What's left as a basis for competition is to execute your business with maximum efficiency and effectiveness, and to make the smartest business decisions possible. And analytical competitors wring every last drop of value from business processes and key decisions.
- Thomas H. Davenport & Jeanne G. Harris*
* All references are available in the article download.
What sets companies apart in a crowded marketplace? Why do some companies outperform their peers while others struggle to stay relevant? In a globally competitive environment companies are exploiting every opportunity to either out-compete or out-innovate you, or both. Processes spanning all value chains, both internal and external, can be a tremendous source of competitive differentiation, especially when companies use dynamic operational analysis to design and operate those processes "better, faster, cheaper" than others.
The path to become a better competitor begins with adoption of a proactive approach to operational execution, decision making, and actions focused on the overall business goals. It is built on the foundation of Predictive Operational Analytics-a systems approach that extends traditional business analytics.
Creating efficiency in a small and isolated area is simple; but to ensure its effectiveness in the overall scheme is complex. Just because a company might be progressing on process efficiency does not guarantee that the results roll up in an integrated manner. Achieving the desired customer, financial, or other results demanded by your various stakeholders, through making the smartest business decisions possible in your dynamic environment, is not a trivial endeavor.
Predictive Operational Analytics is shaped by the confluence of several disciplines that enable organizations to rapidly and dramatically improve operational execution and financial performance: systems thinking, business methods, and dynamic operational analysis. It is a holistic approach that proactively facilitates making the "best possible" operational decisions. Predictive Operational Analytics raises the focus from slow and steady continuous improvement thinking to that of proactive dynamic business optimization.
Brief History of Business Decision Making Frameworks and Tools
It is instructive to briefly examine the history of the disciplines that underpin Predictive Operational Analytics. The following figure highlights a sampling of the three elements of Predictive Operational Analytics: management and finance (top row), information technology (bottom row), and their merger with systems theory and practice (middle row).
Figure 1: Brief History of Business Decision Making Frameworks and Tools
Systems thinking predates all modern business and information technologies. Its applications are generally found in biological, physical and social sciences, philosophy, and theology dating back to the 1600s. Application in a business context crystallized largely in the 20th century. More recently, the field of Predictive Operational Analytics was established by Menawat and Garfein as a powerful business decision making solution and described in the book, Profit Mapping.
Management methods and information technologies come and go over the years, some morph into new and better approaches, and many remain fundamentally sound and relevant. Predictive Operation Analytics closes the gap between business methods and information technology by tightly integrating both through the practical application of systems theory. It does this by emphasizing certain fundamental "requirements" or principles, but without mandating specific methods and tools. This flexible value proposition for decision makers comes from the right combination of "business intelligence" software, business methods, and systems thinking to dramatically improve the impact of complex operational decisions.
Two Tiers of Operational Decision Making
The sweet spot for Predictive Operational Analytics is with greater value, higher risk, and dynamic operational challenges-what we call Tier-II type decisions (see Figure 2). Although the potential payoff for going after the higher hanging opportunities is far greater, the risks are also more severe as "incorrect" actions can severely harm the business.
Figure 2: Two Tiers of Operational Decision Making
In comparison, Tier-I type decision-making deals with critical everyday issues and hotspots that often consume a disproportionate amount of management bandwidth. They represent the assortment of initiatives, actions and countermeasures taken to manage and improve performance, or to get operations back on track if something has gone awry.
Characteristic of Tier-I challenges are the responses to tactical and more immediate, often mission critical problems. This is where you find the typical day-to-day management firefighting. You must deal with issues quickly or the risks have potential to spiral and threaten the business. The lower hanging fruit for improvement are the usual targets in this tier. Tier-I examples include an emergency response to a supply chain issue, a new field service for customers, an employee training program, a new sales group, a technology modernization program, immediate repairs for critical equipment, and so on.
A challenge for Tier-I is that efforts are often for spot-correction and are disconnected from each other and are conducted with little coordination for the larger good. These are the focused activities to eliminate an obvious waste or to correct a local situation. Success of Tier-I activities is an essential component for the success of the organization. Unfortunately, organizations find their attention and energy drawn to these highly visible pieces of the puzzle, often at the expense of the larger more complex interconnected business ecosystem in which they reside.
While Tier-I demands swift and appropriate actions to manage for today, Tier-II is important for sustenance and growth of the business over the long term. Leading companies deal with both tiers simultaneously and effectively. Although the potential performance improvement payoffs are higher in Tier-II, the inherent complexities of these types of challenges are more difficult to conquer and pose greater risks to the business if not handled properly.
Tier-II draws focus on the interdependencies and dynamics of business as it tackles the whole system. A reductionist approach, isolating the problem as is often done in Tier-I, is clearly not viable here. Thus, it reasons that systems based approaches built on sound business methodologies are a natural fit to ensure alignment and effectiveness of decisions and actions. How do you optimally configure multiple products, processes, resources, supply chains and financial parameters all at once to achieve the best possible process and financial performance? How do you best respond to changes in the business environment, such as changing cost structures or customer preferences? What is the best way to introduce new products into the marketplace, or new processing technologies in operations? These are all central Tier-II type questions.
Rather than embarking down a path of costly and uncertain "experimentation" to improve operational performance, which is typically directed at the lower hanging fruit, Predictive Operational Analytics creates a realistic path for performance optimization. It helps create a detailed roadmap for change to achieve the desired results. That is, when faced with several "reasonable" options for reconfiguring a business for better performance, Predictive Operational Analytics helps identify the "best" option based on a business unit's current capabilities, limitations, business objectives, and financial and regulatory requirements.
An Interconnected View of the Business
All we are doing is looking at the time line from the moment the customer gives us an order to the point when we collect cash. And we are reducing that time line by removing the non-value-added wastes.
- Taiichi Ohno, Founder of Toyota Production System
Operational dynamics are central to Predictive Operational Analytics. The challenge for decision makers is to successfully compress the value chain by navigating business and workflow dynamics for superior performance while sustaining the business economics.
At a macro level, business dynamics refers to the systems context of the integral functions of business including sales, accounts receivable, personnel, operations, maintenance, delivery, and the supply chain contained in a business unit. The interrelationships within and between all of these functions generate numerous tactical challenges and curveballs. In managing operations, leaders are laser-focused on daily, weekly, and monthly production related issues. For example, what is the optimal production and personnel schedule for the week given changes in product demand and mix? Planning new ventures or for existing business units is another example, and is done less frequently, such as on a monthly, quarterly, and/or yearly basis.
At a micro level, workflow dynamics refer to the intricacies of the processes, which we call product workflows. The dynamics associated with product workflows derive from the challenge to navigate the right work for the right product at the right place and at the right time despite blockages, starvations, downtimes, failures, and shortages. Clearly, this is very complex. Effective operational design and reconfiguration of the product workflows are strategic endeavors. They focus on the minutia, literally down to the second-by-second operational and financial behavior of a business unit. Companies once had the luxury of long production runs and lots of time to refine operations, but now face short product cycles with razor thin margins for error.
Enterprise software such as ERP and BI tools, as well as spreadsheets, are essential management tools. As we will discuss, however, they are not ideal for managing future performance due to a number of reasons including their "static" rather than "dynamic" analytical basis. Such Tier-I type tools are very important in "accounting" for performance. They also highlight deficiencies where people "just need to get it done" to meet customer requirements. In contrast to this reactionary role, dynamics-oriented tools highlight how "best to do it" while minimizing risk and improving speed and flexibility.
The goal of Predictive Operational Analytics is to quickly make holistic decisions that create the best possible operational and business environment for success. It helps guide the planning and configuration of efficient and effective processes to sustain and grow the business while minimizing risk.
Predictive Operational Analytics is the integration of systems thinking, business methods, and predictive dynamic analytical capabilities embedded in business intelligence software (see Figure 3). While each of these three domains provides unquestioned business value on their own, their convergence into a systematic yet flexible framework for decision making provides benefits far beyond the capabilities of the individual elements.
Figure 3: Predictive Operational Analytics
As evident from the above figure a primary difference between Predictive Analytics and Predictive Operational Analytics is the inclusion of systems thinking. In business execution, systems thinking spans both operational and strategic issues. It has broad applicability across many business functions well beyond the "plant floor" or other operational domains. A systems approach is holistic by definition and emphasizes integrated, forward-looking, and dynamic analysis.
Predictive Analytics typically focuses on mining customer data and is most commonly deployed for marketing efforts such as customized promotions targeting specific customers. In contrast, high risk decisions such as how and where to produce products and services for the best globally landed cost, how to best configure the value chain, and the myriad of related complex operational and business decisions belong to the domain of Predictive Operational Analytics.
Predictive Operational Analytics transforms the culture from proving the success of a decision after its implementation-a passive wait-for-results-to-aggregate approach-to a proactive solution where the value of decisions is "proven" to the business beforehand. Rather than rolling out changes and then waiting for process and financial results to accrue, hopefully for the better, Predictive Operational Analytics virtually guarantees success in advance.
The integration of systems thinking is not a simple add-on to Predictive Analytics to create Predictive Operational Analytics. It has a profound impact on the way both business methods and business intelligence concepts are used. A systems approach reinforces thinking about each element of business collectively as a system comprised of time-dependent interconnected entities. Thinking about business through a systems lens does not change the ways of business, but, when coupled with the right methods and quantitative analytical tools, it reveals a deeper causal insight into the underlying operational and business causes and effects.
Table 1 describes the Predictive Operational Analytics environment.
Table 1: Predictive Operational Analytics Context
We explore these elements in more detail below, with particular attention to the specific domains of systems thinking, business methods, and business intelligence software and how Predictive Operational Analytics seamlessly blends all three for superior business value.
1. Systems Thinking for Business Success
[Increasing the productivity of knowledge] requires systematic analysis of the kind of knowledge and information a given problem requires, and a methodology for organizing the stages in which a given problem can be tackled - the methodology which underlies what we now call "systems research."
- Peter F. Drucker
Wringing every last drop of value from business decisions depends on many factors, and risks are plentiful. As a result of the inherent complexities of managing and improving business performance, the natural tendency is to break execution challenges into smaller more manageable pieces. Implicit with this "reductionist" thinking is the assumption that individual changes and investments will individually and collectively roll-up to yield the desired business results.
A reductionist approach is a good way to improve efficiency of an isolated area. But it does nothing to directly improve its effectiveness towards the overall business goals since these goals are not part of the equation or measurements. Thus, a reductionist approach is only helpful as long as it does not transfer the inefficiency to another part of the business thereby negatively impacting the overall goals. Therein lays a "catch 22" management challenge. How do you guarantee a positive action in an "isolated" area without negatively impacting other areas before making changes and declaring business success?
Synthesis of individual areas and functions into a unified whole creates a better context to understand the interrelationships and time dependencies of business. It prevents the potential of a negative overall impact and provides a platform for systematic integrated change.
In order to adopt a systems approach, the decision-maker chooses the collective impact of actions to be the greater goal over achieving maximum efficiencies in individual areas. The following passage nicely sums up systems thinking and its universal application.
Systems thinking is a discipline for seeing wholes. It is a framework for seeing interrelationships rather than things, for seeing patterns of change rather than static "snapshots." It is a set of general principles - distilled over the course of the twentieth century, spanning fields as diverse as the physical and social sciences, engineering, and management.
- Peter Senge
Let's say for illustrative purposes that the 3-D cube in Figure 4 represents a business or business group in an organization. The cube is comprised of many interconnected sub-cubes representing various functions and activities (marketing, sales, operations, etc.). All of these cubes can pivot in any number of directions, but are nevertheless collectively bound together with a common purpose. Information transpires within and between, which management can leverage for decision making purposes.
Figure 4: Managing and Improving Operational Performance
In this image, the colored boxes, regardless of their individual efficiency or effectiveness in delivering on their own objectives, are out of sync with the rest of the organization and each other. Managers try to move them into correct locations to improve performance. Achieving proper alignment depends on viewing them from the appropriate perspectives - from within the box, from a neighboring box, or from a distance looking at the whole cube. The answers for each perspective are more than likely to be different. Which is the correct one? The short answer is "all of them," but the reality is much more complex.
Strategic Business Unit: A Value-Centric Container
A business is an interdependent collection of things such as people, machines, and operational policies among others, that interact with one another to form a collective whole and provide customer value. We use the concept of a Strategic Business Unit (SBU), a customer focused container for measuring and managing performance, to precisely define a "business system" for management and improvement purposes.
An SBU is a virtual concept for isolating value creation to the smallest organizational segment where value can accrue and financial performance and success can be unambiguously judged. A major benefit of an SBU focus is that it provides the flexibility to address a wide range of business challenges while maintaining a customer focus without being artificially constrained by organizational functions or silos. It can transcend multiple sites and value chains or encompass a small segment within a single facility. The definition depends upon the business and customer objectives.
Companies and managers often stay in their comfort zones, defining what they do in relation to internal functions and silos, with insufficient connection to customers and the underlying operating models for serving them.
Operating models are made up of operating processes, business structure, management systems, and culture, all of which are synchronized to create a certain superior value. At the heart of the operating model sits not one but a set of core processes that make or break an organization's ability to create unsurpassed value at a profit.
- Michael Treacy & Fred Wiersema
The systems concept of an SBU changes one's perspective of problem solving from an internally focused functional driven view to one of delivering products and services that your customers value. It also changes management thinking from an inefficient wait-and-see approach of "waiting for results to accrue following action and hoping they are positive" to a more decisive and proactive approach of "driving business performance by targeted actions that will achieve the business goals" mindset.
Another benefit of a systems approach is that the SBU structure reinforces a laser focus on what is important while pointing out internal and external factors that are irrelevant to the particular challenge at hand, and thus can be "ignored." It is important to focus on the right things because effectively controlling the behavior of a system raises complex measurement and analytical challenges, and you have limited time and resources to achieve success.
The behavior of a system arises from its structure. That structure consists of the feedback loops, stocks and flows, and nonlinearities created by the interaction of the physical and institutional structure of the system with the decision-making processes of the agents acting within it.
- John Sterman
Unfortunately, many organizations see their "system structure" in terms of organizational groups, functions, or silos-not necessarily from a more customer-oriented perspective. Understanding such conditional and nonlinear behaviors requires a specialized framework for analysis, one that goes well beyond the static view of data in isolation.
Conquer System Details and Dynamics for Business Success
An SBU "lens" provides the business focal point. The decision making challenge is to influence the system to perform as desired. How do you reconfigure or adjust an SBU to behave in ways that move it closer to achieving the business and customer objectives? What investments in people, equipment, and facilities are required? What is the optimal timing for the investments? Is production aligned to demand? Do you have the flexibility to respond rapidly to changes in customer demand? How are your costs and quality? How should you react if certain risks materialize?
Systems thinking teaches that there are two types of complexity - the "detail complexity" of many variables and the "dynamic complexity" when "cause and effect" are not close in time and space and obvious interventions do not produce expected outcomes.
- Peter M. Senge
Systems thinking is ideal for holistically overcoming operational complexity, for successfully navigating both the details and dynamics of performance. While it is common to simply overlook the existence of such complexity, it is risky to ignore the operational reality. The following figure highlights these sources of complexity within an SBU.
Figure 5: System Details and Dynamics
The 3-D cube represents the details within a hypothetical SBU, including all of the direct and indirect activities performed by people and equipment to produce and deliver products and services. Notice that Figure 5 focuses on a specific SBU in its entirety.
Figure 5 contains every operation, all equipment and "stations," and all people attached to the SBU in any capacity. It encompasses all value streams associated with those products. In addition are all of the "systems" oriented things such as product workflows, conveyance and transportation, recycle loops, scrap points, stocks, and so on.
System details are not static in nature. To create products, the system must be put into motion. Therein lays the dynamics challenge. For a given demand over a specific time period, products are scheduled, jobs are released to the floor, raw materials flow, people and equipment process materials and parts, parts are conveyed between operations, and so on.
What is the best possible way to configure an SBU given its dynamic behavior to achieve the business objectives including financials? These are all highly time dependent and interconnected activities. Problems can manifest at any point in the flow. Explicitly not incorporating the dynamics is the main reason why static analytical approaches are mismatched to such dynamic business challenges.
Figure 5 also highlights another systems complexity challenge. Namely, each management perspective brings it own interpretation of the business goals, solution thinking, and analytical tools to the table. A CFO sees operational challenges differently than, say an operations manager or an engineer. This often results in apples-to-oranges analyses and disconnections in decision making. Each person sees the same system, but from different "perspectives" resulting in incomplete views of the SBU. To some degree, they are all a little right but also inaccurate at the same time. Without common methods and analytical frameworks it is difficult to balance the needs of all stakeholders to make the right overall decisions for the organization. At the end of the day, everyone needs to speak a common language of business economics.
Even the smallest operational or policy changes from any of the various management "perspectives" in the SBU have ripple effects that are dependent on both the specific and collective changes in the environment. A business is not the sum of its individual parts, but the collection of its interdependent operational relationships.
Apply the Past with Caution in Decision Making for the Future
The future ain't what is used to be.
- Yogi Berra
Another key systems concept has to do with the application of past successes and failures in creating the future. From a systems standpoint, the past is technically irrelevant for effecting future system performance. This is not to be confused with the critical learning, experiences, and "battle scars" that collectively shape our expertise and wisdom.
While history can provide a vital source of knowledge to shape our thinking, it is a poor guide for determining precisely what to do, to what degree, and when for an SBU. The future environment will always be different in some way than the past environment. It is not static, nor invariant. What matters is not how a different SBU previously performed, or where the current SBU has been (such as good or poor historical performance), but where it is today (i.e., capabilities and constraints), the objectives or "destination," and identification of the path in terms of all of the investments in people, equipment, facilities, configurations, and so on required to achieve the objectives.
This is a very empowering point of view. When making decisions to impact future performance, it is not necessary to dwell on past performance. What matters is what you do from this point in time forward, hopefully with an eye towards balancing the multiple near- and longer-term business objectives.
2. Business Methods Ensure Superior Execution and Reduce Risk
Execution is a systematic process of rigorously discussing hows and whats, questioning, tenaciously following through, and ensuring accountability. It includes making assumptions about the business environment, assessing the organization's capabilities, linking strategy to operations and the people who are going to implement the strategy, synchronizing those people and their various disciplines, and linking rewards to outcomes. It also includes mechanisms for changing assumptions as the environment changes and upgrading the company's capabilities to meet the challenges of an ambitious strategy.
- Larry Bossidy & Ram Charan
Predictive Operational Analytics is intended for operational planning and execution, aiming to quickly and systematically find the best possible operational, financial, and customer solutions for business challenges. Without mandating a particular approach, Predictive Operational Analytics requires a formal business method that has a capability to align with systems thinking and analytical rigor. Each organization selects the concepts and methods that best suit their needs and are appropriate for the particular challenge.
A business methodology provides proven and effective ways to synchronize organizational thinking and actions with business objectives and strategy. It provides the body of principles, rules, and postulates with extensions to include analysis of possible actions and outcomes. Business methods and models are as varied as the disciplines they represent, covering everything from strategy, decision making, valuation, communication, organization and change, to leadership and management.
As a word of warning, exercise caution when using less formal "mental models" to guide actions. While they are a helpful starting point and can be handy for troubleshooting, they are less systematic than formal methods and can lead to unforeseen problems. Often, analytical rigor points to "non-intuitive" yet highly effective solutions. Operating under mental models alone instills bias that precludes such potential benefits.
The freedom to select the specific methodology lends flexibility to how problems are defined and solutions are assembled, and prevents dogmatic adherence to concepts that may not be applicable. There is an ample menu of useful business methods from which to choose.
Our intention is not to review or sanction any particular methods here. Your training, discipline, and job title likely already point you toward certain business methods. The point is that every problem no longer looks like a nail just because you own a hammer. Using the right tool begins with properly understanding the problem.
Importance of Proper Problem Definition
If I had one hour to save the world I would spend fifty-five minutes defining the problem and only five minutes finding the solution.
- Albert Einstein
Proper problem definition is an invaluable yet underappreciated aspect of creating successful solutions. Although Predictive Operational Analytics is flexible in terms of specific business methodologies, there is a fundamental "definitional" challenge which impacts whether or not decision makers focus on the "right" things. Business methodologies are as important for defining a problem as they are for providing an effective solution.
A methodology itself can bias the solution approach, particularly with insufficient attention devoted to define the real problem. It is risky to assume, without proper inquiry, that a particular methodology is the right one for a challenge, or if so, that its implementation is necessarily aligned with the business goals. Too often methodologies and tools are applied before properly understanding the problem. This leads to ineffective use of valuable resources and often dooms well-intended actions from the outset without a fighting chance for overall success.
In an interconnected system, it is rare that an observed effect results from one or more causes solely within the same area. Usually they manifest somewhere else as a result of interacting sub-systems and dynamics. This multidimensional cause and effect behavior poses a definitional challenge as it can span multiple locations, and causes do not manifest all effects at the same rate. As the management pioneer Peter F. Drucker observed, increasing the yield from our efforts:
...requires a methodology for problem definition - even more urgently perhaps than it requires the currently fashionable methodology for "problem solving."
At the first indication of a performance problem, an operational manager might, for instance, quickly single out a "bottleneck" at a specific manufacturing operation. A service parallel at a call center, for instance, might center on problem resolution challenges for a specific kind of caller issue. There might be "excess inventory" or "unnecessary processing steps" or "inadequate quality" at one or more operations or "insufficient production flexibility," and so on. In contrast, a financial manager might point elsewhere to labor costs or "cost bottlenecks." But, what is the actual problem? Are these symptoms? They might all be relevant to some degree.
Systems thinking aids in problem definition. What are the objectives of the organization? This establishes what it is trying to achieve-its performance targets. Stated differently, the objectives of the organization define the desired destination for the SBU system. Strategy and objectives provide a coarse filter to assess if an observed issue is really a problem within an SBU. The SBU is the playing field for management decisions and actions, and where operational and business performance is precisely measured.
In practice there is never a single objective such as to reduce waste, improve quality, improve on-time delivery, or reduce costs. The reality is that a business system always has multiple objectives, and maintaining the right balance among them is challenging to say the least.
Thus, a problem definition is really a hypothesis about the nature of a potential problem, potential causes and effects, and where it exists. Accounting for the objectives and needs of the management stakeholders helps to holistically frame this hypothesis. Thus, the problem definition must be clearly understood by each stakeholder and how the solution approach would affect them.
Fostering an environment where people are empowered to question the "why," "how," "where," and "when" of operational challenges contributes to a culture attuned to proper problem definition and adopting the right solution approach for their needs. Otherwise, managers find themselves constantly implementing countermeasures and laboring to correct past errors as opposed to creating a better performing future.
Business methods are a mainstay of Predictive Operational Analytics because you need a systematic framework to identify the objectives, define the problem, specify the solution approach, identify the appropriate set of parameters for change, select the right dynamic analytical tools, and so on. Systems thinking is imperative in order to do this quickly and effectively.
Systems Thinking Helps Business Methods Avoid Local Optimization
In learning and teaching, we do have to focus on the tool. In usage, we have to focus on the end result, on the task, on the work.
- Peter F. Drucker
The emphasis on interrelationships and dynamics of behavior inherently makes systems thinking holistic in its approach. In contrast, the potential benefits fade and risks increase when using "reductionist" reasoning built upon past efforts to solve systemic problems. Consequently, superior coordination between all individual elements for overall improvement loses out to a local optimum. The following quote captures the essence of this often culturally charged context.
Exaggeration, the fallacy [of past success] that if X is good more X is even better, is at the core of . processes that effectively destroy a proven competitive advantage. A tendency to push one's strength to its limits transforms the strength into a destructive weakness. Unfortunately, many stories follow the same line: a winning formula gains adulation, and the heroes or heroines who shaped it become the sole authorities. One right answer prevails. An increasingly monolithic culture produces an ever-decreasing set of alternatives and a narrow path to victory. This limited set redefines the corporate culture, the assumptions, the premises, and the common wisdom that bounds or frames a company's understanding of itself and its industry and drive its competitive strategy.
- Jamshid Gharajedaghi
Local optimization can harm or even break an SBU. It stands to reason that if "air" is good, then even more and perhaps better quality air is even better, up until the point when the balloon bursts - or the SBU fails to meet performance expectations. On the other hand, a systems approach is a collection of cause and effect relationships based on fundamental principles. When properly applied in a business and operational context, the collective performance "facts" percolate to the top for more effective decision making.
Concerns over localized optimization, particularly in the ineffective application of proven business methods, highlight the importance of people for sustaining and regenerating corporate knowledge. Learning and teaching are essential means in this ongoing pursuit. Unfortunately, in the quest to develop and increase the yield from organizational knowledge, companies start to employ cookie-cutter approaches that often lead to loss of the "essentials" of individual or institutional knowledge over time as people change roles or companies. It evolves into an exercise in using tools without fully understanding their purpose.
Many companies deal with the human element to avoid local optimization challenge by developing Sensei, who spend a long time learning and applying their trade before becoming the "grand masters." This reinforces the value of a systematic approach combined with the right people who are properly skilled. Thus, focusing narrowly on single issues or specific tools is a warning sign for missed opportunities. It further creates new problems, particularly when the endgame transforms into perpetuating the tools themselves as the goal, at the expense of proper focus on the business objectives.
3. Dynamic Analysis Software Provides "Perspective" for Success
The entire value chain of activities for creating and delivering products weaves within and between interconnected operational processes, ranging from those inside an organization's "four walls" to those encompassing external partners and the entire value chain. The complexity and interdependencies inherent in these customer driven processes create a tremendous volume of operational data.
The challenge is figuring out how best to leverage these multiple and varied data streams for effective business planning, management, and improvement purposes. The third element of Predictive Operational Analytics addresses this need through the seamless integration of operationally focused analytics software guided by systems thinking and business methods to provide greater insight into the impact of management decisions.
Most organizations are drowning in operational data stored in their various corporate systems and disconnected archipelagos of spreadsheets and other business documents. These extensive records provide important historical insight. However, they provide little guidance of how specific actions will shape the future.
In its most fundamental sense, execution is a systematic way of exposing reality and acting on it. Most companies don't face reality very well.
- Larry Bossidy & Ram Charan
The unforgiving global economy underscores the importance of quickly exposing reality and facing it head on, as the risks for failure are unrelenting. Effective business execution raises two important challenges. First, how do you expose the operational reality for what it is, as opposed to how you perceive it? Second, from a practical standpoint, how do you ensure that management actions will most effectively shape your future operational environment? In other words, how do you holistically quantify the details and dynamics of customer, process, supply chain, and financial implications of your decision options for achieving the business objectives? This is the domain of Predictive Operational Analytics. No other single-focus approach can accomplish these objectives with the same level of insight, speed, and confidence.
Create the Best Possible Operational Solution
What specific operational configuration will help an organization achieve and sustain success? It is vastly more challenging to understand the way an operational ecosystem functions, to see implications for both the forest and the trees with equal perspective. Most software tools simply fall short in providing the level of insight about future performance managers need for making complex operational decisions. Conventional predictive analytical tools are of limited value for this purpose.
Traditional software applied to operations focuses on extrapolating from an organization's past performance into the future. This is like driving a car by looking through the rear-view mirror. It is effective if you are assessing your history, but not very effective for avoiding obstacles in your path.
Experienced managers understand that a reductionist extrapolation approach suffers from a number of limitations. For instance, it loses or assumes away the richness of the actual operational environment. Extrapolation often relies on incomplete data and can be wildly inaccurate, as the past environment on which the assumptions were developed does not accurately represent the future environment. The underlying data and metrics for decision making are simply too disconnected, and traditional "static" analysis tools are a poor fit for understanding the dynamic interconnected operational context.
Predictive Operational Analytics software shifts the analytical focus to creating the best possible solution to operational and business challenges. Rather than embarking down an uncertain path of costly and time consuming experimentation to improve performance, Predictive Operational Analytics proactively creates an execution roadmap to achieve the best possible performance.
As managers identify and sort through several reasonable options to reconfigure an SBU for better performance, Predictive Operational Analytics helps identify the optimal solution based on the organization's current capabilities, limitations, business objectives, investment requirements, customer needs, and so on. It does this through "what-if?" scenarios exploring reconfiguration ideas, equipment investments, adding or moving people, etc. Given, the possible changes on the table for consideration, which one(s) best meet the customer and business objectives?
"What-If?" Scenarios Create an Agile Framework
What happens if an important customer changes his plans in a big way? The operating plan specifies how the various moving parts of the business will be synchronized to achieve the targets, deals with trade-offs that need to be made, and looks at contingencies for things that can go wrong or offer unexpected opportunities.
- Larry Bossidy & Ram Charan
In practical terms, predicting future performance is a matter of describing the details and dynamics of performance under different "what-if?" configurations, with additional risk and sensitivity analysis as necessary. The question is not "what will the future be" but "how will the system perform under different configurations and possible scenarios?" For example, what happens if demand decreases by 40 percent or it doubles? What if the cost of raw materials escalates? What if more or less preventive maintenance is required? What if management wants to assess the potential impact of consolidating several different facilities into one? What if a key supplier misses deadlines or has quality problems? The potential management questions and issues are endless and important.
"What-if?" scenarios are central to Predictive Operational Analytics. They provide the flexibility to address a wide range of operational challenges especially when assessed holistically. Systems thinking and business methods begin by defining the SBU structure and boundary. This serves as a "baseline," representing the current operational configuration and establishes a reference point for comparing options for change. The "what-if?" scenarios capture the operating plan for the SBU in great detail to prepare for dynamic analysis.
The typical response to external or internal pressures is to implement a series of changes as countermeasures, which are often driven by continuous improvement thinking. The enterprise systems' reports over the subsequent few periods give managers their first real opportunity to assess the success of their intervention. Reporting on "what happened" is valuable for accounting for past performance. However, this information is often too late and aggregated such that it lacks precise causal feedback for operational managers to decide in a timely manner on what is working and what is not.
Prediction is difficult, especially about the future.
- Neils Bohr
In contrast to "accounting" analytics that focus on the past, Predictive Operational Analytics seeks "what will happen" in the current or future operational context. "What-if?" scenario analyses define alternative responses for the potential future environment and are the basis to evaluate how the system dynamics would behave holistically.
The future operational environment is bounded by the structure and behavior of the particular system (i.e., SBU). The challenge is to fulfill a given product or service demand and mix given the particular operational capabilities and limitations of the SBU.
The ability to find the best possible operational and business configuration and assess the potential impact of associated risks highlights the parametric nature of Predictive Operational Analytics. Parameters and data sources can be connected to live data streams from enterprise information systems to accelerate decision making and speed to bottom line results, while avoiding analysis paralysis.
Dynamic "What-If" Modeling Without Coding
A model for simulating dynamic system behavior requires formal policy descriptions to specify how individual decisions are to be made. Flows of information are continuously converted into decisions and actions. No plea about the inadequacy of our understanding of the decision-making processes can excuse us from estimating decision-making criteria. To omit a decision point is to deny its presence - a mistake of far greater magnitude than any errors in our best estimate of the process.
- Jay W. Forrester
The better an analytical "model" emulates the operational and business environment the more valuable it is to decision makers. All models have inherent assumptions and are designed for specific objectives. As long as a model is rigorous enough such that the causal relationships between the parameters and the objectives are identifiable and significant then the model serves its defined purpose. Models are of limited business value in the absence of causal relationships with sufficient sensitivity to clearly identify their impact on the objectives.
For example, product and/or service demand and mix provide the customer focus and set the dynamic analytical model in motion. The model must account for "structural" parameters such as all of operations, conveyance and transport, and product workflows in the SBU. It must be capable of creating and dispatching "jobs" to the floor, releasing raw materials, scheduling individual operations and workstations, and managing flow and activities including quality, shifts, transport, buffers, rework, inventory, and parts. From a resources and supply chain standpoint, it needs to manage assets, raw materials, utilities, inventories, supplies, labor, etc. Financially, it must integrate all direct and indirect costs, asset depreciation, produce accurate financial statements, and so on. All of the above factors have direct relationship to the financial and operational performance of the SBU; without these and others, the models are far less useful.
An analytical model is only as helpful as it is practical to deploy. To keep pace with the speed of decision making, it has to be relatively quick to update and provide timely answers. Historically, modeling experts were required to rewire each individual model for every new "what-if?" question and variation. To speed up the modeling process, many important details were either simply ignored or watered down; consequently, the promise of a holistic analytical integration was lost.
One of the software impediments preventing the adoption of Predictive Operational Analytics is that traditional modeling software requires users to laboriously code the models. Furthermore, once built, the models become static due to the data and transactional focus of the software tools. Every little change requires creation of a new model. This approach is less than desirable because it holds most of the system steady while looking solely at the performance of a few variables at a time. This approach, although recognized to be clearly inadequate, has historically been the best solution available to managers.
Developing a complex model and maintaining it is a tall and expensive undertaking. It requires a great deal of expertise and time. Furthermore, decision making requires evaluating many options and under several expected future environments. Stress testing each viable solution for its robustness is vital for success. Creating a new analytical scenario for each decision need is impractical unless you have access to tools designed specifically for this purpose.
A solution to this modeling purgatory is to eliminate manual model specification. The application breakthrough is to build the necessary business intelligence directly into the analytical tool so that it automatically creates a model for each input scenario. Rather than laboriously defining and maintaining each and every model, users spend their time on higher value activities associated with identifying and configuring operational scenarios. This shifts the focus from an exercise in mathematical modeling by IT personnel to an operational and business analysis driven by business leaders and decision makers.
Predictive Operational Analytics Software Requirements
There are a number of technical requirements for Predictive Operational Analytics software to ensure it provides rapid and accurate holistic insight to operational decision makers. The following list highlights several key characteristics for analytical software to meet the minimum threshold required for Predictive Operational Analytics.
- Holistic systems focus
- Future looking
- "What-If?" scenarios
- Activities basis
- Demand driven
- Parametric causal relationships
- Configuration-based with built-in business intelligence
- Comprehensive dynamic analysis
- Integrated process, resources, supply chain, and financial perspectives
- SBU focus, including all direct and indirect activities and financials
Predictive Operational Analytics software is the only tool that can precisely answer complex operational questions such as, "What is the optimal way to configure an SBU to best balance customer, process, and financial and other objectives?" Such software is instrumental in creating highly detailed operational execution and improvement roadmaps, delineating the precise steps and their timings in order to achieve the targets set by the business objectives.
Systems thinking, business methods, and dynamic analytics are the guiding triumvirate for achieving and sustaining the best possible operational and business performance. Predictive Operational Analytics requires the integration of the three principles but does not mandate specific business methods or analytics. These are applied at management's discretion-so long as they meet the minimum requirements outlined in this article-thereby providing tremendous flexibility in application. Predictive Operational Analytics is inclusive by nature, so that whether your preferred methods involve, for example, balance scorecards, lean, Six Sigma, financial analytics, culture/people, or many other approaches, it and accelerates such efforts.
There are multiple benefits to Predictive Operational Analytics. It is:
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Quantitative: Dynamic process and business analysis, including optimization and risk assessment
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Collaborative: Incorporates needs of all roles and stakeholders
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Faster: Rapid to implement, with faster decision making and speed to superior operational and financial results
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Lower Cost: Lower cost and more valuable outcomes than comparable custom internal or external efforts
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Integrative: Integrates customer demand with process, resources, supply chain, and financial performance
Whether the particular business challenge pertains to business dynamics (macro focus) or workflow dynamics (micro focus), the general steps of Predictive Operational Analytics are similar. Business dynamics focuses on execution and financial plans for best business value, typically visited on a monthly, quarterly, or yearly basis. The focus is on holistically coordinating all of the interconnected flows of information within an SBU.
Workflow dynamics explore the micro view for the best way to design or reconfigure processes for their impact on resources, the supply chain, financials, and customers. It concentrates on the details and interconnections at much smaller instances of time-the highly complex second-to-second operational stock, flow, and feedback behavior aggregated across a financially meaningful period of time such as a week, month, or more for an SBU.
Our focus below is on practical application to provide a broad picture of Predictive Operational Analytics in action. In practice, the boundaries between systems thinking, business methods, and software blur into a unified solution approach. Figure 6 shows a high level overview of the general steps and activities of Predictive Operational Analytics.
Figure 6: Predictive Operational Analytics in Action
The methods, systems thinking, and analytics can be very involved; the following highlights aspects of each step.
1. Identify Business Objectives
The goals in this step are to frame the business challenges and multiple objectives, identify the needs of various stakeholders, define the problem, identify execution strategy and options for change, compile a "Canvas for Change" comprising individual parameters, identify the "metrics that matter" for measuring performance and communicating with management, and identify the management and domain experts who will contribute their expertise in formulating "what-if?" operational scenarios, and provide data collection guidance.
Business Dynamics Example: A business dynamics application might focus on new venture creation or an existing business unit in a larger organization. Typical objectives include creating a time-phased execution plan coordinating the activities and investments within and between one or more SBUs for effective growth planning and management. The idea is to appropriately time the investments in people, facilities, equipment, maintenance, etc. to grow at a controlled pace, with the flexibility to respond rapidly to changing customer needs, while minimizing risks associated with actions that can potentially harm the business.
Workflow Dynamics Example: Let's say the high level objective is to identify the changes required to produce the best possible performance in a production area within a manufacturing facility-in a way that balances the needs of the various stakeholders. A sample canvas for change might include emphasis on (a) re-configuring current operations with, (b) minimal investment to, (c) increase flexibility and ability to respond rapidly to changing customer tastes and demand, and (d) create the lowest possible globally landed cost.
For a more specific example, we have observed a common theme among many manufacturers lately where prior investments in automation technologies such as robots and cellular designs have reduced their ability to quickly and cost-effectively respond to changes in product demand (i.e., variability in both volume and mix). These investments were typically justified on the basis for improved processing efficiency and increases in throughput without much regard to fluctuating and ever changing external demands from customers. In such instances, managers seek to understand the process and financial implications of aligning the level of automation to their business needs, and not based on process efficiency issues alone. The ultimate goal is to enhance their ability to meet shortened product life cycles and ever increasing volatility in product demand and mix with the right combination of people and technologies at the least cost.
2. Define the SBU (Customer Focus)
An SBU provides a holistic customer-focused business definition of the system, including all of the products and associated operations, resources, direct and indirect costs, and so on. The SBU is where all customer, operational, and business results "roll-up" and can be unambiguously judged. The SBU owns the business objectives and, thus, is the logical place to align each and every action to ensure superior performance. A challenge for many organizations is that they see and manage themselves in terms of internal disjointed functions, but not from a customer's perspective. This leads to many cascading problems.
As discussed earlier, an SBU is a virtual concept. It is not necessarily a division, a department or a facility. It is a horizontal concept that only exists to deliver value to customers through a single or a set of products. It has the flexibility to represent a service environment such as a call center with various types of calls it services (i.e., individual "products"). Or it can comprise an entire factory or a specific production area within a plant. Alternatively, it might represent the orchestration of several different facilities that collectively serve a defined set of customers.
Without a proper SBU "wrapper" around all of the activities, resources, costs, etc. that go into serving customer demand, it is difficult to precisely measure and manage causal relationship effects. Most importantly, the SBU definition reinforces a business culture with customers at the center, as opposed to an inwardly focused functional or silo view of operations.
In practice, Steps 1 and 2 are iterative. The business objectives may lead one to initially focus on a particular problem in a specific location or for an entire business unit. However, until arriving at an unambiguous definition of the SBU, it is difficult to assess if that is where the best opportunities for improvement lay, or if efforts might be better invested elsewhere. With an SBU definition in hand, it is imperative to revisit the objectives and other activities in Step 1 to ensure proper alignment of effort.
3. Define and Validate Baseline
The "baseline" has two important functions. First, it serves as the reference scenario for structural and quantitative comparison with various "what-if?" options for change identified in the next step. The current or "as-is" dynamic operational capabilities and constraints are an example of a baseline. Alternatively, a baseline might be a proposed configuration for a completely new production facility or service center.
Second, the baseline provides an important opportunity to "validate" that the initial scenario accurately reflects the real process, resources, supply chain, and financial configuration and performance characteristics of the SBU-that it accurately captures the operational reality and the costs are correct. This is akin to the practice called genchi genbutsu or "go to the place and see for yourself" of the Toyota Production System.
Firsthand knowledge derived from seeing and confirming the situation is essential, and establishes a solid foundation for success. However, seeing with your eyes is only part of the validation equation. Holistic dynamic analysis is also required to verify that the actual operations and financials are as expected.
The requirements for a dynamic quantitative analysis at this step also highlight data collection issues. Due to the widespread use of enterprise information systems, the majority if not all data are already available for such analysis. Thus, the issue is primarily one of identifying and gathering relevant existing data, not collecting new data which is generally unnecessary, time consuming, and costly.
4. "What-If?" Analyses and Execution Roadmaps
Cascading the business objectives into an operational context, including the SBU, are instrumental in identifying "what-if?" questions concerning operational configurations and policies. In this step, the management team and domain experts identify potential operational changes including specific parameters and their ranges to evaluate. Here, these parameters are assembled into distinct operation scenarios, driven by the ideas and expertise of the team members. Best decisions are made when there are many practical and viable options.
Each scenario is then configured for subsequent input in dynamic analysis software. For business dynamics planning and management, scenarios explore different options for structuring the SBU to most effectively serve customers and deliver better financial performance. Workflow dynamics optimization scenarios drill deeply down into the details and interconnections of specific processes.
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