Retail Has Transformed – Has Your Distribution Center?

It seems everything changes so rapidly these days with technology leading the way.  What is interesting, though, is how technological changes create a ripple (tsunami) effect in other industries.  Take retail as an example, there is no doubt online purchases have increased and the negative impact that has had on certain retailers is evident.  After all, when is the last time you went to the mall?  Chances are if you are over 18, you can’t remember.

OnLine Purchases Rising

Online purchases are on the rise as demonstrated in the chart above. The impact on some retailers has been bankruptcy filings and the closing of retail brick & mortar locations around the country; Aéropostale, JC Penney, and Sears just to name a few. http://time.com/money/4386499/retail-stores-closing-locations/

What’s the Difference?

Because so many purchases are now online, retailers are facing shipping smaller quantities of goods more frequently.  These shipments will go either direct to stores or direct to customers. Retailers must make accommodations for these changes and adjust their strategies in order to remain successful.  Those retailers who make the appropriate adjustments will have a higher chance to succeed.

So just how do you adjust your existing distribution centers to accommodate these changes? Shipping individual orders to customers or retail stores requires greater speed and accuracy. Distribution operations managers have realized that in order to achieve these greater speeds with more accuracy they must add high-speed conveyors, high-speed sortation systems, robotic palletizers, different picking & packing solutions, etc.

As distribution operations managers make these adjustments and choose the right equipment for their facilities, it also becomes very important to optimize the use of the chosen equipment.  Often, it is not until a state-of-the-art facility is up and running until management really understands how it works and how all of the complex parts and pieces come together.

What Can You Do About It?

This is where predictive modeling can be valuable.  As with any complex system, it is difficult to see and understand all the interdependent cause and effect relationships and overall system behavior.  For example, the tote size and number can affect high-speed conveyor performance, which in turn can affect the packing and shipping processes.

Enter predictive modeling.  A predictive simulation model of your DC can help you understand many aspects of system behavior including:

Order Mix:

  • What percent of orders use full pallets or full cartons?
  • What is the percent of single unit shipments?
  • What is the typical order size?
  • How many line numbers in each order?

Service Level Performance:

  • Do we need to offer overtime or use seasonal staffing to handle seasonal volume?
  • How do we balance the shipping docks to evenly load the work for the stores we service?
  • How do we balance the stores that each distribution center in the network services?
  • Do we have enough people or equipment to complete the day’s work?
  • Do we have items slotted correctly so that the fastest moving products are closest to the shipping doors?

Additionally, a predictive model can help you identify areas for improvement:

Picking strategy:

  • Single order picking or multi-order picking?
  • Order consolidation?
  • Should you use pick waves?

Which in turn will help you determine the design of your pick line. 

  • Straight line
  • Branch or pick zone
  • Serpentine line
  • Pick to conveyor
  • Pick to light
  • Automated conveyors or carousels

Wrapping Up

This shift in retail shopping behavior and delivery expectations is not likely to end anytime soon.  If anything, it will continue to become even more individualized and immediate.  Has anyone had a drone drop off a package yet?   It will be hard for retailers to keep with us overly demanding customers. Maximizing the performance of your DC’s, warehouses and delivery network will likely have to be part of the equation.

Oilfield Equipment Manufacturer Optimizes New Facility Design

CHALLENGES

A leader in the design, manufacture, and supply of oilfield equipment had recently purchased land to build a world class manufacturing facility.  The new location would be designed to capture future growth but needed to be sized correctly; not a wasteful over-construction yet not too small at the same time.

The senior executive team thought simulation modeling would allow them to analyze their manufacturing processes, identify bottlenecks, capture productivity improvements, and properly size the new facility.  After a lengthy vendor sourcing exercise, ProModel Corporation was selected as the best provider to answer this modeling challenge.

OBJECTIVES

  • Model the existing manufacturing processes
  • Identify current process constraints using various customer demand scenarios
  • Simulate maximum throughput potential with the current processes and equipment layout
  • Using LEAN process improvement skills, simulate a more productive manufacturing process and scale that upward to capture growth
  • Simulate the new manufacturing facility and validate the desired growth rates. Upon completion of this step, the layout would be given to the architects for structural design.

VALUE PROVIDED

  • Immediate identification of a critical bottleneck that once resolved, increased cell throughput by 53% and overall production by 19%
  • Throughput has grown 45% since the launch of the initiative due to a much better understanding of their manufacturing methods and related constraints
  • Manufacturing standards used by the production planning team were far from accurate thus creating a workflow imbalance
  • Equipment previously slated for purchase was determined to add no throughput benefit thus saving several hundred thousand in capital expenditures
  • Numerous future state layouts were modeled thus allowing the team to ultimately select the most productive equipment arrangements
  • The simulation model became a powerful sales tool with customers; understanding the flow in the facility and how it could absorb their incremental orders
  • Even during a severe industry downturn, the company continued to capture market share due to improved manufacturing methods.

SOLUTION

A ProModel senior consultant worked with the engineering staff to build dynamic models of their current production facility and planned future construction.

First, a dynamic flexible model of the existing facility was created and validated.  That model was used to define the true capacity of the existing facility, analyze current constraints, evaluate capital improvement options, and test new LEAN concepts that were under consideration for the current and future facility.

A major challenge to creating the model was accommodating the tremendous variety of products manufactured.  A user friendly interface for running the model was developed to provide the ability to run any variation of mix/demand against several operational configurations.

The key learnings from the existing facility model were then applied to the new facility design.  Alternate facility layouts and new material handling concepts were evaluated to ensure the plant of the future would meet all capacity targets.

3D Animation of a Portion of the Plant

3D Animation of a Portion of the Plant

 

An Intern Walks into ProModel

jenn-ross-headshotInauguration Day

There it is. I can see it. Sitting there, small and shiny on an oversized executive style desk is an ominous call bell. Do I ring the bell?  Maybe that is a little weird. Maybe I should cough loudly. Maybe I should shuffle forward quietly. It is my first day as an intern at ProModel and I am nervous. I am relieved from my quandary and greeted warmly by the head of marketing. We tour the office and I shake hands with my new associates. My nerves simultaneously fade as each new face brings kindness and words of welcome.

My first day brings many small challenges and small victories. The nine seasons of office experience I had previously acquired did not have me completely prepared for this new journey. Apparently, many things go into working in an office. Things I did not learn from watching “The Office” on NBC. I learned to push the little red tab in on the hot water spout at the water dispenser and learned the number to dial before you dial the number on the office phones.

I receive a briefing from two passionate and experienced model builders. Discrete event simulation’s ability to predict outcomes under uncertainty leaves me feeling impressed and hopeful. Modeling can aid in reducing patient wait time, improving various military operations, increasing throughput in a warehouse, and solving real world problems. There appears to be no limit to the industry or circumstance in which modeling can provide clarity. There was even one story about a model built for pigs.

jenn-ross-in-her-office

Looking Ahead to My First 100 Days in My Not-So-Oval-Office

This internship will allow me to dabble in many fields. I am looking to launch my career and discover the route that best fits my strengths and interests. I will explore and experiment in model building, finance, marketing, and customer relations. The friendly, team-oriented work environment encourages me to find ways to add value to the company. There is some grunt work involved in being an intern, but there is also the opportunity to play with cardboard boxes and organize filing cabinets.

When the head of marketing first came to me and asked me to write a blog post, it scared me a little. My apprehension quickly turned to excitement as I reflected on my first few weeks experience. This post has been easy to write, as the experience thus far has been superb and the employees could not be kinder.  I anticipate writing future blog posts as my internship progresses!

Jenn Rosscat-1999679_1280

Happy Holidays to You and Your Families From the ProModel Family!

Keith Vadas, President & CEO ProModel Corporation

Keith Vadas, President & CEO ProModel Corporation

The ProModel family would like to wish you and your families a very healthy and happy holiday season!  We thank you for all your support and business this past year and hope we have helped you meet or exceed your performance goals for 2016!

Some of the 2016 highlights include:

  • Awarded a contract for the Joint Staff Operations Directorate requirements associated with GFMDI (Global Force Management Data Initiative)
  • Army LMI-DST (Logistics Materiel Integrator – Decision Support Tool) achieved the major milestone of being listed in the Army Regulation (AR) 710-1 report as “the authoritative source to synchronize the distribution and redistribution of materiel in accordance with Army priorities and directives”
  • Awarded option year for AST which will expand the application to the unclassified network
  • Launch of FutureFlow Rx™ a brand new Patient Flow Analysis solution
  • Significant enhancement of our ship building capacity planning tool
  • Collaboration with the Orlando VA to improve healthcare access for our Vets

As many of you know, we have an extremely dedicated team of customer account representatives, consultants, software developers, and technical support engineers always available to help your organization meet the next business challenge. Looking ahead to 2017, we anticipate another exciting year of launching new products and enhancing current products and services.

Please let me know if you have ideas for products or services that would help you improve your business processes in the comments section below. Thank you, and I wish you and your families a happy holiday and a Prosperous new year.

Best Regards,

 

Keith Vadas

President & CEO
ProModel Corporation

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Just-In-Time for the Holidays!

Kevin Field, Senior Product Manager, ProModel Corporation

Kevin Field, Senior Product Manager, ProModel Corporation

A few weeks back, prior to the Thanksgiving holiday (in the USA), we released Service Pack 1 (SP1) for the 2016 version of our products. For those of you that know the purpose and traditions of this holiday, you also know it is a time to show gratitude as well as a time to feast on turkey, mashed potatoes and gravy, and pumpkin pie. The timing of the release definitely made the holiday more enjoyable for us and enabled stress-free indulging.

However, the release really wasn’t for us but rather for you, our customers! So with more holidays to come this month, I hope you are able to feast on the new features and capabilities we have introduced in the 2016 SP1 release. It is focused around allowing you to capture, collect and extract custom statistics for your process improvement and analysis. Starting lightly with seemingly subtle changes and implementation of logic functions to the real “meat” of a new API for statistics extraction, there really is quite a bit to consume.

(To learn more about the SP1 release of ProModel and MedModel, please see the What’s New and Release Webinar sections below. If you are a Process Simulator user, you can go to the Process Simulator “What’s New?” page on our corporate website and see a detailed description along with an accompanying webinar. )

What’s New in the ProModel / MedModel 2016 SP1 release?

Resource Distance Traveled Statistics

The distance your resources travel over the course of a simulation is now collected and reported in Output Viewer. It is displayed as Total Distance Traveled in Resource Summary reports and tracks the overall distance traveled for individual units.

Identify Captured Resource Units

In addition to determining which resource you’ve captured, you can now find out the specific unit of that resource as well. With the new OwnedResourceUnit() function, you can get the unit number of any resource owned by an entity. This is useful when collecting custom resource statistics at the individual unit level.

In-Process Resource Utilization Statistics

Access the utilization of your resources at any time during simulation. Using the newly modified PercentUtil() function, you can find out the utilization of individual units of a resource or a summary of all units of a given resource type. This allows you to make dynamic logical decisions or write out custom statistics to a CSV or Excel file.

Utilization for a specific resource unit

Utilization of all units for a resource type

Quickly Access Element Definitions

Have you ever been writing or viewing logic, and wanted to quickly see the definition (or details) of a specific subroutine, array, location, etc.? Well, now you can! Simply highlight that model element in logic, press the F12 key, and you will be taken to that element’s specific record in its edit table. For example, if you highlight a subroutine name in logic and press F12, you will be taken to its exact record in the Subroutine table.

Programmatic Export of Statistics

The statistical results of your simulation runs can be programmatically accessed through a new API to Output Viewer. You can get the raw data, down to the individual replication, or have it summarized or grouped (just like in Output Viewer) prior to accessing it. Either way, you can access your results, for example to load into Excel or a database, for analysis outside of Output Viewer.

As an example, this is a Time Plot in Output Viewer where the time series data is averaged over a custom period of 15 minute intervals.

Using the new API, you can have Output Viewer summarize the data into 15 minute intervals prior to exporting it to Excel.

The exported format makes it easy to create a Pivot table and Pivot chart in Excel.

Enhancements

  • When exporting Array data at the end of simulation, the replication number is no longer written out in the Excel Sheet name if “Export after final replication only” is checked.
  • Minitab version 17.3 is now supported

Release Webinar

We even followed up the release with a live webinar showcasing the new capabilities of SP1. So, if after devouring the descriptions above, you still find yourself hungry for additional details, head on over to our YouTube channel or view the webinar directly below.  You can also log into our Solutions Café to have a second or more complete helping of SP1.

And Happy Holidays!

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Brazilian Academic Simulation Awards Given in Honor of Rob Bateman

ProModel friends and associates, last October 12 we lost a dear friend, Rob Bateman and it is very hard to believe that a year has already passed.  Coincidentally, just a few days before the loss of our colleague, on October 6, 2015, the first ever ‘Rob Bateman’ award was delivered in the city of Joao Pessoa (north east coast of Brazil).  Here is the web site of the event:  http://www.abepro.org.br/enegep/2016/index.asp.  The Simula Brazil is a national award for simulation systems, organized and hosted by the portal “www.simulacao.net” which is sponsored by the Belge Consulting (www.belge.com.br). The award has institutional support of ABEPRO (www.abepro.org.br) and SOBRAPO (www.sobrapo.org.br) and is linked to the National Production Engineering Meeting (ENEGEP).

This award aims to encourage young students to use more simulation technology to develop projects and analyze real or fictitious situations through the use of the ProModel modeling and simulation technology (ProModel® or MedModel®) as well as assisting teachers with simulation education. The hope is that this practice will allow for better industrial engineering courses using ProModel and more simulation use in local companies, as well.  This year the award was given to the following recipients:

originalityaward

Marcelo Fugihara of Belge presenting the award for originality to Jacyszyn Bachega of Universidade Federa de Goias

 

complexityaward

Marcelo Fugihara of Belge presenting the award for complexity to Thiago Fernando Rosa Tedoro and Professor Jose Lazaro Ferraz of Universidade FACENS

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

all-students

Here is a photo of all of the students in attendance at the event, called Enegep –  Encontro Nacional de Engenharia de Produção

We hope that this award in some small way pays tribute to our friend Rob Bateman.

Your friend in Simulation,

Alain de Norman & Belge team.

Simulating Impatient Customers-Reneging

Dr. Farhad Moeeni - Professor of Computer and Information Technology

Dr. Farhed Moeeni – Prof. of Computer & Information Technology, Arkansas State University

GUEST BLOGGER – Dr. Farhed Moeeni

In an earlier blog I discussed some of the issues related to modeling customers’ behavior; such as impatience, especially the balking behavior.  Customers’ impatience can lead to certain behaviors such as balking, reneging and jockeying.  Ignoring the case of balking at arrival caused by queue capacity, balking happens when customers assess the waiting line, develop some opinion about the required waiting time (likely by scanning the queue length) and decide whether to join the queue or not. In short, balking happens when the person’s tolerance for waiting is less than the anticipated waiting time at arrival.  Jockeying happens when two or more service channels provide identical services, independently and with distinct waiting lines.  A customer who is presently waiting in one of the queues chooses to leave the current line and join another queue in the hopes of less waiting time.

Reneging, however happens after a person joins a queue.  In other words, the initial anticipated delay was tolerable.  But later, as time passes, the person becomes impatient and abandons the queue without receiving the service because the prospect of being served within the initially anticipated time diminishes or the predicted remaining waiting time exceeds the customer’s tolerance threshold for further waiting.  Other incidents such as receiving an urgent call may also cause abandoning of the queue.  However, this or similar scenarios are not caused by impatience.

Researchers believe the tolerance threshold is not a constant and can change over the course of the waiting period.  For example, studies indicate that the probability of reneging decreases as the person continues to wait, but only for a certain amount of waiting time after which the customer starts losing patience and the probability of reneging increases.  Another interesting conjecture proposed in the literature is that as the number of people behind a waiting customer increases, the likelihood of the customer reneging decreases.  The decision to renege is also influenced by other factors, for example, personal differences, prior experiences with similar situations, the critical nature of the service to be received, attributes and atmosphere of the waiting area’s surrounding (service-scape), and whether or not an alternative date or time for the same service or a similar service is available. Thus, the decision process for reneging is more complex than balking and modeling and simulating reneging is also more complex.

Obviously, it is not possible to directly characterize the complex behavioral aspects of customers into the simulation model.  Each reneging incident is the result of complex interactions among many factors and the result of a thought process that eventually causes a customer to become impatient and abandon the queue. In other words, reneging cases are not random events; however, from the perspective of a simulation modeler or an observer who is watching the queue, the incidents may look like a random process.

In discrete-event simulation, the modeler has a number of mechanisms or tools available. For example, the modeler may apply some probability distribution to characterize the uncertain behavior of customers or formulate some rules to trigger reneging during the simulation runs.  In addition to probability distributions, the modeler may also identify and apply a number of observable predictor variables to fine tune the reneging behavior.  These variables may include the waiting time of each customer in queue before reneging incidents, the position of each customer in queue just before reneging, the number of customers in queue behind the reneging customer at the moment of abandoning the queue, etc.

Incorporating the above mechanisms is not too complex from the modeling and coding perspective. On the other hand, it is difficult to collect relevant data from the field if the required information is not available. A number of obstacles can contribute to these difficulties. First, sufficient observations are needed to estimate the probability distributions, the shapes and parameters with adequate precision.  Unfortunately, in many instances, reneging does not happen often enough to yield sufficient observational data from the field within a reasonable amount of time.

Second, accurate data collection for events such as reneging may need sophisticated data collection procedures because each customer should be tracked while in the system and the values of relevant state variables along with other important information such as the “waiting time in queue before reneging” and “the position of the customer in queue” before abandoning the queue should also be recorded.   Only the information related to those customers who renege is needed. However, those who eventually renege are not known in advance or at the time of joining the queue; thus everyone should be tracked.  Such elaborate data collection system may be costly to implement, intrusive to customers, or may need consent and collaboration from people.  The latter may also influence the behavior and contaminate the collected data.

I discussed some of the important issues surrounding customer’s reneging behavior in simulation.  The focus of the discussion has so far been on physical queues or waiting lines in which customers should be physically present to receive the service.  Some of the complex characteristics and thought processes associated with physical queues may or may not apply to virtual queuing systems such as call centers, web servers, e-commerce sites and the like.  One simplifying advantage of modeling human impatience in virtual queues is that much of the data needed to model reneging behavior may readily be collected electronically without facing the obstacles explained above. Virtual queuing systems have unique characteristics and deserve a  separate discussion.

Check -out more information about Dr. Moeeni.  If you would like more information about ProModel Simulation and simulation studies about queues check out the ProModel services industry information. Contact ProModel to learn more.