Scenario 1: No Formal Link between Strategy and Business Analytics

The first type of link between the deployment of BA and strategy is the absence of any link. This may be a surprising notion, but the most common explanation is that, when developing their strategy, companies often focus on the most visible aspects, such as sales targets, production targets, or cost targets in connection with procurement. In relation with the achievement of these targets, the sales department, production, and procurement will be faced with targets from the business strategy. However, a company also consists of many other functions, such as human resources (HR), finance, product development, strategy, competitive analysis, administration, and BA. These functions are called support functions because they are not adding value in connection with the daily production. However, if they did not exist, the company would soon encounter problems.

From a strategic perspective, the supporting functions are of course expected to support the primary and value creating processes. This may happen when the support functions themselves interpret the business strategy in their daily activities, but more often, the owners of the primary processes place demands on services from the support functions. When we describe a scenario with no link between strategy and BA, it is not a question of completely uncoordinated entities, but rather a case of a filter existing between them. A filter may exist because it is primarily the individual processes' owners, on an ad hoc basis, and not the strategy that defines which information is to be generated by the BA function.

The consequence of the filter is that the BA function prioritizes its tasks according to what best serves the daily target achievement of the company instead of what is best for long‐term strategic projects. Moreover, the BA function tasks are performed based on the driving force of different users requesting information. In terms of reporting, this will result in the development of more or less authorized reports with inconsistent presentations of the business that they are describing. All in all, the quality of BA in this type of organization will typically be an assessment of how quickly a question is answered and how well founded the answer is.

Other reasons for the lack of a formalized link between the strategy and the BA functions may be that the right conditions simply do not exist. There are situations, such as small businesses with one or few customers, where the cost of running a data warehouse is bigger than the value of the decision support created, or companies which have no processes that need to be digitalized. And there are companies that define their strategic targets in a way that is not measurable. If, for instance, a company defines a target such as “we need to establish better relations with our suppliers,” that may be difficult to quantify. Because this definition does not tell us what to measure, we must ask: Is it the number of complaints, the average time per transaction, or the quality of deliveries that are to be improved via this new strategy?

Scenario 2: Business Analytics Supports Strategy at a Functional Level

The second scenario represents what we call an adapted information strategy. Here the BA function is a reactive element, solely employed in connection with the monitoring of whether the defined targets of the strategy are achieved. This process is illustrated in Exhibit 2.2. The recipients of these reports or key performance indicators (KPIs) are the individual departments, which means that there is no feedback to the strategic level provided by the BA function. The BA function supports company performance reporting and processes proactively, but only reactively in terms of how it supports the strategy creation processes. There may be a formalized dialogue between individual functions and BA, but the relation to the strategy function is formalized as a monologue, from strategy to BA function.

In terms of the quality of BA in such an organization, it's important to be good at defining targets based on strategy. These are targets that relate to each other internally and that, combined, make up a whole. It is equally important that the BA function is technically competent when it comes to operationalizing these targets via reports and making those reports both accessible to users and full of the most updated information possible.

As mentioned, based on a strategy development process, individual departments define a number of specific requirements, or targets, they are to achieve. Sometimes a target will simply be given to the sales department: It must increase revenue by 10 percent over the previous year. Alternatively, the department may be given additional information about which segments to grow and with which products. There may also be a message that this must be brought about in cooperation with other departments, such as marketing. It will then be up to the individual functions—with various degrees of autonomy—to decide how they are going to achieve the given targets.

We have illustrated this process in Exhibit 2.3, where a substrategy for HR is developed. In this case, the requirement from the company's overall strategy could be to reduce absence due to illness by 10 percent. How to achieve this will not necessarily be specified. Consequently, the HR department itself will have to come up with an HR strategy that specifies how it intends to meet the target and the deadline for this task given by the company's strategy. In the same way, a substrategy needs to be developed for customer relationship management (CRM), the CRM strategy, and a substrategy for the production department, the production strategy.

Scenario 3: Dialogue between the Strategy and the Business Analytics Functions

The third scenario is based on the existence of an established data warehouse to integrate, store and provide automated digital processes with data, as well as an established BA function with analytical competencies to make use of this data. We are typically looking at a significant investment in software and employees. This scenario is also characterized by a continuous dialogue between the strategy and the BA functions. The reporting methods used at this level for the managing and measuring of operational processes now begin to have different names, such as business performance management (BPM) systems, scorecards, and customer profitability/segment analyses. This signifies that a flow of information is going back to the strategy function based on the created reports.

The information described in this section is feedback information from scorecards and BPM solutions. These types of solutions are normally cyclical and start with a strategy. Based on the strategy, three things occur: Benchmarking is carried out; then there is ongoing measurement and analysis of deviation from targets; and finally, based on the analyses, the strategy is adapted and optimized. We will take a closer look at this in another section of this chapter, “Corporate Strategy's Subsequent Requirements to BA.” Quality for the BA function in this scenario is the ability to deliver relevant information to the strategy function. This is done so that the strategy may be adapted on an ongoing basis for the organization to accommodate changes in the market and within the organization itself. When reports are produced describing whether individual departments are meeting their KPIs, action will, of course, be taken if any major deviations between targets and the achievements are shown. There will therefore always be some form of feedback between target achievement and strategy, although this feedback process may be more or less formalized. An example of the conceptualization of the feedback processes is found in corporate performance management (CPM) and score carding. As Exhibit 2.4 illustrates, this is an ongoing cycle, where the enterprise as its starting point has defined a strategy to be implemented in the various departments that make up the business. Coordination is performed by identifying the so‐called critical success factors, the elements that are essential to the success of the strategy, and making sure that they are coordinated. This is typically done via internal meetings across functions. It is at this stage, too, that we define who is responsible for the various KPIs, and thus who must react to these and in what way.

When the strategy is set in motion, progress is measured on an ongoing basis. Generally speaking, KPIs are rarely hit precisely, but rather a bit over or under. In both cases, learning can be derived based on analyses. Did we overlook any potential opportunities; do we perhaps lack certain competencies in the organization? An optimization of the strategy takes place when we use this learning to improve our business processes and thereby ensure that the organization maintains its agility between the annual strategy processes. Experiences from previous strategy iterations can contribute to the creation of learning in terms of the strategy for the coming iteration.

An alternative way of operationalizing strategic feedback processes is via the “balanced scorecard,” a method introduced in the early 1990s. It connects corporate strategy with the internal processes that will be realizing it, with customer loyalty, and finally—and this was the new thing—with the organization's internal competencies. What the balanced scorecard achieves, therefore, is to link the primary production processes to the development of the business. If we cannot produce enough, do we employ more people or different people, or do we establish a dialogue with our employees and on that basis reward them differently? The method, which was developed by Kaplan and Norton, represents a cornerstone for how to formulate requirements in connection with the implementation of a new strategy.

In regards to automated digital processes, dialogue between BA and the strategy means that the data to support digital processes is closely aligned with the strategy. It also implies that these digitalized processes contain elements of automated embedded decision support—intelligent processes—and that the analytics department will support these processes with models and decision parameters.

Scenario 4: Information as a Strategic Resource

The fourth scenario is about information being regarded as a strategic resource. Such enterprises are characterized by using their analyses of market strengths and weaknesses by systematically thinking about how information, combined with their strategies, can give them a competitive advantage. As illustrated by Exhibit 2.5, this is less about technical solutions and more about people competencies that are required in the strategy development process. In some cases, this may mean that the enterprise needs to ensure it has staff with both strategic and information knowledge represented at the top management level. This is not an altogether surprising conclusion, considering that we live in the age of information.

A typical example of an enterprise that focuses on information as a strategic resource is Amazon.com, which began by selling books but has expanded to other goods via the Internet. Early in their history, Amazon.com saved information about their individual customers' purchases and requests; the information was then processed, with the result that customers were subsequently greeted with offers that are relevant and of service to them. This is a case of improving the relevance of offers to customers based on information, which differentiated Amazon.com in a positive way from some other Internet‐based book shops. Now this trend is emerging among certain retail chains, too, where the segmentation of customers means that services can be customized to local conditions. Moreover, we see a growing sale of information from shops to the manufacturers of the goods sold in the shops (meaning the data also is a sellable commodity itself). This information describes which types of people buy their products, how price‐sensitive the customers are towards products, which products are typically sold together, and so forth. This feedback constitutes essential information for manufacturers in terms of product development, pricing, and promotion in the right places with the right messages.

The strategies created by a company that uses information or data as a strategic asset can be distinguished by looking at certain elements of the company's strategy. If a company does not use information as a strategic asset, it will not, in the strategic implementation plans, have descriptions of how the competitive advantages should be gained via the use of information. If a company does use information as a strategic asset, then next to the objectives of the strategy it will also provide directions as to how the objectives should be reached via the use of information.

An organization that uses information as a strategic asset in its culture is also recognizable because its employees, according to our research, intuitively will think proactively in terms of how they can use information to overcome, for example, a new competitive situation. This sort of a culture will use the information as a strategic asset as a result of a top‐down process as well as a bottom‐up process. This means that if one region learns to improve its processes via the use of information, the news will be captured by the strategy team and spread as a best practice to the rest of the organization as a result of the next strategy creation process.

In Chapter 3, which focuses on business analytics at the functional level, we present a case in which the strategy at the functional level is largely managed on the basis of data warehouse information. Quality in this context is, therefore, being able to understand how the use of information can provide enterprises with an advantage in terms of key competitive parameters. Information at a strategic level must therefore be understood centrally, in connection with the strategy development process, and throughout the organization, where the implementation is carried out. As previously discussed, the use of information as a strategic resource is first about identifying central competitive parameters and second about understanding how this information can ensure that the enterprise differentiates itself from its competitors. We have chosen to introduce a tool that will help determine which information is going to support an organization's business‐critical initiatives, as well as providing a number of examples of how this works in specific terms.

The Product and Innovation Perspective

This perspective is highly prioritized by enterprises that act based on product innovation or product leadership strategies—that is, businesses that adopt as their central competitive parameter that their products and services can be characterized as state‐of‐the‐art. Therefore, the focus is typically on technological aspects and all the things their products are able to do. These are usually enterprises delivering technical sales to specialists. Examples include server solutions, data warehouse solutions, road paving, and windmills.

The knowledge these businesses normally use to differentiate themselves in the market is therefore closely linked to technological knowledge about product development and, to a lesser extent, to knowledge about commercial customer behavior (customer intimacy strategy) or knowledge about how to efficiently produce and deliver services and products (operational excellence strategy). Note that we will be mentioning and discussing some statistical methods throughout the rest of this chapter; these statistical methods will all be presented in greater detail in Chapter 4. Moreover, keep in mind that the analyses presented in this book are primarily built on data that is electronically stored, often in a data warehouse.

One analysis that will always have priority is determining which products deliver the highest revenue, since this is key to the development of future strategies. In other words, look at the sales price and deduct all direct costs related to manufacturing and distribution, as well as to the sales and marketing of the product. Development costs are not taken into consideration here, since attention is given to analyzing internal operational processes.

The purpose of this exercise is to shift focus from the number of sold units of different product types and onto the products that deliver profit. Moreover, the profit per product unit will be an important input for further analyses from the product and segment perspective.

In the market, analyses are prepared by enterprises on a continuous basis, and models are developed to describe the state of the current market, as well as how this market can be expected to change in the future. Combined with forecasting models for individual products, this can give some useful estimates of where revenue will be earned in the future. These analyses should be supported by historical knowledge, too, which we stored from the life cycles of comparable products, information that can be expected to be delivered by a data warehouse. A further dimension of the analytical process is that it is possible, based on the data warehouse information, to examine the development of customer segments. One potential analysis would be looking at which customer segments buy which products, as well as the development of these segments. This analysis can then be broken down by countries, chains, brands, and so on. Often businesses do not just sell one product or service per customer. Some software packages consist of a number of optional modules. Another example is car sales. Special rims cost extra, and the customer can choose not to buy them. Mobile subscriptions typically offer a large number of optional products, a guarantee never to have to pay more than a certain amount per month, favorite contact numbers that can be called for a lower rate, insurance for the phone, and so forth. The analyst must not only compute the profitability of individual products, but also include any cross‐sales related to the product.

If the assumption is that the company is serving only one customer segment, we have several techniques to uncover customers' multiple‐purchase patterns. Correlation analysis can use statistics to prove whether there are, between any two services on offer, any positive or negative purchasing tendencies, and thereby show whether the products are complementary or substituting. Principal component analyses (also known as PCA or explorative factor analysis) are able to provide information about how many multiple‐purchase patterns there are, as well as to describe them. We are not looking only at whether products are sold in twos. A multiple‐purchase pattern can include five products that are sold together.

If we are working with large numbers of different products (let's say more than 100), data mining techniques will start to become the most interesting ones. The methods are called everything from basket analysis to cross‐sales analysis. The technique is based on uncovering which products are sold together. The underlying methodology is based on the same mind‐set as a correlation analysis and involves a simple accounting of how often two products are seen together as a multiple purchase on the same receipt or under the same customer number in the data warehouse.

The knowledge these analyses bring to the strategic planning process is the basic information that describes which products secure earnings (not revenue), and where investment in technology seems to be rendering a positive return estimated over the entire lifetime of the product.

When we begin to relate products to each other, knowledge is created about which basic needs we meet for our customers. Products are positioned in relation to each other and in connection with future marketing initiatives. If it's a matter of whether products can be integrated into each other, such as phones with cameras, GPS units and entertainment, or software packages such as Microsoft Office (spreadsheets, presentation programs, and word‐processing programs), then these analyses also provide us with input for future product development.

Furthermore, these multiple‐purchase patterns tell us something about our customers' needs and, as such, provide us with a basis for segmentation based on needs. Are our customers, for instance, buying large aluminum rims for their cars, or are they buying safety features such as airbags and fire extinguishing equipment? The additional purchases meet different needs, which means that we should not be using the same sales strategies for all customers. So if a business is serving several segments, a cluster analysis is an obvious choice. Cluster analyses serve two purposes: first, to identify how many segments a business is serving, and second, to identify their characteristics. Whereas before it would make sense to discuss whether a multiple‐purchase analysis is a natural part of strategy development, a cluster analysis would constitute a natural part of the identification and description of the customer groups a business is serving.

Once the segments are identified, we can compare their historic development with analyses of the future, and come up with estimates of what our customer mix will look like in the years ahead. Moreover, we will be able to estimate earnings from the different segments and, in this way, prepare strategic plans for the customers we want to serve in the future—and with which products and services.

At the start of this century, we saw some new analytical trends. One trend was driven by the increased availability of unstructured data related to the health care industry. As the ability to analyze and store this data has increased, this industry will be interesting to follow in the years to come, as their way of doing product innovation is now adding the analytics perspective to traditional lab‐based product development. One example is Novo Nordisk, a global market leader in the production of insulin, a vital product for the survival of diabetes patients. Novo Nordisk currently invests large sums in being able to analyze all electronically stored research reports and individual patient files related to diabetes that are made globally available. What we see here is that if companies want to maintain their global market position in the analytical age, they must consider making significant strategic investments in capabilities to store and analyze all available information. To Novo Nordisk, the risk is that the company misses out on important information or fails to be the first to see patterns in the data; then they will risk the lives of their customers and their market position. It is also interesting to note that the information that must be analyzed is written reports and patient files stored in hundreds of formats. To simplify the task confronting Novo Nordisk, this company is about to create its own version of Google specialized within their field of diabetes—this is how Novo Nordisk believes it can win a competitive advantage.

Another example of a new technology that is driving new standards for analytically driven product innovation comes from the field currently described as the Internet of Things (IoT). Products such as cars, televisions, and phones record and continuously report how and where they are used to their producers. Voice recognition programs are even included that can provide input on the emotional state of the user.

In the health care industry, we see the same trend fueled by apps that report on the health of the user for benchmarking purposes, but also to understand how the app is used and how it can be improved. Even in the smallest market leading hearing devices, we now see user logs that are regularly transferred to the producer. With these small logfiles, the hearing aid producers can now follow all their products from cradle to grave and even learn about when their products fail and their users go for repair. For example: Who is doing these repairs? What are the costs and earnings on these repair processes? This is all vital information if a company wants to maintain a profitable market leader position within the discipline of product innovation.

Customer Relations Perspective

This perspective will typically be essential to enterprises with a focus on maintaining good customer relations. Banks, insurance companies, and telecoms, for instance, which are in markets with high degrees of penetration, are all good examples. Enterprises with a high degree of penetration must retain existing customers and at the same time attract customers from the competition. Their focus is customer loyalty. We will discuss this perspective in more detail in the “CRM Activities” section of Chapter 3.

Other types of enterprises, such as Apple, Nokia, Nike, and Coca‐Cola, focus on using customer information actively as well. They usually have less transactional information about their customers than, say, banks, and the relationship is based to a lesser extent on a formal status, such as a subscription. The relationship is more of a psychological bond or a brand. A brand is used to wrap physical products in positive emotions to strengthen customer loyalty. These enterprises typically work with market information generated by “market intelligence.” Information is typically collected for the occasion, often via external partners, competitor monitoring, and questionnaires. So it is not usually information stored in a data warehouse.

The big difference between the product perspective and the customer perspective is whether analyses are prepared based on products or customers. The analytical base table, which is the data set underlying the analysis in its least aggregated form when looking at products, has as many rows as the business has active and historical subscriptions. Further, their unique characteristic will, for instance, be their phone number. For the same telecom company, the analytical base table will have as many rows as the business has active and historical customers, if the analysis took its point of departure in the customer perspective. The difference here is that one customer may have several phone numbers. Just as we would group subscribers based on which product they bought in connection with a product perspective, we would group customers in segments, if our base table were to support a customer perspective.

In other words, the enterprise needs to know which customers buy which products, to be able to prepare its analyses based on a customer perspective. If we imagine that we are a supermarket, and the only electronic information we have is which products are sold together, we can prepare analyses based only on a product perspective, assuming we are working only with data warehouse information. This is why so many retail stores offer customer loyalty cards; it gives the business a possibility of carrying out analyses across several purchases in several stores linked via a customer code, which effectively makes the analysis customer oriented.

With an analytical base table, an obvious first exercise will be to perform a value‐based segmentation. The result of such an analysis is a breakdown of customers in segments of gold, silver, and bronze (or high, medium, and low). The breakdown is relevant because it forms the basis for how to treat customers. The most valuable customers must be retained, the middle ones must be grown via added sales activities, and the least valuable—who often cost the company—must not constitute an obstacle to other customers. Meanwhile, relationship costs to the last group must be minimized. Strategically, this analysis is central, too, as it is based on this information that the two most important objectives are to be defined. First, how large do we want the customer base to be by the end of next year, and second, how large must average sales be per customer, which is the income basis for a typical business?

Other types of segmentation that may be relevant in connection with strategic development processes are based on demand and behavior, respectively. These segmentations are prepared based on product information and transactions over time. They are typically performed for marketing purposes and are often supplemented by market analyses. They are, however, still essential in a strategic context, because they are the ones that tell the organization which trends are experienced in the marketplace. At the same time, the service offerings must be updated based on customer needs and adapted to the future, which is one of the important reasons for developing a strategy. Segmentation can, of course, also build on information about age, gender, geography, and education. But it is important to note that these so‐called sociodemographics do not tell us anything about customer needs.

Only need‐based segmentation delivers this. When it has been performed, sociodemographics can be added to see what is characteristic of the individual segments, enabling the enterprise to ensure that the adopted method of communication to the various segments is appropriate. This is the whole idea of need‐based segmentation—that is, to target marketing activities based on the disclosed needs of the segments, an action that delivers the opportunity to optimize the effect of every marketing dollar spent. To briefly sum up the difference between value‐based and need‐based segmentation; the purpose of need‐based segmentation is to understand the needs of customers and why they choose a company, whereas value‐based segmentation has to do with which specific customers a company must focus on to retain their earnings. Customer lifetime value represents a new type of information that has great potential as a contributor to improved decision‐making at the strategic level. The concept originated in CRM and can be found by asking: “How much can I expect to earn from a given customer in the time he or she is with me?” This can be calculated as:

So‐called churn predictive decision trees can provide us with this exact information. These trees are based on data mining models, which can calculate the risk of each individual customer leaving a company. Based on these models, algorithms can be employed to divide the customer base into different groups based on whether they are going to leave the business within a given period of time. (We describe this technique in more detail in Chapter 4 in the section about data mining.) The gist is that an enterprise can use these models to determine the probability of a customer canceling his or her relationship with the enterprise with a certain percentage, which delivers information about a customer's life expectancy. We therefore know both the average income from and the life expectancy of the customer, and can make a rough estimate of the life expectancy value of the customer, by multiplying the two. With regard to the costs involved in getting the customer, we can choose not to take this into account, depending on whether we have this information on an individual level.

The result of this type of analysis is that customers are divided into segments, with different customer life expectancy values.

In addition, the models can provide us with information about why certain segments decide to leave our business, teaching us a lot about the strengths and weaknesses of our enterprise. These analyses therefore tell us which customers are going to leave us as well as their value to us, and thus which customers we should actively try to retain.

A trend within organizations today is their desire to constantly get feedback from customers, either through a touchpoint survey or through yearly surveys. The touchpoint survey has to a large extent been enabled by the increased digitalization of processes, meaning that companies can automate and reduce their costs of getting this customer input. Touchpoint surveys can be anything from feedback on Web pages to post‐purchase feedback or digital callbacks after contacting customer service.

Organizations focused on customer relationships will often also seek to get yearly feedback. During these feedback processes, customers are asked about their general feedback on loyalty to the company, not just in regard to one interaction. Besides using this feedback in the area between the client agent and the customer, this information can also be summed up to provide essential strategic insights on what is important to the customers and how the organization is performing on those attributes compared to competition. Touchpoint and yearly surveys often coexist in organizations today; the touchpoint surveys typically are used by the process or product owners (e.g., call centers or product development), and the feedback is used to understand the general quality of what is being delivered and to contact individual customers who had bad experiences. The yearly survey is essential for strategic decision support, as it shows the overall strengths and weaknesses of the company, and overall customer satisfaction scores are often used as a strategic target.

For strategic business planning purposes, analytical models are also often used to strategic business case formulation giving answers to questions such as: If I undertake this strategic initiative, what will this mean to my customer loyalty and my subsequent future earnings? These earnings can then be balanced against the cost of undertaking the change, together with the cost of maintaining the future business model. In this way, organizations can focus on their future strategy and on what drives value to the customers.

The Operational Excellence Perspective

When an organization has a strong focus on operational excellence, it means it focuses on effective ways of producing and delivering services to its customers. If a business, for instance, has built its market position on being the cheapest, it stands to reason that intense focus will be on the optimization of internal processes.

Any organization will, of course, be trying to optimize its internal processes. This is a day‐to‐day management task. The real question is whether this constitutes a key competitive parameter for the enterprise.

Organizations for which the operational excellence approach makes sense are typically capital‐heavy businesses with significant initial investments. This makes an efficient return on invested capital essential. A cement factory is a good example of an enterprise that is unlikely to be using a product leadership strategy, since the product cannot differentiate itself technologically. A customer intimacy strategy does not seem relevant either since the assumption would be that the relations one manufacturer is able to establish can be matched by other manufacturers of cement. Therefore, if the cement factory is to secure its survival in the long run, it has to create competitive advantages built on its ability to produce and deliver its products better than the competition. It makes sense, therefore, to invest in technology in terms of the optimization of internal processes—but this is not a product excellence strategy, because the products delivered by the factory do not differentiate themselves technologically.

Other enterprises will be competing on economies of scale. That is, businesses will be focusing on a positive spiral: The more that is produced, the cheaper it gets. Then more products are sold, and as a result, the company can produce even more. Airlines, hotels, general logistics, and production companies are all good examples of this business model. In this case, it is not just a question of efficient production, but of the rate of utilization of capacity having a positive, cumulative effect.

Finally, we often see operational excellence initiatives in businesses right after a merger. These are common in situations where we work in a targeted way to create synergies. Overall, we are talking about organizations that have cost control as a key competitive parameter and that use the operational excellence approach. Market developments are, naturally, a determining factor, too. In a declining market, a business will try to minimize loss, which means a strong focus on costs. It's said that everyone can survive in a growth market, but in a negative market, businesses must prove their worth by retaining their equities and market share via an ongoing adaptation of internal processes. At a strategic level, we can work with two types of information. First, and most obvious, is the measuring of target achievement of internal process as a result of existing strategies. That will tell us where we are. Equally important is knowing where we should have been, and therefore where we need more resources or competencies or both. This is information that is essential in understanding the organization's weaknesses, and that can often be described in more detail in the internal analysis, where we look at whether all the parts of the organization are pulling in the same direction.

The other type of information we should focus on is the organization's key figures. All organizations have this information because it measures exactly what we are focusing on in relation to operational excellence—that is, how to optimize processes in order to minimize the consumption of resources without losing customer loyalty.

This is measurement by financial results that we can work to optimize; we can use the information to compare ourselves with the external accounts of competing enterprises. This, of course, is of great interest when competing on price, as it is the underlying cost structure that shows our competitors' strong and weak points in this kind of competitive situation. On BA‐support.com, we have entered a large number of key indicators along with directions about how to compute them and subsequently interpret them.

At an operational level, we typically find that process owners use tools like control charts and other Six Sigma or Lean tools. The overall purpose is to minimize waste and variance in internal processes by making the processes stable and predictable, because those are the cheapest processes to manage from a performance management perspective. After all, it is only unstable and unpredictable processes that generate waste in terms of overwork, large inventories, and unexpected waiting time between process steps.