Logistics Viewpoints

A complete analysis of logistics trends, technologies, and services

Kellogg’s Use of Automated Guided Vehicles

Material Handling | By •

The ongoing developments in intelligent and flexible material handling systems, which are basically robots, continue to fascinate me. For example, automatic guided vehicles (AGVs) used to be fairly dumb machines that moved from point to point by following a preset trail of magnets in the warehouse floor. Humans loaded them at one end of the warehouse and unloaded them at the other. But AGVs have gotten a whole lot smarter.   

At the RedPrairie user conference a few weeks ago, Tom Grohalski, a project manager at the Kellogg Company, talked about how Kellogg’s is using AGVs with forks on them from Egemin. AGVs take pallets from the end of the production line and stack them in a bulk warehouse. Other AGVs then take product from the warehouse and stage them on the dock. As a result, this warehouse needs very few people. However, “chasers” and “super users” are still needed. Some AGVs stop due to small obstructions, such as shrink wrap dangling down from a pallet, or they go off course and chasers needed to put them back on track. The “super users” handle IT glitches and issues. Kellogg’s also has forklift drivers on the dock to load the truck, but these drivers are now almost three times more productive. 

Here are some takeaways from Grohalski’s presentation:

  • Kellogg’s conducted simulations to figure out how many AGVs it would need to handle peak production in conjunction with various fulfillment scenarios.
  • The RedPrairie WMS tells the system where to put the product. The AGVs have a control system that contains a vision system (i.e., the AGVs could run without reading magnets in the floor), sensors, a digital map of the warehouse, and the move logic. The RedPrairie put-away configuration logic needs to make sure too much product isn’t being unloaded in the same zones; otherwise, the slow-moving AGVs (350 feet per minute) would have congestion problems. “AGVs are polite,” Grohalski commented, “they will patiently wait their turn.”
  • The dimensional pallet and warehouse layout master data needs to be very, very accurate.
  • Kellogg’s ran two pallets high from the production line and would stack pallets up to four high. Pulling from the warehouse to the staging area, AGVs might take one or two pallets. Consequently, there could be bays that would only have three high pallets. Thus, there was a tradeoff in using AGVs. Kellogg gained labor efficiencies but lost some ability to fully utilize all of its warehouse space. 
  • The pallets were scanned off the production line. After that the WMS knew their license plate and where they were to be stored and the pallets did not need to be rescanned in the warehouse.
  • Kellogg’s experienced big reductions in damage and improved safety. 
  • Kellogg’s expects a three year payback period and it is on schedule to achieve that target.

In conclusion, AGVs are becoming more like forklifts, and some forklifts are becoming more like AGVs. If you add a vision control system and control software to forklifts, they begin to act a lot like AGVs (see our posting on “New in Flexible Warehouse Automation: Real-Time Location Forklift Automation”). Meanwhile, AGVs, which used to just tug pallets or move carts, are beginning to act more like forklifts because the latest generation of AGVs have forks on them that move up and down and can stack pallets.

Cummins Filtration Implements a WMS, Seeks to Eliminate Physical Audit

Warehouse Management Systems | By •

Tom Kozenski of RedPrairie (an ARC client) sent me a video case study about a WMS implementation at Cummins Filtration that went very smoothly because of good project management: avoidance of customization, RedPrairie and Cummins folks that worked together seamlessly, and role-based training. 

The benefits Cummins reported are what I would have expected for a paper-based facility moving to a WMS: better labor productivity, increased throughput, and fewer costly shipping errors. However, there was one benefit mentioned in the video that I bet most public companies moving from paper to automation don’t include in their business case: eliminating the need to shut down the warehouse for two or three days to physically count all of the inventory in the warehouse.

The Sarbanes-Oxley (SOX) Act of 2002 was created in response to major corporate accounting scandals (e.g., Enron) that avoided the radar of traditional financial reporting and led investors to lose billions of dollars. The goal of this U.S. legislation was to improve reporting processes at public companies to prevent future financial misstatements. Section 404 of the act is what applies to physical inventory audits.

Section 404 tightens the internal controls at companies. Auditors pay close attention to control points. In the warehousing area, this leads to questions such as “How robust is your cycle counting program and does it affect the amount of inventory you have on your books?” In tightening internal controls, the key issue is materiality. In other words, will the lack of a process control materially affect the financial results a company reports? Following the passage of SOX, more companies decided this was material and moved to physical inventory counts. 

However, a WMS provides strong process control. And if you implement it in a way where a certain number of daily cycle counts are performed, and you can document very high inventory accuracy, accountants are usually willing to agree that a manual audit of the warehouse is not necessary. 

And there is ROI associated this. This is three more days of the year where the warehouse is shipping product and labor is being used for productive purposes.  And this is three days you don’t have to pay outside auditors to supervise the inventory count.

Bottom line: If you are a public company and are considering implementing a WMS, don’t forget the elimination of physical audits as an ROI bucket.

Big News from RedPrairie’s RedShift 2010 User Conference

Cloud/Software-as-a-Service | By •

RedPrairie, an ARC client, held its annual user conference, RedShift 2010, in Orlando last week. It was the company’s largest user conference ever. The two biggest news items from the event were the CEO’s explanation for why RedPrairie was acquired earlier this year by New Mountain Group and RedPrairie’s acquisition of SmartTurn, a software-as-a-service (SaaS) multitenant WMS solution.

RedPrairie had been owned by Francisco Partners, a private equity firm, and the company had filed to go a public last November, which would have allowed Francisco Partners to cash out after owning RedPrairie for five years. Then in February of this year, RedPrairie announced that it was being acquired by a fund affiliated with New Mountain Capital, another leading private equity firm.

Mike Mayoras, RedPrairie’s CEO, said that being funded by a private equity firm had worked well for the company. It had allowed them to grow faster than the overall market and to expand their product offerings. He views this acquisition by New Mountain Capital as a continuation of the previous strategy and the company intends to follow much of the same script. RedPraire wants to grow both via acquisitions and organically from existing products. It doesn’t want to become a company focused on rollups; all acquisitions should complement and expand its product offerings or allow it to expand its global reach. RedPrairie is particularly interested in product offerings that increase its market share among retailers.   

In recent years, RedPrairie has acquired retail store operations solutions based on a SaaS payment model. The company intends to continue growing the percentage of its revenues generated from that model, which brings us to the other piece of news coming out of the conference: RedPrairie’s acquisition of SmartTurn.

While RedPrairie’s retail solutions are sold in a SaaS payment model, they are not multitenant. SmartTurn will be its first multitenant solution, which the company will rebrand as RedPrarie’s On-Demand WMS. RedPrairie does not have plans to make its flagship WMS or TMS products multitenant at this time. RedPrairie remains willing to host its flagship WMS at offsite facilities under its control and let customers pay in a monthly leasing arrangement.

The SmartTurn acquisition is complementary. RedPrairie’s installed base is mostly large companies with complex warehousing solutions. SmartTurn is an extremely low priced solution ($1,200 per month per facility for unlimited users, $4000 in implementation fees) that has been sold primarily to small companies. The traditional RedPrairie WMS solution offers payback in the form of labor productivity (e.g., slotting, labor standards, efficient moves through the warehouse, order clustering). The SmartTurn solution offers payback for facilities currently operating on paper pick sheets. By using SmartTurn and implementing RF their customers gain virtually perfect inventory visibility, which results in fewer returns, not ordering product the company already has, less obsolescence, and other benefits. 

SmartTurn customers get a relatively large, financially sound parent. The SmartTurn organization, however, will remain intact and the group will continue to operate out of Oakland, CA. SmartTurn has sold its solution via telesales and this will continue as well. 

But the RedPrairie sales force has a new product in their portfolio that they can position to existing clients. Many of RedPrairie’s large clients have many small storage locations—e.g., depots, storage in backrooms at stores, pop up facilities designed to handle seasonal storage—where implementing a traditional WMS does not make economic sense. The On-Demand solution will make sense for these types of facilities. Based on the amount of foot traffic at the On-Demand booth, it appeared that many existing customers were exploring this idea at the conference. 

In the longer term, big companies might explore the On-Demand solution in a different scenario. If they want better upstream inventory visibility into their supplier base, this multitenant solution is something they may decide to mandate to their suppliers as the price of doing business with them. Better upstream inventory visibility will allow these large multinational companies to better synchronize their supply chain.

In conclusion, I enjoyed the RedShift show. There was a Midwestern sensibility about it – low on glitz and hype, high on product content—that made you feel RedPrairie is a partner you can trust.

Different Approaches to Dynamic Replenishment

Inventory Management | By •

I’ve been writing about the Demand Signal Repository (DSR) market a fair bit recently (see, for example, “How to Best Describe Demand Signal Repositories”). DSR solutions leverage a variety of different types of downstream data, including POS data, to power more robust and dynamic forms of replenishment. 

As the saying goes, “everyone talks about the weather, but nobody does anything about it.”  Well, when it comes to supply chain management, most companies know about the bullwhip effect, but very few do anything about it. Dynamic replenishment, based on downstream data, finally offers a way for companies to “do something about it.”

When it comes using short-term forecasts for responsive replenishment, software solutions approach this in one of three ways. First, it can be done with flowcasting methodologies. In flowcasting, a store-level forecast by SKU is done. This is converted into a forecast that is propagated upstream from the stores and aggregated into replenishment requirements. This is how RedPrairie (an ARC client) has approached the problem. RedPrairie claims that a technology breakthrough allows its solution to efficiently handle the mountain of detailed data involved in this process. André Martin, the CEO of RedPrairie Collaborative Flowcasting Group, has worked with a major food company using this methodology. For related commentary, see “Eliminating the Bullwhip Effect across the Extended Retail Supply Chain.” 

A second approach taken by some software vendors is to mimic store-level ordering policies. These solutions contain store order policy logic that anticipates what a store will order from the consumer goods manufacturer. One Network (a Logistics Viewpoints sponsor) is the primary practitioner of this style of responsive replenishment. One Network provides both the core data repository and supply chain applications in a software-as-a-service (SaaS) model. The data repository contains retailer and supplier master data, transactional data, and the stores’ replenishment policy information. Del Monte is a One Network client that has publicly discussed its use of this framework. The company is leveraging both Walmart and Kroger downstream data to make better replenishment and transportation decisions. 

Terra Technology, who has had the most success selling short-term forecasting solutions based on downstream data, takes a third approach. Terra takes downstream data and uses pattern recognition. So, for example, for a large consumer goods manufacturer trading with Walmart, the Terra applications would have access to store inventory data, the store forecast of sales, distribution center (DC) withdrawals, DC inventory, and a DC’s open orders. The Terra solution looks at how to combine these data streams to produce the “most likely” forecast. One week out it may be that orders are most predictive. Two weeks out it might be DC withdrawals that have the most influence on the forecast.  Three weeks out, it might be POS sales that have the most predictive power. These weightings can differ by product, and product weightings might also differ based on the warehouse territory they are located in. 

Terra has been very successful with this approach. Procter & Gamble (P&G), after piloting at least one other solution (in which it played the role of coproduction partner) became a client in 2008 and publically stated that the Terra solution allowed it to lower its inventory by $100 million without adversely affecting service.  P&G’s savings on this would be the carrying costs, probably between 15 to 20 percent of the $100 million every year.

In many ways, the DSR market is still immature. However, when it comes to using downstream data for dynamic replenishment, good solutions already exist.

Software as a Capital Expense

Cloud/Software-as-a-Service | By •

Earlier this year, some of my colleagues here at ARC wrote a Strategic Report titled “Capital Expenditure Survey 2009” (available to ARC clients only). The report got me thinking about the differences between how hardware and software are expensed, and how those differences affect a logistics executive’s annual budget. 

I talked to executives at some of our logistics software vendor clients, including i2 Technologies, RedPrairie, Manhattan Associates, and Tecsys, to see how their customers approached this issue. According to these vendors, companies typically depreciate software within three to five years, a much shorter timeframe than expensive capital equipment. However, the ROI model customers use to justify software purchases can be quite different. Manhattan Associates, for example, reports that in helping customers put together a ROI model it often shows costs versus savings, and the time value of money, over a five to seven year timeline. 

How do these budgeting issues affect logistics executives? In my conversations with them, almost all report that the annual budgeting cycle involves cost targets, and these logistics costs roll up to the Income Statement.

For logistics executives with private fleets, the Balance Sheet also comes into play. For these executives, one of their annual budget exercises is to look at their trucks and decide which ones to sell based on their book value, whether a truck is experiencing excessive downtime or increased maintenance or repair costs, and the opportunity costs involved (i.e., the costs of not investing in new equipment). New trucks might have better operating performance, in terms of fuel efficiency, safety, or emissions, so the differences in performance and quality between old and new equipment can be missed opportunities for performance improvements.

So, one key difference between software and hardware is that hardware has a book value, while software does not. The book value for hardware represents the resale value, which is reduced by annual wear-and-tear. Enterprise software contracts forbid customers from reselling their software and thus the software has no book value. Basically, there is an exit scenario for hardware that does not exist for software, and the Balance Sheet exercise described for private fleets would never take place for software.

The issue of wear-and-tear and opportunity costs takes a different form for software. Let’s take the wear and tear issue first. A company that buys a warehouse management or transportation management system would almost always purchase a maintenance contract, at least in the early years. The maintenance contract includes helpdesk support, which helps to keep the software up and running, and helps prevent downtime issues that can occur with aging equipment. If a company does not engage in periodic upgrades, the software might pass a point in time where helpdesk support is no longer provided.

The upgrades included as part of the maintenance contracts also include new functionality that allows for new performance improvements. Software maintenance and implementation services hit the Income Statement as an operating expense.  While some companies I have talked to have decided to get an upgrade after every few releases (or in some cases, after every release), some companies perform an ROI analysis to determine whether the new functionality and performance improvements are worth the new costs that will impact their budget. 

Based on the software’s architecture, some solutions are much easier to upgrade than others. If the software is relatively easy to upgrade, staying current is an easy decision. With other solutions, where upgrades require more costly implementation services, the upgrade decision is more difficult. This is because if you wait too long and there are many releases between the version you bought and the current release, upgrading becomes far more expensive, often rivaling a new implementation in terms of cost and time. In short, while an individual upgrade might not be cost justified when considered in isolation, there can be an opportunity cost associated with not doing periodic upgrades.  From a budgeting perspective, the software’s architecture can matter greatly.

This is where the software-as-a-service (SaaS) model gets interesting. There are no capital expenses associated with SaaS, so software purchased under a SaaS contract doesn’t affect the Balance Sheet. Instead, all the costs and benefits hit the Income Statement. If the SaaS solution is single instance, multi-tenant, then there is minimal costs (often none) associated with upgrades.  From a budgeting perspective, this strikes me as much cleaner.  (For more on SaaS see “A Beginner’s Guide to SaaS and “More Questions about SaaS”).

Task Interleaving: An Advanced Warehouse Productivity Practice

Labor | By •

Task interleaving is an advanced productivity practice enabled by leading warehouse management systems (WMS) with task management engines. Task interleaving is designed to reduce deadheading—i.e., when a worker, for example, picks products and drops them off at a dock and then returns to the picking area without performing any useful tasks along the way. In most operations, some workers are dedicated to picking, while others are dedicated to put-away. Task interleaving is most common in operations with workers that have permission to do both. So instead of a selector returning all the way across a warehouse to a pick zone, they may stop at a nearby receiving dock, pick up a pallet for put-away, and then store it in a location near where their next pick occurs.

I spoke with a warehouse project manager recently whose company started using interleaving across its network of warehouses and distribution centers in 2007. The company has standardized on RedPrairie (an ARC client) as its WMS solution at both the warehouses they operate and the mixing centers managed by its 3PL partners. (A mixing center is a DC that gets products from different factories or divisions so that customers can receive truckload shipments of all the products they want, instead of having to receive multiple less-than-truckload shipments to complete an order). Two days before turning interleaving on, the company moved about 470 stacks of pallets at one of its DCs.  The day it started interleaving, the company moved over 700 stacks. 

These types of productivity gains are not that unusual.  What is unusual, in this instance, is how quickly the gains were achieved. This manager mentioned that when the company first turned on interleaving at its other facilities, productivity typically dropped for a period of time before rising to a level that would have been unachievable without interleaving. This also happened when the company first introduced WMS with more basic task management to existing warehouses. However, in this manager’s experience, the “valley of despair” (the period when productivity falls) lasts longer for task interleaving, but the productivity gains also exceed what is usually achieved with more basic WMS task management functionality. In short, more pain, more gain. You need to set expectations properly or the warehouse manager may want to turn interleaving off before it has had a chance to prove itself.

What was different about this warehouse, and the reason the company was able to achieve instant productivity gains, was that this was a ‘greenfield’ facility—i.e., workers had not yet learned behaviors that needed to be unlearned.

Task interleaving does not just change the way floor-level employees do their job, it also changes things for managers, particularly shipping coordinators. Task interleaving works best at larger facilities where more tasks can queue up in the task management rules engine. This project manager explained to me that prior to interleaving the guys at the dock knew that once a truck was loaded they needed to bring their paperwork to a manager and get new work to do. Now, however, if the task queue is empty, workers tend to slip outside to smoke or talk. Shipping coordinators need to monitor the docks more proactively than they did in the past.

Further, task interleaving may require changes in scheduling, layout, and flow to maximize the productivity gains possible. 

If your warehouse does put-away during the day, and picks and ships at night, your productivity gains are apt to be more limited. You might still interleave cycle counting with put-away, or picking with value-added services, but your ‘bang for the buck’ won’t be the same.

Also, a warehouse layout that has receiving docks on one side and shipping docks on the other is not as conducive to productivity gains from interleaving as a layout that has a few dedicated receiving docks next to a few dedicated shipping docks. Actually, some of the warehouses operated by the manager I spoke with have an even a better layout for interleaving. The company’s newer DCs have dock doors only on one side of the warehouse, and any dock can be used for receiving or shipping. To use a statistics term, the company has built in more degrees of freedom into its layout.

In conclusion, pundits have talked about task interleaving for more than a decade, but only in the past few years have I started to come across warehouses that have actually implemented it . Task interleaving can be a great way to improve productivity, particularly for pallet moves, but change management must be addressed carefully.

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