The data scientists behind the scenes and how they put a spotlight on dark data

Over the past 18 months I have learned a lot about analytics and big data, especially applied to the workforce. I spend a fair amount of my time speaking to customers, analysts and the media and besides the most common question of “do you have any examples you can share” I get asked about the people involved and process of developing these applications. In the spirit of “people are the most important resource a company has” I want to showcase that side of big data.

For timekeeping and scheduling, dark data…( to save you a quick trip to your favorite search engine, I mean data that is collected but not typically used, yet is still required) ….in this case the audit trails of any change made to a timecard or schedule.

In Kronos there are sixteen different types of edits you can make to a timecard or schedule. Each of these edits represents tiny trades of time and money between an employee and the company. Individually most are inconsequential, but in aggregate they represent tens or hundreds of millions of dollars. By and large most of these changes are transactions that everyone agrees to and are necessary… The employee forgot to clock in so the supervisor adds in an “in punch”. Or an employee calls in sick and the supervisor changes a paycode from regular to sick in the schedule.

Occasionally however there are situations where the changes are indicative of an issue. For example, a supervisor changes a couple of minutes around during the week on an employee’s time card and eliminates premium pay. Or a supervisor changes a schedule after the fact to represent that an employee only worked the hours they were scheduled.

These small changes are usually lost in the millions of annual transactions that occur throughout the year. And because they are so small they are usually missed by most reports and audit teams. Only when the employee affected has the courage to speak up does a company become aware of it. By this time the consequences for all involved are significant; from degraded morale on the part of the employee to unnecessary cost in terms of productivity, turnover and financial impact for the company.

As the economy improves and companies feel the pain of turnover and lost performance when employee engagement sags, we have been engaged by companies to understand how they can identify these situations. The companies know the answer is in the data because when someone files a grievance and points out the specific situation and dates, the HR department can immediately see what happened in the transactions.

The challenge is seeing these changes sooner; especially before someone is so frustrated they file a grievance or the behavior becomes obvious to all. This is where one of our data scientists who has a PhD in computer science realized that this is a very similar challenge to what retailers face when they are trying to understand what the millions of customer clicks represent on a website. The customers aren’t telling them why they are clicking the way they do and only a fraction of the clicks result in an order.

So the data scientist applied the same machine learning techniques on timekeeping data that retailers use when they analyze their web server logs. The result of his work however was very difficult to interpret unless you understood machine learning and clustering techniques. To simplify this we had one of our visualization experts re-imagine the output in a way that a lay person could understand. Her interpretation was amazing in its simplicity!

Secondly, the data scientist had created a very flexible tool. The first prototype had a number of tuning parameters requiring the user to take output from past results and enter it in to help weight certain parameters for future analysis. We recognized that aside from a data scientist, we couldn’t expect a typical business user to be able to perform this tuning. So we focused what the tool could do and eliminated the tuning parameters.

clustering dashboard

An example of the machine learning dashboard in Workforce Auditor

We were very nervous and excited about analyzing our first data set (we went in without knowing anything about the customer or their practices to ensure we didn’t bias the analysis). When we researched the results, there it was…we had found an issue that was previously unknown to that company. We tried it a second, third and fourth time. Each time we found something important to the customer that they suspected but couldn’t prove or they were completely unaware of. These small changes were indicative of million dollar +  issues that were looming for these companies but had now been avoided….very exciting stuff.

We found supervisors gaming schedules to improve their own bonuses (the company since tweaked the rules of the bonus). We found a store manager working extremely hard to rebuild her schedule each week because the forecast and automated schedule she received was off (the company immediately re-tuned the forecast for her store). There were many more examples and we realized that we had developed quite a versatile tool. Its power is that it can evaluate the actions of thousands of employees and narrow it down to just a handful of situations that require further investigation in a matter of minutes.

With so many positive results we fast tracked the technology. It’s now available as Workforce Auditor and is included with our Workforce Analytics platform.

Take a ways from this experience?

1) Skills and experience really count in developing big data applications, no one is going from “excel guru” to building a machine learning application overnight and it takes multiple people to get it right

2) involving (internal or external) customers and their data is essential; no one could ever build this without deep domain knowledge and many different data sets to trial

3) By focusing on the business problem rather than the technology we created something that was streamlined and easy to use rather than a feature laden product showcasing the power of machine learning.

When I have a little more time to write, I want to share how the newest member of our team used scheduling data and a network map to uncover undisclosed relationships in a company and what it was costing them….stay tuned!

American Payroll Association Launches its Lean Labor Course

If you are reading this, you are probably aware that I authored a book a couple of years ago by the name of Lean Labor. Its purpose was to apply the philosophy and techniques of Lean specifically to paying and managing the workforce.

I’m pleased to announce that the American Payroll Association (APA) has taken the content from this book and added its own knowledge to create the first Lean Labor educational course. It’s targeted at Payroll Professionals who are looking to run their own operations in a Lean fashion as well as support other areas in their business by providing improved processes and information.

In addition, the APA recruited Dr. Martin Armstrong, Vice President Payroll Shared Services at Time Warner Cable to teach the course with me. Martin, who is also a friend, has implemented many of the techniques found in Lean Labor within his own organization and was recognized for this achievement by the APA with a Prism award. Martin recently wrote an article for training (a leadership development resource) titled The Perfect Paycheck.

We’ve taken the fundamentals of the book and added our experiences over the last three years to provide a practical course in using Lean to improve the performance of an organization driven from a Payroll perspective.

We’ll be teaching the inaugural course July 13-14 in Las Vegas. For more details on the course see the description at the APA site.

Forget 1 + 1 = 3, I’ll show you how 0 + 0 = 1 million

Everyone knows that labor costs show up on the income statement, but the same workforce can’t be found on the balance sheet. It’s considered an intangible asset and does not earn a line on the balance sheet. Did you know that there is another intangible asset that has similar qualities?

It’s the data your company collects every day. Each time an employee fills out or edits a form on a mobile device, laptop, desktop or kiosk, it’s adding to the quantity of data your company owns. This collection of data is relatively expensive and is represented as either a Cost of Goods Sold or an Operating Expense on the income statement chewing away at your profits. Similar to employees it too has no quantified asset value.

CFO’s understand both are extraordinarily valuable but also have a difficult time articulating it as anything more specific than goodwill.

What I have seen in the last year however is that an increasing number of companies are putting these two intangible assets together and finding million dollar insights about their businesses. I am specifically differentiating from companies who have business analysts hard at work tearing apart their data and those companies that are empowering a broader population of employees with increasing amounts of information.

The reason that this is important is that business analysts are relatively few and far between. When the broader workforce can begin making data-driven decisions on a daily basis and view situations with perspective and context based on facts rather than relying on their instincts and week old reports, the value creation is exponential.

Why are some doing better than others in capitalizing on this opportunity? Analytics and Big Data are a frequent topic of discussion with every company I visit. Everyone recognizes the potential. But as with most emerging opportunities, most are talking about it and formulating plans. The successful ones are diving in, learning and profiting. Companies that have accelerated their labor analytics journey are finding they can…

  • Identify who is not regularly following the corporate policies put in place from clocking, to editing time sheets to aligning a schedule to demand
  • Identify employees that have figured out manipulate the data they enter into so they can game controls and metrics in a way that current reports can’t identify. This results in either fraudulent behavior or boosting performance metrics at someone else’s expense.
  • Identify employees who are not following policy and outperforming others resulting in new best practices.
  • Identify employees who are overwhelmed with manual production and employee scheduling edits due to smaller batches and increases in product mix. This change in production speeds and patterns with no accompanying improvements in scheduling techniques is resulting in more unplanned Saturday shifts and overtime to accommodate less than optimal schedules
  • Locate where cost accountants have made mis-allocations of labor costs due to a lack of visibility to where and when the costs actually occurred.
  • Rank employees who are receiving volatile schedules from week to week that can result in increased fatigue and turnover.

I didn’t recognize how big this was becoming until I noticed a trend in the conversations I have been having with companies that provide strategic consulting. Some of these consultants are telling me their jobs are getting harder and in some cases their revenue is falling. Why? One of their bread and butter techniques of identifying opportunities is by interviewing multiple employees from different departments and then aggregating data from different systems. What they are experiencing is that the “low hanging fruit” they could always depend on is gone. Customers have already figured it out. The Aha! moments consultants are famous for are getting harder to find with their traditional techniques. They must evolve too.

Where is your company in maximizing its ROIA (Return On Intangible Assets)? The following list highlights some of what the companies that I visit with were experiencing before they changed tactics. To see how you relate, take a survey of your spreadsheets and business analysts to see if you can identify these situations:

  • Is there so much data available that the reports are now being heavily summarized, and detailed and history is not being carried along?
  • Is there significant manual massaging, data wrangling to use a fancy term, to reconcile data from different systems…is this causing latency in delivering the information?
  • Are different functional areas frustrated with the lack of support in getting at data and beginning to start reporting processes of their own, especially as you move to middle management ranks?
  • Have intrepid employees begun adding thousands of lines of macros to spreadsheets to make them automated and interactive to the point that the spreadsheets themselves are unstable?
  • Do spreadsheets going to front line managers have hundreds of rows or more and multiple tabs to make sure they have all the information they need?
  • Do your spreadsheets analyze mainly the data that users enter on forms or through hardware but ignore the data that the hardware and software generates itself in logs and audit trails?
  • Are people frustrated by the fact that they know the data they need is resting on servers within your organization but even your best report writers are not able to put it together to answer the questions and hypotheses posed?

If you see signs of this in your organization, then a review of your reporting processes, skills and tools is in order. You have an opportunity to change the way you think about math.

Lean Workforce in Hospitals

I’ve been a big fan of Mark Graban’s for years, but only just had the opportunity to work with him. About 2 weeks ago we delivered a webinar focused on Lean in hospitals. If you haven’t heard of Mark, he’s a highly respected Lean expert and author of Lean Hospitals among other books on the subject.

Mark provided several examples of how Lean techniques can be used to improve operations and steps ensure that a hospital’s staff is respected and supported.

If you haven’t had a chance to hear Mark speak about Lean, this is a great opportunity. He shares specific examples and outlines a process that everyone can use to improve outcomes.

Becker’s Hospital Review hosted it and you can see it here

Additionally Mark will be holding full seminars soon. If you are looking to learn more about Lean in a healthcare environment I would suggest looking into one of these:

Building Successful Lean Teams

Boston, March 31, 2015

Kaizen: On-Site Experience, Franciscan St. Francis

Indianapolis, April 22,23

The case for (and against) predictive analytics

It’s been a busy couple of months, and I’ve been learning quite a bit about business intelligence, big data and the opportunities and challenges in this space.

One area that has been a frequent topic is predictive analytics. As a lean guy, anything that promises improved business results by predicting the future immediately makes me suspect. I’ve been indoctrinated by the Lean philosophy to depend less on forecasts and more on the ability to observe and react to current demand and disruptions in a process.

That being said, I really depend on weather forecasts to get my kids dressed in the morning, so maybe I need to keep an open mind.

Predictive analytics is the next evolution in a long history of forecasting solutions that technology providers offer. For example, Kronos provides a labor forecast for retailers to help them in creating a labor schedule for the next week. This can be really helpful in supporting store managers in that it is really difficult to aggregate all the different patterns and unique events that are significant in scheduling a store. For example, day of the week is a fairly repeatable pattern and can be predicted fairly easily. Black Friday is also consistent. So rather than force the store manager to figure it out on her own, why not automate that in a forecast.

But ask a store manage if they completely rely on that forecast (or any other vendors forecast for that matter) and they will tell you that they use it for guidance. The reason for this is that there are many factors that affect a local store that aren’t used as drivers in creating the forecast. For example, if there is construction in the area that makes it more difficult for customers to get to the store or if a product is out of stock that week, the local manager will know that sales will not meet the forecast. This is why it’s so important to have someone knowledgeable about the local practice with the ability to react quickly to changing conditions.

Predicting levels of absence at a store or plant level is significantly easier that predicting individual absence. Make sure you understand the probability of success in a prediction. If you are providing guidance at the individual level, and the probability of a correct prediction is 60% then that means you are wrong 40% of the time. What actions are you asking managers to take based on this prediction and what’s the impact financially and in terms of system trust if it’s wrong?

A slight improvement over the status quo is good enough if a manager is already making the same decision frequently at an individual level. Hiring guidance for a similar job that has high turnover is a great example of an area where this works. Infrequent decisions are or if you are asking someone to take an action based on a predictive result is a different story. If it’s only a couple of percent better than the current method, internal customers are going to not understand the nuance of improvement and the project will be difficult to sustain.

When someone is having difficulty at home, it is likely their work will suffer. How is this behavior measured? While there are some outcomes that are measured like increases in absenteeism, these can also be attributed to difficulty with a supervisor or co-worker or a health issue.

Behavior is an area where correlation and causation can easily be confused. While we sense through data that something is wrong, it’s still going to take personal discussion to find out what the cause is. If you try and let software predict what the cause is and the manager takes a wrong action, it won’t be too long before the software isn’t trusted.

It’s very tempting to look for patterns in the data we already have. And no doubt there is lots of value hidden away in there that we have yet to mine. But we need to be careful to not try and solve every problem we have with existing data. In many cases, new data will be required to capture the true drivers of an event or behavior. This is a much more difficult endeavor.

So is there a future for predictive analytics? Absolutely. We just need to treat it like any other tool in our bag and not go around thinking that every employee problem we face is now a nail for predictive analytics to hammer.

Where are areas where predictive analytics excels?

Are there significant consequences for missing something?

Safety is something that comes to mind. If we can improve our ability to predict an increase in safety risk by even a few percent, the savings can be significant both in life, limb and dollars. This is a great area for exploring. Part of the solution needs to include understanding the drivers required to reduce the risk once an increase is predicted.

Will improvements in processing power or improved algorithms provide better insight than before?

This is the case for weather prediction. The data and algorithms were overwhelming the processing capabilities. As capacity to process improved, outcomes improved. This is also the case for customer behavior analysis. With lots of new data and increased granularity lower costs for processing have changed the game. Do you see similar opportunities with respect to labor analytics in your company? This could be a rich area to explore.

Predictive analytics is an interesting area, but let’s balance these efforts with the basics of making information more available and easier to use. Only then will we truly empower our employees’ decision making capabilities.

Optimized Labor Scheduling for Quality Control Labs

Brazil is no doubt the best place to enjoy the World Cup this year, but Belgium is definitely celebrating the event with colors, team gear and soccer centric activity everywhere. I had the opportunity to be in Antwerp the night Belgium beat Algeria last week and spirits were running high.

World Cup Enthusiasts in Antwerp

But of course that was not the main reason for my visit last week. Among other things I was there to spend time with a very smart group of folks at BlueGrass Consulting. BlueGrass has a strong legacy in supply chain consulting focused often on inventory, processes and logistics. Over the last couple of years, it has recognized the opportunities for improving the effectiveness of the workforce. I haven’t seen the BlueGrass team in almost a year and they were excited to share with me their work on process improvement within the Quality Control labs at several pharmaceutical companies. These labs face a couple of workforce related challenges. First is that they have several streams of work that flow through their labs. Checking the quality of products throughout the production process, mixing reagents in order to support the testing procedures and running experiments on new products are some of the major ones. Some of these streams are dependent on each other. For example, running more production tests requires more reagents. The second challenge is that they can’t predict with certainty when work will come into the lab. They can generally narrow it down to a thirty day window, but for many types of work that’s about as precise as they can get. Also challenging is that there are certain competencies required to perform certain tasks. So if a critical person is missing or already busy, production is delayed. Additionally, competencies of the employees are constantly changing as people leave the organization and as others complete training.

There are some opportunities to improve throughput as well; Often the same kind of testing will come through in multiples during the same time period. When this happens, the work can be grouped, reducing the number of set-ups required.

The flow of work can be predicted from demand drivers such as the production schedule created in ERP, which has a rough cut version planned out over the next year.

Through their software application BINOCS (Binoculars) BlueGrass has linked the production schedule to the employee schedule and through a heuristic process, optimized the employee schedule around production. This schedule ensures that the work can be processed without delay. As the production schedule is refined, the application can be re-run to ensure the appropriate people with the right skills continue to be available.

Geert VanHove, a principal at BlueGrass, was understandably very happy during my visit when he received the first employee schedule that their customer had created on their own through BINOCS.

Branded pharmaceutical manufacturers continue to become more demand driven and shrink finish good inventories. The ability to remove one more potential production bottleneck without creating excess capacity is obviously extremely valuable. Goooal!


India poised for a new age

I just returned from Kronos’ first customer conference in Mumbai. It’s an exciting time for the Kronos India team as they now have over 100 customers locally.

During the week Narendra Modi took office as the new Prime Minister. The media and citizens have an optimistic vibe. Modi’s messages include increased transparency, eliminating nepotism and other forms of corruption and improved economic conditions. He has a history of welcoming foreign investment and a take charge attitude. Already there are examples of families of government officials who are now rejecting long time government perks saying to the media that they want to be treated the same as everyone else people. Indians are ready and hopeful for some good news.

Indian RetailA traditional and common view of retail in India

I’ve been visiting India for the last seven years. Over that time, economically, there have been significant ups and downs. For many of the companies I’ve been visiting there has been tremendous progress in terms of how they operate including managing their workforce. For retailers, there is an increasing appreciation for larger chain stores as compared to the small stalls that line the streets each specializing in just a handful of products. While retail chains and larger grocers are still in the minority they are leaping forward in their thinking. Last week I spoke with the Head of Operations, Hemant, for a retailer of electronic and electrical consumer products that has over 100 locations across India. It has just completed the transformation from a push to a pull strategy with respect to moving their inventory from DC’s to the store. As a result of this strategy they are experiencing increases in revenue as stock-outs are reduced. Margins are improving due to the reduction of discounts of excess inventory. The return trips of unsold inventory to the DC has more than paid for the incremental expense of smaller, more frequent shipments. Hemant is now moving his sights onto the workforce. He recognizes that pursuing a low-cost labor strategy won’t work. “How do we differentiate our stores when our competitors have the same products with the same types of employees? We need to pay more for highly skilled employees to guide our customers to the product that is right for them.” To pay for this increase in skill, Hemant is looking to make sure the staff is scheduled when the customers are there. This is more difficult than for most retailers in the U.S. as its employees are all full-time. Increased utilization isn’t even the main priority. Hemant continues…”During slow periods when employees have completed some training and refreshed inventory and still have time on their hands they become bored and sluggish. It’s tough for them to get their energy back when customers begin entering the store again. It’s important to make sure they stay busy and energized throughout the day.”

retail storeModern retail chain in India

I visited with a number of manufacturers and there is a widening gap in their approach to labor. The head of HR at one large exporter of textiles felt very strongly that there is no place for technology in managing people. Their supervisors manage the 17,000 people at one plant just fine according to this executive. If there is a problem, adding a couple of extra people is no issue because their wages are low. He did however acknowledge a machine utilization problem. This is being addressed by adding sensors to the machine to let management know when it the machine goes down. I’m looking forward to visiting him in the future to see if his perspective changes.

Diametrically opposed to that perspective is a manufacturer of cellular phones who uses technology to analyze the behaviors of supervisors to understand if they are favoring one gender over the other in scheduling overtime or if they are showing favoritism in granting leave requests. This company has also identified 250 out of 20,000 employees who are critical to keeping the lines moving and know instantly if they are late for work so management can begin reacting right away.

There is no shortage of talent in India, let’s hope Prime Minister Modi is successful in his efforts so that India’s talent can be converted to economic success.

Government employees are making good labor decisions, they just don’t see the whole picture

I was pleasantly surprised during my time at the Governing Leadership conference in Maryland held last week.

First was the energy and diligence expended by the elected and appointed officials in attendance to continue to work through the challenges they face. It was a refreshing change from the articles frequently found in the media about less than stellar government performance.

Secondly, I was impressed by the prevalence of Lean methodology in place within different areas of government. As I told the attendees during my panel discussion on performance budgeting; the good news is that organizations in the private sector have been through this transformation and survived. Examples of how to use Lean are readily available, it continues to get easier to implement Lean as the number of successes grow and are publicized.

What struck me during the day was the complexity of planning and operations. While there are several examples, the one that hit home for me was the impact many small decisions  have on a government’s finances. For example, a simple choice between who works an overtime shift and who doesn’t can have a decades long financial implication that far outweighs any wage difference or even premium pay difference. But this cost implication is hidden from the decision makers and is often never connected.

It becomes easy to see how governments can get a poor reputation for managing its finances over the years because of what in hindsight look like obviously poor financial decisions.

When labor makes up to 90% of a municipal government’s labor budget (their stat not mine). It would seem like there would be more visibility and control over the decision-making process. The challenge for governments is that an hour worked today is impacted by benefit rules that take into account many years previously worked and then generates many years of commitment ahead. The supervisor making the daily decision has no visibility to this and therefore cannot take it into account.

If we could provide the information that shows the real cost of an hour of labor, supervisors would be enabled to make better decisions and I would bet that government would see immediate benefits from improved use of tax dollars without having to resort to tax increases or layoffs.

Consider these easily measurable areas of influence on the cost of an incremental hour :

  1. First is the cost of the wage paid. This is the easiest to measure and is often what is used in labor reporting and decision-making.
  2. Will this have an impact on benefit costs? (If the employee is classified as part-time, will they now be eligible for benefits under the Affordable Care Act? Do they generate any additional vacation, comp time or paid sick/personal time?)
  3. Are they now eligible for overtime? It could be 1.5 or higher based on negotiated contracts
  4. Will this hour impact their pension? Depending on pension rules, this incremental hour of work could increase their pension. Depending on the contract, it’s not just the last couple of years of a person’s wages that are used, but, for example, the highest average pay during a continuous 60 month span, whenever that occurs in a person’s career. This incremental hour of time could have an impact on government cost that lasts for 20 or more years!
  5. Is this work occurring due to a State or Federal grant? Has the amount from the grant already been used up? If so the department is now responsible for paying the overage must now find those funds from another area of the budget.


Every day the result of these calculations change for each individual, they can even change from one hour to the next!

It’s no wonder that supervisors can unwittingly rack up expensive labor bills at the end of the week when they are doing their best to apply resources to the work at hand. Even worse the may never realize what they have done because some of these costs never show up in their daily or monthly cost reports.

What’s the answer? This same problem is faced by supply chain managers all over the world. The cost of a good is impacted by many different factors during its life from raw material to delivered good. Rather than looking at the cost to manufacture the good (Raw Material + Production Labor + overhead) which often produces an incorrect cost, manufacturers look at the large drivers of cost across the entire supply chain and calculate a “Cost to Serve”. The cost to serve is a very useful calculation when making decisions (e.g. We could get cheaper labor in Vietnam but the lead time, shipping and warehousing costs go up too much.)

This same principle can be applied to an hour of labor. With benefit costs and regulatory compliance becoming a significant part of total wages, it’s time to provide the total labor cost to supervisors so that they can make better decisions about how they use labor or who is assigned the next shift.

The best part of this opportunity is that unlike manufacturing Cost to Serve where data collection costs are high but calculation requirements are low, the total cost of the next hour of government labor is already available, it’s a matter of aggregating that cost into a one simple dollar figure on a daily basis so that a supervisor understands the impact their decisions are having on their organization.

If your organization is having trouble containing its labor costs then it’s time to understand what the next hour of labor is really costing your organization. You have the data, you have the rules, it simply a matter of sharing them with the people who are deciding how much to spend in the next hour.

Is it worth the effort? Considering the 2103 PEW Trusts report found that 39 of 40 of the cities it analyzed do not have fully funded pensions and the amounts are measured in 10’s of millions and even billions, I would say this is an area worth looking into.


Adding more labor can be beneficial to your bottom line

It’s been about eight years since I stepped into the manufacturing vertical in Kronos and I’m excited to announce that Kronos has asked me to take what I’ve learned here and apply it to a new initiative. This is a project to demonstrate that labor is not just a cost and compliance burden, but rather a strategic resource that is responsible for differentiating a company.

For those of you who have had a chance to read my book, Lean Labor, you know that I have espoused this proposition for many years. The challenge when I wrote that book is for many companies it is easy to measure the cost and compliance risk of an employee. What’s more challenging is to equate an individual’s effort to more positive outcomes such as higher quality, increased revenue or improved conformance to policies and procedures. The reason is that measuring cost can be recorded easily and accurately down to the minute. Labor’s impact to other factors can often take days or months. Additionally a single or group of employee’s impact is often mingled in with other factors such as weather, sales promotions or the performance of other downstream operations.

As any Lean follower knows, this is very similar to the Lean philosophy and I’m looking forward to sharing what I discover in the future here as well.

This initiative goes outside of Manufacturing. Healthcare organizations are evolving from measuring by activity to improving outcomes. Retailers are working to differentiate by providing the right types and levels of service to their customers just when they need it.

This is potentially valuable work for a company because when the only data driven linkage to labor is around cost, compliance and immediate output, the so called “war on labor” will continue. Once we can link the more positive outcomes experienced by a company to labor, companies will have another option to use in achieving their strategic goals. I would predict that this increases their use of labor because executives will have the metric and proof they need to feel confident in the outcomes.

Why do I have confidence in saying that? Today the only role whose performance is easily measured to a strategic goal, in this case revenue, is sales. Broadly speaking sales is always the role that is first to be grown and last to be cut. They are often the highest paid as well.

Fortunately, I’m not alone in this effort. There are examples already occurring in the market and academics who have already been proposing these ideas. A recent article in the New York Times Thinking Outside the (Big) Box provides some more specifics around the idea.

I look forward to communicating our findings and what others are doing in this area in the near future.

Those office workers have it made

How frustrating to walk off the production floor after a grueling variance meeting and see office workers surfing the web or standing around having a cup of coffee. Even worse, urgent engineering change or variance requests disappear into the ether unless they are constantly expedited. How about customers waiting for weeks to obtain an answer to why a product failed in the field? Should they change their installation practices? Was it a material problem?

Increasingly I’m being asked about back office processes. If it was just a few people causing inefficiency in a department, that’s an easy fix. But it is never quite that simple. It seems like there are waves of busy times and then periods of slow times. Depending on who does the work (we all have our goto people at corporate), the outcomes and response times can be very different.

These are typical scenarios:

Recently I was taking a plant tour with a large Auto OEM and the managing director felt that their production process was in pretty good shape. He then opened a door to a large room full of QC engineers and stated that he wasn’t sure if he had too many, too few or just the right number of engineers working.

A couple of weeks ago I was speaking at a workforce management seminar in Belgium and a manager from a large pharma manufacturer asked about improving the productivity of their QC department whose employees work in a lab environment.

I always ask the same questions. How volatile is their work? Is it slow sometimes and busy others? Are there small jobs and big jobs that flow through the department? Does work get re-prioritized based on production or customer demand? Are the people working in the department reasonably happy and skilled employees?

The answer is generally yes to all four. What I then draw is the curve described by the Kingman formula. What it shows is the relation between utilization and wait time. The drivers of the function are the variability in arrival times and variability in the cycle time.

A good introduction to the formula is available on Wikipedia.

An example of the curve is shown below. What is immediately obvious is that wait times increase dramatically as utilization approaches 100%. The result of this is that departments with little control over the variability of demand and cycle times must run at lower utilization rates in order to maintain acceptable service levels.

kingmans formula

And just because a process is documented and looks efficient doesn’t mean that the variability has been driven out of it. Just like the routing on a production floor, an office process is generally documented assuming perfect conditions…open capacity, consistent workload, fully skilled employees and no interruptions. In other words, it assumes perfect standardization.

We are all familiar where high variability in the process and the financial pressure for high utilization causes long wait times. The doctor’s office is a familiar one. For those with appointments at the end of the day, patients can be waiting for 30 minutes or more after their scheduled appointment. It’s tough for the office to schedule the right amount of time for each patient because it’s difficult to know what care each patient will require. It also has to deal with patients arriving late. But if the office doesn’t book the schedule pretty full, the office can’t be run profitably. A doctor’s office is a relatively simple example and many have implemented fixes to ensure higher levels of service while maintaining high levels of utilization. Canceling appointments for patients who arrive late, increasing flexible capacity by adding Nurse Practitioners and scheduling different amounts of time based on the predicted effort for a scheduled patient are a few examples that increase utilization and maintain service levels.

Knowing that standardizing the process and shaping the arrival times of the work will help maintain service levels while allowing for the increase in utilization, the next challenge is identifying where the variability is the greatest. I’ll assume that the first place to look for that answer is with the employees themselves. This is a good start and take what improvements can be identified. The next level gets a little harder. Workflows often cross departments where priorities change and individuals don’t have knowledge of the entire process. This is where some data is going to be required. Often this is also where the improvement efforts slow down. Collecting data around office processes can be challenging. From employee resistance to complex flows it becomes difficult to know what to track. One change in environment that seems to be going underutilized is that office employees are moving to electronic records. From Engineering Changes, to electronic lab notebooks to CAD systems to document management, employees are logging on to applications, doing their work and logging off or checking into another piece of work. As compliance around every aspect of our lives continues to increase, technology producers are tracking our every move to produce a record of what we have done and when. Often this information goes unused unless it is needed for some type of inquiry.

This information is an untapped goldmine for understanding workflow in the office. This electronic trail reconciles work and employee and time. With this you can generate metrics to understand when it is busy, when it is slow, and how long do different types of work take to traverse through a process. When this information is connected to a workforce management system you go from understanding what happened to predicting issues and being able to address them immediately by shifting capacity to where it’s needed and prioritizing work before it is late.

As with production, the idea is not to work the office staff harder. It’s to improve and standardize the workflow through the office so service levels improve without increasing capacity. Imagine lab results returning faster. Engineering requests approved on time without follow-up. Customer inquiries and complaints responded to more quickly. How would this impact your production lead times and competitive stance in the market? All with no increase in labor cost. Kingman has done the hard part by showing you how to improve utilization and service times and what the ROI will be. Now it’s your turn to drive variability out of the office.