How Warehouse Simulation Informs Supply Chain Choices

by   |   October 17, 2013 7:08 am   |   1 Comments

A Stingray robotic shuttle in a Warehouse Simulation

Stingray robotic shuttles in action at a warehouse. TGW Logistics Group photo.

At WIX Filters’ Master Distribution Center in Gastonia, N.C., automated robotic “shuttles” will soon be whipping along rows of densely-packed shelving, picking packs of filters to fulfill orders for the company’s automotive, agricultural and industrial customers.

Already producing more than 12,000 different product numbers, the robotic “Stingray” shuttles, sourced from Austria’s TGW Logistics Group, are part of a plan to boost the product range still further.

Related Stories

The role of analytics in the race for the supply chain of the future.
Read the story »

Visibility into real-time sales data keeps sandwiches moving at Greencore.
Read the story »

5 steps supply chain executives can take to harness big data.
Read the story »

More supply chain coverage at Data Informed.
Read the story »

“As we continue to grow our business and increase our product offering, the Stingray shuttle system is another step forward in customer satisfaction by providing smaller quantities on a consistent basis,” says Karl Westrick, WIX’s Master Distribution Center distribution manager.

Automation appears on its face to be a sound strategic move for a distribution center. But will it work? Turn the clock back a few years, and vast expensive warehouse automation projects were notorious for proving in practice to be inflexible gridlocks, a bottleneck in the flow of goods heading from factories to customers.  In the U.K., for instance, the high-profile collapse of a warehouse automation project almost ten years ago led to board-level departures and write-offs in the tens of millions of pounds at upmarket grocery retailer J. Sainsbury.  Further back, the failure of a warehouse automation project is seen as a contributory factory in the bankruptcy of FoxMeyer Drugs, then the United States’ fourth-largest pharmaceutical distributor.

But while people are far more flexible, and come with a much cheaper incremental pricetag, labor-intensive warehouse operations rapidly eat into profit margins.

Throw in the fact that warehouses need to be optimally located in order to balance inventory holdings against drive times for the trucks ferrying orders out to customers, and it’s clear that the design and placement of warehouses has a lot riding on it. Such factors have some big companies working to show they can gain competitive advantages through their supply chain prowess. Retailers, both online giants like and established brands like Wal-Mart, are facing off in a much-publicized battle to dominate the market to fulfill customer orders on a next-day or same-day basis from windowless warehouses (dubbed “dark stores”) scattered across the United States.

The Warehouse Simulation at Wix Filters

For its automated distribution center, Wix turned to computer simulations to anticipate performance issues and optimize its choices. Using AutoMod, a 3D graphical simulation tool, TGW Logistics Group’s simulation experts carried out a simulation project, modeling the operation of the newly-automated warehouse in intricate detail, like an animated, mechanized ballet.

TGW carries out such simulations for three distinct reasons, explains David Vessier, head of simulation at TGW. First, simulation provides an element of assurance that a proposed automation design is viable. Second, clients themselves may request a simulation, or want to have a model developed for them for subsequent use by their own people—a trend that is growing, he notes. And thirdly, simulation helps to optimize the use of resources, ensuring that the warehouse is equipped with the right number of fork lift trucks, for instance.

“We have a list of ‘what if’ scenarios, where we run simulations to examine the consequences,” says Vessier. “We cycle through the test scenarios, and look at the impact on critical key performance measures.”

At Wix, for instance, the simulations explored the interaction of shuttles and pick stations, and how various layouts impacted pick station performance. “The simulations allowed us to optimize the layout in terms of racking layout, pick station performance, and the allocation of orders to each pick station—as well as the retrieval of items from the shelving,” sums up Vessier.

Talk to those close to supply chain and warehouse management, and such simulations are on the increase.

Paul Wilson, consulting director at Davies & Robson, a specialist supply chain consultancy, says that increase partly reflects a different corporate agenda. A recession-induced era of retrenchment and cost-cutting has given way—as with the Wal-Mart and Amazon same-day delivery tussle—to one of expansion and warehouse-building.

But there’s also, he says, a growing awareness among supply chain executives that simulation can deliver real value, by providing answers to questions that could previously only be found by trial and error.

“‘How many forklift trucks do I need? Will a higher-grade forklift truck reduce the number of people required? What is the impact on the size of the pick face?’  The answers to questions like these can have an important bearing on operational performance and cost,” Wilson says.

A recent illustration: In Europe in 2010, a warehouse simulation exercise helped Coca‑Cola Enterprises to increase the volume of product moving through its Northampton, U.K., distribution center by 18 percent, using simulation technology from Lanner. The simulation looked at variables such as traffic flows, shift patterns, time of day, and different truckload configurations, and helped Coca-Cola create the optimum mix of lanes, bays and parking spaces for the center to cope with extra volume—handily also saving a reported £1.7 million (about $2.6 million) in operating costs, Coca-Cola reported.

Fitting a Warehouse Simulation into an Existing Supply Chain

As in the Coca-Cola case, the best warehouse simulations place the warehouse in the context of the supply chain in which it sits, adds Dan Bausch, director of analytics and product management at specialist consulting and logistics software firm Insight, of Manassas, Va.

“There’s the cost of getting products to the warehouse, the cost of passing them through the facility itself, and finally the cost of getting the product to the consumer: you need to look at all three aspects of the supply chain, and often companies just look at one or two,” says Bausch. “Look at all three, though, and you can reach a quite different conclusion as to how many warehouses you need, how large they should be, and where they should be located.”

In other words, look at the flow of product through the network, taking into account incoming transport costs, inventory holdings, required service levels, and outbound transport times and costs. Then size the resulting warehouse. And then model its detailed operation to make sure that it can efficiently handle the desired distribution network strategy.

“It sounds easy, but moving from strategic to tactical is more difficult than it sounds,” says Bausch.

Dominic Regan, senior director for value chain execution at Oracle, agrees. Oracle has recently supplemented its long-established Strategic Network Optimization simulation and network design tool with a product called Oracle Transportation Management, specifically to model detailed transport flows, and explore the effect of altered freight rates or network disruption. It takes a business’s existing operational database as its raw data source, working on real product and network flows, not assumed hypothetical ones.

“The demand for simulation capability is increasing, and in part it’s because there’s a lot more data available, and companies are asking themselves why, with all the data that they now have, they aren’t using it to better understand the costs and potential benefits of operational changes,” Regan says.

Synthetic Data for Simulations as Alternative to Real Data
Intriguingly, back at consultants Davies & Robson, simulations are going in the other direction, away from real data and towards a painstakingly built-up catalog of synthetic data, as used in time and motion studies.

“We’ve moved away from the warehouse simulation tool that we’ve traditionally used, because it was too data hungry, too time hungry, and increasingly too expensive for us to offer to our clients,” says consulting director Wilson. “A simulation with traditional tools took 20 to 40 man-days; these days we use spreadsheets and CAD tools, which yield a comparable result much sooner.”

And thanks to the use of synthetic data, says Wilson, “it’s not unusual for us to be asked to simulate every activity taking place in a warehouse—goods receipt, putaway, picking, replenishment of pick faces, packing and dispatch, working through a day or a week’s workload at 15-minute intervals.”

Which means that even before a warehouse is built, or a single sod of earth turned, its future managers can know exactly how it will operate, and what equipment to order.

In the competitive retail battleground being fought over by Wal-Mart and Amazon, both sides are tight-lipped about their plans for same-day delivery networks.

But warehouse automation is key to at least one of them: Amazon. The company last year acquired robotic warehouse automation specialist Kiva Systems in a $775 million deal. As its warehouse-building program rolls out, it would be surprising if simulation wasn’t informing executives’ decisions.

Malcolm Wheatley, a freelance writer and contributing editor at Data Informed, is old enough to remember analyzing punched card datasets in batch mode, using SPSS on mainframes. He lives in Devon, England, and can be reached at

Tags: , , ,

One Comment

  1. dcliquidators
    Posted December 29, 2013 at 3:31 am | Permalink

    Simulations can definitely give us a better idea of how things will run and if there are things that need fixing.

Post a Comment

Your email is never published nor shared. Required fields are marked *

You may use these HTML tags and attributes: <a href="" title=""> <abbr title=""> <acronym title=""> <b> <blockquote cite=""> <cite> <code> <del datetime=""> <em> <i> <q cite=""> <s> <strike> <strong>