The OLAP Report: Applications

You can contact Nigel Pendse, the author of this section, by e-mail on NigelP@olapreport.com if you have any comments or observations. Last updated on July 11, 2005.

Introduction

We define OLAP as Fast Analysis of Shared Multidimensional Information — FASMI. There are many applications where this approach is relevant, and this section describes the characteristics of some of them. In an increasing number of cases, specialist OLAP applications have been pre-built and you can buy a solution that only needs limited customizing; in others, a general-purpose OLAP tool can be used. A general-purpose tool will usually be versatile enough to be used for many applications, but there may be much more application development required for each. The overall software costs should be lower, and skills are transferable, but implementation costs may rise and end-users may get less ad hoc flexibility if a more technical product is used. In general, it is probably better to have a general-purpose product which can be used for multiple applications, but some applications, such as financial reporting, are sufficiently complex that it may be better to use a pre-built application, and there are several available.

We would advise users never to engineer flexibility out of their applications — the only thing you can predict about the future is that it will not be what you predicted. Try not to hard code any more than you have to.

OLAP applications have been most commonly used in the financial and marketing areas, but as we show here, their uses do extend to other functions. Data rich industries have been the most typical users (consumer goods, retail, financial services and transport) for the obvious reason that they had large quantities of good quality internal and external data available, to which they needed to add value. However, there is also scope to use OLAP technology in other industries. The applications will often be smaller, because of the lower volumes of data available, which can open up a wider choice of products (because some products cannot cope with very large data volumes).

Marketing and sales analysis

Most commercial companies require this application, and most products are capable of handling it to some degree. However, large-scale versions of this application occur in three industries, each with its own peculiarities:

	Consumer goods industries often have large numbers of products and outlets, and a high rate of change of both. They usually analyze data monthly, but sometimes it may go down to weekly or, very occasionally, daily. There are usually a number of dimensions, none especially large (rarely over 100,000). Data is often very sparse because of the number of dimensions. Because of the competitiveness of these industries, data is often analyzed using more sophisticated calculations than in other industries. Often, the most suitable technology for these applications is one of the hybrid OLAPs, which combine high analytical functionality with reasonably large data capacity.
	Retailers, thanks to EPOS data and loyalty cards, now have the potential to analyze huge amounts of data. Large retailers could have over 100,000 products (SKUs) and hundreds of branches. They often go down to weekly or daily level, and may sometimes track spending by individual customers. They may even track sales by time of day. The data is not usually very sparse, unless customer level detail is tracked. Relatively low analytical functionality is usually needed. Sometimes, the volumes are so large that a ROLAP solution is required, and this is certainly true of applications where individual private consumers are tracked.
	The financial services industry (insurance, banks etc) is a relatively new user of OLAP technology for sales analysis. With an increasing need for product and customer profitability, these companies are now sometimes analyzing data down to individual customer level, which means that the largest dimension may have millions of members. Because of the need to monitor a wide variety of risk factors, there may be large numbers of attributes and dimensions, often with very flat hierarchies.

A marketing example

In response to a sudden management panic near a quarter end, a marketing analyst is given a few minutes to analyze the market acceptance of new products. She decides to group 20 products that were introduced between six and nine months ago and compare their sales with a comparable group of 50 products introduced between two and three years ago. She simply defines two new, on-the-fly, product groupings and creates a ratio of the new group to the older group. She can then track this ratio of sales revenue or volume by any level of location, over time, by customer sector or by sales group. Defining the new groupings and the ratio takes a couple of minutes, and any of the analyses take a matter of a few seconds (depending on the product: in a ROLAP, it might take minutes) to generate, even though the database has tens of thousands of products and hundreds of locations. It doesn’t take more than a total of 15 minutes to spot that some regions have not accepted the new products as fast as others.

Then, she investigates whether this was because of inadequate promotion, unsuitability of the new products, lack of briefings of the sales force in the slow areas or if some areas always accept new products more slowly. She looks at other new product introductions by creating new groupings of products of different ages, and finds that the same areas are always conservative when introducing expensive new products. She then uses this information to see if the growth in the ‘slow’ areas is in line with history, and finds that some areas have taken off even more slowly than previously.

Management can now decide if there really is a problem, what it is and what to do about solving it.

Most of the data will usually come from the sales ledger(s), but there may be customer databases and some external data that has to be merged. In some industries (for example, pharmaceuticals and CPG), large volumes of market and even competitor data is readily available and this may need to be incorporated.

Getting the data right may not be easy. For example, most companies have problems with incorrect coding of data, especially if the organization has grown through acquisition, with different coding systems in use in different subsidiaries. If there have been multiple different contracts with a customer, then the same single company may appear as multiple different entities, and it will not be easy to measure the total business done with it. Another complication might come with calculating the correct revenues by product. In many cases, customers or resellers may get discounts based on cumulative business during a period. This discount may appear as a retrospective credit to the account, and it should then be factored against the multiple transactions to which it applies. This work may have been done in the transaction processing systems or a data warehouse; if not, the OLAP tool will have to do it.

There are analyses that are possible along every dimension. Here are a dozen of the questions that could be answered using a good marketing and sales analysis system:

The benefits of a good marketing and sales analysis system is that results should be more predictable and manageable, opportunities will be spotted more readily and sales forces should be more productive.

Clickstream analysis

This is one of the latest OLAP applications. Commercial Web sites generate gigabytes of data a day that describe every action made by every visitor to the site. No bricks and mortar retailer has the same level of detail available about how visitors browse the offerings, the route they take and even where they abandon transactions. A large site has an almost impossible volume of data to analyze, and a multidimensional framework is possibly the best way of making sense of it. There are many dimensions to this analysis, including where the visitors came from, the time of day, the route they take through the site, whether or not they started/completed a transaction, and any demographic data available about customer visitors.

Unlike a conventional retailer, an e-commerce site has the ability – almost an obligation, it would seem – to be redesigned regularly and this should be based, at least in part, on a scientific analysis of how well the site serves its visitors and whether it is achieving its business objectives, rather than a desire merely to reflect the latest design and technology fashions. This means that it is necessary to have detailed information on the popularity and success of each component of a site.

But the Web site should not be viewed in isolation. It is only one facet of an organization’s business, and ideally, the Web statistics should be combined with other business data, including product profitability, customer history and financial information. OLAP is an ideal way of bringing these conventional and new forms of data together. This would allow, for instance, Web sites to be targeted not simply to maximize transactions, but to generate profitable business and to appeal to customers likely to create such business. OLAP can also be used to assist in personalizing Web sites.

In the great rush to move business to the Web, many companies have managed to ignore analytics, just as the ERP craze in the mid and late 1990s did. But the sheer volume of data now available, and the shorter times in which to analyze it, make exception and pro-active reporting far more important than before. Failing to do so is an invitation for a quick disaster.

Many of the issues with clickstream analysis come long before the OLAP tool. The biggest issue is to correctly identify real user sessions, as opposed to hits. This means eliminating the many crawler bots that are constantly searching and indexing the Web, and then grouping sets of hits that constitute a session. This cannot be done by IP address alone, as Web proxies and NAT (network address translation) mask the true client IP address, so techniques such as session cookies must be used in the many cases where surfers do not identify themselves by other means. Indeed, vendors such as Visual Insights charge much more for upgrades to the data capture and conversion features of their products than they do for the reporting and analysis components, even though the latter are much more visible.

Database marketing

This is a specialized marketing application that is not normally thought of as an OLAP application, but is now taking advantage of multidimensional analysis, combined with other statistical and data mining technologies. The purpose of the application is to determine who are the best customers for targeted promotions for particular products or services based on the disparate information from various different systems.

If these goals are met, both parties profit. The customers will have a company that knows what they want and provides it. The company will have loyal customers that generate sufficient revenue and profits to continue a viable business.

Database marketing specialists try to model (using statistical or data mining techniques) which pieces of information are most relevant for determining likelihood of subsequent purchases, and how to weight their importance. In the past, pure marketers have looked for triggers, which works, but only in one dimension. But a well established company may have hundreds of pieces of information about customers, plus years of transaction data, so multidimensional structures are a great way to investigate relationships quickly, and narrow down the data which should be considered for modeling.

Once this is done, the customers can be scored using the weighted combination of variables which compose the model. A measure can then be created, and cubes set up which mix and match across multidimensional variables to determine optimal product mix for customers. The users can determine the best product mix to market to the right customers based on segments created from a combination of the product scores, the several demographic dimensions, and the transactional data in aggregate.

Finally, in a more simplistic setting, the users can break the world into segments based on combinations of dimensions that are relevant to targeting. They can then calculate a return on investment on these combinations to determine which segments have been profitable in the past, and which have not. Mailings can then be made only to those profitable segments. Products like Express allows the users to fine tune the dimensions quickly to build one-off promotions, determine how to structure profitable combinations of dimensions into segments, and rank them in order of desirability.

Budgeting

This is a painful saga that every organization has to endure at least once a year. Not only is this balancing act difficult and tedious, but most contributors to the process get little feedback and less satisfaction. It is also the forum for many political games, as sales managers try and manipulate the system to get low targets and cost center managers try and hide pockets of unallocated resources. This is inevitable, and balancing these instinctive pressures against the top down goals of the organization usually means that setting a budget for a large organization involves a number of arduous iterations that can last for many months. We have even come across cases where setting the annual budget took more than a year, so before next year’s budget had been set, the budgeting department had started work on the following year’s budget!

Some companies try the top down approach. This is quick and easy, but often leads to the setting of unachievable budgets. Managers lower down have no commitment to the numbers assigned to them and make no serious effort to adhere to them. Very soon, the budget is discredited and most people ignore it. So, others try the bottom up alternative. This involves almost every manager in the company and is an immense distraction from their normal duties. The resulting ‘budget’ is usually miles off being acceptable, so orders come down from on high to trim costs and boost revenue. This can take several cycles before the costs and revenues are in balance with the strategic plan. This process can take months and is frustrating to all concerned, but it can lead to good quality, achievable budgets. Doing this with a complex, multi-spreadsheet system is a fraught process, and the remaining mainframe based systems are usually far too inflexible.

Ultimately, budgeting needs to combine the discipline of a top-down budget with the commitment of a bottom-up process, preferably with a minimum of iterations. An OLAP tool can help here by providing the analysis capacity, combined with the actuals database, to provide a good, realistic starting point. In order to speed up the process, this could be done as a centrally generated, top down exercise. In order to allow for slippage, this first pass of the budget would be designed to over achieve the required goals. These ‘suggested’ budget numbers are then provided to the lower level managers to review and alter. Alterations would require justification.

There would have to be some matching of the revenue projections from marketing (which will probably be based on sales by product) and from sales (which will probably be based on sales territories), together with the manufacturing and procurement plans, which need to be in line with expected sales. Again, the OLAP approach allows all the data to be viewed from any perspective, so discrepancies should be identifiable early. It is as dangerous to budget for unachievable sales as it is to go too low, as cost budgets will be authorized based on phantom revenues.

It should also be possible to build the system so that data need not always be entered at the lowest possible level. For example, cost data may run at a standard monthly rate, so it should be possible to enter it at a quarterly or even annual rate, and allow the system to phase it using a standard profile. Many revenue streams might have a standard seasonality, and systems like Cognos Planning and Geac MPC are able to apply this automatically. There are many other calculations that are not just aggregations, because to make the budgeting process as painless as possible, as many lines in the budget schedules as possible should be calculated using standard formulae rather than being entered by hand.

An OLAP based budget will still be painful, but the process should be faster, and the ability to spot out of line items will make it harder for astute managers to hide pockets of costs or get away with unreasonably low revenue budgets. The greater perceived fairness of such a process will make the process more tolerable, even if it is still unpopular. The greater accuracy and reliability of such a budget should reduce the likelihood of having to do a mid year budget revision. But if circumstances change and a re-budget is required, an OLAP based system should make it a faster and less painful process.

Financial reporting and consolidation

Every medium and large organization has onerous responsibilities for producing financial reports for internal (management) consumption. Publicly quoted companies or public sector bodies also have to produce other, legally required, reports.

Accountants and financial analysts were early adopters of multidimensional software. Even the simplest financial consolidation consists of at least three dimensions. It must have a chart of accounts (general OLAP tools often refer to these as facts or measures, but to accountants they will always be accounts), at least one organization structure plus time. Usually it is necessary to compare different versions of data, such as actual, budget or forecast. This makes the model four dimensional. Often line of business segmentation or product line analysis can add fifth or sixth dimensions. Even back in the 1970s, when consolidation systems were typically run on time-sharing mainframe computers, the dedicated consolidation products typically had a four or more dimensional feel to them. Several of today’s OLAP tools can trace their roots directly back to this ancestry and many more have inherited design attributes from these early products.

To address this specific market, certain vendors have developed specialist products. Although they are not generic OLAP tools (typically, they have specific dimensionality), we have included some of them in this report because they could be viewed as conforming to our definition of OLAP (Fast Analysis of Shared Multidimensional Information) and they represent a significant proportion of the market. The market leader in this segment is Hyperion Solutions. Other players include Cartesis, Geac, Longview Khalix, and SAS Institute, but many smaller players also supply pre-built applications for financial reporting. In addition to these specialist products, there is a school of thought that says that these types of problems can be solved by building extra functionality on top of a generic OLAP tool.

There are several factors that distinguish this specialist sector from the general OLAP area. They are:

Special dimensionality

Normally we would not look favorably on a product that restricted the dimensionality of the models that could be built with it. In this case, however, there are some compelling reasons why this becomes advantageous.

In a financial consolidation certain dimensions possess special attributes. For example the chart of accounts contains detail level and aggregation accounts. The detail level accounts typically will sum to zero for one entity for one time period. This is because this unit of data represents a trial balance (so called because, before the days of computers you did a trial extraction of the balances from the ledger to ensure that they balanced). Since a balanced accounting transaction consists of debits and credits of equal value which are normally posted to one entity within one time period, the resultant array of account values should also sum to zero. This may seem relatively unimportant to non-accountants, but to accountants, this feature is a critical control to ensure that the data is ‘in balance’.

Another dimension that possesses special traits is the entity dimension. For an accurate consolidation it is important that entities are aggregated into a consolidation once and only once. The omission or inclusion of an entity twice in the same consolidated numbers would invalidate the whole consolidation.

As well as the special aggregation rules that such dimensions possess, there are certain known attributes that the members of certain dimensions possess. In the case of accounts, it is useful to know:

	Is the account a debit or a credit?
	Is it an asset, liability, income or expense or some other special account, such as an exchange gain or loss account?
	Is it used to track inter-company items (which must be treated specially on consolidation)?

For the entity (or cost center or company) dimension:

	What currency does the company report in?
	Is this a normal company submitting results or an elimination company (which only serves to hold entries used as part of the consolidation)?

By pre-defining these dimensions to understand that this information needs to be captured, not only can the user be spared the trouble of knowing what dimensions to set up, but also certain complex operations, such as currency translation and inter-company eliminations can be completely automated. Variance reporting can also be simplified through the system’s knowledge of which items are debits and which are credits.

Controls

Controls are a very important part of any consolidation system. It is crucial that controls are available to ensure that once an entity is in balance it can only be updated by posting balanced journal entries and keeping a comprehensive audit trail of all updates and who posted them when. It is important that reports cannot be produced from outdated consolidated data that is no longer consistent with the detail data because of updates. When financial statements are converted from source currency to reporting currency, it is critical that the basis of translation conforms to the generally accepted accounting principles in the country where the data is to be reported. In the case of a multinational company with a sophisticated reporting structure, there may be a need to report in several currencies, potentially on different bases. Although there has been considerable international harmonization of accounting standards for currency translation and other accounting principles in recent years, there are still differences, which can be significant.

Special transformations of data

Some non-accountants dismiss consolidation as simple aggregation of numbers. Indeed the basis of a financial consolidation is that the financial statements of more than one company are aggregated so as to produce financial statements that meaningfully present the results of the combined operation. However, there are several reasons why consolidation is a lot more complex than simply adding up the numbers.

Results are often in different currencies. Translating statements from one currency to another is not as simple as multiplying a local currency value by an exchange rate to yield a reporting currency value. Since the balance sheet shows a position at a point in time and the profit and loss account shows activity over a period of time it is normal to multiply the balance sheet by the closing rate for the period and the P&L account by the average rate for the period. Since the trial balance balances in local currency the translation is always going to induce an imbalance in the reporting currency. In simple terms this imbalance is the gain or loss on exchange. This report is not designed to be an accounting primer, but it is important to understand that it is not a trivial task even as described so far. When you also take into account certain non current assets being translated at historic rates and exchange gains and losses being calculated separately for current and non current assets, you can appreciate the complexity of the task.

Transactions between entities within the consolidation must be eliminated (see Figure 2).

A typical consolidation error

Figure 2: Company A owns Companies B and C. Company C sells $100 worth of product to Company B so C’s accounts show sales of $500 (including the $100 that it sold to Company B) and purchases of $200. B shows sales of $1000 and purchases of $600 (including $100 that it bought from C). A simple consolidation would show consolidated sales of $1500 and consolidated purchases of $800. But if we consider A (which is purely a holding company and therefore has no sales and purchases itself) both its sales and its purchases from the outside world are overstated by $100. The $100 of sales and purchases becomes an inter-company elimination. This becomes much more complicated when the parent owns less than 100 percent of subsidiaries and there are many subsidiaries trading in multiple currencies.

Even though we talked about the problems of eliminating inter-company entries on consolidation, the problem does not stop there. How to ensure that the sales of $100 reported by C actually agrees with the corresponding $100 purchase by B? What if C erroneously reports the sales as $90? Consolidation systems have inter-company reconciliation modules which will report any inter-company transactions (usually in total between any two entities) that do not agree, on an exception basis. This is not a trivial task when you consider that the two entities will often be trading in different currencies and translation will typically yield minor rounding differences. Also accountants typically ignore balances which are less than a specified materiality factor. Despite their green eyeshade image, accountants do not like to spend their days chasing pennies!

Management reporting

In most organizations, management reporting is quite distinct from formal financial reporting. It will usually have more emphasis on the P&L and possible cash flow, and less on the balance sheet. It will probably be done more often — usually monthly, rather than annually and quarterly. There will be less detail but more analysis. More users will be interested in viewing and analyzing the results. The emphasis is on faster rather than more accurate reporting and there may be regular changes to the reporting requirements. Users of OLAP based systems consistently report faster and more flexible reporting, with better analysis than the alternative solutions. One popular saying is that “what gets measured, gets managed,” so senior management will often use a reporting system to give (subtle or not) direction to subordinates.

Many organizations have grown by acquisition, and may have two or more organizational structures. There will be a legal structure, which will include dormant and non-trading subsidiaries, and will often be largely ignored in the management reporting. There may also be a different business structure, which might be based on products or market sectors, but may blur the distinction between subsidiaries; it will be based on the company’s management structure, which may be quite different to the legal structure. There may also be a marketing structure, which could reflect a virtual (matrix) organization, crossing both the legal and the management structures. Sometimes the same reporting tool will be expected to produce all three sets of reports.

Management reporting usually involves the calculation of numerous business ratios, comparing performance against history and budget. There is also advantage to be gained from comparing product groups or channels or markets against each other. Sophisticated exception detection is important here, because the whole point of management reporting is to manage the business by taking decisions.

The new Microsoft OLAP Services product and the many new client tools and applications being developed for it will certainly drive down ‘per seat’ prices for general-purpose management reporting applications, so that it will be economically possible to deploy good solutions to many more users. Web deployments should make these easier to administer.

EIS

EIS is one branch of management reporting. The term became popular in the mid 1980s, when it was defined to mean Executive Information Systems; some people also used the term ESS (Executive Support System). Since then, the original concept has been discredited, as the early systems were very proprietary, expensive, hard to maintain and generally inflexible. Fewer people now use the term, but the acronym has not entirely disappeared; along the way, the letters have been redefined many times. Here are some of the suggestions (you can combine the words in any way you prefer):

With this proliferation of descriptions, the meaning of the term is now irretrievably blurred. In essence, an EIS is a more highly customized, easier to use management reporting system, but it is probably now better recognized as an attempt to provide intuitive ease of use to those managers who do not have either a computer background or much patience. There is no reason why all users of an OLAP based management reporting system should not get consistently fast performance, great ease of use, reliability and flexibility — not just top executives, who will probably use it much less than mid level managers.

The basic philosophy of EIS was that “what gets reported gets managed,” so if executives could have fast, easy access to a number of key performance indicators (KPIs) and critical success factors (CSFs), they would be able to manage their organizations better. But there is little evidence that this worked for the buyers, and it certainly did not work for the software vendors who specialized in this field, most of which suffered from a very poor financial performance.

Balanced scorecard

The balanced scorecard is a 1990s management methodology that in many respects attempts to deliver the benefits that the 1980s executive information systems promised, but rarely produced. The concept was originated by Robert Kaplan and David Norton based on a 1990 study sponsored by the Nolan Norton Institute, the research arm of KPMG. The results were summarized in an article entitled, ‘The Balanced Scorecard — Measures That Drive Performance’ (Harvard Business Review, Jan/Feb 1992). Other HBR articles and a book (The Balanced Scorecard, published by Harvard Business School Press in 1996) followed. Kaplan is still a professor at the Harvard Business School and Norton, who was previously president of the strategy group in Renaissance Worldwide, is now president of the Balanced Scorecard Collaborative.

Renaissance says, “a Balanced Scorecard is a prescriptive framework that focuses on shareholder, customer, internal and learning requirements of a business in order to create a system of linked objectives, measures, targets and initiatives which collectively describe the strategy of an organization and how that strategy can be achieved. A Balanced Management System is a governance system which uses the Balanced Scorecard as the centerpiece in order to communicate an organization’s strategy, create strategic linkage, focus business planning, and provide feedback and learning against the company’s strategy.”

Figure 3: The Balanced Scorecard provides a framework to translate strategy into operational terms (source: Renaissance Worldwide)

The basic idea of the balanced scorecard is that traditional historic financial measures are an inadequate way of measuring an organization’s performance. It aims to integrate the strategic vision of the organization’s executives with the day to day focus of managers. The scorecard should take into account the cause and effect relationships of business actions, including estimates of the response times and significance of the linkages among the scorecard measures. Its aim is to be at least as much forward as backward looking.

The scorecard is composed of four perspectives:

	Financial
	Customer
	Learning and growth
	Internal business process.

For each of these, there should be objectives, measures, targets and initiatives that need to be tracked and reported. Many of the measures are soft and non-financial, rather than just accounting data, and users have a two-way interaction with the system (including entering comments and explanations). The objectives and measures should be selected using a formal top-down process, rather than simply choosing data that is readily available from the operational systems. This is likely to involve a significant amount of management consultancy, both before any software is installed, and continuing afterwards as well; indeed, the software may play a significant part in supporting the consulting activities. For the process to succeed, it must be strongly endorsed by the top executives and all the management team; it cannot just be an initiative sponsored by the IT department (unless it is only to be deployed in IT), and it must not be regarded as just another reporting application.

However, we have noticed that while an increasing number of OLAP vendors are launching balanced scorecard applications, some seem to be little more than rebadged EISs, with no serious attempt to reflect the business processes that Kaplan and Norton advocate. Such applications are unlikely to be any more successful than the many run-of-the-mill executive information systems, but in the process, they are already diluting the meaning of the balanced scorecard term.

Profitability analysis

This is an application which is growing in importance. Even highly profitable organizations ought to know where the profits are coming from; less profitable organizations have to know where to cut back.

Profitability analysis is important in setting prices (and discounts), deciding on promotional activities, selecting areas for investment or divestment and anticipating competitive pressures. Decisions in these areas are made every day by many individuals in large organizations, and their decisions will be less effective if they are not well informed about the differing levels of profitability of the company’s products and customers. Profitability figures may be used to bias actions, by basing remuneration on profitability goals rather than revenue or volume.

With deregulation in many industries, privatization and the reduction in trade barriers, large new competitors are more likely to appear than in the past. And, with new technology and the appearance of ‘virtual corporations’, new small competitors without expensive infrastructures can successfully challenge the giants. In each case, the smart newcomers will challenge the incumbents in their most profitable areas, because this is where the new competitor can afford to offer lower prices or better quality and still be profitable. Often they will focus on added value or better services, because a large incumbent will find it harder to improve these than to cut prices. Thus, once a newcomer has become established in areas that were formerly lucrative, the established player may find it hard to respond efficiently — so the answer is to be proactive, reinforcing the vulnerable areas before they are under attack.

This takes analysis. Without knowledge of which customers or products are most profitable, a large supplier may not realize that a pricing umbrella is being created for new competitors to get established. However, in more and more industries, the “direct” labor and material cost of producing a product is becoming a less and less significant part of the total cost. With R&D, marketing, sales, administration and distribution costs, it is often hard to know exactly which costs relate to which product or customer. Many of these costs are relatively fixed, and apportioning them to the right revenue generating activity is hard, and can be arbitrary. Sometimes, it can even be difficult to correctly assign revenues to products, as described above in the marketing and sales application.

One popular way to assign costs to the right products or services is to use activity based costing. This is much more scientific than simply allocating overhead costs in proportion to revenues or floor space. It attempts to measure resources that are consumed by activities, in terms of cost drivers. Typically costs are grouped into cost pools which are then applied to products or customers using cost drivers, which must be measured. Some cost drivers may be clearly based on the volume of activities, others may not be so obvious. They may, for example, be connected with the introduction of new products or suppliers. Others may be connected with the complexity of the organization (the variety of customers, products, suppliers, production facilities, markets etc). There are also infrastructure-sustaining costs that cannot realistically be applied to activities. Even ignoring these, it is likely that the costs of supplying the least profitable customers or products exceeds the revenues they generate. If these are known, the company can make changes to prices or other factors to remedy the situation — possibly by withdrawing from some markets, dropping some products or declining to bid for certain contracts.

There are specialist ABC products on the market and these have many FASMI characteristics. It is also possible to build ABC applications in OLAP tools, although the application functionality may be less than could be achieved through the use of a good specialist tool.

Quality analysis

Although quality improvement programs are less in vogue than they were in the early 1990s, the need for consistent quality and reliability in goods and services is as important as ever. The measures should be objective and customer rather than producer focused. The systems are just as relevant in service organizations and the public sector. Indeed, many public sector service organizations have specific service targets.

These systems are used not just to monitor an organization’s own output, but also that of its suppliers. There may, for example, be service level agreements that affect contract extensions and payments.

Quality systems can often involve multidimensional data if they monitor numeric measures across different production facilities, products or services, time, locations and customers. Many of the measures will be non-financial, but they may be just as important as traditional financial measures in forming a balanced view of the organization. As with financial measures, they may need analyzing over time and across the functions of the organization; many organizations are committed to continuous improvement, which requires that there be formal measures that are quantifiable and tracked over long periods; OLAP tools provide an excellent way of doing this, and of spotting disturbing trends before they become too serious.

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OLAP applications

OLAP technology can be used in a wide range of business applications

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