Logistics Viewpoints

A complete analysis of logistics trends, technologies, and services

Guest Commentary: Effective Interoperability between Transportation Planning and Execution

Guest Commentary | By •

The Time Gap
Within the realm of supply chain there has always been an inherent separation between the notion of planning and that of execution. Historically, this separation was easily attributed to the time required to plan, as well as the gap in time between planning and when orders begin to be placed. In some aspects of supply chain planning, this time gap can be measured in months. As supply chain planning technology improves, the gap shrinks.

In the realm of transportation management, however, this is not the case. As transportation is an intrinsically execution-level activity, the gap between planning and execution is naturally limited, and in some cases, non-existent. The consequence is that plans resulting from transportation optimization technology must be operationally executable from the start. If not, planners are forced to react — sometimes to significantly less than optimal or negative results.

To combat this challenge, leading transportation solutions focus on improving the processes on either end of the spectrum: the ability to plan effectively and create a better, more actionable result, or to create more nimble execution processes allowing users to more efficiently react to imperfect plans or network fluctuations. With the former approach, technology providers focus on creating more comprehensive models that factor in as many network variables as feasibly possible while still allowing for reasonable solving times, resulting in a point-in-time approach that does not allow for the inevitable variability that occurs during execution.

Similarly, the latter approach — while providing a semblance of agility — puts all the onus of enablement at time of execution and consequently invalidates much of what any sophisticated planning engine was trying to achieve in the first place. This causes significant value leakage.

A Different Approach
A third option exists, however, in the form of creating an active interoperability between the planning activity and the ever-changing dynamics of the transportation network. This approach requires viewing transportation optimization in a different light, creating an environment where the notions of planning and execution become an intertwined, iterative set of inputs and processes versus the traditional approach of two distinctive and disparate independent activities. To effectively enable this approach, three distinct concepts must be actively invoked.

Iterative Optimization
The first of these concepts is an iterative and incremental (instead of point-in-time) approach to transportation optimization. The solution must be able to take an initial plan and incrementally improve it based upon new information. An iterative and incremental approach to planning changes the paradigm to one where planning becomes an ongoing function that does not really end but instead continuously functions, ever improving on itself and releasing moves just in time to be executed. Additionally, in this model, the concepts of orders and constraints become organic and are carried across subsequent optimization runs so that each round of planning understands what happened before it and consequently understands the current operating environment.

Comprehensive and Dynamic Constraints
The second of these concepts is the inclusion of more discrete network constraints and subsequently the ability to consider those constraints dynamically over time. Transportation planning engines have become more sophisticated not only in their ability to consider complex routing and consolidation scenarios, but also in the expansion of the constraints that they can consider. To extend this further, the deeper those constraints can represent execution-level processes, the greater the interoperability. Yet expanding consideration of these constraints is not enough. Solutions must be able to dynamically consider how those constraints change over time, as well as layer in the previously stated fundamental of an iterative and incremental approach to optimization.

Examples of this type of execution-level constraint are the representation of available dock capacity and throughput and the availability of network asset capacity. In both cases, not only is it critical to have a representation of these constraints within the planning paradigm, but there also needs to be an ability to understand and process ongoing network changes.

Network Collaboration
The third concept in enabling effective interoperability between planning and execution — related to the consideration of comprehensive and dynamic constraints — is the inclusion of network constituents in the collection of actionable information. Many of the execution-level constraints that an effective solution should consider, such as dock availability and asset capacity, are dynamic based on the inputs of external parties, with carriers being the most prominent. It is critical that any solution have an established method or set of methods to capture this information in real time for consideration in any subsequent optimization solves.

A critical value driver in any transportation network is the ability to keep assets moving. Trying to create these opportunities can be challenging, particularly in a static planning environment where variability can occur between when an extended movement is planned and when the move has to actually occur. Many solutions leave this as an execution-level function but lose out on the impact that potential moves may have on actual upstream load building and carrier selection decisions.

The ideal solution can incrementally build backhaul or continuous move opportunities in the current planning cycle while leveraging moves that are already in transit, maximizing not only the value but the likelihood of successful execution.

Conclusion
Transportation management is an execution-level function, with little room for error between effective planning and flawless execution. The ideal solution combines these two traditionally separate domains into an actively interoperable and interactive cycle maximizing value creation and minimizing disruption.

Fabrizio (Fab) Brasca is vice president, global logistics at JDA Software. In this global role, he develops innovative transportation and logistics strategies across all industry verticals, strengthens executive-level relationships with JDA’s key customers and prospects and advises companies on best practices. Brasca joined JDA as part of the i2 Technologies acquisition in January 2010 after spending more than 10 years at i2 overseeing the company’s global transportation practice. His previous employers include InterTrans Logistics Solutions, IBM, Tandem Computers and ACNielsen. He holds an Honours Bachelor of Mathematics, Co-op degree with a specialization in business and information systems from the University of Waterloo, Ontario.

Goya Foods Transforms Its Supply Chain to Enhance Profitable Growth and Service

Food & Beverage Industry | By •

The title of this posting comes from a speech Peter Unanue, Executive Vice President at Goya Foods, gave at the JDA Software user conference last month.

Goya Foods is the largest family-owned Hispanic food company, and the number one Latin brand, in the US. The company’s Americas supply chain contains six factories and fourteen distribution centers (DCs). Goya has about 350 vendors and it carries 1,600 authentic Latin products, including beans, rice, regional specialty foods, frozen foods, condiments, beverages, and other types of food. That is a lot of SKUs, but according to Unanue, “Hispanics are united by language but separated by the bean.”

What is most interesting about Goya’s supply chain is that it is a direct store delivery (DSD) organization. I would not have expected canned foods, for example, to be a DSD product. In metropolitan areas, Goya may deliver to two different bodegas, separated by just a few blocks, but that order very different products based on the ethnic backgrounds of their customers. Smaller stores and chains in particular appreciate the personal service and labor-saving activities delivered by a DSD supplier.

Goya has 500 salespeople who visit stores and take orders. Orders placed during the day are picked, loaded onto trucks, and delivered to the stores the following day. There are no backorders. If inventory is not in the DC, it is a lost sale. On the other hand, shelf space is extremely important. Goya does not want to lose slots in the store because of bad service.

Goya set itself the goal of achieving a 98 percent service level at the shelf with no increase in inventory. It had been running at 95 percent. Improving service levels would increase sales. The company also wanted to sell more types of food. Historically, it sold longer shelf life products, such as canned products. Goya wanted to expand into shorter shelf life products too. Finally, in taking on more products, the company did not want to hire more buyers, so buyer productivity had to improve.

Although the family-owned company had grown aggressively, it still had a small-company culture. But growth requires changes and the company needed new systems. Goya examined many different types of solutions and it concluded that having better demand planning and fulfillment capabilities would best meet its needs. Goya implemented JDA’s Demand Planning, Order Optimization, and Fulfillment solutions beginning in April of 2009. The solution was brought live in phases, with the final phase completed in September of 2010.

Historically, Goya’s demand planning was based on an 8-week moving average. This methodology was particularly inaccurate in forecasting seasonal and promoted items. The demand planning solution uses statistical forecasting methods with automatically-tuned, best-fit algorithms by SKU.

The Fulfillment solution ensures that the right amount of inventory is at the right locations. Goya rarely has to move inventory from one DC to another anymore. The solution contains a Bill of Materials–i.e., the inventory needed to meet demand for a particular product can be expanded out into the raw materials that will be needed by the factories to produce that product. This has helped Goya’s production process. In the past, from time to time, the company would be ready to start a production run and discover it did not have one of the key ingredients. That does not happen anymore. Now the company has fewer changeovers.

Goya has also used the Fulfillment solution to test vendor managed inventory (VMI) on certain SKUs between one of its factories in Puerto Rico and some of its east coast DCs. The company’s DC in New Jersey, for example, could place an order for an SKU for next week. Meanwhile, its DC in Miami could place an order due in two weeks. Goya’s production planners can now see inventory across all locations, see the forecast, look forward, and create its own production orders. Again, this improves production efficiencies by allowing for longer production runs.

Finally, the Order Optimization solution takes that demand and translates it into optimal purchase orders for raw materials for Goya’s factories and finished goods for its warehouses. The cube and weight limits of 40 or 28 foot trucks are taken into account in the ordering process. Goya got an unexpected bonus from this solution; the order minimum logic helps to insure that its trucks are fully loaded more consistently.

Previously, Goya’s PO generation process had been time consuming. Now, it is highly automated. Buyers are required to review and approve the JDA-generated POs. Ninety percent are approved. Building an order used to take 20 to 30 minutes, now it just takes 2-3 minutes. This allows buyers to spend more time developing new vendors and working on vendor relationships.

In summary, Goya achieved its main goals: a 98 percent service level without increasing inventory, more efficient buyers, and the ability to add more short-life items. But the company also achieved savings in transportation and production that it had not expected.

The next steps? Driving service levels up to 99 percent while actually reducing inventory levels, and expanding its VMI program. Alas, a supply chain manager’s job is never done.

(Note: JDA Software is an ARC client)

The Catch-22 of Supply Chain Risk Management

Supply Chain Risk Management | By •

One of my favorite books is the classic “Catch-22” by Joseph Heller. In the book, the main character, Yossarian, is desperate to find a way to avoid combat missions. Those missions, in the long run, would almost certainly get him killed. So, he wanted to be classified as insane to avoid those missions.

“There was only one catch and that was Catch-22, which specified that a concern for one’s safety in the face of dangers that were real and immediate was the process of a rational mind.” If you were crazy, you could be grounded, all you had to do was ask; but as soon you did, you would no longer be crazy and you would have to fly more missions.

Following the coverage of large natural disasters, like the tsunami and nuclear meltdown in Japan, it strikes me that a similar kind of double-bind thinking occurs in these situations too.

The ultimate point of supply chain management is to help companies be cost competitive while maintaining the bare minimum of inventory consistent with achieving a targeted service level. Global outsourcing, reducing the number of suppliers you do business with, and using optimized planning to reduce inventory are common techniques companies use to accomplish a cost-effective supply chain.

But all of these things that make for a lean and mean supply chain can also cause a supply chain to become brittle and break in the face of disasters. Toyota, for example, is facing a large drop in market share because of the Japan earthquake.

Supply chain risk management says that you can avoid this outcome by having redundant factory lines in different geographic locations, using multiple suppliers, and carrying more inventory in locations around the world. These actions, of course, are the very opposite of what it takes to be a low-cost supply chain.

This double bind is most acute for companies that compete mainly on price rather than service or product attributes. If these companies practice the prescribed risk management practices, they might find themselves losing a little market share year after year to competitors that don’t engage in supply chain risk management. Eventually, the company becomes an afterthought in the market.

On the other hand, if a company runs a brittle, low-cost supply chain, the company may gain a little market share year after year until a disaster occurs. The firm then experiences a huge drop in market share all at once.

Now there are risk management practices that any company can adopt that do not fall into the category of a double bind. For example, a firm can do contingency planning upfront on who will do what when a facility goes down. The company can look for components widely used across its products that would put significant revenues at risk if the supplier of that component was to experience difficulties; for these components, the company should look at dual sourcing.

And companies can have preset playbooks in place for disasters that are more likely to occur. JDA (an ARC client) is doing product development in this area. For example, many retailers know that hurricanes are apt to strike the Gulf region every year during certain months. These retailers also know what products would be in demand when a hurricane hits, so they could carefully track hurricane forecasts and move the necessary inventory forward to key DCs before a hurricane strikes.

But for companies that differentiate mainly on price, the core practices associated with supply chain risk management will continue to represent a Catch-22.

Nestle DSD Makes Optimal Production Sourcing Decisions

Food & Beverage Industry | By •

Nestlé USA has a Direct Store Delivery (DSD) division focused on several of its frozen food brands, including Edy’s and Dreyer’s (ice cream) and Digiorno, Tombstone, and Jack’s (frozen pizza). The company’s move to DSD began in 2006 when it acquired Dreyer’s Grand Ice Cream and the scope of its operations has grown since acquiring the pizza brands.

DSD can be a competitive route to market. Food companies often pay to have their products placed on retailer shelves. But if a food company has DSD capabilities supported by an efficient supply chain organization, it doesn’t have to “pay to play” (or at least not to the same extent). This is particularly true for smaller stores and chains.

Brad Alford, Chairman & CEO of Nestle USA, gave a presentation last September where he provided some facts about Nestle’s DSD operations. For its ice cream brands, Nestle pre-sells and pre-picks items before delivering them to freezers at the back of the store. Later, merchandisers arrive at the store, pull the product out of the freezer, and bring it to the front of the store. Merchandisers visit the stores multiple times a week. This business generates $2.2 billion in annual revenue and is supported by 2,200 full-time employees.

In contrast, pizza DSD is based on a “rolling inventory model,” which I assume means the drivers arrive at the stores, check the inventory, and then deliver what is necessary. This business generates $1.9 billion in annual revenue and is supported by 1,500 full-time employees.

Nestle has the fourth largest, company-owned DSD system in the US food industry and it is by far the largest in frozen foods. To support this business, the company has 30 distribution centers and about 200 cross-docks. This is a large number of warehouses, but in DSD you need warehouses close to the stores.

At JDA’s user conference last month, two Nestle DSD supply chain managers gave a presentation on how the company is using a strategic network design solution from JDA—Production & Sourcing Optimization—on a quarterly basis to determine optimal sourcing by lane. In the ice cream DSD supply chain, transportation and frozen storage costs are high. Consequently, Nestle has redundant plants and production lines across the US. The network is made more complex by the fact that demand for ice cream is seasonal (greater demand during the warmer months). Thus, the decision about how far ahead to forward load production and where production should be done is complex.

The JDA solution answers the question, “Where should we make a product based on storage, handling, manufacturing, transportation, service, and inventory carrying costs?” Service costs are based on a penalty logic that is parameterized into the solution. The thinking is that retailers don’t like it when their shelves are empty, and some retailers even impose fines. The solution can also look at the profitability by case of the different SKUs. In instances where demand is outstripping supply, the solution can determine which cases should be cut.

Running these plans quarterly is a bit unusual; strategic planning is typically done on an annual basis. In Nestle’s case, however, demand changes are significant enough that doing it quarterly makes sense.

In Nestle’s first year using this solution, the company saved $5.5 million!

To get there, the company had to move to centralized production planning and it had to create a centralized strategic sourcing group to manage these long range plans (the group contains some math-savvy planners – i.e., industrial engineers rather than just business undergrads). The company also had to clearly explain to factory managers, at its annual planning meeting, the reasons behind its sourcing decisions.

You can’t argue with success. Over five million dollars in annual savings—now that’s how I spell “ROI”!

(Note: JDA Software is an ARC client)

For Large, Complex Transportation Networks, It’s All About “The Solve”

Cloud/Software-as-a-Service | By •

Regular readers of Logistics Viewpoints know that Adrian and I are fans of multi-tenant, network-based transportation management systems (aka software-as-a-service TMS). These solutions take the burden of connecting to carriers and cleansing EDI messages off the back of the shipper. They also make it easier for a shipper to add new carriers if they have a problem lane. And shippers can leverage the network to access benchmark analytics in a way that is not possible with a single-tenant solution.

But, ultimately, what matters is ROI.

For most shippers, the biggest payback from a TMS comes from mode selection, LTL-to-TL consolidation, and routing optimization. In large, complex transportation networks, all of the advantages associated with multi-tenant solutions pale in comparison to the savings associated with transportation optimization.

At the JDA FOCUS 2011 conference last week, a presentation by PepsiCo brought this point home for me. What PepsiCo is doing with JDA’s single-tenant TMS solution amazed me.

PepsiCo is running a single instance of the TMS across the Americas to support its food and beverage products. The TMS supports a complex distribution network of over 100 manufacturing locations, 600 warehouses, and 200 suppliers. The network includes inbound moves from growers and suppliers to the plants, outbound moves to distribution centers, repackers, copackers, bins that support vending machines, the company’s direct store delivery operation, and shipments to customer DCs. PepsiCo executes 1.5 million shipments per year via its private fleet, dedicated fleets, and common carriers. The company’s transportation spend exceeds $2.2 billion and all of this is managed from a centralized TMS.

The TMS handles constraints and decision logic based on delivery commitments, product, customer, capacity commitments, preset appointments, preferred carriers by business unit and/or lane, and other factors. PepsiCo generates continuous moves for its private fleet. In some cases the optimization may be regional (e.g., West Coast), in other cases national. In some cases the same truck can be used only for one business unit (e.g., Tropicana), in other cases the same truck can be used to carry goods from different business units (perhaps, Quaker Oats and Lays). Its drop down for optimization encompasses 20 criteria. In short, this was the most complex and holistic transportation optimization problem I have ever come across.

The presenter did not disclose the savings PepsiCo has achieved, but I suspect it is massive. Procter & Gamble, for example, has told financial analysts that better transportation optimization will save it $200 million annually.

Thus, what really struck me is that for a network like PepsiCo’s, if an optimization engine from one TMS vendor performs even one tenth of a percent better than an engine from a different vendor, the difference could be worth millions of dollars in savings. Then it is necessary to tune the engine to get the most out of it. That is probably worth even more.

A couple of weeks ago, the TMS team from Manhattan Associates came in to brief us. Manhattan also prides itself on being able to solve complex optimization problems involving inbound and outbound, private fleets, and common carriers. Manhattan made the point that for big, complex problems, it is willing to customize the optimization engine—i.e., put in special math to solve a peculiar problem – to produce better savings. At the time, I thought customization is usually the wrong way to go. However, after hearing about PepsiCo’s network, I have rethought this. Again, if customization improves the solve even one tenth of a percent, it would be worth it, even if the customization makes the implementation take longer and future upgrades more difficult. Multi-tenant solutions, of course, are never customized.

In conclusion, multi-tenant solutions have a lot going for them. But for companies with very complex networks and large transportation spends, it really is all about “the solve.”

(Note: JDA Software and Manhattan Associates are ARC clients).

Guest Commentary: Three-Dimensional Thinking: Reaping the Benefits of Containerization in the Global Supply Chain

Guest Commentary | By •

In today’s challenging environment, it’s a given that companies across all industries must strive to optimize the process of moving goods from one place to another – whether from factory to distribution center, distribution center to store, or any combination in between – locally, regionally or internationally.

Leading companies are reexamining their current business processes, looking to new strategies and applying innovative uses of technology to improve performance and enhance efficiencies. Web services and service-oriented architecture have made it easy to deploy individual component capabilities as a flexible alternative to all-or-nothing comprehensive systems, and this strategy offers immense opportunity to drive results. By deploying component capabilities as callable functions and finding additional ways in which to leverage these capabilities, companies can realize rapid, pinpointed advancements that make a big business impact.

Containerization: Downstream and Upstream Benefits

Containerization is one of these areas of opportunity. Deployed as a callable capability, companies can leverage containerization in downstream transportation and logistics functions, as well as in more unexpected upstream supply chain planning functions.

How does it work? In its simplest application, containerization provides the ability to maximize the loading efficiency of objects into a shipping container to optimally build out a three-dimensional space while respecting constraints such as orientation, crush factors, dimensionality and stacking rules. Containerization is especially useful for companies that move many disparate product types in varying weight categories. Both heavy and light freight can be intelligently co-loaded and shipped together to utilize all available container capacity, with heavy freight on the bottom and more delicate freight on top. The result is increased resource utilization and reduced number of containers required to move the same amount of goods.

In addition to its basic load configuration function, the containerization concept can also be leveraged in other applications. Fundamentally, the solution operates as a callable engine that can work alone or in cooperation with other optimization capabilities to solve a number of problems. Consider the following expanded methods in which companies can use containerization to drive performance and achieve improved utilization rates, better distribution center throughput, reduced miles driven and more intelligent ordering:

Take transportation management systems to the next level. Incorporate containerization capabilities into the end-to-end transportation and logistics management process to get better results from corporate transportation management systems. Containerization can be used as a part of the dynamic routing solution validating that complex, multi-stop routes are built to maximize asset utilization.

Containerization enables a company to confidently build loads that utilize 100 percent of available container capacity instead of estimating and leaving a safety buffer that results in shipping space under-utilization. The ability to ship more merchandise in a single container facilitates the routing process and enables the inclusion of an extra stop that was previously not possible.

Intelligently split distribution orders as an extension to the fulfillment plan. The containerization concept can also be applied to an order-splitting paradigm upstream in supply chain planning. This entails splitting distribution orders into intelligent, executable shipments that take into account both date constraints as well as inventory policies. For example, a company can leverage containerization in its distribution plan by intelligently segmenting an aggregate group of distribution orders. It does this by pushing orders forward or pulling orders back based on date parameters to build optimal truckloads before sending them out into the network for fulfillment.

Banish high-level capacity assumptions in the order management process. Another way companies can use containerization in the upstream supply chain planning process is as a callable service to help them determine when an order has reached an actual full truckload versus estimated full truckload when qualifying orders for discounted pricing. Companies fulfilling customer orders frequently offer advantageous pricing for “full truckload” purchases, but determining when an order meets a legitimate full truckload is difficult without visibility into actual data. The common remedy is to establish an assumptive truckload threshold based on a designated order amount. This imprecise method means that companies often end up leaving money on the table by offering discounts on less-than-truckload orders. Containerization provides a probe-and-response capability to keep order takers apprised regarding when a legitimate full truckload threshold is reached during the order-taking process.

Optimize ocean container utilization in international transportation. Containerization can be used as a loading optimizer for the intelligent stuffing of ocean containers. A common scenario in ports today is a mismatch between the incentives of freight forwarders and the incentives of shippers – while freight forwarders simply charge by the container, it is in the interest of shippers to ensure that the container is full. Infusing containerization capabilities into the process provides the opportunity to ensure maximum capacity utilization.

The Bottom Line

Bringing transportation functions like containerization into the greater supply chain planning process offers tremendous strategic and performance potential. By leveraging containerization as a callable capability and incorporating it into downstream transportation and upstream supply chain workflows, companies can improve asset utilization, use fewer trucks, drive fewer miles, reduce fuel costs and enhance distribution center operations.

Fabrizio (Fab) Brasca is vice president, global logistics at JDA Software. In this global role, he develops innovative transportation and logistics strategies across all industry verticals, strengthens executive-level relationships with JDA’s key customers and prospects and advises companies on best practices. Brasca joined JDA as part of the i2 Technologies acquisition in January 2010 after spending more than 10 years at i2 overseeing the company’s global transportation practice, including marketing, presales, roadmap development and services functions. His previous employers include InterTrans Logistics Solutions (acquired by i2), IBM, Tandem Computers and ACNielsen. He holds an Honours Bachelor of Mathematics, Co-op degree with a specialization in business and information systems from the University of Waterloo, Ontario.

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