The future of the project profession: Transforming culture and developing talent

We are in a time of extraordinary change. The new normal, whilst filled with societal change and disruptive technologies, is arguably more evolutionary than revolutionary. For organisations to thrive they must harness change. The future is looking to be one of constant shifts and continual transformations, but also great potential.

In PMI’S recent Pulse of the Profession report it was speculated that; “Organizations are undergoing a fundamental paradigm shift in which projects are no longer adjacent to operations but instead primary to how work gets done and problems get solved.” With this comes not only a transformation in culture, but also a real need to develop talent.

Here we take a look at the findings of the report, with specific regard to the future of the project profession.

Company culture is transforming

With projects and operations no longer polar entities, company culture is transforming to become more united and more collaborative. Within this progression, organisational leaders are rethinking how work gets done. They are recognising that change happens through projects.

53% of organizations surveyed say they place a high priority on building a culture receptive to change.

Leaders are prioritising agility and building a company culture which celebrates agile practices – reacting to change, as well as valuing reflection. As with any cultural shift in a workplace, leaders must gain buy-in from team members and all parties must be committed to the transition. Companies may look to onboard staff through agile training and know-how, and also through practice so that agile working becomes the norm.

59% of organisations report using design thinking at least some of the time to explore and solve problems.

Progressive companies are shifting to a culture which incorporates a range of practices and approaches for problem solving. The Pulse data showed that seventy percent of organisations surveyed place a high priority on delivering customer value. They see that in order to find solutions for customers and clients, they must innovate. To solve issues effectively, professionals must explore new approaches to challenges and address pain points from every viewpoint.

For organisations reimagining their vision and reforming their culture, it is paramount that project leaders are on the same page. It’s down to project professionals to recognise the value of the change for the business. They must also show an eagerness and commitment to leading their teams to adopt new practices and ways of thinking.

Developing talent

In the era of transformation, project professionals too must evolve. As key drivers of cultural change within organisations, project leaders must expand and build upon their talents in order to thrive.

Pulse data show that more than half (51 percent) of organizations require project professionals to hold some type of certification for their role.

Future-thinking companies are raising the bar on their expectations and are even ensuring career development plans are in place for project managers. In fact, the report found that a huge 61% of companies already provide some form of project management training.

Pulse data shows most organizations place nearly an equal emphasis on developing leadership skills as they do technical skills (65 percent and 68 percent, respectively).

In order to be future-ready, businesses must prioritise upskilling project professionals in various areas. It is no longer enough to just provide training on new technologies. Progressive organisations must be ready to invest in developing leadership skills for the future. Soft skills are also of great importance and it is becoming increasingly apparent that people-skills are vital to the role of a project manager. Empathy alone has the power to strengthen collaboration and reduce the risk of conflict.

For project professionals to succeed within a changing culture, they must be equipped with leadership talents, interpersonal skills, and technical proficiencies. Projects are central to transformation, and so project leaders must have a full set of competencies in order to be ready to turn ideas into reality.

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Case Study: Streamlining a Hiring Process

A Lean Six Sigma (LSS) team leveraged its acumen in process improvement and operational excellence to overhaul an organization’s human resources (HR) talent acquisition (TA) operation. By coupling fundamental LSS tools like fishbone diagrams, value stream maps and cause-and-effect matrices with Lean concepts like visual management, the team was able to identify areas for improvement and implement sustainable countermeasures for the TA department. By the end of the project, the overhaul had saved the organization more than $165,000. More, the project transformed the TA department from business line “order takers” into a troupe of trusted, efficient and tactful human resource leaders.

Background

The need to update the hiring process came on the heels of an organizational restructuring The HR department had pivoted from a geographically oriented structure to a competency-specific structure. In the previous structure, the client used HR generalists who handled all the HR duties in a particular region (i.e., talent acquisition, employee relations, benefits administration, etc.).

The new model was selected to bring together each HR competency into its own center of excellence. This was done anticipating that increased specialization would generate improvements in effectiveness and efficiency. Once this was complete, the TA department determined it needed further expertise in process improvement and operational excellence to realize these aspirations of increased department efficiencies.

The Current State

The project to revamp hiring began about 90 days after the restructuring had been completed. The first step was to assess the current state of the TA department. Nine recruiters were interviewed about the processes in use to recruit talent across multiple business lines. Concurrent to these interviews and with the participation of these front-line leaders, the project team created value stream maps for each recruiting process in use (Figure 1). Immediately, it was revealed that there was no standard process the recruiters were following to recruit candidates to the organization. After the initial mapping, the same nine recruiters were engaged to identify possible roadblocks in each step of their processes. Together, the recruiters identified 48 obstacles that specifically posed challenges to how they recruit. These obstacles ranged from how they contact candidates for phone screens to the vendor they use as a background check.

Figure 1: Value Stream Map of Recruiting Process (Click to Enlarge)

The interviews allowed the recruiters to share best practices between business lines and help newer recruiters navigate the organizational machinery they had not yet figured out. The conversations between recruiters also served to illuminate how the team truly operated. Oftentimes this did not correspond to the recruiters’ intuition. These conversations eventually led to a consolidation of recruiting processes and adoption of best practices for all recruiters. Figure 2 shows one “bone” of a fishbone diagram; each process step had its own bone.

Figure 2: Piece of Fishbone Diagram

Objectives

After the current state of operations in TA was mapped, the next step was to identify what was most important to the center of excellence and their stakeholders. Next, the recruiting management team was guided through a brainstorming session. The end goal? Identify how each of the 48 roadblocks identified by the recruiters would affect the performance of the department.

A critical first step in this process was to identify criteria that could be used to evaluate the potential impact of each roadblock. The three most important criteria the team identified were:

1. Time to fill (synonymous with a manufacturer’s lead time)

2. How much time a recruiter spent

3. The quality of candidates

The LSS team facilitated the discussion with the TA management team to evaluate each of the 48 roadblocks based on the selected criteria. A cause-and-effect matrix (Figure 3) helped the team identify the most impactful parts of the overall recruiting process.

Figure 3: More Brainstorming with Cause-and-Effect Matrix

Countermeasures

With opportunities and priorities firmly in hand, the next step was to create actionable countermeasures – that is, solutions to the roadblocks –  that would drive the TA team to meet their goals.

Using a countermeasure priority matrix, the project team grouped the evaluated roadblocks into four quadrants across two factors (high/low effort) and (high/low impact) as shown in Figure 4. The team identified the roadblocks that were most critical. Once plotted, the countermeasure priority matrix allowed the formulation and prioritization of action items that aligned with the established goals. Then countermeasures were created. Each countermeasure clearly defined what was to be done, who was to do it and when it was to be done by (Figure 5).

Figure 4: Priority Matrix

Figure 5: Countermeasures Mapped to Priority Matrix

The team then participated in (another!) brainstorming activity to identify waste in their current processes. The waste identified were steps in the process that added little to no value, but had significant impact on the departmental objectives (Figure 6). In one such example, a recruiter was identified who would reach out to candidates by telephone much more frequently than other recruiters. Because this behavior had a large impact on the recruiter’s time and did not reduce time to fill or improve the quality of the candidate, this was determined to be non-value added and was removed from the recruiter’s value stream.

Figure 6: Itemized Implementation Plan for Countermeasures (Click to Enlarge)

Visual Management

Throughout the project, it became clear that in addition to an overhaul of the candidate hiring process, there needed to be a better way for the management team to understand the current state of the department, notice trends and make informed decisions quickly. Additionally, due to recent poor performance, the TA team had to earn back the trust of their colleagues from other business lines.

Early in the project, a strategy was devised to help these managers communicate their successes, make informed decisions and build trust across the enterprise. These ends were accomplished by making use of data already available. (See Figures 7 and 8.)

The LSS practitioners created a suite of management tools that focused on summarizing the health of the department and communicating their efforts to business line customers. One such tool in the suite was the recruiter funnel dashboard (Figure 9). This tool, built entirely in Excel, allowed the recruiting team to illustrate the recruiting activity in each business line by state and provided a quick view of why candidates were not moved forward.

Figure 7: High-Level Dashboard (Click to Enlarge)

Figure 8: Dashboard for Recruiting Managers (Click to Enlarge)

Figure 9: Recruiting Funnel (Click to Enlarge)

Results

Time to fill an open position was one of the most important metrics identified. By the end of the 90-day project, the time to fill metric was reduced by 11 percent. Figure 10’s red box demonstrates that the new process is more capable of hitting the expected time to fill a position. An analysis of variance (ANOVA) test was conducted to confirm that this reduction was statistically significant. (See Figure 11.) The two groups compared in the ANOVA test were requisitions filled up to 180 days prior to the project’s inception and subsequent requisitions filled within 180 days after the completion of the project. This reduction resulted in a cost avoidance savings of over $165,000, mainly achieved through a computed cost of vacancy.

Figure 10: Box Plot of Improvements

Figure 11: Before/After ANOVA Output

Perhaps more important than the reduction of the mean time to fill an open position, a decrease in the time to fill variance was observed. Using an F-test for equality of standard deviation with the same two groups mentioned above, a statistically significant reduction in the time to fill variation was observed (Figure 12). This had an incredible impact on the trust and confidence business lines had in the TA department. Now, the TA department is more capable of consistently hiring a candidate within a set window of time.

Figure 12: Before/After F-test for Variance Output

The tools and data-driven management style that materialized from this project has revolutionized the way the TA team manages and communicates. Upon following up with the management team, they estimate they spend an average of four hours every day working with the tools that were provided to them – roughly 2,000 hours a year.

Along with the concrete results above, the organization has benefited in other, less tangible but highly impactful, ways. TA discussions now revolve around real, objective facts that have bolstered business line confidence in the recruiting team’s ability to meet hiring demands. Additionally, the lessons learned from participating in this project have shifted the culture; the team no longer fights daily fires, but is deliberate and tactical in how they approach each new challenge.

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The New Normal and Optimizing IT Service Desk Cost

At the point when the COVID-19 crisis hit, the need to rapidly develop an organization's IT service management (ITSM), including IT support, got basic: from the need to help better access for working for remote employees, to the speeding up of slow-moving digital transformation systems. This produced new demands for IT, bringing about both new and transformed IT administrations and backing rehearses. 

Presently, different associations attempt to return some similarity to ordinariness, they will probably need to "tighten their belts" to imitate the drop in incomes experienced during the crisis and possibly beyond. 

This may be seen as a return of the past, after some time of cost-cutting after the 2008 financial emergency. But, the business world is diverse now – there's a requirement for "better, quicker, less expensive." With this rational key present-day business needs, for example, the attention on results and worth creation, agility, and limiting employee lost productivity. Furthermore, that the expectation for employees keeps on rising dependent on their regularly unique customer world encounters of administration and backing. 

Moreover, there are additionally the digital transformation needs of different business capacities. All things considered, their methods of working and financial plans are influenced as well – and they'll probably be taking a brant at the more prominent utilization of self-administration and mechanization or just the utilization of innovation to help better approaches for multi-area working. 

This may all appear to be overwhelming, however, it's an extraordinary open door for IT to achieve long past due change. To help, this blog traces three key facilitators of IT service desk cost advancement alongside three selection tips for each. 

3 Key Facilitators of Cost Optimization 

This blog features three key facilitators however there may likewise be a requirement for improved monetary and better value showing through ITSM limited, for example, financial management, IT asset management, portfolio management, demand planning, reporting, and analysis, and persistent improvement. Furthermore, the total cost of ownership (TCO) of your ITSM apparatus may be questioned versus the worth it delivers. 

Facilitator #1 – Knowledge Management 

The latest HDI State of Technology and Operations report shows knowledge management as the second-most received ITSM process for IT support associations, with 67% of organizations utilizing the cycle. Just incident management is more popular. Numerous advantages of knowledge management fall into the range of "better, quicker, less expensive". Knowledge management enables the IT service desk to support the work area and its people to convey better arrangements all the more quickly, bringing down both IT and business costs subsequently. 

In any case, despite the high level of take-up, many help work areas despite everything need to improve. The equivalent HDI report found that 40% of associations just use information articles for less than 10% of tickets; 64% for less than 30% of tickets; and 78% for less than half of tickets. 

If your organization is struggling with knowledge management, at that point, the accompanying three hints will help. 

3 Knowledge Management Tips 

Understand that knowledge management is more about people's change than innovation. Since it's a change to the usual method of working, there's a need to utilize hierarchical change the executive's devices and strategies, instead of including information the board innovation. 

Knowledge sharing capacities must be included in the business-as-usual operation. They can't just be extra to existing methods of working. Without this, information the board will probably be viewed as irrelevant and something to do later. 

Focus on knowledge use – it's the place the worth is made. Try not to put all your improvement exertion in knowledge capture. All things considered, knowledge has little worth – other than hazard relief – until it's utilized. 

Facilitator #2 – Automation and AI Technology 

Artificial Intelligence (AI) innovation is the foundation of "better, quicker, less expensive" and cost or digital transformation. Regardless of whether it's assisting with the hard work, the weighty reasoning, or both. You can peruse more about what's conceivable here. 

In any case, similarly, as with knowledge managers, because there's innovation accessible it doesn't imply that the advantages will show up for your organization. 

3 Automation and AI Technology Tips 

Manage both co-worker desires and concerns. There's a need to get everybody in the same spot about what's practically understandable with automation and AI innovation at present and how this will progress after some time. It's additionally essential to distinguish and oversee staff concerns over professional stability specifically as a feature of standard change the board efforts. 

Take ideas from past innovation adoption failures. Try not to commit similar errors made with knowledge management and self-service innovation. Specifically, missing to perceive that the presentation of AI-empowered abilities is again a human change activity that requires authoritative change the board devices and procedures to succeed. 

Address the impact on the performance of the management areas and targets. At the point when you get things right, mechanization and AI will influence your key assistance work area measurements. Overall, the more-confused and tedious tickets. 

Facilitator #3 – Self-Service 

The recently referenced HDI report found that 68% of help associations are taking care of more tickets year-over-year, ordinarily with a similar degree of staffing. Add to this that MetricNet's benchmarking research has a self-improvement exchange at a tenth of the expense of an IT-administration work area dealt with ticket, and you have a convincing case for IT self- service management selection and its advantages. 

3 Self-Service Tips 

Cost decrease shouldn't be the primary inspiration for self-service. Examination shows that there's a need to rather make the key inspiration "better employee experience and results." Otherwise, there's no close to home inspiration, and conceivably a second rate client experience, for representatives – bringing about restricted use and advantage acknowledgment. However, if you can get employees to utilize it in high numbers, at that point the predicted cost-related advantages will come.

Focus on the employee selection "empowering agents." Your self-administration capacities should be comparable to their shopper world partners. Along these lines, hope to meet worker desires across variables, for example, convenience, admittance to self-improvement, and a decision to help channels. 

Organizational change management is required for self-service achievement. Once more, there's simply the need to treat the presentation of self-administration as a people change activity since it's a change to the customary methods of working. With the hierarchical change the executive's devices and methods expected to assist agents with getting tied up with the change, pioneers will see better generally speaking appropriation of the innovation. 

These cost-optimization openings can apply outside of the IT department. There might be comparative open doors for the different business capacities inside your association – for example taking enterprise management the boarding point of view of cost improvement. A viable IT Service Management programming with across the board capacities can be utilized over the endeavor to bring down costs and increment efficiency.

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Estimation Method Helps Analysis of Truncated Data Sets

When working with data sets, practitioners sometimes encounter metrics, such as out-of-roundness and loss-of-moisture measurements, with physical limits. In these scenarios, the data distribution is truncated at the value of physical limitation, creating a distribution outside of the criteria of a normally distributed population. With non-normal data, estimates and predictions using the normal distribution are not accurate, creating the need for alternative methods of analysis to assess the data.

Standard Methods

Typically, when data does not fit the normal distribution and prediction or estimation calculations are made using the assumption of normality, data is transformed and assessed for normality. If the transformed data fits the normal distribution, then calculations are performed using the transformed data with transformed specification limits. Alternatively, if other distributions are found that fit the non-normal data, the capability of the process can be calculated using an alternative distribution, which better fits the data. However, if no alternative distribution is found that fits the data and the data cannot be transformed into a normally distributed data set, other methods of analysis are necessary.

Alternative Method

Due to the nature of truncated data sets, which have a point of central tendency at a physical limit, common transformation methods such as Box–Cox and Johnson are often not sufficient. The following method of estimating the population’s standard deviation for the normal distribution is a practical method that gives a realistic estimate of the standard deviation. It also avoids violation of the assumption of normality when using the Cpk calculation based on the normal distribution. This correction provides practitioners with the ability to predict the spread of the data and assess capability in the direction of the upper specification limit. Prior to using this correction method, however, practitioners must verify that the sample data is of adequate size to approximate the normal distribution.

Empirical research and data results, gathered from both theoretical and production data and analysis, support the theory that estimating the standard deviation is possible for physically limited data by proceeding as if the data were not truncated. Theoretically, this means extending the data beyond the physical limitation of the measurement.

The empirical evidence provides a ratio, or correction factor, between the truncated distribution standard deviation and the theoretical normal distribution. The equation is:

where

is the standard deviation calculated from the physically limited data set truncating one side of the data.

 

is the standard deviation calculated for the population if the data were not truncated.

The coefficient of 1.7 correlates these two parameters.

This ratio can act as a correction factor for the standard deviation, allowing practitioners to calculate the Cpk based on the assumption of normal data. An accurate calculation of process capability or any other estimate or prediction made using the normal distribution is not valid without this type of correction. In the following example, the standard deviation is estimated for the population using the correction factor.

Example Data Description

In the figure below, the distribution is truncated as it approaches approximately zero readings of moisture. This truncation is due to the physical limitation of the zero bound on a moisture reading (i.e., a product cannot have less than zero units of moisture present). Hence, the data is not able to follow the normal distribution.

Moisture Readings in Batch 3

This truncation can cause the central tendency measurement to match the physical limit value if that is desirable. With out-of-roundness and loss-of-moisture measurements, often there is only an upper specification limit and it is desirable to have low values, as is the case with the data in Figure 1.

The standard deviation for this example data set is 0.3735 units. The estimated (or corrected) standard deviation for the example data set as a normally distributed data set is 0.3735 units multiplied by 1.7, which is equal to 0.6350 units. The mean from the example data set is 0.2746 units. The specification limit is a one-sided upper specification limit (USL) of 8 units.

The following equation is typically used to calculate process capability:

, where

USL and LSL are upper and lower specification limits

is the population standard deviation

is the population mean

However, because no LSL exists in this case, the equation is reduced to:

This alternative process capability estimation can be used for further analysis.

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How to Enjoy ITSM implementation

The comparison for having a great time in your ITSM execution to me is especially basic. You have to "plan". 

"Plan". Truly, it's a decent four-letter word, yet the absence of planning is frequently a primary step of disappointment in an ITSM usage. Too often, ITSM usage looks something like this: 

1." How about we do ITSM!" 

2. Gather a group—as a rule people that aren't doing whatever else at that point 

3. Get a few instruments and programming ("we're going to require them in any case") 

4. "We should crush in some preparation"— so a couple of individuals disappear for three days and get a confirmation 

5. "Alright, presently how about we design the device" 

6. Turn the new instruments and programming out to an objective IT bunch alongside a couple of slides that discussion about how to utilize the device 

7. Put out a few flames that came about because of misconfiguration in the device

8. Constrain other IT gatherings to utilize the device; after some time, disregard the ones that don't utilize the device 

9. Following a couple of months, begin to ask why things aren't working so well OR (even better) proceed onward – rapidly—to the following venture 

Possibly my representation above is somewhat extraordinary, however a large number of you perusing this may perceive these traits in your current (and maybe battling) ITSM execution. I will disclose to you that while an arrangement won't make ITSM usage a breeze, it will help smooth the way. However, neglect the basic advance of arranging and you'll be searching for that next venture (or work) sooner than you might suspect. 

So how would you build up a decent ITSM plan? A Google search "on the most proficient method to design" will restore a great many outcomes, however, I think great ITSM arranging comes down to a basic equation: "Plan your work, at that point work your arrangement". 

Plan your work: 

◉ Characterize clear objectives and targets for ITSM. 

◉ Guarantee the objectives and targets are adjusted to business vision, objectives and destinations 

◉ This helps put ITSM in a business setting and causes you to guard against "nerd talk". While you will probably (and should!) have a couple of specialized destinations of the ITSM execution, the better you can tie ITSM to business objectives and goals, they better ITSM will go. 

◉ Characterize the technique for accomplishing objectives and goals 

◉ "System" is frequently mistaken for and abused as "vision and objectives". These two terms don't mean anything very similar. "Technique" is the way to accomplish "vision and objectives", and gives the guide to arriving. Characterize and record your system. 

◉ Characterize and recognize jobs (counting the regularly ignored "support" job), capacities, assets, measures, and the time required for accomplishing the objectives and targets. 

Work your Plan: 

◉ Execute your technique. Do what you said you would do. 

◉ Convey triumphs, just as misfortunes (you will have a few) and what you're doing to remediate those difficulties, on a standard, intermittent premise. 

◉ Keep your support locked in. You have to empower your support so she can thump down hindrances and help with unanticipated occasions to keep you on track. 

◉ Praise victories, regardless of whether that be the accomplishment of achievements, hitting those measures you characterized in your arrangement or the ID of new learnings. 

Intermittently audit your arrangement and modify technique varying without dismissing the objective. Keep in mind, the "objective" is the objective. The "plan" is how you will arrive, not why you're doing it. Here and there people become so secured in running the arrangement that they neglect to see that they're going off base toward accomplishing objectives. 

The best piece of making arrangements for us? Watching the arrangement work and accomplishing those objectives and destinations characterized inside the arrangement. How fun is that?

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What Is Six Sigma Certification?

Many people have written to iSixSigma asking various questions about Six Sigma certification, like:

◉ How can I get Six Sigma certified?

◉ Where can I get Six Sigma certified?

◉ Is Six Sigma certification transferable if I leave a company for another?

The list goes on and on.

This article will help answer these questions regarding the ubiquitous and confusing “Six Sigma certification” topic.

For instance, millions of people have applied for, tested and been granted a driver’s license in the United States. It certifies that a person has passed the minimum guidelines and requirements for driving. The individual must pass both a written and hands-on driving test in order to “certify.” Not all individuals in the United States, however, have the same proficiency in driving – even though they may have passed the tests and been granted a driver’s license. The same is true for Six Sigma certification. Certification alone does not summarize the worth of a true quality professional.

What Is Six Sigma Certification?

Six Sigma certification is a confirmation of an individual’s capabilities with respect to specific competencies. Just like any other quality certification, however, it does not indicate that an individual is capable of unlimited process improvement – just that they have completed the necessary requirements from the company granting the certification.

What Is Involved In Six Sigma Certification?

As with attaining a driver’s license in the United States, Six Sigma certification entails learning the appropriate subject matter, passing a written proficiency test and displaying competency in a hands-on environment. The materials can be purchased from almost any Six Sigma training and consulting company, but almost always comes bundled with classroom training. Usually you or your company will purchase a training session, which has different bodies of knowledge and durations for each Six Sigma level (Green Belt, Black Belt, Master Black Belt, etc.).

The written proficiency test may be given by the training company or the business hiring the training company. Typically, companies new to Six Sigma will defer to the training company’s proficiency test. Companies that have been performing in-house training for years (such as Motorola or GE) have created and administer their own written proficiency tests.

After a quality professional has completed training, they must complete one or two quality projects and display competency in applying the concepts learned in the classroom training. This is where certification companies diverge – this part of the certification is the most fuzzy and undefined. Some organizations require a certification candidate to complete one project if a Green Belt, and two projects if a Black Belt or Master Black Belt; others require less or more. In addition, there is no standard for what passes and what fails to display an individual’s competency.

So, what’s the takeaway from certification? Any worthwhile certification involves training, a written proficiency exam and a hands-on competency display of the methodology to real world problems. The specifics around each of these three requirements vary from company to company. Will it ever become standardized? Probably, but not in the near future.

How Can I Get Six Sigma Certified?

There is no single body designated to provide Six Sigma certification to the quality profession. Almost every one of the tens of companies providing Six Sigma training and consulting also provide certification. Why is this? Because individuals and companies are spending a great deal of money, sometimes in excess of $30,000 per individual, to become trained, and they feel like they should have something to show for it. Hence, certification became a popular add-on service for consulting companies because it allowed them to differentiate between skills levels, as well as charge additional fees.

Why Isn’t ASQ The Sole Six Sigma Certification Body As It Is For CQM?

Quality certification titles such as Certified Quality Manager (CQM), Certified Quality Engineer (CQE) and Certified Quality Auditor (CQA) may only be attained from the American Society for Quality (ASQ) – as they are the sole certification body for these titles. No other company or body offer these certifications as it would be difficult to convince individuals and companies that the certification has the same value as that of ASQ’s certification.

There are many reasons why ASQ did not automatically become the de facto certification source for Six Sigma Green Belts, Black Belts and Master Black Belts as it has for CQMs, CQEs and CQAs. First, ASQ only started offering certification around 2000, although Six Sigma quality has been popular since the mid-1990s. Second, the methodology was originally proprietary to Motorola (although most, if not all, of the individual tools that make up Six Sigma are public domain), and it was not apparent how great an impact Six Sigma would have in the quality community and business world until the late 1990s. Finally, there have been complaints within the quality profession that ASQ is a slow-moving organization, and at times has tried to control the evolution of Six Sigma, which created a barrier to adoption by the industry and by businesses.

Should I Get Six Sigma Certified?

The reasons for certification are the same for any other industry certification:

◉ To display proficiency in the subject matter

◉ To increase desirability by employers

◉ To potentially increase your salary

Ultimately, certification is a professional decision that can only be made by you. In some cases, it will be required for you to advance within an organization. For instance, at some companies it is a requirement of every salaried employee to be Green Belt trained and certified if they want to be promoted within the organization. In other cases, Six Sigma certification will display your energy and intent to be a leader within the quality profession.

Can I Transfer Six Sigma Certification Between Organizations?

The answer is both yes and no. As with attaining a driver’s license in a particular state in the United States, the main driving body of knowledge is the same and transferrable. Certain laws vary from state to state, however, requiring an individual to learn the specific state laws and test within the state of residence. Six Sigma certification is similar in that certain requirements or variations exist between businesses implementing the Six Sigma methodology. Some companies may also require you to re-certify to display competency.

Most likely a company will value an individual’s certification if attained from a leading Six Sigma business, such as Motorola, GE and Allied Signal. The businesses that have only over the past few years embraced Six Sigma will also have value, but not to such an extent. Attaining certification from a second-tier consulting company will have less value and, finally, being trained without certification will have the least amount of value.

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Assessing Forecast Accuracy: Be Prepared, Rain or Shine

Just as people follow weather predictions to know if they should carry an umbrella, organizations use forecasting to predict and prepare for future events. Across industries, companies attempt to determine what will happen – they forecast for product or raw material prices, market demand, exchange rates, and numerous other key metrics. Based on these forecasts, production sites are built, new products are launched and business entities are re-scaled.

While such forecasts are crucial to making significant business decisions, they may not be as accurate as people think. Organizations often assess the accuracy of forecasts in an elementary way. They monitor, for example, the last available forecast in comparison to the current actual values. Or they may compare the current to the last forecast for a given business quarter.

In his book The Black Swan: The Impact of the Highly Improbable (Random House, 2007), Nassim Nicholas Taleb points out the shortcomings of forecasting in light of the randomness involved in business processes. To address this randomness, practitioners can assess the accuracy of forecasts using control charting and analysis of variance (ANOVA). Screening a corporation’s forecasts with these two tools will reveal the evolution of forecast bias and consistency over time.

Forecasting Basics

In corporations, there are typically two methods for compiling a forecast. In one way, past performance is extrapolated into the future. This approach assumes that the underlying mechanisms remain the same. For example, hotels might forecast their occupancy rate across seasons and plan accordingly for hiring temporary staff. Or, an organization might aggregate multiple individual anticipations of what will happen into one system. For example, predictions from sales agents are consolidated in one database.

To analyze properly the accuracy of forecasts provided by either method, practitioners must understand the following terms:

Forecast horizon describes how far into the future a metric is forecast. For example, weather forecasts typically have a horizon of up to 10 days.

Forecast bias measures how much, on average, forecasts overestimate or underestimate future values. For example, a sales forecast may have a positive (optimistic) or a negative (pessimistic) bias. A positively biased sales forecast, on average, predicts higher sales than what is later achieved. Such a bias can occur when business units get allocated production capacity according to their forecasts and thus have an incentive to be optimistic.

Forecast consistency quantifies the spread of forecasts. People expect a forecast with a short horizon (e.g., the high temperature provided in a one-day weather forecast) to be more consistent than a forecast with a long horizon (high temperature 10 days from now). The reason is that for longer horizons, unknown or non-understood influences typically play a more important role, and the high temperature 10 days ahead may deviate significantly in either direction from what is forecast today.

Forecast bias and consistency are two important elements of forecast accuracy. Notice that bias and consistency are aggregated quantities based on multiple offsets between forecasted and actual values. In other words, they usually are determined using predictions and actual data over a period of time. The offset typically is measured in one of two ways:

1. Forecast – Actual

When a time series, or a sequence of data points, can be considered stationary

2. Forecast – Actual

Actual for a time series with a long-term trend

A prominent example of a long-term trend can be seen in computer memory capacity, which has, over the past 50 years or so, followed a trend first observed by Intel co-founder Gordon Moore. As Moore’s Law predicts, about every two years there is a doubling of the number of transistors that can be placed on an integrated circuit, allowing for a consistent increase in memory.

Figure 1: Factors Influencing Forecast Bias and Consistency

The fishbone diagrams in Figure 1 introduce the two independent parameters, time and forecast horizon, that influence forecast bias and consistency. Also shown are the tools used to analyze them.

Case Study: Forecasting Price of a Subassembly Component

As an example, consider the price forecast data over 20 months of a component used for a subassembly (Figure 2). Price forecasts were made over a horizon of one to six months in advance. For instance, a three-month forecast (3mFC) for the price in Month 9 is actually made in Month 6. This data is derived from a supply-and-demand based model, which considers production capacity to determine future supply and demand, and derive sales prices.

Figure 2: Actual Component Price Data vs. Forecasts for Different Horizons

Using control charts, it is possible to confirm that the dip of the actual price from Months 8 through 10 is significant across the time frame explored here (Figure 3). Until Month 10, forecasts are for the most part positively biased, whereas later they display a significant negative bias (Figure 4). In Figure 4, the time section “Before” the dip displays a lower control limit of about zero, which means that the average of forecasts minus actuals is significantly larger than zero, and thus positively biased. For the time section “After” the dip, the upper control limit is about zero, which shows that forecasts are now negatively biased.

Figure 3: Individuals Control Chart of Actual Prices

Figure 4: X-Bar Chart of Forecasts Minus the Actual Values of Horizons of One to Six Months in the Future

The relation of the price forecasts to the actual price of the component can be understood from a business context. The company’s purchasing and production departments celebrated the price reduction after Month 8 as a permanent breakthrough, and simply brushed off the idea of prices regaining their previous levels. Forecasters, who received most of their information from the purchasing department, were thus induced to adapt parameters in their models according to this general belief.

Unfortunately, such occurrences appear to be commonplace; because a “think positive” attitude is often rewarded in the corporate world, experts with an understanding of complex market mechanisms may be listened to too late. To avoid this, organizations should ask the experts to quantify the likelihood of the perceived risk, assess its potential impact and prepare a contingency plan accordingly.

Setting the Time Period

Is a one-month forecast any better than a six-month forecast? Translated into mathematical terms, this question is asking: “Do one-month forecasts spread around the actual values significantly less than six-month forecasts?” Using the forecast-minus-actual metric, practitioners can perform an ANOVA test for equal variances to answer this question. For the data above, when the time frame of the studied 20 months is considered, forecast bias does not significantly depend on the forecast horizon (Figure 5). The dots indicate the standard deviations and the lines their 95 percent confidence intervals.

Figure 5: Test for Equal Variances of the Forecast Minus Actual Data

Determining Confidence Limits

The analysis of the forecast-minus-actual price data can be used to estimate forecast confidence limits based on past forecast performance. Assuming that the forecast process delivers results with the same performance as before, consistency (Figure 6) and bias (found not to be significantly different from zero) from the previous ANOVA assessment can be used to display confidence limits around the forecasted values.

Figure 6: 1-sigma Confidence Limits Around Forecasted Values

There is a roughly 30 percent chance that the actual price values will land outside these 1-sigma confidence limits (15.9 percent chance for a value above the upper 1-sigma confidence limit and 15.9 percent for a value below the lower 1-sigma confidence limit). Typically, decisions are based on a 95 percent or higher confidence interval with far wider limits; here, only a 70 percent confidence interval is considered. The price three months in the future could either be at $3.90 or at $2.90, with a 30 percent chance it would be beyond these two values. The actions that would need to be taken for either of the two cases are of a completely different nature.

From the practical point of view for managing the subassembly business, forecasts with such accuracy at this confidence level are considered virtually worthless. A useful forecast is one in which the upper and lower confidence limits, at a reasonably high confidence level, lead to about the same practical consequence.

Considering Models

While a supply-and-demand-based model of forecasting is applied in the case study example, there are many potential forecasting models. The more elaborate a model, the more the forecasts of such models tend to be taken for granted. The methods discussed in this article can gauge the validity of forecasts at any level of sophistication.

Addressing Inaccurate Forecasts

Faced with a forecast that does not provide useful data, a team is left with two alternatives:

1. Significantly improve the forecasting process and its accuracy

2. Improve the reaction time of the process such that less forecast accuracy is needed

The impossibility of the first in many business contexts led Taleb to write that “we simply cannot forecast.” But not forecasting is not an option in today’s business world. Because of the resulting necessity of the second alternative, Six Sigma teams may find forecasting accuracy a tremendous source for improvement projects. For example, if a product mix forecast is inaccurate, a Six Sigma team could launch a project to reduce lead times in order to improve reaction time to changing demands. Or, if forecasts for product and raw material prices are inaccurate, a team could lead a project to decrease waste in the supply chain. As Taleb describes eloquently, the hardest part may not be to assess the accuracy of forecasts but rather to make the results generally accepted throughout the organization and to translate them into a consistent approach for improving and managing business processes.

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How Six Sigma Reduces Process Cost?

You ever hear people using the phrase “Good old saying”?  Well, in the world of Total Quality Management, that “Good old saying” definitely is Six Sigma. Sure, it has been around for quite some time now but hasn’t lost its charm even for a bit.

In our previous blog “Top 5 Six Sigma Principles, Benefits and Features”,  we told you all about Six Sigma in detail. But, how does Six Sigma reduce the process cost really? We didn’t talk about that much. So for that, we are going to present some real-life examples in front of you,  with the help of case studies where Six Sigma has been implemented.

As a starter, let’s mug up our Six Sigma knowledge a little bit, shall we?

The Origin Of Six Sigma:

Six Sigma is a set of practices developed by Motorola. After that, Jack Welch, former CEO of General Electric, implemented Six Sigma throughout his company for the first time. And that came with great results. In fact, the 1997 GE Annual Report states that Six Sigma added $300 million to GE’s bottom line. In its first five years of implementation, Six Sigma saved GE approximately $10 billion. Partly due to the notoriety of GE’s achievements, the value of Six Sigma training and certification became widely known.

This goes without saying, after this, this concept spread quickly like wildfire to private and public firms in industries such as manufacturing, finance, aerospace, and automotive, as well as in government and military organizations. In finance alone, Six Sigma’s methods have been adopted as a proven route to error-free processes and waste reduction. Thus, accountants as well as those preparing to become a CPA, have begun learning this methodology as part of their accounting academia and exam preparation as six sigma has proven to be beneficial for any business entity that entails a product or service.

Advantages of six sigma methodology in cost reduction

Now the question is, how does Six Sigma help companies to reduce process cost? Well, imagine you are buying something plastic, like a small toy. Material is melted and then fed into the injection machine. The melted plastic goes into a mold and solidifies to form the toy.

Can you guess what happens if there is variability in the time that you open the mold? If you open it too early or too late, the product may deform in different ways due to the drastic change in temperature. So both timing and temperature, in this case, are extremely important in manufacturing a product that satisfies the customer.

Depending on the product, there can be anywhere between tens and hundreds of processes. Each process may have its own key points to control in order to reduce variability.

This is why Six Sigma is so important and why qualified inspection and process auditing are often strongly recommended. People can easily be stuck in a rut. And often it takes an outside perspective to point out those areas that may cause issues.

How Ford used Six Sigma Principles to lower costs and improve quality

Well, let’s see some real-world figures and case studies here.

You might have heard of Six Sigma stories of Starbucks (Youtube Video) from a lot of people. A few months back, GSDC’s blog “5 reasons why Six Sigma is still relevant in 2020” stated the Six Sigma journey of Starbucks in a quite amusing way.

But, what about seeing the numbers going high this time?

 In 2009, Ford Motor Company found that basecoat consumption for two models at their plant in Saarlouis, Germany was 3.74 kg/unit in 2007, while current consumption was higher at 4.18 kg/unit. Right here, Ford saw an opportunity of applying the Six Sigma principles to find a solution.

How?

Let’s find out!

What are the principles of Six Sigma used by Ford?

In our previous blog “Overview of Six Sigma-Top 5 Principles, Features, Benefits”, we have discussed about Six Sigma Principles in detail. Six Sigma principles solely depend on DMAIC methodology that is consist of 5 following steps:

1. Define

2. Measure

3. Analyze

4. Improve

5. Control

Define

The "Define" phase of Six Sigma expects to distinguish the key partners included and structure objectives that address the necessities of those partners. When characterizing partners – those individuals by the wastefulness – Ford showed up at three gatherings, comprising of end clients, process owners, paint shop laborers, and others.

Of course, specific goals were set to address the problem.

Here’s how:

1. Reduce costs – lower production costs by $1.5 million annually by lowering the consumption of paint in manufacturing.

2. Improve customer satisfaction – meet customer needs better with a reduction of 127.000 ppm (defective parts per million).

3. Reduce environmental impact – lower consumption of certain paint compounds to achieve a 50,000-kg reduction in Volatile Organic Compounds (VOCs).

Measure

The “measure” step in Six Sigma estimates current procedures that identify with your concern or wastefulness. This part is urgent to gathering information uncovering main drivers that can be tended to. 

For Ford's situation, they estimated the accompanying basic components:

1. Daily basecoat consumption

2. Paint coat thickness

3. Paint consumption per robot (for automated paint stations)

4. Consumption per human painter (for manual paint stations)

1. First-time through rate versus consumption; and

2. Technical problems with application equipment

Analyze

The "analyze" step plans to decide the root cause(s) identified with the difficulty you need to address, in view of significant information you've accumulated. Ford's group investigated the information gathered when estimating the six factors above and pinpointed one significant contributing element to the issue. 

There were two likely main drivers found for overabundance paint utilization during creation as follows:

1. Higher consumption in robots applying paint to the liftgate.

2. A fault in the solvent recovery valve of the application equipment. This meant that paint that should have been applied was leaking into the sprayers’ recovery tank.

Improve

"Improve" is a stage that centers around discovering answers for or improvement activities to dispose of the underlying drivers discovered during the past advance. The group at Ford discovered they could supplant the computerized procedure of painting lift doors with manual application to tackle the utilization issue there. They additionally discovered four expected answers for development that tended to the entirety of their objectives:

Goal	Target	Result
Lower costs	$1.5 million annually	$2 million annually
Improve customer satisfaction	127.000 ppm reduction	129.000 ppm reduction
Reduce the environmental impact	Lower VOCs by 50.000 kg annually	Lowered VOCs by 70.000 kg annually

1. Replace plastic valves with more durable stainless steel valves

2. Create an automated system to check valve recovery

3. Manually check valves weekly; and

4. Eliminate the solvent recovery process

Following test preliminaries, the group found that supplanting the plastic valves and making the mechanized valve check framework were the two arrangements that best met their objectives. At the point when the progressions were executed, the plant really observed improvement past what they set as focuses on the objectives they set.

Control

The last step in the DMAIC technique for Six Sigma is "control". This progression is basic to keeping up the enhancements acknowledged when actualizing estimated to address the underlying drivers of an issue. 

Ford kept on utilizing its new computerized framework to check valve recuperation. They additionally remembered this for the models for ISO 9001 quality system management audits. Routine reviews help to guarantee paint utilization is ceaselessly overseen and inside determinations.

Source: novelvista.com

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