hoc data rebuild, resulting in multiple versions of the truth, with the likelihood of a different answer to any one question every time it is asked.
Consolidate -- At this stage, a company has pulled together its data at the departmental level. Here, a question gets the same answer every time, at least within the department. However, departmental interests and interdepartmental competition can skew the integrity of the output and result in multiple versions of the truth.

Integrate -- At this point in the evolution, a company has adopted enterprise-wide data and bases its decisions on this more complete information. This company is beginning to have a true awareness of additional opportunities for the use of BI to improve processes and profits.

Optimize -- At this stage, the company's knowledge workers are very focused on incremental process improvements and refining the value-creation process. Everyone understands and uses analysis, trending, pattern analysis, and predictive results to increase efficiency and effectiveness. The extended value chain becomes increasingly critical to the organization, including the customers, suppliers, and partners who constitute intercompany communities.

Innovate -- This level represents a major, quantum break with the past. It exploits the understanding of the value-creation process acquired in the optimize stage and replicates that efficiency with new products in new markets. Companies operating at this level understand what they do well and apply this expertise to new areas of opportunity, thus multiplying the number of revenue streams flowing into the enterprise. Armed with information and business process knowledge, organizations approaching the innovate level will introduce truly innovative products and services that reflect their unique understanding of the market, their internal strengths and weaknesses, and an unfailing flow of ideas from continuously engaged employees.
We are finding that most large retailers have reached or are approaching the integrate stage, with many making great strides toward the optimize and innovate levels. There is an enormous opportunity for the evolution to continue -- within every retail organization.

The Presence of BI in the Retail IT Infrastructure:

In the typical retail IT infrastructure, there are two fundamental categories of systems: transactional/operational systems, such as POS and purchase order management systems; and analytic/BI systems.

business inteligence innew era

As 21st century is on ramp we need to think about business processes to come up in 2012 onwards. It will be people oriented business processes .I mean the People whom company is holding. product will have little less importantance as every body will have product.But companies will diffrenciate with Human cepital behind it.