The financial advantages of the lean supply chain: companies are investing in software, hiring consultants, and reconfiguring their physical supply chains in order to capture the promised returns from lean supply chain management. Yet the returns from these investments can be elusive. What these companies may not realize is that it's not about expense reduction or profit enhancement--it's about creating capacity for growth. (Finance).
Rate-Based Planning and Execution
Lean supply chain management is planning, executing, and designing across multiple supply chain partners to deliver products of the right design, in the right quantity, at the right place, at the right time. (2) A lean supply chain uses lean manufacturing principles and RBPE. RBPE helps companies respond to demand by determining the average rate of demand for both end products and component parts. Exhibit 1 on page 44 shows the six key differences between RBPE and conventional materials requirement planning (MRP) and execution approaches, which can be summarized as follows:
1. Capacity accommodates demand variation. Under RBPE, demand variation is accommodated with changes in capacity rather than inventory. (3) To use an analogy, consider the snow-capped mountain and the farmer in the valley. In the natural world, the snow on the mountain melts into a river that is used by the farmer in the valley. The imbalance between the rate of supply and demand is managed by building dams throughout the river system. This is similar to building inventory in a supply chain. That is, the imbalance between supply and demand is handled by releasing "inventory" or building "inventory." In contrast, the lean supply chain does not use "dams." Rather, the water flow rate in the riverbed is increased or decreased to match the demand for water by the farmer. The use of tactical capacity in the supply chain is increased when demand increases and reduced when demand falls.
As a more practical illustration, consider the scenario for John Deere. When demand for skid loaders changes, the final-assembly-line rate is adjusted to match the demand rate. The rate is expressed as takt, which is a measure of demand in cycle terms. (4) Inside the plant, the production rate in the paint line, welding cells, shearing cells, and machining cells is matched to the new assembly rate. In addition, all of the in-process inventories (kanbans) are sized to support the demand rate. Outside the plant, supplier delivery rates are also adjusted to the new line rate. Thus, for example, the transmissions arrive at a rate that matches the assembly rate. The techniques for doing this are part of the remaining rate-based elements.
2. Forecasts are used for planning. For the supply chain to adjust to higher and lower rates of demand, supply chain participants must have advance warning of the expected execution rates and changes in those rates. In rate-based planning and execution, this is the purpose of the forecast. The forecast is not used for releasing orders in anticipation of demand (a build-to-stock strategy), except for parts with long lead-times. As we know, when forecasts are used to release orders in anticipation of demand, stock is built prior to known demand. The longer the time period between order release and sale, the greater the opportunity for misallocating capacity for a given product.
Rather, the forecast is used to prepare suppliers for changes in demand rates. For example, if the demand rate is expected to increase in the future, suppliers must be warned in advance. They then will be prepared to accommodate the increased velocity of kanban replenishment signals (or trips) when the demand does increase. In this way, planning should significantly reduce the level of surprises at or near the build date. Minimizing surprises and disruptions, in turn, reduces the likelihood of misallocated capacity.
3. Rates are established for end items and components, and the supply chain builds to those rates. Rate-based planning uses the forecast to establish demand rates at the end-item level and consumption rates at the component level. This differs from the conventional approach, which does not establish rates. Instead, the conventional method ties an end item to a bill of material (or ingredient card) of components (or materials) for execution purposes. Under this approach, a manufacturing order is released to produce a specific item due on a specific date. This manufacturing order may be generated from customer orders or production (stock-point replenishment) orders. That is, the order could be for make-to-stock or make-to-order manufacturing. When the manufacturing order is released, MRP commits all of the subassemblies and components to a particular end item, which are linked together through the bill of material.
All is well if everything goes according to plan. But it never does. If the customer changes the due date of the end item, then the entire set of component due dates will change with it. If you lose a component because of a yield problem, there are no other components to use--everything is already committed. So we rob Peter (another order) to pay Paul (this order). Under the conventional system, assembly is managed by launching manufacturing orders, then expediting them forward. As a result, order completion dates become more and more uncertain, which require even more leadtime to guarantee completion. Long leadtimes cause customers to precommit orders, and the true demand becomes contaminated with artificial demand, which consumes extra capacity. Or else, capacity is consumed by work-in-process and finished-goods inventories that help allay nerves but at the cost of working capital and speculatively consumed capacity.
In contrast to building to manufacturing order, rate-based planning establishes an average rate of demand for end items and components and then builds to that rate. These rates help determine manufacturing cell capacity, kanban sizes, transportation capacity, and warehouse plans. Under this system, a planning bill of material connects components to end items for planning purposes only. At the execution level, components and end items are disconnected from one another. Components are no longer built to a particular end item; rather they are built according to replenishment signals.
From an operations perspective, the process works as follows: Customer demand pulls end items from a warehouse. The warehouse then sends a finished-goods kanban replenishment signal (such as an empty container or an electronic message) to the factory. Within the factory, subassemblies are pulled to build the final assembly, and the components for the subassembly are pulled from the supplier. Execution is accomplished by an interlocking system of kanbans tied to each component, subassembly, and end item. Response time is quick, because the customer does not have to wait for the complete item to be pushed through the supply chain. Finally, as leadtime is reduced, the supply chain becomes more flexible and able to handle demand variation without inventory. Capacity is freed up as all resources are used to satisfy actual demand rather than artificial demand.
4. Rates are broadcast throughout the supply chain. The planning bill of materials also determines the expected demand rates for items purchased from suppliers. Suppliers receive rate plans for components and use them to prepare for kanban replenishment signals that will flow at the planned rate. There can be no rate surprises, or else the kanbans will trip "into air"--in other words, the supplier will not have enough components ready to fulfill the replenishment request. This rate-based method differs markedly from the traditional approach of managing the supply chain through a series of purchase orders and due dates that are tied back to end items through the bill of material. This conventional approach just extends the problems identified in point three back to the supplier. The supplier can execute to rates only if there is demand rate visibility, rather than discrete orders with discrete due dates that are always changing.
5. Demand variation is critical to determining capacity bounds. Rate-based planning establishes not only the end-item demand rate but also the possible variation from that rate during the short term. In this way, end items and components throughout the upstream supply chain can be managed by rates and bounds. The bounds form the upper and lower possible consumption rates based on the historical pattern of demand. (5) This means that day-to-day or week-to-week replenishment quantities will not be an exact set amount. Rather, the replenishment will vary around a rate but within a boundary of anticipated demand. The supply chain "river" discussed at the beginning of this article can now adjust its flow rate (or capacity) up and down the supply chain to respond to these short-term changes in the underlying demand rate.
6. Flexible capacity boundaries are planned as a function of leadtime. (6) The amount of flexible capacity inside the supply chain depends on its planning horizon, as shown in Exhibit 2. Exhibit 2 illustrates a supply chain's product response profile and shows the flexibility of the supply chain as a function of leadtime. That is, the more planning time the upstream supply base and transportation resources have, the greater their ability to respond to variations in demand. The additional leadtime provides time to increase or decrease capacity in more significant amounts, such as by adding Saturday overtime, eliminating a line, or adding railcars. As the execution time period draws nearer, the rate-based plan constrains the forecast demand rate into narrower boundaries. Thus, at execution, the actual kanbans are flowing inside the near-term rate-based execution band.
Putting It Together: An Example From Wendy's
To show how all of these principles can work together, let's look at how Wendy's Restaurant uses them to build a "custom configured" hamburger. Wendy's builds a rate-based plan at the component level for a typical lunchtime demand rate. Let's say it determines that the hourly demand rate is between 160 and 240 hamburgers during lunch. A planning bill of material will tell it how the components are matched to the orders. For example, each hamburger gets a bun, but only half get pickles. Given that half the burgers get pickles (on average, with variation), it is also able to determine the pickle rate and boundary--80 to 120--and so forth for each ingredient. These rates determine the amount of ingredients that are available for immediate consumption at the dressing counter. Each ingredient is uncommitted until final assembly.
At the dressing station, the burger is picked off the grill and dressed for immediate delivery. The space on the grill is a kanban square that signals the upstream cook to replenish with a new burger. In this way, the customer does not wait for the burger to be cooked, which would be a form of conventional push scheduling. As the day progresses, the average rate is adjusted downward, meaning that the number of burgers cooking on the grill will decline to match the new demand rate. Though there may have been 16 on the grill at noon, there are only eight on the grill at 1 p.m.
This strategy provides customers with high configurability as well as quick response--which is the well-documented Dell Computer strategy in action. But this strategy cannot be accomplished by building end items ahead of order based on guesses of what people will want (build-to-stock). Wendy's has too many possible combinations to plan and stock for such a strategy. Nor can releasing build orders for each unique hamburger and then committing every ingredient to the hamburger at the point of initial release meet the customer's time requirement. The leadtime to fulfill the order is too great. Neither Wendy's nor Dell does this. Instead, they establish rate-based plans at the ingredient (component) level, based on the historical mix information coming from the order management system. They then prepare and pull the ingredients at the point of use based on actual orders. When orders are received that violate the rate-based plan boundaries, the customer can be given a commitment date that is realistic and allows the supply chain time to adjust to the exception condition.
All six of the rate-based planning principles reduce the total capacity of a business's product delivery system because capacity is used only for product that is actually demanded or consumed. Capacity is not allocated to make product that is not actually required. In the language of Wendy's, capacity is not used to build burgers before they are actually ordered. This method avoids the problem of lost product (and the capacity needed to make it) that occurs under the build-ahead strategy.
As this example shows, lean supply chain management frees up capacity. Unfortunately, excess capacity often does not have an immediate positive financial impact. To obtain the full benefits of the lean supply chain, the capacity must be used to fuel business growth. To accomplish this next step toward realizing the financial benefits, companies must look at their product strategy.
Lean Supply Chain and Product Strategy
Many businesses that have not embraced lean supply chain methods argue that they are "job shops" or that their products are too complex and customized to establish average demand rates. Our experience causes us to question this reaction. To understand how RBPE and other lean manufacturing techniques can be integrated into different product strategies, we've classified products in the following way:
* S products: Standard (catalog) products with an established bill of material.
* C products: Configurable products. Either the product has options and a base bill of material, or there is no established bill of material at the end-item level because there are thousands of possible combinations. However, the modules and components used to make the end item are standardized.
* E products: Engineered-to-order products with custom fabrication and assembly.
Except for the E products, these product types can be either build-to-stock (BTS) or build-to-order (BTO), as shown in Exhibit 3. The majority of S products and some C products use build-to-stock strategies. Initially, it would seem that RBPE techniques could not be applied to these products because using forecasts to create inventory in anticipation of demand runs counter to RBPE. There is a trend, however, toward creating build-to-order markets for some types of S and C products. For example, Dell Computer took a classic build-to-stock product and offered a base computer with options within a build-to-order supply chain platform. But not all products will necessarily migrate to build-to-order. The impulse-buy nature of some products will support the cost of finished stock availability to the customer. For example, many products (such as groceries, gasoline, and cosmetics) are available for immediate consumption to satisfy our impulsive shopping habits. However, aspects of RBPE can be applied even to these build-to-stock supply chains. Coca-Cola, for example, is in the process of incorporating RBPE into its supply chain.
It would be impractical to manage those configurable (C) products that have almost unlimited end-item definitions (such as a Cisco network) as make-to-stock. There would be too many end-item stock points and forecasting errors to support this type of supply chain. These products need to use RBPE techniques; otherwise, their manufacturers will struggle with long leadtimes and inefficient use of capacity.
Only E products are truly engineered to order, and thus, fit the classic "job shop" definition. When a product is managed as an E, a customer places an order and then has to wait for the order to work its way through engineering and final manufacturing. In actuality, however, many of these products are hidden C businesses inside what many companies view as a job shop environment. The end products may appear highly diverse or complex. Inside the bills of material, however, there are streams and rivers of repetitiveness that could be eliminated through rate-based planning and then addressed at the component and module levels. Furthermore, there is often ample opportunity for engineers to redesign product structures so that they have fewer E characteristics.
To illustrate, a fabricator of truck bodies managed its product and supply chain as a BTO-E business. Each truck body had unique dimensions, materials, and functions. Customers would custom order their truck bodies, then wait 12 weeks for them to be finished. The company managed its business without the benefit of rates, boundaries, and flows. Instead, it used discrete manufacturing orders, purchase orders, and leadtime planning offsets from extended due dates. Upstream suppliers managed to the due dates of purchase orders, which were never stable. The unstable purchase orders resulted in missed purchased parts shipments, factory confusion, unstable processes, and supplier defections. In response to these problems, the company began to use lean principles by first creating some standard (S-type) truck bodies. These S-type truck bodies had standard designs that were 95 percent of what most customers wanted. They also had a standard bill of material and could be planned and built using rate-based techniques. The complete supply chain from supplier to customer worked within a one-week leadtime. Thus, this truck body could be sold to time-sensitive customers.
By adopting this strategy, the truck body company realized twofold benefits. First, by applying RBPE and other lean manufacturing techniques to part of its business, it created excess capacity from what was previously misused capacity. Second, this new capacity could be immediately redeployed to a new market niche, quick response customers. Such customers are contractors that have an immediate need for functional trucks in order to begin work on newly won contracts. Speed is more important to them than functional specification. In this industry, time-sensitive customers were a large and untapped portion of the market. Furthermore, now that customers had a choice between fast-response S trucks and long leadtime E trucks, more and more of the market selected the S products. This shift in demand created enough business to justify expanding into more S and some C designs. The company was able to expand revenues significantly by serving a new market without having to spend capital to expand capacity. We have seen this same strategy play out in the executive jet, office furniture, and power control businesses. Namely, lean manufacturing and RBPE strategies are introduced in tandem with fast-response products designed around standard configurations or bills of material. The fast-response products' open new sources of demand, which are satisfied with the newly released capacity. This is a powerful financial one-two punch.
Company-Level Financial Analysis-ROA
I have suggested both a planning/execution system that will create capacity and a product strategy that will produce the revenue growth to fill that capacity. How can these benefits be demonstrated financially?
The DuPont Formula. The classic DuPont formula helps start us off. The formula states:
Return on Assets (ROA) = Profit Margin x Asset Efficiency
NOPAT * / Assets = NOPAT / Sales x Sales / Assets
ROA = NOPAT * / Assets
Asset Efficiency = Sales / Assets
Profit Margin = NOPAT / Sales
* Net operating after tax
Return on assets (ROA), the classic financial measure of success, (7) is used to evaluate interest-bearing investments. Those investments that provide more interest have a better return on assets than those that provide less. Basically, ROA is composed of two components, profit margin and asset efficiency. The best combination, naturally, is high asset efficiency and high profit margin. Fortunately, following the lean supply chain and product strategy principles outlined earlier can lead to both. That's because a lean supply chain helps companies use their capacity (or assets) more efficiently, which increases the sales per asset dollar. In turn, products will be sold to customers that value responsiveness, thus allowing more capacity to be used for those products that produce a high profit margin.
To illustrate how the DuPont formula works, consider the tale of two computer companies, shown below:
Net Income (as a % Asset Return on Company of sales) Utilization Assets Dell Computer 6.83% 2.37 16.19% Co. Compaq 1.40% 1.82 2.56% Computer Corp.
As a recognized leader in lean supply chain management, Dell has implemented lean supply chain techniques (including RBPE) more robustly than Compaq has. The chart clearly shows that Dell's ROA is superior to Compaq's. That's a direct result of Dell's stronger profit margin and more efficient asset utilization.
ROA Footprint. Insight into the differences between Compaq and Dell Computer--and thus into how lean techniques can positively influence ROA--can be further understood via the ROA footprint displayed in Exhibit 4 on the following page. Exhibit 4 shows the different components that make up ROA and the factors that influence those components. As demonstrated earlier, ROA is determined by multiplying the profit margin and asset efficiency. The profit margin can be decomposed into its components, gross margin as a percentage of sales (gross profit divided by sales) and the sales, general, and administrative expenses (SG&A). The table below again compares Dell and Compaq.
Dell Compaq Computer Computer Gross margin as percentage of sales 20.20% 23.50% SG&A as a percentage of sales 10.01% 14.26% R&D as a percentage of sales 1.51% 3.47% Operating margin as a percentage 8.35% 5.78% of sales (before tax)
Compaq's gross margin is greater than Dell's because of its more profitable mix of server products (hence the higher R&D costs). However, Compaq's sales, general, and administrative expenses are higher than Dell's. The higher SG&As can be partially traced to Compaq's more complex supply chain, which includes a retail component that requires distribution resources and additional inventory management effort. In contrast, Dell is able to deliver made-to-order computers directly to the home and avoid significant logistics costs.
However, the significant difference between the two companies is reflected in their respective asset efficiencies. Dell is able to generate $2.37 of revenue out of every dollar of assets, while Compaq is only able to generate $1.82 of revenue per dollar of assets. Dell is more efficient in using its assets than is Compaq. Or, in the language of the lean supply chain, Dell is better able to match its asset "pipeline" capacity with demand. Asset waste is minimized, while revenue throughput is maximized. The ROA footprint model shows that we can decompose the total asset efficiency into subasset categories, such as fixed-asset, inventory, and receivables efficiency. The individual subasset category efficiencies for Dell and Compaq are shown as follows:
Fixed-Asset Inventory Receivable Efficiency Efficiency Efficiency Dell Computer 32.02 79.72 11.01 Compaq Computer 12.35 19.61 5.05 Fixed-asset efficiency = sales/average property, plant, and equipment Inventory efficiency = cost of sales/average inventory Receivable efficiency = sales/average receivables
We can now see that Dell generates superior asset efficiency in all three major asset subclasses. Dell's success in two of these subclasses--fixed asset and inventory efficiency--can be linked to its strong RBPE strategies. (Dell's receivable efficiency is superior because Dell does credit card sales on the Internet, which is the same as cash.) Dell's inventory turnover is much better than Compaq's because of the well-documented success of its configure-to-order strategies, which employ RBPE and other lean manufacturing techniques. Its fixed-asset efficiency is superior because Dell is able to match its property, plant, and equipment capacity to demand, again through RBPE. In addition, Dell avoids lost capacity at the component level because it is an integrator and only needs fixed-asset capacity for final assembly. Add some growth to this equation through effective product strategies, and Dell ends up with some very attractive financial benefits. Does all of this translate into shareholder return? Well, Dell's five-year accumulated market return was nearly 1,000 percent, while Compaq's was near zero for the same period.
Simplified Product Level Analysis--GMROI
We've used Dell to illustrate the power of lean supply chain strategies and demonstrated the financial impact of these strategies on a company as a whole. Oftentimes, however, lean supply chain strategies are implemented by product lines across the supply chain. Given this implementation pathway, organizations often need to show the financial benefits of their lean supply chain strategies at a disaggregated, product-line level.
Companies trying to identify the financial benefits at the product-line level face a number of challenges. First of all, many costs occur below the gross margin line and are difficult to assign to product lines. An activity-based costing system can help. With activity-based costing, companies can assign the warehouse moves, sales order administration, and other general and administrative (G&A) support costs to the product line. However, without this degree of system granularity, the SG&As can only be assigned to product lines using arbitrary allocation approaches. Naturally, supply chain work should not be justified on the basis of accounting allocations. As a result, the net operating profit line is often difficult to identify at the product-line level without more sophisticated information systems and software. Additionally, the assets dedicated to a product line can be difficult to determine. There will be many assets, such as warehouses, transportation, G&A, and plant assets that are shared by multiple product lines. This increases the difficulty of making nonarbitrary asset assignments at the product line level.
Gross margin return on investment (GMROI) is a shortcut financial metric that can be helpful when it's too impractical to assign costs and assets to the product line directly.
GMROI is a variant of ROA and is calculated as follows:
GMROI = Achieved Gross Margin / Average Inventory Investment
The numerator is the achieved gross margin for the product line. The achieved gross margin is calculated less all markdowns, trade discounts, promotion dollars, and other price discounts and is calculated above the SG&A expense lines. Thus, the achieved gross margin avoids spurious SG&A allocations to the product line. The gross margin should be available for the product-line level from conventional accounting systems because manufacturing costs and revenue allowances must be assigned to products for inventory valuation and revenue recognition purposes. Achieved gross margin penalizes product lines that are not well matched to demand because companies with excess supply (or inventory) often have to give price discounts or markdowns. Because RBPE helps companies more closely match their capacity and inventory levels to demand, this discounting can be avoided.
The denominator includes only the inventory. The rationale is that the accounting system identifies inventory at the product-line level for balance sheet valuation purposes. It might be possible to include other assets in the denominator if such assets can be associated directly with a product line, such as plant assets in a manufacturing cell or shelf space in a retail environment.
The ratio shows the amount of gross margin earned per dollar of inventory. Higher values of the ratio would be associated with improved efficiency in generating margin from inventory assets. This makes GMROI a good financial metric for demonstrating the main benefits of the lean supply chain. Namely, a company using RBPE and lean manufacturing strategies gains more achieved gross margin by using less inventory.
In addition, GMROI is a good measure to use at the product-line level because it acts as a proxy for ROA. This relationship is illustrated in Exhibit 5. In each of the four pairs of companies shown in Exhibit 5, the second one in the pair (Best Buy, Wal-Mart, Toyota, and Intel) is a recognized leader in managing its supply chain. The exhibit shows that GMROI and ROA are directionally consistent and supports the claim that GMROI can serve as a proxy for ROA.
The same relationship can also be seen by looking once again at our Dell and Compaq comparison. In that example,
GMROI = Gross Margin % x Inventory Efficiency x 1/(1-GM%)
Using this formula, the GMROI for Dell and Compaq at the company level is as follows: (8)
Gross Inventory Margin % Efficiency 1/(1-GM%) GMROI Dell 20.20% 79.72 1.253 2,018% Computer Compaq 23.50% 19.61 1.307 602% Computer
Even though GMROI does not take into account the below-goss-margin expense efficiencies and noninventory assets efficiencies, we can see that Dell still demonstrates superior GMROI to Compaq, at a nearly four to one ratio. The example demonstrates the usefulness of GMROI, when product level asset and expense information is not available for the more classic ROA calculation.
In conclusion, Rod Tidwell was eventually "shown" the money. Similarly, supply chains that use lean supply chain techniques will be able to demonstrate financial improvement as capacity is freed to satisfy rapid-response markets. These improvements can be communicated to the CFO by using GMROI as a short-cut financial measure at the product-line level. On a companywide or division level, improvement will become evident over time through improved ROA that, when combined with revenue growth, will yield strong stock market returns. This is an answer that should satisfy any CFO.
EXHIBIT 1 Rate-Based vs. Conventional Planning and Execution Rate-Based Planning Conventional Planning and Execution and Execution 1. Capacity accommodates 1. Inventory accommodates demand variation. demand variation. 2. Forecasts are 2. Forecasts are used for used for planning. execution (build ahead). 3. Rates are established for end items 3. No rates are established and component parts. Build to rate. for end items or parts. Build to manufacturing orders. 4. Rates are broadcast 4. Orders used to communicate throughout supply chain. requirements sequentially throughout the supply chain. 5. Demand variation is critical 5. Demand variation to determining capacity bounds. is unknown. 6. Flexible capacity boundaries are 6. Flexible capacity planned as a function of time. boundaries are not established. EXHIBIT 3 Product Matrix S Products C Products E Products Build to Stock Diet Coke Automobile N/A (BTS) Sub-assemblies on Dell Computer, Build to kanban (such as Cisco network, Custom power Order color matched office furniture plant valve (BTO) wheel assembly installation for a vehicle) EXHIBIT 5 GMROI Comparison for Company Pairs Five-Year Cumulative Company GMROI ROA Market Return Circuit City 162% 4% -33% Best Buy 208% 8% 1,025% Kmart 109% -2% 62% Wal-Mart 199% 8% 284% DaimlerChrysler 172% 7% -47% * Toyota Motor 228% 12% -26% * Motorola 423% 3% -11% Intel 2,507% 22% 55% * Two-Year Cumulative Return
(1) See also "Want to Grow? Think Lean First," interview of Tom Greenwood by John Sprovieri in Assembly, August 2001.
(2) This definition is similar to other supply chain definitions, such as those of: Mentzer, J.T., W. DeWitt, J.S. Keebler, S. Min, N.W. Nix, C.D. Smith, and Z.G. Zacharia. "Defining Supply Chain Management," Journal of Business Logistics 22 (2), (2001):1-25.
(3) Seasonal businesses may use prebuild strategies if the capacity is too expensive relative to the seasonal production spike.
(4) Takt is the designed capacity divided by the unit demand per period. If the plant has a capacity of eight hours per day and 80 units are demanded per day, then takt is six minutes per unit (480 min./80 units). Thus, the demand in units is translated into cycle time. All other resource cycles, such as labor time per unit and machine time per unit, are then matched to takt. That is, all resource capacity is designed to takt (demand) rate.
(5) Naturally, there are product and component scenarios with very high demand variation. Under these circumstances, speculative safety stock may be employed based on cost and variation considerations.
(6) See also John R. Costanza, The Quantum Leap ... In Speed to Market, John Costanza Institute of Technology, 1996.
(7) The discussion here applies to ROA and its variants, such as return on capital employed (ROCE) and return on net assets (RONA). Closely related to ROA is economic value added (EVA).
(8) This analysis is done at the company level because product-level data are proprietary.
James M. Reeve is the Deloitte & Touche Professor of Enterprise Information Management at the University of Tennessee.
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|Author:||Reeve, James M.|
|Publication:||Supply Chain Management Review|
|Article Type:||Statistical Data Included|
|Date:||Mar 1, 2002|
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