Senior Manager- Quality

Job Description

Senior Manager- Quality-HEA001338

Job Description – Black Belt

Designation- Senior Manager

Location: Noida

Job Description

A Lean Six Sigma Black Belt in Genpact is responsible for managing and executing a roadmap of High Impact Projects that solve for the business problems/opportunities of key customer(s) through usage of Six Sigma, Lean and other methodologies

The role would be responsible for the effective delivery of the project through working with cross functional global teams to favorably impact the business outcomes of the customer.

Minimum prior qualifications:

Post-Graduation in Quantitative sciences or MBA

Lean six sigma BB certification (Preferably)

Specific Competence (Essential)

Strong Quantitative and problem solving ability: Ability to conceptualize complex problems and develop an Analytical road map for them

Insurance, specially P&C Domain /Industry knowledge is must

Strong Influencing skills and clarity of thought

People Leadership: Ability to coach & mentor people

Demonstrates the ability to facilitate meetings to generate ideas and make key decisions

Creates a team environment of accountability and commitment for reaching project goals

Specific Competence (Desirable)

Consulting / Strategic Initiatives group / Lean Six Sigma experience

Key Roles and Responsibilities:

Lead Quality Projects for the business, individually complete 2-3 high impact projects per year per corporate guidelines.

Identifying areas of significant Customer Business Impact and improvement opportunities therein and provide strategic direction & thought leadership

Focus on Process improvement and cost reduction for clients to deliver tangible benefits

Lead and Implement business process management system for new clients

Drive and Track Quality DNA – training, testing & certification, Lead any other analytics

and productivity initiatives that come up

Job Posting Jan 18, 2017, 2:27:39 AM

Unposting Date Feb 10, 2017, 1:29:00 PM

Salary: Not Disclosed by Recruiter
Industry: BPO / Call Centre / ITES
Functional Area: ITES, BPO, KPO, LPO, Customer Service, Operations
Role Category: Quality Assurance/Quality Control Manager
Role: Quality Assurance/Quality Control Manager
Keyskills:  Consulting, Analytical, Analytics, Coaching Ideas, Business process management, Process improvement, Usage Cost reduction.

Manager – Six Sigma Operational Excellence F&A

Job Description

Role Purpose:-
Build Business Excellence Capability across Finance Shared Services Center (FSSC)through Lean / Six Sigma / Continuous Improvement techniques
Drive Continuous Improvement Program of bringing Cost of Delivery down by 10% year on year
Drive Continuous Improvement Program of generating Value / Benefit

to customer as per defined Target
Training and Coaching on various Lean/Six sigma tools and techniques to people
Conducting the process improvement workshops at various levels in the organization
Continual Improvement support during transition and their processes
Build Knowledge Management Practices, Processes for FSSC
Consulting services to the Business Partners ( Need based)

Key Accountabilities:-
Continual Improvement in typical Finance & Accounting Domain
Work with the Stakeholders along with Service Leads in driving continuous improvement Culture.
Support and Contribute to Operational Excellence Operating Model
Identify, Select, Execute and Close business improvement projects w.r.t. effectiveness, efficiency KPIs or any other area based on management review inputs.
To collect, coordinate data collection, perform trend analysis of business/operations data and drive for root-cause and drive Continual Improvement projects
Drive Idea generation and best practice sharing across Service Lines and conduct CI trainings
Contribute to designing and upgrading the Improvement methodologies
Work in collaboration with Corporate teams in line with group CI frameworks
Maintain & Provide various reports on CI projects progress status, business benefits etc.

Behavioral/ Personality Specifications required:-
Deliver CI and cost saving results as decided by the business that meet the scope of the Business Excellence function and deadlines set as deliverables by management
Ability to observe, analyze and give constructive feedback.
Extremely good verbal & written communications skills
Strong understanding of Finance and Accounting Domain / Shared Services set up.
Should be Solution and Customer service Oriented

We encourage you to register on Vodafone careers against job code 000000171104 if you are willing to explore this opportunity on below link

Salary: Not Disclosed by Recruiter
Industry: BPO / Call Centre / ITES
Functional Area: Accounts, Finance, Tax, Company Secretary, Audit
Role Category: Finance/Budgeting Manager
Role: Finance/Budgeting Manager
Keyskills: Six Sigma, operational excellence, lean, F&A Finance accounting.

ACCENTURE HIRING BUSINESS EXCELLENCE LEAN SIX SIGMA ROLE

JOB DESCRIPTION

Accenture Hiring Business Excellence Lean Six Sigma Role Leaders with 6 to 12 Years Of Experience

Location: Mumbai / Gurgaon / Hyderabad / Bangalore / Chennai

Domain Specification In Business Excellence: Financial & Accounting, Banking Financial Service & Insurance, Contact Center, Robotic Process Automation (Kindly Note Domain With IT/Manufacturing/Infrastructure do not apply).

Job description

Roles & Responsibilities:

Drive Improvement projects on processes to improve
Be responsible for the Operational Excellence Framework for one/multiple deals or a towers/Site
Acts as a mentor to Six Sigma and Lean projects for his influence
Drive business transformation through Lean Six sigma and Analytics
Lead global projects in Business Excellence
Six Sigma Black Belt / Master Black Belt and Green Belt with good knowledge of Lean practices
Certification in Lean and other quality practices added advantage
Strong ability to influence

Desired Skills and Experience:

Must Need Skills: Six Sigma Green Belt, Black Belt & Master Black Belt with good knowledge of Lean practices.
Strong Knowledge of Quality Principles and Techniques essential.
Needs to have worked in a BPO Operational Excellence (or similar) function /operations.
Certification in Lean and other quality practices added advantage.

If you, or a friend, match these requirements, please send in your resume to parthiv.m.trivedi@accenture.com, marking Lean Six Sigma Business Excellence in the subject line.or connect me @ 9699327245 to have a Telephonic disucssion

Overall Exp:
Relevant Business Excellence Exp-Process Improvement
Current Ctc:
Expected CTC:
Domain Specialized In Lean Six Sigma:
No. Of Green Belt , Black Belt & Master Black Belt Projects Conducted:
Lean Six Sigma Certification:

Accenture is an equal opportunities employer and welcomes applications from all sections of society and does not discriminate on grounds of race, religion or belief, ethnic or national origin, disability, age, citizenship, marital, domestic or civil partnership status, sexual orientation, gender identity, or any other basis as protected by applicable law

Salary: INR 8,00,000 – 18,00,000 P.A. Weekend off / Transportation provided both ways

Industry: BPO / Call Centre / ITES
Functional Area: ITES, BPO, KPO, LPO, Customer Service, Operations
Role Category: Team Leader-Quality Assurance/Quality Control
Role: Team Leader-Quality Assurance/Quality Control
Keyskills: Lean Six Sigma, Business Excellence, Operational Excellence, Six Sigma, Certified Business Transformation, Green Belt, finance Transformation ,Process Improvement, Business Improvement, Digital Transformation, DMAIC, Lean,  F&A ,Master Black Belt.

So What is “Lean” Anyway?

I’ve been involved in manufacturing since 1967, initially as a manufacturing engineer in a precision machine shop and later managing multi million dollar programs. Not to brag on myself, but I’ve met many smart people!

Initially our goal was limited to meeting quality requirements, by getting through quality inspections and testing in order to deliver on time that month. It was a pretty simple life then, but we didn’t know it.

A little later, I managed the manufacturing services department of a geophysical exploration company, and my boss asked me to analyze the flow of our hydrophones and ocean-going seismic cables. Not knowing what he meant by flow, I went to our final assembly area to look around. This was way before Gemba was a word we had heard.

I found that, yes; we were building in batches because of the large setup times–and my boss knew that.

So began my career of looking for continuous improvements.

Now, quite a few years later, we have all used the broad words for change such as Lean, continuous improvement, Toyota Production System, and before that Total Quality. Still, despite the widespread use of the terms, I’m concerned that perhaps they are being used without a full understanding.

Toyota has been given a lot of press and acknowledgement for their approach to creating TPS, and rightly so. But in today’s implementation of Lean, how many organizations buy into the total culture change that TPS and Lean really require?

It seems to me that, too often, companies run a pilot in their assembly or machine shops to see if it works. If they get good results, they train a few manufacturing employees on 5S, 3P, Poke Yoke, the seven wastes and all the other tools we know.

Usually if done well, there are immediate cost savings from reducing waste, so Lean tools are expanded across the manufacturing department. Cost savings become the key metric looked at by management. But after a few years, the low hanging fruit has been diminished and cost savings plateau or recede. Then management asks, “Ok, we’ve done Lean, what’s next?”

This is what I call a “manufacturing lean project,” which can lead to short term gains, but no transformation.

Let’s look at what a Lean Transformation entails. By looking at TPS, the two pillars it is built on are easy to identify:

1. Continuous  Improvement

  • Part of the culture and expectations
  • By everyone, every day
  • In every department, from the top down
  • Management goes to the Gemba to view the work being done

2. Respect for People

  • Management asks questions as a form of mentoring, so that workers decide for themselves what is best.
  • Each worker is unique and should be treated with respect and helped by management to fulfill their capabilities and dreams.
  • Communication to all about the companys’ goals, plans and results assures that everyone is on the same page.

These two pillars are true to Lean as well. Many lean practitioners may not understand that the “Respect for People” pillar is the basis for everything else–trust, motivation, continuous improvement and outstanding performance.

It’s a big step to adopt a Lean strategy as the Lean Management System for the entire company, but it’s important that everyone has the same goals and expectations, i.e., one language. For example, management should be teaching the Lean classes and frequently inspecting for both continuous improvement and respect for people every day! Then everyone knows it’s important.

Some enlightening questions about a Lean Transformation

  1. Does everyone in the company understand that this is a long-term commitment?
  2. Does the company have a Lean Management System in place that defines these expectations and live by it daily? Research identifies this as a best practice for companies that have been on the Lean journey for 20 to 30 years.
  3. Does management have standard work? Yes, this includes top management, marketing, engineering, purchasing, quality and everyone else.
  4. Is the company continually looking at the customer’s needs today and tomorrow? For example, is the company willing to change what works today for what will work tomorrow?

Ask yourself these four questions to see if your company is on a Lean Transformation or just doing a Lean manufacturing project.

Simulation Modeling Best Addition to Analysis Toolkit

Because of the rapid growth and increased competition in information technology (IT), business process outsourcing (BPO) and other service sector industries in India, quality and cost of operations have become the major distinguishing factors among such companies. Survival, growth and profits depend on how an organization controls its costs and satisfies its clients or customers.

Many organizations have adopted quality improvement programs, the important ones being Six Sigma and Kaizen. They also have modified the techniques of these programs to best suit the organization’s needs. To generalize, the choice of the quality philosophy has been made on such factors as scope and duration of the projects, the organization’s product or processes, and the statistical intensity required to analyze and improve.

Irrespective of the quality program used, many organizations have found limitations in some of the quality improvement tools they use. At the same time, they are discovering the advantages of using simulation modeling and analysis as a problem-solving tool.

Limitations of Quality Tools Used

The reasons why companies are finding that some analysis methodologies provide sub-optimal results include:

  • Complexity of the System Under Study – The business scenario has become highly complex with continuous changes with which organizations must cope. When initiating a project on quality for a highly complex new or existing system, often there are too may factors affecting the performance of the system. Even Six Sigma may fail as it becomes impossible to statistically analyze the system or provide statistical alternatives to the existing system. This has prompted project teams to provide ad hoc alternatives as solutions.
  • Sensitivity or Robustness Required – Analysis methods provide a solution to the problem at hand, but a slight change in input or a minor business decision requires the quality project team to “reinvent the wheel” by kicking off a new project to solve the “new problem.”
  • Verification of Analytical Solution – There is no pedagogical pattern to reinforce or verify the solution arrived at. Most quality methodologies include having to implement and measure the solution provided to determine if the required quality level (or Sigma level) is reached and then control the system to stay at that level. If the project has not met the expectations, it will need to be restarted. This is highly costly to the organization which must change the processes or work force or even make business decisions based on the project’s analysis. Cost also is incurred when actual experimentation (design of experiments) is done on the system.
  • Inability to Analyze a Stochastic System – When the outcome of an activity can be described completely in terms of the input, the activity is deterministic. When the effects of the activity vary randomly over various possible outcomes, regardless of the complexity of the system, the activity is stochastic. Many systems currently used in the industry are stochastic and cannot be easily modeled or studied in the current quality methodologies. The solutions provided to such systems are ad hoc and never satisfactory. Statistical modeling is necessary to study such systems.
  • Inability to Visualize the System – When studying a system for bottlenecks, lead-time reduction and process changes, it can become difficult to visualize it. The quality team requires a scale model to assist it in spotting bottlenecks. Mere numbers such as average handling time (mean time) or standard deviation can be misleading. Even system changes need to be visualized.

These limitations of quality processes can be dealt with by implementing an operations research technique called simulation modeling and analysis.

Sources on Simulation

Discrete Event System Simulation by Jerry Banks, John S. Carson II, Barry L. Nelson and David M. Nicol, (third edition) Pearson Education.

System Simulation by Geoffrey Gordon, (second edition) Prentice Hall.

“Simulation as a Tool for Continuous Process Improvement” by Mel Adams, Paul Componation, Hank Czarnecki and Bernard J. Schroer in Proceedings of the 1999 Winter Simulation Conference, IEEE Press.

An Introduction to Simulation

Simulation is imitation of the operations of a real world process or system over time. It involves the generation of artificial history of the system and the observation of that artificial history to draw inferences concerning the operating characteristics of the real system.

Operations research and simulation modeling have been used in the past by upper management for decision-making in various areas, including supply chain management, manufacturing applications, semiconductor manufacturing, construction engineering and military applications.

 

Simulation is now in use in service industries to model and analyze call flows, human resource management and forecasting. Usage had generally been a one-time effort due to various disadvantages of the simulation concept, but current technology and development have actually converted these disadvantages into advantages. Some of the important ones are:

 

 

  • Data Availability – Simulation requires a large amount of data. In the past, data was generally not available and had to be collected, which was a strenuous activity and took a lot of time. Now the use of enterprise resource planning software and customer relationship management programs provide large volumes of data. That data can be used as input for simulation.
  • Cost of Modeling – In the past, companies developed their own simulation software to supplement their analysis. That software was costly to procure. Now, many off-the-shelf simulation software products have been developed. They are cheaper and easy to use, and can be applied in different business scenarios. The software packages also provide graphical representations of the model.
  • Extensive Knowledge of Probability and Statistics – Simulation modeling requires the use of probability and statistics to model the system. This was a hindrance in the past as many system modelers were reluctant to bury themselves with probability and statistics. The current simulation software packages have input analyzers – this also is available with many quality software tools – which help with the data conversions. Only a working knowledge of statistics is a prerequisite to effective simulation modeling.
  • Time to Run Simulation – Thanks to the current computer processor speeds, simulation can be run quickly or even be slowed down to assist the project team.

Figure 1: Quality Improvement Framework

Figure 1: Quality Improvement Framework

Integrating Simulation Modeling and Analysis

Most organizations have modified the quality techniques to suit their requirements, but the basic project methodology for continuous quality improvement remains. The projects would follow the basic outline as shown in Figure 1.

Simulation modeling and analysis as a tool can be best used in Steps 2 and 3 in the framework above. It is most useful when studying the system, designing the system, evaluating alternatives and backing up the results of the improved process.

A typical example of how it can be done is shown in Figure 2 for a Six Sigma DMAIC (Define, Measure, Analyze, Improve, Control) process. DMAIC applies to an existing process that needs improvement. It is best applicable to continuous defect reduction in a cross-functional/uni-functional environment.

Figure 2: Six Sigma DMAIC with Simulation Tool as an Option

Figure 2: Six Sigma DMAIC with Simulation Tool as an Option

Conclusion: Enhancing Current Methods

Simulation modeling and analysis can be used in a quality improvement framework as an enhancement to current methods. Some key points to remember when deciding to use simulation are:

  • GIGO (garbage in, garbage out) applies to simulation. The way the system is modeled and data is entered will determine the efficiency of the model itself. This will mean that the project team or at least an analyst must be trained in the use of simulation. The analyst or team must know the system well enough to gauge the factors and level of detail to be simulated.
  • Simulation must not be performed if the team does not have time or resources for a detailed quality project. This would obviously mean a sub-optimal solution or a stopgap solution.
  • Various off-the-shelf simulation software is available in the market. A detailed feature study needs to be made before purchase so the software will best fit the organization’s needs.
  • The simulation software is still a cost to the company. The monetary gains will be seen only after successful completion of the project.

Simulation modeling as a tool currently is the best addition for a continuous improvement process. Organizations have a lot of new challenges when it comes to quality of service. These new challenges can only be dealt with by taking it up with newer ways of finding solutions. The quality framework needs to be upgraded as the situation demands.

What Will Process Excellence Look Like in 2025?

It is safe to say that 2016 was a year of immense and unexpected change. As the global political, economic and regulatory environment shift, the way we do business in the future will fundamentally change. The rule book is being torn up and disruptive technologies are shaking to the very core how organizations operate.

In the face of unprecedented change, I am reminded of a quote I read in my friend and colleague Diana Davis’ paper, Emergence: the Future of Operational Excellence:

“The average lifespan of an S&P 500 firm is 18 years today down from 61 years in 1958. By 2027, new firms will replace 75 percent of the companies that were in the Index in 2011″.- Source: Innosight Consulting Research.

It is too early to tell who will be the winners in 2025, so I reached out the PEX Network Global Advisory Board to find out what they thought process excellence would look like in 2025. Here is what two of our key advisors had to say:

“Process Excellence has to do with strategy and the tactics used in its application. However, there can be no single correct strategy of excellence because there are too many variables which are different in each situation and organizational environment including resources, mission, and society.

What can be done is to develop and implement basic principles which will result in process excellence in widely varying circumstances. Therefore, to the extent that organizations focus on improvements in their ability to develop and implement ways to better serve their constituents within their environments, process excellence will improve on into the future.” –  William A. Cohen, PhD, Major General, USAF, Ret. & President, The Institute of Leader Arts

And…

“It will be entirely different than today. Surviving companies will have adopted an ongoing Enterprise Transformation philosophy that is led by a Digital Transformation strategy that is defined within the context of business strategy. This will set the design requirements for a modernization of IT and result in a new IT infrastructure architecture and portfolio.

Applications will be divided into three groups – core applications (HR, Finance, Legal, etc., that must be in place but offer no advantage) will form the first group. These will never provide a competitive advantage and can be supported on licensed software.

The second group will be custom built backroom applications. BPMS generated applications created by a collaboration between the business and IT will replace current legacy applications in this group. These applications actually are the workhorses of the company.

The third and final group are the applications that provide competitive advantage, which will be created by BPM/BPMS staff located in the business units.

The second and third groups of applications will be generated by people in the business areas using low code (by then probably “no code”) BPMS tool suites. These business process specialists will bridge the business/IT gap and redesign the business in the BPMS environment and then generate the solutions. The OPEX people located in the business areas will work closely in an open collaboration model with IT data, hardware, tool, communication specialists and others. This will produce a process excellence capability that is nimble, responsive, collaborative and both low risk and low cost.” – Dan Morris, Managing Principal, Wendan Consulting

Innovation

What will the future hold? What are the demographic and economic trends that will shape markets and businesses tomorrow? Which technologies will drive fundamental changes to our ways of working, living and developing as individuals and employees? More importantly for PEX Network‘s community, what will the impact of these big picture changes be on approaches to Operational Excellence and the profession itself.

Find out how leading practitioners and businesses are positioning themselves for success in PEX Network’s exclusive report “Emergence: The Future of Operational  Excellence.”

  • How the rise of the Millennial Generation is reshaping the customer experience and driving the need for simpler, faster processes
  • Why operational excellence programs need to get more strategic and how you can make the shift
  • The ways that new technologies – Robotics, Low Code, Artificial Intelligence, Data Analytics, and Process Automation – are causing fundamental change to business processes and how you can start to effectively capitalize on them to promote operational excellence
  • The skills and capabilities you need to develop now and in the future to better support your business to achieve true process and operational excellence

Performance Metrics that Matter: Effective Productivity Measures

The final metrics I’ll address in this series are for productivity.  This is a slippery slope.  Frankly, I can count on one hand the plants I’ve seen with an effective measure over the last 10 years.  Most are not robust enough to represent reality, if they exist at all.

I also find there is often “discounting” going on for things not directly in control of the shop floor that affect product costs.  The most notable, of course, is raw materials, which can have a huge impact on the cost of goods produced.  These costs must be effectively managed by the sourcing/purchasing function, which is typically overseen by the senior operations or supply chain executive.

Bottom line:  I recommend three distinct productivity measurements for the operations function:  shop-floor productivity, plant productivity and total operations productivity.  Let’s take them one at a time.

Shop Floor Productivity Metric = OEE (Utilization x First Pass Yield x Efficiency)

OR, use the following option if you don’t yet have robust OEE reporting:

Actual Cost of Production Dollars current year compared to Actual Prior Year Cost of Production Dollars — adjusted for the new year’s volume and product mix

Our readers may remember references to OEE in previous articles.  If the plant organization, top to bottom, understands the budgeted OEE that must be met on constrained work centers (as well as other critical support work centers), that’s a pretty good way for operators and first line supervision to do their part on delivering productivity.

OEE exposes yield issues on raw materials, quality cost issues and labor cost issues; and these metrics are very easily monitored visually so that reactions to unfavorability occur in real time.  For example, kanbans or FIFO lanes that start drifting off the plan, i.e., are starving for material or are building inventory cause supervisor/operator intervention on the spot.  So I like OEE as the shop-floor productivity measurement.  The value stream managers/production managers own this one.  Here’s a recent experience I had that makes the point well about how plant leaders should be thinking and responding — and it isn’t like this particular plant manager.

I counseled a young plant manager a couple of years ago who was so full of himself that he thought he was the best plant manager in the company.  I know because that’s what he told me.  He couldn’t understand why the corporate HR trainer had invited me in to spend some time with him.  He thought he was running the best plant in what is a multi billion dollar enterprise.  I suggested that his expectations of himself and his team were far too low.  Of course he was offended and disagreed.  I then attended his staff meeting later that morning, and his plant controller put up a graph that showed significant shortfalls in plant performance to budget for the first two months of the year. March was already more than half over and would result in the same unfavorable outcome.

No analysis of causes or any attempt at corrective action had been done.  I did a quick calculation and asked the staff team if they knew how much extra productivity they must deliver each of the last nine months of the year, from April through December, to make the budget plan for the year.  Of course they did not.  The plant manager quickly spoke up and said that he didn’t believe in extrapolating two or three months of performance into a 12-month projection.  My response:  Then show me the projects and corrective action that will give us all confidence that you and your team will close the gap by year end.  There were none.

OEE is often used as a measure of plant productivity, though I usually find issues with the reporting when I dig into it on my plant visits.  Most plants should use the alternative shop-floor productivity measure until robust OEE accounting is in place.  Further, and highly problematic, is that other functional areas typically don’t understand the mechanics of the OEE calculations and can’t tie them directly to products and mix, which is a more common language for them.  Also, leaders around the entire business often think that productivity is the responsibility of manufacturing, and they become cheerleaders instead of doing their part in their own functions.  These leaders also tend to think more in terms of the overall plant and business financials.  The alternative method of reporting I suggested dollarizes the results and have a direct link to the financials.  OEE is the shop-floor piece of the plant productivity measure.  The costs of products are either going up or down when compared quarter-over-quarter, year-over-year.

But so are the “fixed/period costs.”1

Let’s Talk Fixed Costs

The fixed cost piece has the plant manager’s oversight and each functional leader is accountable for making their numbers accordingly.  All staff managers are accountable for their respective pieces.  And the plant manager, of course, is accountable for the sum total of the OEE and fixed costs within the plant organization.

The plant manager must expect that his/her team will remove any obstacles that cause delivery of the promised performance to be negatively affected.  And, of course, that expectation is universal for anyone in a leadership position.  This leads us to our second measure, the plant productivity metric, which is designed to capture the total variable cost spend and the fixed cost spend compared to prior year spending.

Plant Productivity Metric = OEE results dollarized +/- Actual Period Costs spent vs. Prior Year Actual Spend

This simple measurement collects all of the plant spending except for capital spending, which is excluded.  Capital is appropriated project by project and has to meet certain pay back criteria.  The productivity that results from capital spending is calendarized into the budget calendar and captured according to the actual reporting compared to how it was included in the budget process.

The final measurement I propose is the Total Operations Productivity Metric:

Shop Floor Productivity +/- Year-Over-Year Changes in Fixed/Period Costs +/- Raw Materials Cost Changes (adjusted for the new year’s product mix)

These three measurements clearly assign productivity metrics for which very specific groups are accountable.  It’s not surprising that if you hold plant managers accountable for unfavorable purchase prices, they’ll protest and become discouraged since they have little if any influence on them.  Holding the sourcing team accountable for purchase prices is where it belongs and eliminates the frustration of factory people.  Thus purchase price changes + or – are reported in the Total Operations productivity number.  The VP of operations is accountable for all of this.

For those of you who may be struggling to create a viable productivity metric, I hope you’ve found some help here to put reality-based productivity plans together that you can track all year long to a successful outcome at three levels of operations accountability.  Finally always remember this:  If the productivity you report can’t be found on the income statement and/or balance sheet, then it didn’t happen.  From a financial standpoint that’s the final word.

Forget Sales Dollars

Some of you may be wondering by now why there is no measurement here that compares plant performance to any kind of a sales number as many of the measurements I’ve seen in the field measure manufacturing output vs. some form of sales dollars.  Here’s why.

The biggest mistake I see in manufacturing organizations is the tracking of manufacturing production costs as a percentage of sales dollars.  It doesn’t matter whether the scorecard is on net sales or gross sales.  It’s just the wrong way to think about it.  There are three compelling reasons why this is true.

  • First, the amount of sales dollars in a specific period has no relevance to the shop floor’s cost performance.  Even if your plant is delivering on a very short lead time, the accounts payable cycle alone usually puts the recording of a sales dollar into a different month or quarter from when the goods were produced.  Sales dollars simply have no connection to the actual time of when the product was manufactured.
  • The second reason is that several non-manufacturing causes affect the calculation.  Marketing may be running a promotion and selling at discounted prices.  Does a lower sales number have anything to do with today’s cost of manufacturing?  What if the mix of product sales is significantly different than what was being produced in the factory?  (See previous article re: poor S&OP planning.)  This could result in windfall productivity or unfavorability.  Neither has anything to do with the period’s production costs.
  • The third reason for a 35-year manufacturing guy like me is just short of criminal.  Too often companies still measure cost by the long outdated formula of “standard cost + margin = price.”  So the factory team busts their tails delivering year-over-year cost reductions, which should flow directly to the bottom line  (Isn’t that the goal?).  The effect:  Cost reductions get passed straight through to the customer.  Pretty discouraging for factory folks and a very negative outcome for the business.  I’ve seen this occur time after time.

    The only formula I know of that really works is to expect sales and marketing people to be close enough to the market that they know what the market price is that is being supplied by their best competitors.  Sales and marketing people are the final implementers of manufacturing productivity by using the formula “market price – cost = margin.” If the prior year margin on a product was 30%, and manufacturing is coming off a 5% productivity year, the new calculations yield a margin of 35% using the market price – cost = margin formula.  It’s a simplistic example, but you get the point.  When the cost + margin = price formula is used, sales/marketing/accounting take the new 5% lower manufacturing cost and add a 30% margin and the entire amount of productivity gets passed straight through to the customer.

In the above example, if margins had dropped from 30% down to 25% the productivity gain would result in simply getting margins back to the desired 30%.  In either case, Total Operations Productivity would be calculated the same way.

“There are so many people who can figure costs, and so few who can measure values.”

 

Simulation Modeling Best Addition to Analysis Toolkit

Because of the rapid growth and increased competition in information technology (IT), business process outsourcing (BPO) and other service sector industries in India, quality and cost of operations have become the major distinguishing factors among such companies. Survival, growth and profits depend on how an organization controls its costs and satisfies its clients or customers.

Many organizations have adopted quality improvement programs, the important ones being Six Sigma and Kaizen. They also have modified the techniques of these programs to best suit the organization’s needs. To generalize, the choice of the quality philosophy has been made on such factors as scope and duration of the projects, the organization’s product or processes, and the statistical intensity required to analyze and improve.

Irrespective of the quality program used, many organizations have found limitations in some of the quality improvement tools they use. At the same time, they are discovering the advantages of using simulation modeling and analysis as a problem-solving tool.

Limitations of Quality Tools Used

The reasons why companies are finding that some analysis methodologies provide sub-optimal results include:

  • Complexity of the System Under Study – The business scenario has become highly complex with continuous changes with which organizations must cope. When initiating a project on quality for a highly complex new or existing system, often there are too may factors affecting the performance of the system. Even Six Sigma may fail as it becomes impossible to statistically analyze the system or provide statistical alternatives to the existing system. This has prompted project teams to provide ad hoc alternatives as solutions.
  • Sensitivity or Robustness Required – Analysis methods provide a solution to the problem at hand, but a slight change in input or a minor business decision requires the quality project team to “reinvent the wheel” by kicking off a new project to solve the “new problem.”
  • Verification of Analytical Solution – There is no pedagogical pattern to reinforce or verify the solution arrived at. Most quality methodologies include having to implement and measure the solution provided to determine if the required quality level (or Sigma level) is reached and then control the system to stay at that level. If the project has not met the expectations, it will need to be restarted. This is highly costly to the organization which must change the processes or work force or even make business decisions based on the project’s analysis. Cost also is incurred when actual experimentation (design of experiments) is done on the system.
  • Inability to Analyze a Stochastic System – When the outcome of an activity can be described completely in terms of the input, the activity is deterministic. When the effects of the activity vary randomly over various possible outcomes, regardless of the complexity of the system, the activity is stochastic. Many systems currently used in the industry are stochastic and cannot be easily modeled or studied in the current quality methodologies. The solutions provided to such systems are ad hoc and never satisfactory. Statistical modeling is necessary to study such systems.
  • Inability to Visualize the System – When studying a system for bottlenecks, lead-time reduction and process changes, it can become difficult to visualize it. The quality team requires a scale model to assist it in spotting bottlenecks. Mere numbers such as average handling time (mean time) or standard deviation can be misleading. Even system changes need to be visualized.

These limitations of quality processes can be dealt with by implementing an operations research technique called simulation modeling and analysis.

Sources on Simulation

Discrete Event System Simulation by Jerry Banks, John S. Carson II, Barry L. Nelson and David M. Nicol, (third edition) Pearson Education.

System Simulation by Geoffrey Gordon, (second edition) Prentice Hall.

“Simulation as a Tool for Continuous Process Improvement” by Mel Adams, Paul Componation, Hank Czarnecki and Bernard J. Schroer in Proceedings of the 1999 Winter Simulation Conference, IEEE Press.

An Introduction to Simulation

Simulation is imitation of the operations of a real world process or system over time. It involves the generation of artificial history of the system and the observation of that artificial history to draw inferences concerning the operating characteristics of the real system.

Operations research and simulation modeling have been used in the past by upper management for decision-making in various areas, including supply chain management, manufacturing applications, semiconductor manufacturing, construction engineering and military applications.

 

Simulation is now in use in service industries to model and analyze call flows, human resource management and forecasting. Usage had generally been a one-time effort due to various disadvantages of the simulation concept, but current technology and development have actually converted these disadvantages into advantages. Some of the important ones are:

 

 

  • Data Availability – Simulation requires a large amount of data. In the past, data was generally not available and had to be collected, which was a strenuous activity and took a lot of time. Now the use of enterprise resource planning software and customer relationship management programs provide large volumes of data. That data can be used as input for simulation.
  • Cost of Modeling – In the past, companies developed their own simulation software to supplement their analysis. That software was costly to procure. Now, many off-the-shelf simulation software products have been developed. They are cheaper and easy to use, and can be applied in different business scenarios. The software packages also provide graphical representations of the model.
  • Extensive Knowledge of Probability and Statistics – Simulation modeling requires the use of probability and statistics to model the system. This was a hindrance in the past as many system modelers were reluctant to bury themselves with probability and statistics. The current simulation software packages have input analyzers – this also is available with many quality software tools – which help with the data conversions. Only a working knowledge of statistics is a prerequisite to effective simulation modeling.
  • Time to Run Simulation – Thanks to the current computer processor speeds, simulation can be run quickly or even be slowed down to assist the project team.

Figure 1: Quality Improvement Framework

Figure 1: Quality Improvement Framework

Integrating Simulation Modeling and Analysis

Most organizations have modified the quality techniques to suit their requirements, but the basic project methodology for continuous quality improvement remains. The projects would follow the basic outline as shown in Figure 1.

Simulation modeling and analysis as a tool can be best used in Steps 2 and 3 in the framework above. It is most useful when studying the system, designing the system, evaluating alternatives and backing up the results of the improved process.

A typical example of how it can be done is shown in Figure 2 for a Six Sigma DMAIC (Define, Measure, Analyze, Improve, Control) process. DMAIC applies to an existing process that needs improvement. It is best applicable to continuous defect reduction in a cross-functional/uni-functional environment.

Figure 2: Six Sigma DMAIC with Simulation Tool as an Option

Figure 2: Six Sigma DMAIC with Simulation Tool as an Option

Conclusion: Enhancing Current Methods

Simulation modeling and analysis can be used in a quality improvement framework as an enhancement to current methods. Some key points to remember when deciding to use simulation are:

  • GIGO (garbage in, garbage out) applies to simulation. The way the system is modeled and data is entered will determine the efficiency of the model itself. This will mean that the project team or at least an analyst must be trained in the use of simulation. The analyst or team must know the system well enough to gauge the factors and level of detail to be simulated.
  • Simulation must not be performed if the team does not have time or resources for a detailed quality project. This would obviously mean a sub-optimal solution or a stopgap solution.
  • Various off-the-shelf simulation software is available in the market. A detailed feature study needs to be made before purchase so the software will best fit the organization’s needs.
  • The simulation software is still a cost to the company. The monetary gains will be seen only after successful completion of the project.

Simulation modeling as a tool currently is the best addition for a continuous improvement process. Organizations have a lot of new challenges when it comes to quality of service. These new challenges can only be dealt with by taking it up with newer ways of finding solutions. The quality framework needs to be upgraded as the situation demands.

Quantifying the Benefits of Quality: Employee Training and Incentives

This four-part series looks at what helps drive financial benefits from quality, including looking at the relationship between financial benefits and:

  1. the role and uses of quality,
  2. governance and standardization of quality,
  3. quality training for suppliers, and
  4. quality incentives and training for staff.

In part three of this series we expanded the discussion on the benefits of transparency and cross-functional integration with external partners, namely suppliers. What we found was that organizations that establish training for their suppliers tend to reap higher financial benefits from quality efforts. Training provides a common language and helps suppliers understand the impact that defects or other setbacks like delays will have on the end customer, ultimately resulting in a unified focus on the customer and increased financial benefits.

The same ideas of transparency, common language, and understanding the impact of role on quality should also hold true for the organization’s employees. Hence in this final article, we will look more closely at the relationship between employee training and incentives.

Training Employees and Financial Value

Training programs help develop competencies, ensure employees understand their role in creating quality for the customer, and establish a quality-focused culture. Hence respondents were asked to indicate if they had a formal quality-related training program. Though the majority of organizations do not have a formal training program, more organizations (43%) are investing in a training program than were in 2013 (32%).

Though it can be argued there is intrinsic financial value in offering quality training, there are still unanswered questions such as: Who should receive the training and what training should we provide?

Who Should Get Training?

The majority of respondent organizations (56%) offer (either through direct training or compensation for external training) quality management training for staff involved in quality actives.  Almost half of the respondent organizations (44%) also offer quality-related training to all employees, likely driven by the need to embed a quality-focused culture within the organization.

To understand where organizations should focus their training resources—in regards to ROI—we ran analysis against the employees offered training and the organizations’ financial benefits of quality (Figure 1).

Figure 1: Employees Trained and Financial Benefits of Quality

Though the conventional wisdom would be to increase quality training to all employees to develop a shared perspective and bolster a culture of quality, the analysis indicates a drop-off in financial benefits for organizations that offer quality training to all employees. Instead the largest increase in financial benefits comes from providing training for quality-related staff and those who specifically request training.

What Training Topics Matter?

The majority of organizations focus their training on quality fundamentals, auditing, ISO, quality management principles and quality tools. However, few organizations include training on more customer-value concepts such as the customer experience, Net Promoter Score (NPS) and lean. To understand what training supports increased financial benefits from quality, we ran analysis against the type of training provided and the organizations’ financial benefits of quality (Figure 2).

Figure 2: Gap Analysis: Employee Training Topics and Impact on Financial Benefits

What we found was that almost all of the types of training discussed in the survey correlated to improved financial benefits. However, organizations that provided training on customer-value related concepts like NPS, lean, Six Sigma and the customer experience were more likely to reap higher financial benefits. This makes sense given the relationship between customer value and financial benefits discussed in the first article.  Organizations that use quality as a competitive differentiator — for the benefit of the customer and enhance brand image — reap higher financial benefits.

Role of Incentives

Similar to training, incentives help reinforce preferred behaviors—such as a quality focus. To understand the role that incentives play in quality respondents were asked, “What incentives, if any, do you use to encourage employees to meet critical quality targets?”

Given that most organizations do not include quality measure-based goals in their variable performance compensation, it makes sense that financial and variable compensation are not readily used incentives.  Instead, organizations use informal recognition by management to encourage employees to meet quality targets. Though immediate recognition can help create buy-in on a person-by-person basis, using formal recognition and tying quality goals into performance can help generate a widespread cultural change faster.  However we wanted to see if specific incentives also had a direct financial impact (Figure 3).

Figure 3: Incentives and Financial Benefits of Quality

What we found was that all types of incentives increase the financial benefits of quality. However informal manager recognition — followed by honorary awards — has the greatest impact on financial benefits. This does not mean organizations should not still consider financial incentives such as tying quality measures to performance goals.  As noted earlier, awards and informal recognition are excellent tools for engagement and ongoing motivation, but financial measures tend to increase cultural changes in the long run.

Conclusion

Best-in-class organizations use training to drive a commitment to quality and help employees understand their role in quality — including their impact on the end customer and driving value.  However, organizations need to consider the purpose of their quality efforts before making decisions on incentives, the types of training, and even which employees to target for training. If the organization’s goals are to create a widespread culture of quality, then casting a wide net for measures tied to quality and training all employees on the fundamentals of quality and impact on the customer could reap the most benefits. However if the organization is specifically leveraging quality to provide customer value — and potential price premiums — then it should consider incorporating training aimed at the customer experience and related concepts such as lean and NPS.

Holly Lyke-Ho-Gland is process and performance principal research lead with APQC, a member-based nonprofit and one of the leading proponents of bench marking and best practice business research.

Start with Leaner Tools to Ease Non-Belts into Six Sigma

Six Sigma offers a variety of powerful tools that help organizations make data-driven decisions. Yet most people in an organization do not hold a degree in statistics and may feel that filling out endless data forms is pointless. When first starting a deployment, it is best to make things as easy and painless as possible for the non-Belt community. Once Six Sigma has gained momentum, Belts can enhance the statistical aspect and refine the methods they use.

Here are three examples for leaner tools that could be used to ease process owners and other non-Belts into the method during an initial deployment:

1. Failure Mode and Effects Analysis (FMEA)

If a Six Sigma team does everything manually in the standard FMEA template, it may need to fill in somewhere between 20 and 30 columns per row. To do that, team members may need to get thousands of data records from the process owner. And once the FMEA is complete, will the Champion even care if the risk priority number is 441 or 810?

When starting out, people may not even be capable of telling whether a defect occurs 7 percent or 70 percent of the time. But they do know what you need to be looking for – their most obvious pains. Most likely, the information Belts need from the process owner is this: What and where could something happen? Why would it happen? How bad is it? Who is going to do what about it, and is it effective?

That is a total of seven questions that almost everybody should be able to answer about their process. Asking these questions allows practitioners to get some data quickly, without misunderstanding or redundancy. As the initiative becomes more sophisticated, practitioners can work to refine the FMEA assessment process.

2. Analytic Hierarchy Process (AHP)

The AHP consists of simply going through a list of options and asking for each possible pair, Is (the first) more important than (the second) – and if so, by how much? But it can be tedious for larger amounts of options.

Time can be saved, however, by reviewing and optimizing the list beforehand, removing the unnecessary comparison questions. If the team already knows that gadget production is three times more important than widget production, why ask later if widget production is more important than gadget production? Taking that to the next level: If the team knows that gadget production is a factor three over widget production and that widgets are twice as important as trinkets – why waste stakeholder time by asking whether trinkets beat gadgets?

Optimizing AHP requires a bit of thought and definitely some information technology support. But for Belts doing the AHP on six factors, completing optimization first makes the difference between discussing 30 comparisons or nine. The AHP session may be condensed from two hours to 30 minutes, which key decision makers will appreciate.

3. Quality Function Deployment (QFD)

QFD is a support process for innovation and change, and also helps in assessing the status quo. It is nearly a science, and performs best in the hands of trained experts.

The information needed to first introduce QFD is not necessarily related to interactions, benchmarks and development status. What practitioners really need to know is: who is doing what, and why?

When practitioners know what requirements a process realizes, and what groups are engaged in the operation of the process, they have a solid basis for process improvement. They can still build intricate houses of quality later, when there is at least a formal requirement process.

Create Other Simplified Tools

The list does not stop here. With a small time investment studying a tool, chances are practitioners can find a simplified, leaner version that provides the information Belts really need from process owners in order to produce initial results.