Category Archives: Distribution Center Warehouse Logistics

Latest Process Simulator and Process Simulator Autodesk Edition Updates

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We are pleased to announce the latest updates to the Process Simulator and Process Simulator Autodesk Editions.

Enhancements and Improvements

  • Improvements to window reload behavior (i.e. Scenario Manager window)
  • Improvements with add-in load behavior
  • Improvements with array stability when using 64-bit Office
  • Improvements with Output Viewer load speed
  • Licensing stability enhancements
  • Improved model error report
  • Updated localization

Material Handling and Autodesk Edition

  • Path Network refinement
  • Auto Orientation of Entities on Conveyors – users can now toggle on or off auto orientation of entities within Process Simulator Material Handling and Process Simulator Autodesk Edition

Download a 30-day evaluation of Process Simulator from the Process Simulator Eval Page

Download a 30-day evaluation of Process Simulator Autodesk Edition from the Autodesk App Store

Purchase a Process Simulator Autodesk Edition subscription from the new ProModel Online Store

Let us know what you think by leaving a reply below. Thanks!

ProModel and Autodesk – Partners in Optimal Factory Design

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Autodesk, the creator of AutoCAD® and Inventor® software, and ProModel have teamed up to bring you the best of both worlds of factory design and process optimization.  ProModel’s Process Simulator Autodesk Edition and ProModel Optimization Suite Autodesk Edition connect to Autodesk’s AutoCAD, Inventor, and Factory Design Utilities in order to streamline model building, process optimization and ultimately facility design.  You can get a 30-day evaluation version of both products from the Autodesk App Store

Process Simulator Autodesk Edition Eval

ProModel Optimization Suite Autodesk Edition Eval

Check out these great new Process Simulator Autodesk Edition videos:

Executive Overview – 2 min

 

Detailed Overview – 6 min


 

Product Launch – Process Simulator Material Handling Edition

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Aaron Nelson Product Dev. Manager Process Simulator

Aaron Nelson Product Manager

I am excited to announce the launch of the Process Simulator Material Handling Edition. This edition of Process Simulator now supports modeling material flow.

Along with the drawing environment being scaled, you now will have access to Stations, Conveyors, Path Networks and Nodes.

Image_PCS MH MFG Demo Model

Stations – A new type of activity created to enhance material handling. The station can have capacity or capacity can be turned off—designed to be used with conveyors.

  • The user can insert an on-board station into a conveyor.

pcs-station

pcs-station-properties

Conveyors – A new connector with properties to control how entities flow from activity to activity. Introducing stations in a conveyor will enhance the ability to control flow from conveyor to conveyor

  • Control speed of conveyors
  • Control distance of conveyors
  • Control orientation of entities
  • Control accumulation of entities

pcs-conveyor

pcs-conveyor-properties

Path networks – Resource movement can be added to enhance your model with travel, pick up, and deposit time.

pcs-path-networks-1

pcs-path-networks-2

Material Handling usage in a manufacturing environment is obvious, but maybe not as obvious in a Healthcare Environment, but it can be valuable there too.  One example is delivering prescriptions from the hospital pharmacy to a pick-up point on each floor, from where they would be picked up and delivered to patient rooms.

Image_PCS MH HC Demo Model

You can see additional features on the Process Simulator Material Handling Edition webpage.  You can also watch our introductory webinar on how to use this new edition on the website refresher course page

Thanks for your continued business and support.  We wish you and your family good health!  Let me know if you have any questions or comments on the material handling functionality.

Aaron

 

Predictive Variables, Artificial Neural Networks and Discrete Event Simulation in Manufacturing

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Rebecca Santos

Rebecca Santos ProModel Tech Support Engineer

During my Master’s program at BYU, I worked with a team to complete my thesis.  The work turned into a published article by the International Journal of Modelling and Simulation!  Since I work for ProModel, it’s only appropriate that I share it with my fellow simulation enthusiasts.  Please let me know what you think by commenting below.

Discrete event simulation (DES) is a powerful tool that can help users make better decisions. Over the years tools such as ProModel and Process Simulator have been developed to simplify the application of simulation, decreasing the learning curve and increasing its use. One of the advantages of simulation is that it is able to create lots of data with valuable information. However, the data analysis process can be challenging and relevant information may not be fully analyzed.

Observing the opportunity to more fully learn from the data, we decided to use data mining algorithms to help in the data analysis process, and more than that, to guide the modeler to the variables that most impact the outcome of the system being modeled.

The data mining algorithm picked was Artificial Neural Networks (ANNs) which has been good at learning from the data and making accurate predictions, according to the scientific literature. We applied ANNs to the data generated by the simulation model and we were able to create ANN models that could predict the simulation model results. The ANN models created were then interpreted and through the interpretation it was possible to rank variables according to their impact on the output results. This makes it possible for decision makers to know how to prioritize and where to place their investments.

Here is the abstract from the article:

This research used a discrete event simulation to create data on a shipment receiving process instead of using historical records on the process. The simulation was used to create records with different inputs and operating conditions and the resulting overall elapsed time for the overall process. The resulting records were used to create a set of predictive artificial neural network models that predicted elapsed time based on the process characteristics. Then, the connection weight approach was used to determine the relative importance of the input variables. The connection weight approach was applied in three different steps:

(1) On all input variables to identify predictive and non-predictive inputs

(2) On all predictive inputs and

(3) After removal of a dominating predictive input.

This produced a clearer picture of the relative importance of input variables on the outcome variable than applying the connection weight approach once.

You are welcome to access the published article at the journal’s website or the full original paper at the ProModel website.

 

Decision Support Tool Promotes Army’s Supply Chain Readiness

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Two Department of Defense publications recently ran articles on the Army’s continuous pursuit of  supply chain excellence.  Our men and women in the military cannot do their jobs without the right materiel at the right place, at the right time, in the right quantity, and the right condition. ProModel Corporation is the software developer behind the Decision Support Tool (DST) featured in both articles.

Learn more about ProModel and our products, in booth #200 at the upcoming AUSA Global Force Symposium and Exposition on Mar 26-28 at the Von Braun Center in Huntsville, AL.

Decision Support Tool Promotes Army’s Supply Chain Readiness

“DST gives materiel managers the capability to realign equipment on their property books to maximize readiness or fulfill high-priority requirements. A plan that once took days to create can now be completed in minutes. With just a few clicks of the mouse, property book officers can optimize their formation, synchronizing existing equipment on-hand against authorization.”

– Lt. Col. Rodney Smith, chief, Distribution Integration Division, DMC.

Read full Army.mil article

AMC (Army Materiel Command) Restoring its Atrophied Repair Parts Inventory

Using a new system called the Decision Support Tool that Army Sustainment Command runs out of Rock Island Arsenal, Ill., “for the first time in my career, we can really see ourselves, and so we know where every single piece of equipment in the Army is, whether you are in the active component, the Reserve component or the National Guard. It’s never been that way; it’s very powerful.”

Army Materiel Command – General Gus Perna

Read full Defense News article

ProModel AutoCAD App for Warehouses and Distribution Centers

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Steve-Courtney-100-x100

Steve Courtney, ProModel Sr. Consultant

I have several years of experience in supply chain and logistics modeling helping clients who have large warehouses and distribution centers.  These models are often very large (thousands or tens of thousands of locations), which can be very time consuming to model.  I’ve found the old adage to be very true: “Necessity is the Mother of Invention”, so I developed a ProModel App that is used from within AutoCAD which enables us to quickly build the graphical portions of the model using OLE automation.  This capability is also very useful when experimenting with several different layouts.

The types of Warehouse / DC modeling questions that can be answered include:

  • Slotting questions – where should my SKUs go?
  • Racking questions – which type of racking is best (flow rack, bin shelving, single pallet deep, double pallet deep, drive-in racking, etc.)?
  • How high should our racking go 5 levels, 7 levels, etc?
  • Which material handling devices are best – narrow aisle, forklifts, single/double/triple pallet jack, reach trucks, side loaders, clamp trucks, electric/propane/natural gas, etc.?
  • Staffing questions – how many of each type and when?

I recently gave a webinar on this topic which you can view here

The requirements for using the app include:

  • Current AutoCAD drawing
  • AutoCAD not AutoCAD Light
  • Know where each location is physically on the drawing
  • Location levels 2-X should be mapped to the level 1 location
  • Build indexed location file in the order you plan to add to the drawing
  • Know which material handling device accesses each location

If you would like to discuss this further, or have other ideas that can help us all improve warehouse and distribution center modeling, please comment below.  Thanks and Happy Modeling!

Thanks, Steve Courtney

 

Retail Has Transformed – Has Your Distribution Center?

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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.

Teaching Supply Chain Management with ProModel

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profshannonPatrick W. Shannon, Ph.D., is a professor of operations and supply chain management at Boise State University. He taught graduate and undergraduate courses in business statistics, quality management, lean manufacturing and other areas of operations and supply chain management. Professor Shannon developed a curriculum for his supply chain class, using ProModel Simulation which he used for over 10 years.

To provide you some insight into how you can use ProModel in the classroom, Professor Shannon was kind enough to allow us to share the materials he used.

Attached are PDFs of his course materials.

  1. Tri-Star Manufacturing: A Case Study in Lean Implementation
  2. The Tri-Star Simulation Model
  3. Project Requirements and Rules
  4. ProModel Instructions

Dr. Shannon served as dean of the College of Business and Economics from 2008-2014 and has lectured and consulted on statistics, lean manufacturing and quality management, project management, statistical modeling, and demand forecasting for over thirty years. He has co-authored 11 university level textbooks, and he has published numerous articles in such journals as Decision Sciences Journal of Innovative Education, Business Horizons, Transportation Research Record, Interfaces, Journal of Simulation, Journal of Production and Inventory Control, Quality Progress, and Journal of Marketing Research, Quality Management Journal, and The International Journal of Quality and Reliability Management.

He completed his BS and MS at the University of Montana and his Ph.D. in Statistics and Quantitative Methods at the University of Oregon. In 2015 he presented at the National Kidney Registry (NKR) Symposium in New York City. The presentation, authored by Shannon and Phil Fry, professor of operations management, is titled “Kidney Life Years” and describes the research Fry and Shannon have conducted with the NKR. The purpose of the research is to develop a statistical model to identify the donor characteristics that impact the length of time live donor kidney transplant will last.

Click here to view his LinkedIn Profile.

If you are a professor interested in learning more about ProModel’s Academic offerings, please email cbunker@promodel.com for more information.  You may also check out the following: www.promodel.com/industries/academic

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