Practical Simulation

 

At times, simulation software’s glitz factor has eclipsed its promise for testing material handling systems. Here are some common-sense ways to separate the cinema from the science.

 

by Christopher Trunk, managing editor

 

Simulation software can fall victim to its own strengths.  The software offers sparklingly realistic, 3-D animation that charms the eye. The challenge lies in that a snappy warehouse animation, loaded with hidden and untested production bottlenecks, can easily tempt the buyer. After all, it’s human nature to desire what is beautiful.

 

“Simulation can be all glitz and no substance, depending on who creates the model,” says Debbie Kotlarek, director of simulation services for HK Systems. Simple animations are a valuable educational tool in illustrating a complicated material handling concept. “But making simulation practical requires a mix of artistic rendering, expert engineering, accurate production data and statistics,” she observes.

 

Practical powers of simulation

 

Part of simulation’s practicality lies in its slight cost, in comparison to overall project cost, and in its ability to reduce buffer inventory, lop off unneeded machines from a design and prevent production snafus by reallocating workers and machinery — to name a few.

 

Simulation offers exceptional value when studying complex systems with multiple variables that change frequently. “Simulation is the only way to examine the capacity of each piece of equipment and find the pinch points created by an avalanche of inventory and insufficient capacity,” advises Brad Moore, sales director, retail division for Swisslog N.A. “You can also test for pinch points when all machines hit peak throughput simultaneously,” he says.

 

Simulation’s specialty is examining those peak levels. “Simulation can determine just how many inventory items are generated in a peak situation and how frequently that peak occurs — maybe once every two years,” Moore says. “Then, it’s a determination of: do you overbuild your facility to handle those rare spikes, or do you make sure the devastating peak doesn’t hit,” he adds. Tweaking the model can rearrange machinery and reallocate staffing to eliminate these production earthquakes.

 

John Sidell, co-founder of esynch, a consulting and systems integration firm, applies simulation’s educational powers. Sidell guides executives through automated facilities, prompting them to want the same material handling systems in their plants, too. “Simulation shows executives how a box would flow from receiving through storage, orderpicking through shipping. It creates a glass-window view of automation, and that’s valuable in breaking down a monolithic system into understandable chunks for people,” adds Sidell.

 

Know your data

 

Rule of thumb: Bring good data. Typical production data is critical to a realistic simulation. Kotlarek says, “Oftentimes when customers bring data, they’ve never glanced at it. In discussing data, customers point out how it doesn’t reflect their order streams, the contents of trucks at the dock, etc.”

 

The data need not be cumbersome or be intensely detailed. “Gathering can simply mean asking an experienced worker how often a machine goes down and how long it takes to repair. Good data doesn’t have to come out of a computer,” observes Kotlarek.

 

Question your objectives

 

Here are sample questions to bring to your first meeting with a simulation vendor:

 

• What if my workers change the way they take breaks?

 

• What would happen if our receiving all took place on one shift, and then the contents of the truck changes?

 

• How would throughput change at various levels of production?

 

• What happens if a machine breaks down? How long would it be until we’re stacking inventory on the floor?

 

• What can happen if my order mix changes?

 

These and many more questions are fair game for simulation. Kotlarek warns that most customers develop questions only after the software model is complete. “Then they ask, ‘What would happen if we just had five people at this station?’ To answer that question would require rebuilding the entire simulation,” she laments. Her advice: Come armed with a fistful of variables — factors that could affect operations.

 

Optimize versus analyze

 

“Flashy graphics have grabbed too much attention over the years, and I think some who simulate may have forgotten the real reason: to better understand how a particular material handling system operates under specific conditions,” says Matt Rohrer, director of simulation products and services for Brooks Automation.

 

Systems vendors who perform simulation are careful never to use the word “optimize” or “optimal.” Even with great simulation success stories like the U.S. air traffic control system, federal highways and major telephone networks, vendors point out that simulation provides one result for a given situation.

 

Kotlarek says “other software packages may take a simulation through many iterations, but at HK we’re modeling material handling systems, not looking at a million alternatives.” It takes an engineer to first develop an efficient design. Then software’s task is to refine it, detect and rework bottlenecks.

 

Balancing efficiency and service

 

“At times, efficiency and customer service are opposing factors,” says Jan Young, director of business development for Catalyst International Inc., “and measuring the acceptable amount of each is a tough choice.” Rather than rely on a worker’s gut feeling about a procedural change, Young suggests you simulate a change’s impact on both handling efficiency and customer service.

 

Young discovers that managers commonly make changes to a material handling system without recognizing the second- and third-order impact. “They perform a static engineering study, proving efficiency. But later problems crop up with replenishing new picking locations and keeping them staffed. It hurts customer service, causing a net loss over the previous arrangement,” adds Young. A simulation would have waved red flags over how a small change in one part of the warehouse can damage both throughput and service.

 

Convinced?  Ted Clucas, vice president of systems engineering for Alvey Systems, advises, “In a large project with a host of integrated automated material handling systems, inventory management, in-process manufacturing, kitting, sequencing or value-added work, simulation is the best, single tool for practically proving your concept.”

 

With simulation as your backup on an expensive and complicated project, it’s the science you’ll need to make the confident decision.

 

For more on simulation, see “E-Commerce Retrofit: Manage Complexity With Simulation,” May 2001. MHM

 

 

 

Simulation Pushes Fast Forward

“The future of simulation is digital engineering,” says Jim Higdon, sales engineer for Ann Arbor Computer. He predicts integrating simulation software with other aspects of material handling engineering — creating electronic engineering drawings, testing them with simulation and carrying those results through to system controls. “The future is coming closer. Simulation is no longer a throwaway, one-time-use effort. The software is already moving into controls design and emulation, allowing controls to be tested long before a system is installed,” says Higdon. Advance testing snips time off the end of a project, saving money.

 

Debbie Kotlarek, director of simulation for HK Systems, also envisions that within 10 years, multi-purpose simulation software will embrace front-end, electronic engineering design through simulation testing, to emulation and finally to detailed software controls for running automated material handling equipment.

 

She says this leap will require that today’s separate engineering, simulation and controls computer languages be recast as a unified code.

 

Simulation is reality

 

“I remember 25 years ago when simulation was a batch job that lumbered for hours on a huge mainframe computer,” recalls Kotlarek. While the software has catapulted in speed, ease of coding and graphical interface, the purpose of modeling remains fixed.

 

Kotlarek says that using simulation as an ongoing tool is new. “Already, it’s easier now for the customer to simulate experiments and change data sets on his own,” she adds,

Higdon describes ongoing simulation breaking into the real world. “I’ve seen a Coca-Cola bottling operation in which a palletizer gantry robot builds different mixes of Coke products. A continuous simulation is run of the gantry robot’s arm to test the options for building each new, mixed palletload. The practical goal is minimizing time lost to excess palletizer moves,” observes Higdon.

 

Matt Rohrer, director of simulation products and services for Brooks Automation, spies the future of simulation taking shape today with industry templates. “I see simulation developers creating more easy-to-use templates that fit a specific industry, e.g., automotive, food and beverage, etc,” he says. Then an industry site can be more quickly plugged into a simulation without creating each model from the ground up. Rohrer believes this method dispels the mirage of a one-size-fits-all simulation.

 

 

 

Questions Answered

Contact these sources:

Clucas, info@alvey.com

Eskay, simulation provider, info@eskay.com

Higdon, jhigdon@jervisbwebb.com

Kotlarek, debbie.kotlarek@hksystems.com

Moore, brad.moore@swisslog.com

Rohrer, matt.rohrer@brooks.com

Sidell, john.sidell@esync.com

Young, jyoung@catalystwms.com

 

 

 

Smart Applications for Simulation

 

Reducing Damage and Downtime

At this major, Midwest daily newspaper, the top priorities are keeping the presses from starving and protecting heavy — but fragile — paper rolls from damage. The publisher considered an automated storage and retrieval system (AS/RS) to replace lift trucks that had been unwrapping and feeding paper rolls into the press. That process yielded damaged rolls and press downtime. “We simulated the AS/RS to determine how many AS/RS cranes would be required to meet throughput and how much motor capacity the machines needed. Then we tested ways to remove a paper roll’s outer cover safely,” says Jim Higdon, sales engineer for Ann Arbor Computer. Now, the AS/RS feeds paper presses without starving them and handles paper with care.

 

 

Cutting Equipment Purchases

A snack food manufacturer consolidated several plants into one Georgia facility. Management questioned whether its existing fleet of 24 automatic guided vehicles (AGVs) was sufficient to carry the increased number of palletized loads. The AGVs shuttle snacks from receiving docks to palletizers, to a pallet storage warehouse, to a finished goods AS/RS machine and finally to a shipping dock.

 

HK Systems located the simulation model it had originally used to design the 24-vehicle system and updated it to test for higher pallet throughput. “With AutoMod and Microsoft Excell software, we found that by revising the AGV schedule to better distribute the vehicles around the plant, the waiting at the automated handling stations was reduced,” says Debbie Kotlarek, director of simulation services for HK Systems. The manufacturer canceled plans to buy six more AGVs.

 

 

Capacity Hits 100%

 

At this Pennsylvania bottling facility, a mix of high- and low-volume inventory is handled, including dairy products, iced tea, water, eggs, etc. Swisslog simulated a 16-crane automated storage and retrieval system (AS/RS) to see if it could handle all cranes dumping product from one order simultaneously onto the conveyor system. The model proved the design had insufficient capacity. An engineering change now has eight cranes feeding one conveyor, and the other eight feeding another conveyor. “Simulation added practical value by saving the expense of reworking a final installation. It also increased capacity from 75% (37.5 cases/min) to the intended 100% performance (50 cases/min) — avoiding headaches for everyone,” says Brad Moore, sales director, retail division for Swisslog N.A.

 

 

Eliminating Bottlenecks and Overtime

This major warehouse and distribution center supplies goods to 7-11 convenience stores. Management sought to shorten the 11 hours it took to pick the day’s orders. Brooks Automation, a simulation vendor, tested alternatives to how workers pick from gravity flow rack into totes. The totes are placed onto take-away conveyor [Conveyco Technologies], and the conveyor sorts and shunts them to the right dock location.

 

Matt Rohrer, director of simulation products and services for Brooks, recalls, “The simulation verified the design of the main conveyor merge that releases waves of picked items onto the sortation conveyor (see top of next column). The model analyzed how to control the merge release, manage recirculation and combine split orders for better wave management.”

 

The simulation revealed that both improper sorter logic and misallocating workers created bottlenecks. Improvements included speeding the takeaway conveyor at a merge point to reduce the glut of recirculating totes, giving higher priority to certain merging conveyor legs and reallocating workers more flexibly. Picking fell to just eight hours a day. Daily overtime costs shrank, and order fulfillment rates jumped. Simulation service like this costs from $25,000 to $40,000.

 

 

Next-Day Delivery Assured

This manufacturer supplies service parts and promises next-day shipment using 30 workers with lift trucks and hand carts in a 150,000-square-foot, palletized facility. The company receives most of its parts at the inbound dock, manufactures some parts on site and has its share of stock orders and hot orders. Management decided to simulate various orderpicking strategies to improve the effectiveness of new warehouse management system (WMS) software.

 

“There are a lot of knobs and dials with any WMS,” says Jan Young, director of business development for Catalyst International. “We examined the geography and sequence for each picker and lift truck driver, and how many individual orders should fall into each batch pick. The simulation examined how each variable affected productivity and customer service, allowing for the best decisions,” he adds.