What makes a goal achievable?  In my work as a Continuous Improvement (CI) coach and consultant, I have seen some common practices setting a numerical goal using, for example

1. A target set by management, e.g. a productivity standard for the site
2. Customer requirements, e.g. a minimum process capability
3. Some benchmark value from a similar process
4. A number with sufficient business benefit, e.g. 10% improvement

At the first glance, these methods seem reasonable.  In practice, they are problematic for two reasons.

First, the goals are based on what is desirable, not sound understanding of the opportunity using data.  How do we know if a desirable goal is achievable?   In many organizations, a numerical goal is “set in stone” when the project starts; failing to achieve the goal can have potential career repercussions.  While management tends to aim for aggressive targets, the project leaders are more concerned with the risk of failing to achieve them.  They prefer a more “realistic” target that can be met or even exceeded and negotiate with the sponsors to make the desirable target a “stretch” goal.  In the end, no one knows what the real improvement opportunity is.

Secondly, the practices create a mindset and behavior inconducive to the CI culture.  I have seen too many organizations’ Lean, Six Sigma, or other CI initiatives focus only on training and project execution.  They fail to build CI into their daily decisions, operations, and organization’s culture.  Quality improvement cannot be accomplished by projects alone – numerous incremental improvement opportunities exist in routine activities outside any project.  Projects, by their nature, are of a limited duration and are merely one mechanism or component of continuous improvement. Most improvement does not require a project.  Depending on projects to improve a process is a misunderstanding of CI, reinforces reactive (firefighting) behavior, and sends a wrong message to the organization that improvement is achieved through projects, and even worse, by specialists.

Creating a project with only a desired target leads to high uncertainty in project scope, resources, and timelines – a lot of waste. 

To be effective, a CI project should have a specific opportunity identified based on systematic analysis of the process.  Furthermore, the opportunity is realized through a project only if it requires additional and/or specialized resources; otherwise, the improvement should be carried out within routine activities by the responsible people in collaboration. 

What kind of systematic analysis should we perform to identify the opportunities?

One powerful analysis is related to process stability.  It requires our understanding of the nature and sources of variation in a process or system.  In a stable process, there is only common cause variation – its performance is predictable.  If a process is not stable, there exists special cause variation — its performance is not predictable.  Depending on process stability, the opportunity for improvement and the approach are distinct. 

The first question I ask about the goal of any improvement project is “Is the current performance unexpected?”  In other words, is the process performing as predicted?  No project should start without answering this question satisfactorily in terms of process stability.  Most often the answer is something like “We don’t really know but we want something better.”  If you don’t know where you are, how do you get to where you want to be?  This is a typical symptom of a project driven by the desirability rather than a specific opportunity based on analysis.  If the process stability was examined, most likely the first step toward improvement would be to understand and reduce process variation, which does not need a project.

For people familiar with Deming’s 14 Points for Management, I have said nothing new.  I merely touched point 11 “Eliminate management by numbers, numerical goals.”  His original words¹ are illustrative.

“If you have a stable system, then there is no use to specify a goal.  You will get whatever the system will deliver.  A goal beyond the capability of the system will not be reached.”

“If you have not a stable system, then there is again no point in setting a goal.  There is no way to know what the system will produce: it has no capability.”

A goal statement that sounds SMART does not make a project smart.  A project devoid of true improvement opportunity achieves nothing but waste.  But if we follow the path shown by Deming, opportunities abound and improvement continues. 

1. Deming, W. Edwards. Out of the Crisis : Quality, Productivity, and Competitive Position. Cambridge, Mass.: Massachusetts Institute of Technology, Center for Advanced Engineering Study, 1986.

We operate in a highly unpredictable environment.  The increasing interest and practice of Lean and Agile methodologies is a clear acknowledgement of the unknowns and unknowables.  Unable to predict the future with high confidence, we have to learn and adapt as we go. 

How can individuals and organizations be more effective in dealing with this new reality?

In project management, multi-generational project planning is used to transform a large project into a series of smaller ones, each achieving a significant milestone relatively quickly.  In addition, the lessons learned and knowledge acquired in each stage refine the goal and reduce the uncertainty in the subsequent planning and execution. 

This is an example of analytical thinking — a critical competency of the project manager — which includes

1. Breaking down a large, complex problem into smaller, manageable components
2. Prioritizing the components based on a set of criteria, such as risk, effort, impact, and interdependency
3. Sequencing the efforts in such a way that minimizes risk and cost while maximizing the desired outcome

In today’s work, analytical thinking is not just a required competency of project managers but any knowledge worker who has to solve technical or business problems.  I use it as an essential criterion for evaluation of job candidates and development of employees.

Fortunately, everyone can improve their analytical thinking at work through continued learning and practice of some well-established conceptual frameworks and scientific methods, for example

1. Product development using Design of Experiments (DOE) 

Frequently, there are numerous variables that we need to understand in order to design a product or process or improve its performance.  When a one-factor-at-a-time or a full factorial design is not most effective, a screening design can be used first to identify the critical few among many potential factors.  Then less resources are required to study the few more thoroughly, e.g. characterizing their interactions and the Response Surface, to achieve optimal outcomes.  When designed strategically, each study augments the previous ones, avoiding unnecessary repeats.

2. Quality improvement by understanding variation

Reducing defects and improving customer satisfaction is a main goal of quality.  Unfortunately, in many organizations, quality issues persist despite repeated improvement efforts.  While subject matter expertise is important, sustained improvement requires understanding of process stability (i.e. lack of special cause variation) and capability (i.e. probability of the process producing a result that meets customer requirements).  That is why process improvement methodologies, such as Six Sigma, rely on the principle of Statistical Process Control (SPC) to identify and separate special cause variation from common cause variation.  Only after eliminating special cause variation, can we truly characterize and improve the process.  Then, if necessary, we can improve process capability by reducing common cause variation and/or re-centering the process to the desired target.

3. Lead time reduction using Lean concepts

The ability to consistently design and deliver a product or service faster than competition is critical to business success.   The lead time (e.g. request-to-delivery time) of many business processes remains long and variable. These processes often involve numerous and convoluted steps across multiple functions.  It is impractical and unnecessary to analyze and improve all steps. Seeing from a customer’s perspective, Lean thinking brings us clarity by separating value-added from non-value added activities (i.e. waste) that impede the continuous flow of value to the customer.   By reducing or abolishing waste, such as waiting, overproduction, and inventory, we can simplify and speed up the processes without investing in new capacity.

These are but a few examples in which proven scientific and management methods help break down complex problems into manageable components that lead to effective solutions. 

Buzzwords like “work smart,” “be agile,” and “fail fast” may create the initial awareness or inspiration.  But they rarely lead to operational effectiveness or material change.  Building an organization that is Lean, Agile, responsive, or adaptive is transformative and requires systematic identification and development of required competencies, such as analytical thinking.

Adapting to a changing world is a challenge that demands analytical thinking as well.   I am optimistic that individuals and organizations will continue to develop new competencies by embracing sound problem-solving methodologies.