Regular Paper A Computational Theory of Enterprise Transformation Zhongyuan Yu, William B. Rouse,* and Nicoleta Serban Tennenbaum Institute, Georgia Institute of Technology, Atlanta, GA 30332-0210 A COMPUTATIONAL THEORY OF ENTERPRISE TRANSFORMATION Received 13 August 2010; Revised 20 December 2010; Accepted 20 December 2010, after one or more revisions Published online in Wiley Online Library (wileyonlinelibrary.com). DOI 10.1002/sys.20188 ABSTRACT This paper translates a qualitative theory of enterprise transformation into a quantitative, mathematical theory. This enables computational exploration of the phenomena outlined in the theory, as well as the sensitivity of these phenomena to a range of parameters intended to reflect the elements of the theory. The overall question addressed is, “What should an enterprise do in response to perceptions of impending substantial change?” A mixture of three types of responses is considered—predict better, learn faster, and act faster. As these responses all require investments, it is not the case that an enterprise should always pursue all of them. Indeed, there are conditions under which an enterprise should not pursue any of them. This paper elaborates the nature of these conditions, explains why they arise, and discusses the implications for enterprises entertaining transformation. © 2011 Wiley Periodicals, Inc. Syst Eng 14: Key words: enterprise transformation; change strategies; computational modeling; change point detection 1. INTRODUCTION change. For example, the direction of the change may be clear, but its timing and magnitude may be uncertain. The second type of uncertainty concerns how best to respond to change. What should one’s offerings and processes become? Given insights into or answers of these questions, how can one best allocate resources between the enterprise one has and the enterprise one is striving to become? Thus, the leaders of these enterprises must wrestle with the question of whether they should invest in becoming better at what they are currently doing versus investing in doing new things that will better match emerging market desires. In other words, should they focus on business process improvement or enterprise transformation? These are difficult issues. Gerstner [2002, p. 64] portrays this difficulty, indicating “Reengineering is like starting a fire on your head and putting it out with a hammer.” His approach to enterprise transformation was to turn IBM into a marketdriven rather than internally focused, process-driven enterprise. A recent study of four successful transformations—Lockheed Martin, Newell Rubbermaid, Reebok, and UPS—has shown that there are a variety of Contemporary enterprises face enormous uncertainties. Defense procurements may no longer be dominated by traditional weapon systems. Healthcare delivery may transition from pay for services to payment for health outcomes. Intelligent sensing and control technology may morph the energy ecosystem. There are many examples of substantial uncertainties in other industries such as education, finance, and food [Rouse and Basole, 2010]. There are, at least, two types of uncertainty. First, the enterprise may not be sure of the nature of the impending *Author to whom all correspondence should be addressed (e-mail: [email protected]). Contract grant sponsor: This research was supported, in part, by the Mark & Kimberly Miller Charitable Foundation. Systems Engineering © 2011 Wiley Periodicals, Inc. 1 2 YU, ROUSE, AND SERBAN approaches to transformation, but some common underlying competencies including vision, leadership, strategy, planning, communication, and cultural change [Rouse, 2011]. While current levels of uncertainty may seem unprecedented, this is not the case. We are just entering the 40th decade of American experience, at least from the perspective of European immigrants. In only seven of these decades has there not been a war or economic crisis. The 1880s was the most recent of such decades. Change and uncertainty are strongly woven through American history. In a study of 200 companies over the past 200 years in the transportation, computer,1 and defense industries in the United States, it was found that many attempted transformation in the face of change—and most failed [Rouse, 1996]. More recently, The Economist reported that the Fortune 500 has seen a 200% turnover in the past 25 years [Schumpeter, 2009]. Thus, the challenge of enterprise transformation has been, and continues to be, very significant. To provide a framework for exploration of the nature of enterprise transformation, Rouse [2005, 2006] proposed a theory of enterprise transformation. This theory focuses on why and how transformation happens, as well as ways in which transformation is addressed and pursued in terms of work processes and the architecture of these processes. This theory is stated as follows: Enterprise transformation is driven by experienced and/or anticipated value deficiencies that result in significantly redesigned and/or new work processes as determined by management’s decision making abilities, limitations, and inclinations, all in the context of the social networks of management in particular and the enterprise in general. The purpose of this paper is to translate this qualitative statement of the theory into a quantitative, mathematical theory to enable computational exploration of the phenomena outlined in the theory, as well as the sensitivity of these phenomena to a range of parameters intended to reflect the elements of the theory. The overall goal is to address the question, "What should an enterprise do in response to perceptions of impending substantial change?” A mixture of three types of responses is considered—predict better, learn faster, and act faster. As these responses all require investments, it is not the case that an enterprise should always pursue all of them. In fact, there are conditions under which an enterprise should not pursue any of them. This paper elaborates the nature of these conditions, explains why they arise, and discusses the implications for enterprises entertaining transformation. 2. CONCEPTUAL FORMULATION 2.1. Model Constructs The central constructs of the theory are value, work processes, decision making, and social networks. Value is a measure of 1 The roots of the computer industry in the second half of the 20th century can be traced back to the cash register and typewriter industries in the 19th century. Indeed, the leaders in these two industries were among the key players in the early computer industry. Systems Engineering DOI 10.1002/sys the extent to which an enterprise provides a market what the consumers in this market want. Increasing variations of offerings from what consumers want results in decreasing value. We hasten to acknowledge that value can also accrue from providing customers an offering they did not expect, e.g., Apple’s iPhone. This leads to innovation whereby the market changes its desires. Market innovators are usually not transformers; their innovations cause the other players in the market to have to transform. Thus, for example, Wal-Mart innovated in the retail marketplace; K-Mart and Sears were thereby forced to transform. Work processes are the means for translating an enterprise’s intentions into market offerings. These processes depend on people, information, facilities, and equipment. Changing work processes requires investments, often substantial investments. There are several aspects of decision making. One concerns what is happening in the market. Is the value of offerings increasing or decreasing or holding steady? Another decision concerns how best to adapt to needed changes. Should one invest in predicting better, learning faster, or acting faster? As elaborated later, another possibility is doing nothing different. This has the advantage of being easy to do and perhaps not requiring any additional investment. Indeed, we have found in working with numerous large enterprises that the status quo is often a very compelling alternative. We later discuss the circumstances under which the status quo is the best choice. Social networks play several roles. First, of course, they constitute how work gets done in terms of who does what, who knows what, who knows who, and so on. They can also facilitate or impede change. Social networks can facilitate change by embracing and rapidly diffusing change as has been seen recently for e-mail and smart phones. Social networks can also impede change, acting in ways analogous to an enterprise immune system [Rouse, 1998]. One way to impede change is to deny that it is needed. This delays recognition of market signals that the value of an enterprise’s offerings is declining. Another impedance is reluctance to change work processes. Competencies that provided competitive advantage in the past may be preserved, perhaps receiving sustained investment, despite their decreased relevance to competing in the future. 2.2. Conceptual Model Figure 1 provides a high level conceptual model of the theory of enterprise transformation that forms the basis for the mathematical model introduced in the next section. This mathematical model makes the concepts in Figure 1 quite precise. The key elements of this model include the following: • Management: Represents decisions to allocate resources to remediate value deficiencies and maximize expected utility • Production: Represents mapping of resources, via labor, to products and services over time A COMPUTATIONAL THEORY OF ENTERPRISE TRANSFORMATION 3 which contributed to Senge’s more recent view of organizational learning [Senge, 1990]. Markides [1999] addressed the dynamic nature of strategy in terms of continuous reassessment and reformation. Miles and colleagues [1978] reviewed how organizations define strategies relative to product-market domains and then align structure and processes to pursue these strategies. Moncrieff [1999] contrasted planned and emergent strategy in terms of five elements: Figure 1. Elements of the theory. • Market: Represents mapping from products and services to value that leads to revenues, profits, and cash flows over time • Social Network: Represents allocation of human attention to deploy resources, including provision of information for decision making. The overarching question to be asked of this model concerns how market uncertainties and social network characteristics affect the decisions management must make to allocate resources, including how the information available, plus predictions, affects the possibility of making well-informed decisions. 2.3. Broader Theories Before addressing the mathematical formulation of the conceptual model in Figure 1, it is important to discuss the extent to which this model aligns with contemporary management theory. In other words, how does the theory of enterprise transformation underlying this model fit in with broader theories of business strategy? It has long been recognized that strategy formation is not a static mapping, nor a simple mapping, from industry structure to strategic plans to exploit this structure to maximize profits [Porter, 1991]. Even earlier, Schendel and Patton [1978] outlined the multidimensional nature of strategy, where profit is but one element. Mintzberg [1978] elaborated the interaction of the organization with the environment, • • • • • Organizational intentions Organizational response to the environment Dynamics of the organization Alignment of action with intentions Strategic learning. These elements are reflected in Figure 1, albeit with more contemporary terminology. There are additional important subtleties. Eisenhardt and Martin [2000] elaborated a resource-based view of strategy and contrasted moderately versus highly dynamic markets. They summarized evidence that analytical strategies work best for the former, and experimental approaches work better for the latter. However, such differences are not inherent. Kelly and Amburgey [1991] reported that organizational change is not always related to discontinuous environmental changes, nor does it always relate to chances for survival. Thus, it would be folly to argue that the theory presented in this paper reflects how companies will inherently respond to circumstances that should prompt enterprise transformation. Instead, the goal is to elaborate the strategic choices available to companies who address transformation in this manner and the conditions under which these choices make sense. 3. MATHEMATICAL FORMULATION The goal is to convert the above conceptual model to as simple a mathematical model as possible that will enable representation of the central phenomena of interest and support computational exploration of the nature of these phenomena, including their sensitivity to key parameters. Although our model is simplified given the complexity of enterprise trans- Table I. Summary of Key Parameters Systems Engineering DOI 10.1002/sys 4 YU, ROUSE, AND SERBAN formation, it enables fundamental understanding of how key characteristics outlined in Figure 1 impact transformation at a higher level. The model elaborated in this section has four key parameters as summarized in Table I. These parameters reflect central elements of the theory of enterprise transformation. The next subsection presents a series of simulation experiments aimed at illustrating the sensitivity of market performance to the model parameters and the role of these parameters in investment strategies and in driving fundamental changes faced by an enterprise. 3.1. Market and Value Models The market defines value. Hence, elaboration of the model begins with the market. The evolution of the market’s desires is described by XM(t + 1) = α XM(t) + (1 – α) W(t). (1) The market’s desire at time t + 1, XM(t + 1), is driven by its desire at time t, XM(t), and a stochastic process denoted by W(t). While W(t) could take many forms, in this paper it is assumed to be a Gaussian variable with mean of 100 and variance of 10. According to this model, the smaller alpha is, the more weight placed on the randomness of the market implying an instable market. The market’s desire, XM, should be viewed as an abstract set of product or service characteristics sought by the market, e.g., price, performance, fuel economy, etc. Changing market desires requires a company to change their offerings accordingly if these offerings are to continue to be desired by the market. Value is defined as a function, F, of the difference between market desires, XM, and the characteristics of a company’s offering, XO, as denoted in VM(t) = F[XM(t), XO(t)]. This function could take on various forms. The functional form used for the simulation experiments discussed in this paper is given by VM(t) = EXP{–λ ABS[XM(t) – XO(t)]}. (3) Thus, market value VM is assumed to decrease exponentially with absolute differences between market desires and market offerings. Markets with higher lambda are more discriminating than markets with lower lambda. Figure 2 depicts the impact of alpha on market desires (upper plot) and perceived market value (lower plot). When alpha is large (e.g., alpha ≥ 0.8), the market is more stable, resulting in more predictable market desires and market value. When alpha is smaller (e.g., alpha < 0.5), the market becomes Figure 2. Impact of market dynamics. Systems Engineering DOI 10.1002/sys (2) A COMPUTATIONAL THEORY OF ENTERPRISE TRANSFORMATION noisy, resulting in a more random market behavior regardless of whether there is a significant trend or not. Later results will show that as alpha decreases, a company’s abilities to change successfully greatly diminish. This has a direct impact on the choice of the investment strategy. Figure 3 shows the impact of lambda, representing the extent to which customers are highly discriminating relative to differences between XO and XM. Small lambda indicates forgiving customers, while larger lambda indicates discriminating ones. As expected, perceived market value decreases substantially when lambda increases. An important issue for a company concerns how quickly they learn about market value, especially when the nature of value is changing [Eisenhardt and Bourgeois, 1989]. Unfortunately, many companies are sometimes slow to recognize that they are no longer delivering value as in the past. This is due in part to the practicalities of data collection and interpretation. However, there are social factors that affect how quickly a company learns, as elaborated below. The delay in recognizing value changes is represented by differentiating perceived value, VP, from actual market value, VM, as defined by VP(t + 1) = δ 1 VM(t) + δ 2 VM(t – 1) + δ 3 VM(t – 2) + ⋅ ⋅ ⋅. (4) Ideally, δ1 equals 1 and the higher order deltas δ2, δ3, δ4, ... are zero. Unfortunately, the higher order deltas may dominate what a company perceives; e.g., what used to be highly valued may be seen by the company as still highly valued, although it may not be, as recently illustrated by the automobile companies. In this paper, to avoid confusion and extensive notation, when we say that delta equals 2, we mean that the second order delta δ2 equals 1 while the other deltas are all zero, for example. The challenge for a company in a changing market is to get XO as close as possible to XM to yield the highest VM, subject to the difficulty of only knowing VP. “Closeness” is most important for highly discriminating markets, although 5 large variations between XO and XM will be strongly penalized by all markets. 3.2. Work Process Model The work processes of a company determine the characteristics of the company’s product and service offerings. If XM changes suddenly, perhaps due to an economic crisis, a company may find it difficult to immediately shift XO to match XM. This difficulty is represented by XO (t + 1) = β XO (t) + (1 – β) XF (t + T) (5) A company’s offerings at time t + 1, XO(t + 1), are highly influenced by its offerings at time t, XO(t), as well as its forecast, XF(t + T), of future market desires, XM. Offering design, equipment, and facilities may be such that they cannot be morphed quickly; e.g., shifting from producing pickup trucks to economy cars may take time. Beyond being a practical issue, such changes may be impeded by social forces, as discussed below. Figure 4 illustrates the impact of the beta parameter on the market offering XO (upper plot) and on the perceived market value VP (lower plot). For example, in the upper plot, at time t = 120, the market desire is around 220. However when beta equals 0.8, the company can only respond with an offering of about XO = 180, which will result in lower market value. Thus lower values of beta imply that the company can act more quickly and is better in tracking changes in market desires. On the other hand, the sluggishness of the company has a substantial impact on its responsiveness to change. As indicated in Eq. (5), the characteristics of a company’s offerings are affected by previous offerings XO(t), as well as the company’s market forecasts, denoted by XF(t + T) with prediction horizon T in Eq. (6). ^ ^ ^ ___(t), XM ___(t – 1), XM ___(t – 2), ⋅ ⋅ ⋅] XF(t + T) = F[XM (6) Figure 3. Impact of market discounting. Systems Engineering DOI 10.1002/sys 6 YU, ROUSE, AND SERBAN Figure 4. Impact of enterprise social network. The many nuances and subtleties of Eqs. (1)–(6), including the impacts of α, β, δ, and λ, are discussed in the subsequent section describing the simulation experiments. 3.3. Decision Making In this section, attention shifts to the nature of management decision making in the context of the proposed computational theory of enterprise transformation. Succinctly, what choices does management have and how should resources be allocated among these alternatives? There are four major types of decisions to be addressed. First of all, given a sluggish company with delayed market feedback, management could decide to invest in making better and longer-range predictions, i.e., predict better. This is a good idea to the extent that alpha is large enough to enable accurate predictions. For example, this works really well for sales that are driven by demographics, e.g., it is easy to predict how many young children will reach school age next year. Another, complementary, decision would be to learn faster. This would involve investing in the infrastructure and people for information gathering and interpretation in hope of shifting the weights in Eq. (4) towards more recent market data. There can be, as noted earlier, social forces that can inhibit this. To counteract such forces, companies often employ focus groups of consumers to get external views of the value of their Systems Engineering DOI 10.1002/sys products and services. Later discussions consider the impact of such investments on ∆V/∆δ , which should inform investment decision making. A third complementary decision would be to act faster. This would involve investing in people, processes, equipment, and so on to decrease beta in Eq. (5). The result would be a less sluggish company that can change XO much faster in response to forecasts of changing XM. A good example of this is Honda’s investment in flexible production lines that enabled shifting production to the smaller cars in its lineup when demand for larger cars faded. The impact of such investments on ∆V/∆β is also discussed later. Finally, management could focus on more quickly detecting market changes, i.e., detect faster. This involves the topic of change point detection, which is elaborated in the discussion of the simulation experiments. Put simply, there is a tradeoff between the “power” of change point decisions, e.g., the probability of correctly rejecting the null hypothesis of no change, and the time it takes to conclude that a change has happened. The power threshold chosen can be highly influenced by social forces and cultural attributes of an enterprise. 3.4. Social Networks A company is not just a set of human, physical, and financial resources organized around creating and delivering what the A COMPUTATIONAL THEORY OF ENTERPRISE TRANSFORMATION market desires. A company is also a social system that tends to have enormous—positive and negative—impact on the enterprise’s abilities to change [Rouse, 1993, 2005, 2006, 2007 and the references therein]. This social system can facilitate change via rapid communication and support of appealing changes. It can also inhibit change, much like an immune system, when the changes being entertained conflict with the concerns, values, and perceptions of key stakeholders. Beta and delta within the model elaborated in the previous section reflect social phenomena just as much as they reflect practical limitations on change. Acting faster, by decreasing beta to enable doing new things, will usually compete for resources with the status quo. Indeed, in times of stressful change, the “as is” enterprise can consume enormous resources trying to sustain the incumbent business model. Jobs, livelihoods, and political support may hang in the balance. For example, shifting investments from bending metal to coding software will likely encounter stiff resistance from those weaned on bending metal. Learning faster, by shifting delta to gain market knowledge more quickly, will compete with organizational belief systems about market value and the extent to which the status quo remains competitive [Rouse, 1993, 1998]. Denial of change is often easier to argue than acceptance of change coupled with a lack of action. The insularity typical of large successful enterprises is likely one of the reasons the Fortune 500 experiences such high levels of turnover [Schumpeter, 2009]. There is also a social aspect to the statistical power required for agreement that change has happened. If the threshold is very high, say 90–95%, then the company will be quite delayed in reacting to change. On the other hand, if the threshold is quite low, perhaps 50–60%, then the company will react to apparent changes that have not actually occurred. There are typically very strong social forces against abandoning a business model that was the source of previous success. This perspective can be quite reasonable when it is not at all clear what new business models will succeed. 4. SIMULATION EXPERIMENTS To support the qualitative insights into the enterprise transformation theory, computational experiments were conducted to assess the sensitivity of a company’s performance (i.e., average market value realized) to market trends and model parameter settings (i.e., α, β, δ, and λ). Since the theory of transformation is driven by recognition of experienced or anticipated value deficiencies, these experiments required a mechanism for a company to detect change. Consequently, this section begins with consideration of change point detection. With this mechanism defined, focus then shifts to the three strategic decisions that a company should entertain— predicting better, learning faster, and acting faster. 4.1. Change Point Detection Change Point Detection Theory. The observations in this study, Yj, j = 1, . . . , n, are the perceived market values VP 7 realized at different time points tj, j = 1, . . . , n. A company is interested in deciding whether there is a change in VP within a given period of time. A simple linear model for the perceived market values is given by Yj = b0 + b1 ∗ tj + εj for j = 1, . . . , n, (7) where the εj’s are error terms and assumed to be identically distributed but correlated. Under this model, the hypothesis of a change point in the perceived market value is H0: Yj = b0 + b1 ∗ tjj for j = 1, . . . , n, (8) vs. HA: Yj = b0 + b1 ∗ tj if j < ρ and Yj = b∗0 + b∗1 ∗ tj if j ≥ ρ (9) where b0 ≠ b∗0 or/and b1 ≠ b∗1. That is, under the alternative hypothesis the regression line changes its slope and/or its intercept, which is an indication of a change point at tj in the observed data. For detecting a change point, one computes a test statistic Uj at a given time point tj using the approach elaborated in the Appendix. High U values indicate a potential change in intercept and/or slope at the corresponding time points. However, a high U value alone does not guarantee a change point; one has to make an inference of how high the U value needs to be using the test p-value. In this study, this is achieved by using bootstrap sampling, since the distribution of the test statistic under the null hypothesis does not have a closed form expression. This is because the observations Yj, j = 1, . . . , n, are serially correlated, and, therefore, the classical assumption of independence does not hold. Application of the bootstrap sampling procedure for obtaining the significance level and the p-value is described in the Appendix. If the p-value is lower than a preset test significance level, specifically 0.05 in this paper, one accepts the alternative hypothesis of a change point. In the context of this study, the p-value corresponds to the probability of deciding a change in the market value at a given time point although no change has happened. The smaller the p-value, the smaller is the probability of making an error of deciding that there is a change in the perceived market value when there is in fact not a change. An important aspect of the testing procedure discussed above is the power of detecting a change in the market value given market trends and parameter settings (i.e., α, β, λ, and δ). For this, Monte Carlo simulations are employed as discussed in the Appendix. The power of the test provides a probabilistic assessment of the effectiveness of the change point detection procedure described in this paper. The higher the power, the more accurate the decisions are. The change point detection procedure is applied to selected observed time points—in this study, the actual change point is time point 101 and the selected observed time units are from 101 to 110. Therefore, for each selected time point, we derive their corresponding U values, U101, U102, . . . , U110 and Systems Engineering DOI 10.1002/sys 8 YU, ROUSE, AND SERBAN p-values, p101, p102, . . . , p110. Then for the same parameter settings, the above simulation procedure is repeated 60 times. The power of the detection test at each of the selected time points is obtained by counting the number of simulations that have p-values less than the 0.05 preset significance level. Detection time is defined as the smallest number of time points needed to detect a change. That is, the detection time is the first time point among t101, . . . , t110 for which the corresponding power of the detection test is larger than a company’s decision threshold. The length of time to discover a change in the market value varies from one company to another and from one business setting to another, as reflected by market trends and model parameters (i.e., α, β, δ, and λ). A company may choose a different decision threshold, i.e., power threshold, to detect a change. For example, an aggressive company may set the decision threshold as 60%, implying the company will take action when it is 60% sure that there is a change, whereas a conservative company may set this threshold as high as 95%. Change Point Detection Results show how trend, alpha, beta, lambda, and delta, as well as the company’s decisionmaking criteria, affect the delay in discovering that change has happened in the market according to the simulation. Effect of trend: Not surprisingly it is easier for companies to detect sudden and rapid changes than slow and stable changes. Effect of market dynamics (α): Stable markets (higher α) naturally will display higher power to detect a change point in the market value. However, when β is quite high (β ≥ 0.5), the sluggishness reduces the effect of alpha; therefore, the differences in market value trends for varying alpha are not significant. Effect of enterprise social network (β): Sluggish companies (higher β) can detect change more easily, although they cannot act sufficiently quickly to compensate for this change. In this way, a company’s sluggishness can act as a filter on market signals, which makes change more pronounced. Effect of market social network (δ): Increasing delta will directly delay detection time. The increased delay of detection time caused by the delta is approximately the same as the observation delay of the market value due to larger δ. Assume the actual change happens at period 101, when there is no delay in learning the market value (δ1 = 1), it is quite possible to detect the change at period 102. When there is 2-time-unit delay (δ3 = 1), one will likely detect change at period 104. Effect of market discounting (λ): Discriminating customers (higher λ) are those that sharply discount the value of products or services that do not closely match their desires. Such discounting will delay the detection of change, because the volatility of market value caused by crisply defined market desires rather than trend changes impedes the detection of change. 4.2. Decision Making Predict Better. In a stable market, given a sluggish company with delayed market feedback, management could decide to invest in better and longer-range predictions to force the company to change by using predictions of future market desires to drive the current market offering, much in the same way that a supertanker captain uses the longer term desired path to drive shorter-term steering decisions. This is accomplished by setting XO(t + 1) = β XO(t) + (1 – β) XF(t + T) with prediction horizon T (T time units ahead), where T can be determined by beta, the sluggishness of a company. Figure 5 shows how different look-ahead periods can affect market value. If the enterprise social network (β) is equal to 0.5, it is better to look ahead 2 time units, as the dotted curve has the highest market value. For each beta, there are always one or two best look-ahead periods. Figure 6 shows how look-ahead time units should be adjusted for beta, and how different look-ahead time units affect average market value. Figure 5. Market value vs. number of time units look ahead. Systems Engineering DOI 10.1002/sys A COMPUTATIONAL THEORY OF ENTERPRISE TRANSFORMATION When beta equals 0.5, it is better to look ahead 2 time units, as we can see the dashed curve has the highest average market value. The trend and alpha do not change where the maxima occur, but change how pronounced the maxima are. Learn Faster. The delay of change point detection time leads to lost value. The lost value is calculated by the timecumulative value until the company catches up with the target value that would have occurred had detection not been delayed (the area between the nondelayed and delayed detection time). Results show that lower α and higher β, i.e., sluggish companies in unstable markets, experience greater loss when there is a delay in detection of a change. The market social network (δ) will directly affect the detection time. The increased delay of detection time caused by delta is approximately the same as the delta increase. Figure 7 shows ∆V/∆δ, which is defined as the average difference of total lost value caused by the 1 unit detection time change as a function of the market dynamics (α). From this figure we can see that higher α results in shorter detection times and smaller total lost value. Also, the longer companies wait to make decisions, the more loss per time unit they will encounter. Act Faster. One reason for low market value is the sluggishness of a company (reflected by high beta). A decision to act faster (decrease beta) involves investing in people, processes, equipment, and so on. An enterprise is interested in the 9 impacts of such investments on ∆V/∆β. These results are sufficiently compelling to discuss them in the next section. 4.3. Summary of Results The simulation results discussed in this previous section can be summarized as follows: Change point detection time decreases with increasing trend and alpha, and increases with increasing lambda and delta. The best case is a strong trend in a stable market for a responsive company that learns quickly and has forgiving customers. The worst case is a mild trend in a noisy market with a sluggish company that is slow to learn and has highly discriminating customers. Interestingly, sluggishness is good in the sense that it decreases detection time but, on balance, is bad in that it inhibits responding to what is detected. Investments in predicting better can compensate for high beta, with the optimal prediction period increasing with beta, independent of alpha and trend. Thus, if one’s company is more like a supertanker than a speedboat, longer-term predictions should drive decisions regarding how best to track market desires. Noisier markets will result in less accurate predictions, but nevertheless these predictions should drive decisions. Learning faster results in less lost value due to delayed detection of change points, which provides greater returns Figure 6. Average market value vs. beta. Systems Engineering DOI 10.1002/sys 10 YU, ROUSE, AND SERBAN Figure 7. Investments in learning faster, ∆V/∆δ. (less lost value) for companies with low beta in markets with high alpha. Thus, learning faster helps most when the market is more predictable and a company can respond quickly. Acting faster by investing in decreasing beta yields improved market value, independent of alpha for low initial market value, but very much dependent on alpha for higher initial market value. An enterprise can only approach maximum value in very predictable markets when it can act quickly. 5. IMPLICATIONS FOR INVESTMENTS These results beg the question of how best to allocate scarce resources to improve market value. Acting faster is powerful, particularly in stable markets with strong trends. If the market is very noisy or the trend is weak, then investing to reduce beta may not be worthwhile. In such situations, it may be best to stay with the status quo—after all, this may not require any additional investment and the company already knows how to do it. Changing, in contrast, will require investment and, if the market is sufficiently noisy, may result in a situation just as bad (or worse) as not changing at all. The overarching question concerns: When to act and when not to act? We answer this question by assessing the return ∆V/∆β for varying values of alpha, beta, and lambda. In general, ∆V/∆β should exceed some threshold to warrant Systems Engineering DOI 10.1002/sys investing in change. The threshold will depend on how ∆V and ∆β are monetized, i.e., what ∆V is worth and what ∆β costs. One could easily imagine, for example that ∆V/∆β >1 could be the investment criteria. Figure 8 shows how much market value increases for beta decreasing from 0.1 to 0. Under this scenario, the return is high when alpha and lambda are both high. In other words, if the market is more predictable, customers are discriminating and one can act quickly, the return is high. On the other hand, if the market is noisy or customers are not discriminating, the likely return (∆V) from investing is not worth the cost of the investment (∆β). Figure 9 illustrates how much market value increases for beta decreasing from 0.5 to 0.4. Under this scenario, the return is high for median alpha and median lambda. This may seem counterintuitive. The reason is that there are two underlying phenomena. • When customers are forgiving, it is not worth investing as one is already “hitting the target” of maximum value. • When customers are highly discriminating, it is not worth investing because one is very unlikely to “hit the target.” • When the market is less noisy, the investment is worth more until the market becomes highly predictable because in this case one would already be “on target.” A COMPUTATIONAL THEORY OF ENTERPRISE TRANSFORMATION 11 Figure 8. Investments in acting faster, ∆V/∆β (β = 0.0–0.1). Figure 10 illustrates how much market value increases for beta decreasing from 0.9 to 0.8. If customers are discriminating (high λ) and one acts slowly (high β), one is unlikely to hit the target. On the other hand, if the customers are not discriminating and the market is noisy, it may be worth investing to act a bit faster as it increases the chances of hitting the target, considering how poorly one is currently performing. As indicated earlier, predicting better and learning faster can always be justified, with the prediction horizon adapted to the level of sluggishness of the company. The returns on such investments will be greater for more predictable markets and less sluggish companies. Considering the likelihood that such investments will be much smaller than those needed to decrease beta, these investments are much easier to justify. Figure 9. Investments in acting faster, ∆V/∆β (β = 0.4–0.5). Systems Engineering DOI 10.1002/sys 12 YU, ROUSE, AND SERBAN Figure 10. Investments in acting faster, ∆V/∆β (β = 0.8–0.9). To summarize, investing in transformation is likely to be attractive when one is currently underperforming and the circumstances are such that investments will likely improve enterprise performance. In contrast, if one is already performing well, investments in change will be difficult to justify. Similarly, if performance cannot be improved—due to noisy markets and/or highly discriminating customers—then investments may not be warranted despite current underperformance. 6. CONCLUSIONS The computational theory elaborated in this paper predicts that companies will transform their enterprise by some combination of predicting better, learning faster, and acting faster, as long as the market is sufficiently predictable to reasonably expect that transformation will improve the market value the company can provide. If this expectation is unreasonable, then companies will sit tight and preserve resources until the market becomes more fathomable.2 The theory elaborated in this paper is premised on the notion that companies make transformation decisions in response to the dynamic situations in which they find themselves. These decisions are affected by both what the company knows (or perceives) and the company’s abilities to predict, learn, and act. Indeed, decisions to transform abilities to predict, learn, and act reflect desires to fundamentally change the company’s overall ability to create market value. In this way, transformation decisions can enhance a company’s abilities to address the ongoing and anticipated transformations needed for success in dynamic markets. APPENDIX: TEST FOR A CHANGE-POINT IN LINEAR REGRESSION The observations are (Xj, Yj), j = 1, . . . , n, where X jg = (1, xj,1, . . . , xj,p are a set of predictors. In this article, the response Yj corresponds to perceived market value whereas the predictor is time (p = 1). The null hypothesis of interest is that the observations satisfy a linear regression model: H0: Yj + Xjgβ + εj for j = 1, . . . , n. (10) The alternative hypothesis is HA, that there is a change-point ρ such that Yj = Xjgβ + εj if j < ρ and Yj = Xjgβ∗ + εj if j > ρ for β ≠ β∗. (11) Here εj for j = 1, . . . , n are errors assumed to be identically distributed but correlated. In this study, the likelihood ratio test provided by Kim [1994] was applied to detect changes in linear regression models with the modification of the p-value. The likelihood test statistic is defined as follows: For k = 1, …, p, let ak be the (p + k) × 1 vector with 1 and –1 at the (2k – 1)st and the (2k)th component, respectively, and 0 in all other components, and let Xk,j be the n × (p + k) matrix such that Xk,jek,i = (1jg, 0jg) for i = 1, (12) Xk,jek,i = (0jg, 1jg) for i = 2, (13) 2 This can be observed during the current (2010) economic conditions where companies are making strong profits, but hoarding cash rather than investing in increased capacity and/or new offerings due to great uncertainties about where the economy is headed. Systems Engineering DOI 10.1002/sys A COMPUTATIONAL THEORY OF ENTERPRISE TRANSFORMATION g Xk,jek,i = x i−1 ,1, . . . , x i=1 ,j 0n−j for i = 3, 5, . . . 2k−1,(14) 2 2 g Xk,jek,i = 0n−j , x i−1 ,j+1, . . . , x i−1 ,n for i = 4, 6, . . . 2k,(15) 2 2 Xk,jek,i = x i−1 ,1, . . . , x i−1 ,n for i = 2k + 1, 2k + 3, . . . (, 16) 2 2 Xk,jek,i = x i ,1, . . . , x i ,n for i = 2k + 2, 2k + 4, . . . , (17) 2 2 where 1j is the length vector of 1’s, 0j is the j length vector of 0’s, and ek,i is the (p + k) length vector with 1 in the ith component and with 0 in all other components. For k = 1, …, p, we further define g g a k,g j(X k,j X k,j)−1Xk,j Y , Uk(j) = 1 − g g 2 g a k,j (X k,j X k,j)−1ak,j (18) where Y = (Y1, . . . , Yn)′. Lastly, the test statistic referred as the U value in this paper is ^ ^ U(Y) = σ−2 max ||U(j)||2 = σ−2 max {U21(j) + ⋅ ⋅ ⋅ + U2p(j)} p ≤ j ≤ n−p p ≤ j ≤ n−p (19) ^ −2 is the maximum likelihood estimate of σ2 under where σ H0. Worsley [1983] studied the likelihood ratio test (LRT) for testing H0 against HA, and provided an upper bound for the significance level of the test. However, in this case study, the assumption of independence among Y1, . . . , Yn does not hold as they are serially correlated, and therefore, existing LRT results do not apply. Instead a resampling method called parametric bootstrap is used to sample from the null distribution of the test statistic defined above which will further provide an approximation of the p-value of the change point hypothesis test. Specifically, a sample of the test statistic is obtained under the null hypothesis as follows: 1. Fit a linear regression under the null hypothesis: ^ ^ where β ^ are estimated coefficients. Extract H0: Yj = Xjgβ ^ , j = 1, . . . , n. the residuals Rj = Yj − Xjgβ 2. Permute the residuals R1, . . . , Rn to obtain a new sample from the error distribution: R∗1 , . . . , R∗n. 3. Obtain a new sample from the null hypothesis: ^ + R∗, j = 1, . . . , n. Y∗j = Xjgβ j 4. Repeat 2–3 for B (= 1000) times. Using this re-sampling technique, we obtain B samples ∗b from the null hypothesis Y∗b = (Y∗b j , . . . , Yj ) for b = 1, . . . , B and for each sample we can compute the U value, U∗b = U(Y∗b). We approximate the p-value using B 1 p-value = B ∑ b=1 I(U(Y) ≥ U(Y ∗b)). (20) 13 An important evaluation criterion of a hypothesis test is its power, defined as the probability of rejecting the null hypothesis when the alternative hypothesis is true. In the context of this problem, the power of the test is translated as the probability of detecting a change when a company indeed has a change in the market value. Similarly to the computation of the p-value, because one does not have a close form expression for the distribution of the test statistic, Monte Carlo simulations are employed to obtain an approximation of the power of the change point detection test. Specifically, given the parameter settings (i.e., trend, α, β, λ, and δ), S = 60 samples are generated from the model. One then evaluates the number of times the p-value is smaller than a significance level (0.05 in this paper) divided by the number of samples, S. Remark: In change point detection theory, one tuning parameter that may affect the reliability measured by the p-value and the accuracy measured by power of the test hypothesis for detecting a change is the number of observations set before (past) and after (future) a test time point. The study shows that the test results are not sensitive to the selection of the future and past points as long as the ratio between them is less than 2/3 and as soon as the number of past time points is between 20 and 40 and the number of future time points is 5–15, respectively. However, when, for example, the number of past time points is too large or the number of future time points is too small, the accuracy of the decision regarding change in the market decreases. REFERENCES K.M. Eisenhardt and L.J. Bourgeois, Making fast strategic decisions in high-velocity environments, Acad Management J 32(3) (1989), 543–576. K.M. Eisenhardt and J.A. Martin, Dynamic capabilities: What are they? Strategic Management J 21(10–11) (2000), 1105–1121. L.V. Gerstner, Jr., Who says elephants can’t dance? Inside IBM’s historic turnaround, Collins, New York, 2002, 64. E.S. Hanawalt and W.B. Rouse, Car wars: Factors underlying the success or failure of new car programs, Syst Eng 13(4) (2010), 189–403. D. Kelly and T.L. Amburgey, Organizational inertia and momentum: A dynamic model of strategic change, Acad Management J 34(3) (1991), 591–612. H.-J. Kim, Tests for a chance-point in linear regression, IMS Lecture Notes, Monograph Series, Volume 23, Institute of Mathematical Statistics, Beachwood, OH, 1994. C.C. Markides, A dynamic view of strategy, Sloan Management Rev 40(1) (1999), 55–63. R.E. Miles, C.C. Snow, A.D. Meyer, and H.J. Coleman, Jr., Organizational strategy, structure, and process. Acad Management Rev 3(3) (1978), 546–562. H. Mintzberg, Patterns in strategy formation, Management Sci 24(9) (1978), 934–948. J. Moncrieff, Is strategy making a difference? Long Range Plan Rev 32(2) (1999), 273–276. M.E. Porter, Towards a dynamic theory of strategy, Strategic Management J 12(S2) (1991), 95–117. Systems Engineering DOI 10.1002/sys 14 YU, ROUSE, AND SERBAN W.B. Rouse, Catalysts for change: Concepts and principles for enabling innovation, Wiley, New York, 1993. W.B. Rouse, Start where you are: Matching your strategy to your marketplace, Jossey-Bass, San Francisco, 1996. W.B. Rouse, Don’t jump to solutions: Thirteen delusions that undermine strategic thinking, Jossey-Bass, San Francisco, 1998. W.B. Rouse, A theory of enterprise transformation, Syst Eng 8(4) (2005), 279–295. W.B. Rouse (Editor), Enterprise transformation: Understanding and enabling fundamental change, Wiley, Hoboken, NJ, 2006. W.B. Rouse, People and organizations: Explorations of human centered design, Wiley, Hoboken, NJ, 2007. W.B. Rouse, Necessary competencies for transforming an enterprise, J Enterprise Transformation 1(1) (2011), in press. W.B. Rouse and R.C. Basole, “Understanding complex product and service delivery systems,” Handbook of service science, P. Maglio (Editor), Springer, London, 2010, pp. 461–480. D. Schendel and G.R. Patton, A simultaneous model of corporate strategy, Management Sci 24(15) (1978), 1611–1621. J. Schumpeter, Taking flight, The Economist (September 19, 2009), 78. P.M. Senge, The fifth discipline: The art and practice of the learning organization, Doubleday/Currency, New York, 1990. K.J. Worsley, Testing for a two-phase multiple regression, Technometrics 25 (1983), 35–42. Zhongyuan (Annie) Yu is a Ph.D. student in the School of Industrial and Systems Engineering of Georgia Tech and a Graduate Research Assistant at the Tennenbaum Institute. Her research focuses on economic decision analysis and human decision making. Zhongyuan has various intern and project experiences in fields ranging from manufacturing to supply chain management, to airline operations research, to real estate consulting and banking, and she has published related papers, such as “The Application of Industrial Engineering in Manufacturing Management” and “Modeling and Solving the Spatial Block Scheduling Problem in Shipbuilding Industry using Particle Swarm Optimization.” Zhongyuan received a B.S. in Mechanical and Industrial Engineering in Tongji University (Shanghai, China) with a minor in Journalism at Fudan University (Shanghai, China), and an M.S. in Industrial Engineering at Georgia Tech. Bill Rouse is the Executive Director of the Tennenbaum Institute at the Georgia Institute of Technology. He is also a professor in the College of Computing and School of Industrial and Systems Engineering. His research focuses on understanding and managing complex public-private systems such as healthcare, energy and defense, with emphasis on mathematical and computational modeling of these systems for the purpose of policy design and analysis. Rouse has written hundreds of articles and book chapters, and has authored many books, including most recently Economic Systems Analysis and Assessment (Wiley, 2011), People and Organizations: Explorations of Human-Centered Design (Wiley, 2007), Essential Challenges of Strategic Management (Wiley, 2001), and the award-winning Don’t Jump to Solutions (Jossey-Bass, 1998). He has edited or co-edited numerous books including Engineering the System of Healthcare Delivery (IOS Press, 2010), The Economics of Human Systems Integration (Wiley, 2010), Enterprise Transformation: Understanding and Enabling Fundamental Change (Wiley, 2006), Organizational Simulation: From Modeling & Simulation to Games & Entertainment (Wiley, 2005), the best-selling Handbook of Systems Engineering and Management (Wiley, 1999, 2009), and the eight-volume series Human/Technology Interaction in Complex Systems (Elsevier). Among many advisory roles, he has served as Chair of the Committee on Human Factors of the National Research Council, a member of the U.S. Air Force Scientific Advisory Board, and a member of the DoD Senior Advisory Group on Modeling and Simulation. Rouse is a member of the National Academy of Engineering and has been elected a fellow of four professional societies—Institute of Electrical and Electronics Engineers (IEEE), the International Council on Systems Engineering (INCOSE), the Institute for Operations Research and Management Science (INFORMS), and the Human Factors and Ergonomics Society (HFES). Nicoleta Serban is an assistant professor in the School of Industrial and Systems Engineering at Georgia Institute of Technology. She received her Ph.D. and M.S. in Statistics from Carnegie Mellon University. She also holds a B.S. in Mathematics and an M.S. in Stochastic Processes and Theoretical Statistics from the University of Bucharest. Dr. Serban’s research crosses multiple disciplines including methodological statistics, molecular biology, healthcare, industrial engineering and socio-economics. Her primary methodological contributions in statistical research are for the analysis of multiple time-varying random functions. Dr. Serban’s research has been published or accepted in more than 15 journal articles, most in top journals in statistics, engineering and biology. Systems Engineering DOI 10.1002/sys
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