comments 2

A salary analysis of industrial engineers

By Zubin Ajmera

When I was new to the field of industrial engineering, I had zero idea on what the field was about. What do industrial engineers do? Where do they work? What do they do on a daily basis?

These were the type of questions almost everyone has when they start out. But sometimes the hidden truth behind all those questions is that we want to know the salaries of industrial engineers – how much do they earn, which industries within IE are more lucrative, what’s a good benchmark salary for an IE, etc., to see if this field is worth the academic investment.

So I started doing some basic research and I discovered there is no good resource which can provide valuable information on this topic. And it couldn’t have been a better idea to put this valuable information on the platform of IISE – the hub of industrial engineers.

In this blog, we’re going to talk about salaries of industrial engineers. The good thing is we won’t just talk about salaries, but we will talk about the factors behind them.

Ultimately, a salary boils down to the three most important variables

  1. Experience: Are you a new to the industry or have had experience?
  2. Degree/Education: Have you earned a bachelor’s degree, master’s degree and/or a Ph.D.?
  3. Location: Where are you working? Atlanta, New York, Mumbai, Chicago, Delhi, Seattle, Boston, etc.?

Of course, there are many variables like company size, performance review, interviewing skills, negotiating skills and others, but I consider the three listed above to be the most important in determining your salary.

I narrowed down the best major cities to work in based on a mix of data from reliable sources, references and my experience:

  • Atlanta, New York, Boston, Chicago, Austin, Houston, Los Angeles, San Francisco, Charlotte, Seattle, Detroit, Columbus (Ohio).

I also took in-depth data from salary.com since it provides reliable information regarding the actual salaries that industrial engineers are offered. I also factored in my experiences after working in four states for seven different employers as well as interviewing with almost 50 companies.

Zubin_Pic2

Source: Salary.com

Let’s begin.

Graph 1: Salary Analysis for Bachelor’s Degree Recipient with Experience by City

Below you see that “x” is the years of experience. Typically:

  • A recent college graduate has less than one year of full-time work experience
  • A mid-level professional has anywhere between one to three years of experience
  • A senior professional has more than three to five years of experience

Zubin_Pic3

Highlights:

  • Highest Salary: $82,045
  • Lowest Salary: $59,543
  • Average Salary: $68,980

Graph 2 : Salary Analysis for Master’s Degree Recipient with Experience by City

Zubin_Pic4

Highlights:

  • Highest Salary: $83,236
  • Lowest Salary: $60,348
  • Average Salary: $69,847

Graph 3: Salary Analysis for Ph.D. or Advanced Degree Recipient with Experience by City

Zubin_Pic5

Highlights:

  • Highest Salary: $84,535
  • Lowest Salary: $61,233
  • Average Salary: $70,880

Key Takeaways :

  • Salaries lie between $60,000 per year to $70,000 per year: Almost all of the salaries fit in this range. When I started my career, 1) I never found a good resource to rely upon; and 2) The answers varied from $60,000 per year to $80,000 per year, and even $100,000 per year. But now you have a good benchmark by which you can compare.
  • Not all salaries will encompass the industrial engineering spectrum: As we know, the field of industrial engineering is broad. There are positions in supply chain, manufacturing, consulting, data analytics and more. What we have done in this blog is captured a bird’s eye view for the position of industrial engineering so we get a good idea for all of its domains.
  • Most high earning salaries come from the U.S. west coast: This is for obvious reasons and true for any major or job, so don’t confuse this when you do your math.
  • Your navigation map: Finally, take this analysis as your navigation map and not the final answer. At the end of the day your salary will be affected by many factors. Also, these numbers may change over time, but for the immediate future, you can refer back here and review to get a good benchmark to help you find the right job that pays well and starts your career.

About the Author

Zubin has a master’s degree in industrial engineering and currently works for a logistics company. He has experience in multiple roles among manufacturing, consulting, and data analytics. He aims to help aspiring industrial engineers in their career endeavors.

 

comment 0

Supply Chain Analytics offer big improvements to Lockheed Martin Aeronautics Company

ATTN: Business and company leaders whom aspire to excel,

Meet Drew Harnish, P.E. (B.S. and M.S., industrial and systems engineering, Oklahoma University), senior project engineer, Lockheed Martin

lm_logo

There are tens of thousands of unique parts that are assembled to make the F-35 Joint Strike Fighter and dozens of locations across the globe that require shipments of parts. The bulk of the part consumption remains at production sites but as more jets are delivered to our customers, there is increased demand for spares and upgrades at military bases around the world. The part demand requirements and associated supply replenishments are captured in our Material Requirements Planning (MRP) system which allows analysts and buyers to see tabular snapshots of real time demand and supply for any given site at any time. As our production rate begins to ramp to triple the current rate and more F-35 bases are stood up, it has become an increasingly manual task to make decisions on supply allocations when challenges arise.

Consider this scenario: a fielded jet is grounded in Arizona due to a damaged part and the base requests a spare to be delivered ASAP. An items analyst sees this request, notices a part available in the Texas warehouse that hasn’t been allocated to an aircraft, requests a transfer, and solves the problem. The base is happy. Easy, right? Next, a production buyer receives a notification that there is not enough supply on hand to meet discrete demand in the factory. The buyer is confused because there was enough on order and there have been no scraps on the line. The reallocation results in a part shortage on the production line, increased labor cost to work around it, and increased material cost to expedite a single part which had already been purchased as part of a block buy. Program management blames production, production blames supply chain management, supply chain management blames sustainment, and everyone is really doing their best to deliver for their customer. There are countless shortage meetings to heroically solve problems just like this on a daily basis. They all stem from the same two root causes; there is no single source of total aggregate part demand or a model to account for quantity risk from unplanned part demand. This happens to a lesser extent when parts are scrapped on the production line with no safety stock in place but is exacerbated when multiple locations introduce unplanned demand into the supply chain community.

drewharnish_lm

In 2014, Lockheed Martin Aeronautics Company stood up an Enterprise Integration team. This team combines the use of advanced analytics and predictive modeling capabilities with operational excellence skillsets to solve systemic problems that cut across functions. One of the first projects our team led was a multi-pronged demand and supply planning optimization effort that eventually resulted in the creation of a new organization with new capabilities. As one of five project leads, I was responsible for leading the team that developed standard business rules for solving demand/supply problems like the scenario discussed above using newly developed predictive analytics. The primary analytics model was an integrated risk-adjusted demand requirements model referred to as “Big D.” This tool aggregates all of the discrete part demand data from our MRP systems and stacks a risk-based order quantity (RBOQ) factor on top which is modeled from historical experience such as scrap, repair, unplanned field demand, and other less-than-lead-time demand. The integration of this data with the risk models allows Big D to plot two critical visuals: 1) a month-over-month demand profile from all sources including overall risk for each part which allows the user to identify parts needing stop-gaps such as safety stock before long term fixes can be put into place, and 2) a month-over-month supply profile that shows discrete and risk-adjusted line of balance (netting of supply vs. demand) to highlight areas where MRP shows healthy supply plans but the risk model indicates high potential for problems.

For three pilot areas, we analyzed the line of balance 60 days outside lead time for over 100 unique part numbers. Using the data from Big D, we developed a decision tree to triage each part based on its risk factors in order to categorize short and long term solutions for recurring issues. The methods we developed formed the basis for a new supply plan health analyst role in the company that did not previously exist. There are now multiple analysts dedicated to analyzing part issues just outside lead time to mitigate shortage risks before problems occur. This has resulted in an increase in collaboration between our production, sustainment, and supply chain teams.

The overall project team responsible for leading this enterprise transformation was over 70 people from all facets of the business and a number of consultants. The specific team discussed in this case study was made up of 16 people from Lockheed Martin and Deloitte Consulting, 5 of which were industrial engineers.”

Industrial and System Engineers provide incredible value to any organization in any industry and I am really excited to share these stories and inspire you and your company to hire ISE’s.

Blessings to you all!

Best Regards,
Michael Foss
President, Institute of Industrial and Systems Engineering
www.iise.org

comment 0

When to Fear the Sudden Fall

When there is a sharp drop off down from a cliff, we instinctively fear the heights and the risk. The same emotions kick in when the stock market indexes fall, triggering a fear of loss. And there are other applications where you should worry about the sudden drop in your trend lines.

A sudden spike on a statistical process control chart may be an outlier if it only happens once. Repeated spikes or a new plateau represent a problem, though whether with the machine or incoming product requires further study.

An unexpected shift in customer satisfaction levels and similar marketing metrics means there is a problem, though whether your marketing campaign went unexpectedly viral or the new product release bombed requires asking more questions.

A dramatic change in cycle time represents a problem if it wasn’t planned, such as a shift in the product mix or new personnel training. Are key staff occupied and leaving machines waiting for material or unloading of completed product? Is equipment breaking down? Has some critical item gone offline, leading to a bottleneck?

Sharp drops in profits can occur when there is a major increase in expenses like a legal settlement or financial charge off or sales dropped off significantly. Any sudden drop you didn’t expect is a threat to your business’ survival.

Sudden drops and spikes in IT metrics may represent hack attacks taking up all the bandwidth or someone copying your database before taking it with them. Any dramatic and unexpected shifts in key IT metrics should be investigated by system administrators – and if you weren’t planning maintenance or found a major problem like a downed server or super-user consuming all the resources, IT security.

Sudden changes can happen and sometimes do, but we need to pay attention to them as often as we do gradual changes in order to prevent the fall from becoming a crash landing, in any application.

comment 0

Improving Access to Care through Decision Support Algorithms

ATTN: Business and company leaders whom aspire to excel,

Meet Freddie Weiss (B.S. industrial and systems engineering, Georgia Tech), Beth Garcia, Anita Ying, M.D., Laura Burke and Gwen Tate

MD Anderson Cancer Center

Improving Access to Care through Decision Support Algorithms

The University of Texas MD Anderson Cancer Center, based out of Houston, Texas, provides cancer patient care, research, education and prevention.  MD Anderson has been ranked the leading cancer hospital for 11 of the past 14 years by the U.S. News & World Report’s “Best Hospitals” survey.  The hospital provides over 1 million outpatient clinic visits, treatments and procedures each year.  In FY15, the hospital processed approximately 68,000 new patient referrals with 39,000 new patients seen.

The goal of this project was to improve the patient experience through ease of access and to reduce the time between initial contact and the scheduling of a new patient appointment where appropriate. Previously, an appointment could not be offered to a patient until medical records were received and reviewed by the clinical team to determine if it was medically appropriate for the patient to come to MD Anderson and if yes, which service and provider the patient should see.  This process could take anywhere from 3 to 15 days.  Data on cancellation rates showed that the top reasons for cancellation were “Unknown,” “Shopping for Options,” and “In Treatment Elsewhere.”  Although there were some inconsistencies with how the cancellations were labelled, these reasons pointed to the same issue.  By the time the staff member contacted the patient to schedule the appointment, many times the patient had already elected to seek treatment elsewhere.  Historically, the hospital had a 35%-40% cancellation rate.

The team used a systems engineering approach to evaluate the new patient process.  The team evaluated the process using a process map to identify key areas for improvement and focused on the desire to provide a patient with a new patient appointment during the initial call.  A pilot study was conducted in the Endocrine Center to establish proof of concept.  As the access staff were not clinical personnel, an algorithm (or decision tree) was needed to aid the staff in determining when an appointment could be given without additional medical review and with which service/provider the patient should be scheduled.  The team then worked with the provider team to document the clinical decisions made when reviewing medical records. Algorithms were designed to allow access staff to gather relevant medical information from the patient.  Where appropriate, the staff could then schedule a new patient appointment for patients who did not require a more detailed review.  When developing the questions for the algorithms, the medical staff focused questions on information that the patient would readily know, for example, “Have you had surgery to treat your cancer?” and “Have you been told that your cancer has spread to other parts of your body?”

Additional tools were implemented to enhance the benefits of the algorithms, including:

  1. Scheduling priority and timeframe which indicated the timeline for scheduling appointments (i.e. within 5 business days).
  2. Physicians’ diagnosis preference list that identified which physicians see which diagnoses.
  3. Terminology guide that defined which diagnoses were included in the algorithm and common terminology used by patients and referring providers (i.e. types of brain tumors).
  4. Standardized medical record request by diagnosis which outlined the medical records needed for the appointment (This document is sent to the referring physicians office).

These documents were combined with the algorithms into a toolkit for the access staff to use throughout the new patient process.  They were also used to train new employees to the process and measure performance.

The team monitored the percentage of patients that were given an appointment within one business day of referral initiation as well as the cancellation rate for new patient referrals.

After the implementation of the algorithm toolkit, the Endocrine Center saw an increase in the percentage of patients with an appointment created within one day of the referral to 69%.  The cancellation rate for new patient referrals to the center reduced from 53% to less than 15%.

Based on the results of the pilot study, the project scope expanded to include the remaining access centers. The institutional rollout began in November 2014 and as of May 2016, 10 centers have been completed and four are in progress. Preliminary data analysis has shown a similar trend with an increase in the percentage of patients with an appointment created within one day of the referral.  The team continues to monitor this metric as well as the referral cancellation rate. In addition to improving patient satisfaction and creating a more consistent experience, the project is expected to aid in staff training and retention, provide physicians with more time to focus on patient care, and help the institution capture a higher percentage of targeted patients.

Organization: The University of Texas MD Anderson Cancer Center

Team Members (L to R):

Freddie Weiss (B.S., ISE, Georgia Tech)
Healthcare Systems Engineer, Quality Measurement and Engineering

Beth Garcia
Director, Process Improvement & Quality Education

Anita Ying, M.D.
Associate Professor, Endocrine Neoplasia and HD

Laura Burke
Healthcare Systems Improvement Specialist, Quality Measurement and Engineering

Gwen Tate
Clinical Administrative Director, Brain & Spine Center


Industrial and System Engineers provide incredible value to any organization in any industry and I am really excited to share these stories and inspire you and your company to hire ISE’s.

Blessings to you all!

Best Regards,
Michael Foss
President, Institute of Industrial and Systems Engineering
www.iise.org