The evolution of the “cost research” function at Honda of America illustrates this concept. Initially, Honda employed a group of 20 to 25 experts in a central purchasing function who worked cooperatively with suppliers to develop sophisticated cost models. Over the past decade, Honda has systematically codified the expertise into “cost tables” that can be used by anyone – not just a commodity expert – to cost out a part quickly based upon key drivers. Now the central function consists of only a half dozen individuals; cost modeling has become an organizational capability, not just an individual skill.
Unfortunately, the typical purchasing organization has not achieved Honda’s level of sophistication. Accordingly, the following methodology describes a process for developing the capability by beginning simply and advancing over time. By concentrating resources and approaching the problem systematically, a company can begin to develop sophisticated models quickly.
1. Baseline and segment the spend.
Baselining and segmenting the spend helps a company combine individually purchased items and services into logical groupings called commodity families. Although baselining the spend may seem straightforward, it often is not. Few companies have good commodity-coding systems and those that do often find the codes are inconsistently applied. Furthermore, large companies
often find that each business unit has completely different purchasing systems with incompatible data formats.
Even in companies with standard, company-wide purchasing systems, a significant amount of outside purchases (such as advertising and travel expenses) is not processed through the purchasing function. After baselining the total spend, the purchases should be aggregated into logical groupings conducive to cost modeling. As an example, consider the simple pie chart in Exhibit V illustrating a baseline of the total spend of a hypothetical manufacturer of industrial equipment. In developing segments this company could define all castings as a commodity or separate them into ferrous and non-ferrous materials. Another topology might be process, or types of mold. Sand casting and permanent mold casting could be considered separate categories regardless of the material, as could horizontal and vertical molding. Generally, the best segmentation groups the items by “supply industries” or “process technology” because understanding supplier economics provides the foundation of the initial cost models. The least effective segmentation
scheme (one used by many companies) groups parts based on the end-product application. For example, castings used for a compressor are grouped separately from castings used in a motor, even though both could come from the same supplier and be manufactured on the same type of casting equipment.
2. Quantifying significant elements of the cost of ownership.
Once the overall spend is documented and segmented into logical groupings, a general total-cost of-ownership model should be developed. Clearly, such a model should capture obvious cost elements such as transportation and material rejections. However, there are other cost elements that are less obvious yet often significant – and typically difficult to quantify. For example, many companies capture the cost of the purchasing function in ordering, expediting, managing returnsand qualifying suppliers. However, capturing the materials-related costs of down time, warranty and disposal are less obvious and sometimes forgotten.
At this stage the cost-of-ownership model may be at the company-wide level only. For example, the best an organization may be able to measure is that inbound transportation costs average 2 percent of material purchases or that materials-related warranty is estimated to be 60 percent of total warranty costs. Though not particularly accurate, such estimates broaden the organization’s
thinking about the purchasing process and materials costs by highlighting the magnitude of such costs.
3. Use cost drivers to build total-cost-of-ownership model at the commodity level. Although simply capturing the absolute value of numbers helps, the analysis should not stop there. As noted before, an effective model captures cost drivers – not just elements. For example, an obvious driver for the cost of supplier certification is the number of suppliers. For transportation, however, part weight, travel distance and transportation mode are critical drivers. With this type of information, the overall total-cost model can be refined to allocate cost differently across commodities. To demonstrate how an understanding of the cost drivers and the elements of total cost of ownership are combined, look again at the die-casting example for the industrial products company.
The total-acquisition cost model encourages thinking about the sourcing strategy for a commodity. However, at this stage, the modeling has only illustrated the broader set of issues beyond purchase price. To move further, there needs to be an understanding of the drivers of cost within the suppliers’ operations because purchase price is still likely to be the largest component
of total cost.
4. Build a supplier-level, total-cost model based on key drivers.
The cost model resulting from the third step is actually a compendium of costs by a mix of suppliers. If done well, the “ownership costs” reflect the fact that the suppliers are not all the same. For example, shipping costs are a higher percentage of price from a supplier that is farther away. However, the commodity-level model does not capture the difference in production costs of different suppliers. For example, one could have lower labour rates; another could have lower overhead costs due to economies of scale. Even fairly similar suppliers might have different costs due to differences in capacity utilization and the resulting overhead absorption rate.
Building the supplier-cost model follows the same path as building the total-cost-of-ownership model at the commodity level. First, break the supplier’s overall cost structure into key components: direct labor; materials; manufacturing overhead; selling, general and administrative costs; and profit. With the exception of profit, most suppliers are willing to provide such detail as part of a site visit, even if you have fairly adversarial relations with them. Supplier estimates will be somewhat variable and understanding the variances provides the initial insight into cost drivers.
For example, if one company has low direct-labor but higher manufacturing overhead than another, it probably indicates a difference in the degree of automation. However, it may also mean that one pays lower labor rates. Is one unionized and the other not? Or if comparing suppliers across countries, are wage rates driving the differences? If material costs are significantly lower, it probably means that the supplier is more vertically integrated, buying raw materials and performing the basic “transformation processes” in-house rather than simply doing final assembly.
The next task in building a solid cost model at a supplier facility level is to get quantification of the key drivers for each major element. For example, an initial facility model will capture the number of hourly employees and their annual wage rates. If a more sophisticated model is desired, it may be appropriate to separate direct laborers from indirect hourly laborers and capture their different wage rates. Though the mix between direct and indirect labor may be more detail than needed at this stage, such information can provide insight into manufacturing practices at different suppliers. Lean manufacturers tend to have proportionately fewer indirect laborers versus direct laborers because much of the material handling and off-line inspection is eliminated.
By applying the principle of adding complexity only as needed, a facility-level model can be simple but powerful. For example, a comparison between a high-labor-cost-country supplier and one with the same facilities but lower labor cost requires knowing only the differences in wage rates between suppliers – or maybe simply the differences in average wage rates between countries. This analysis alone might be adequate to convince senior management to support efforts to begin sourcing from emerging markets.
However, most sourcing decisions are more complicated: suppliers are seldom found with identical facilities across countries. For example, high-labor-cost suppliers typically offset their disadvantage by investing in automation and have larger facilities that provide “economies of scale.” Also, productivity levels, duties and transportation costs can often offset low-labor-cost manufacturing, which reconfirms the need to model total cost and not just supplier price.
5. Build cost tables at the item level
Creating item-level cost models drives the process to the next level of detail. Facility-level models are adequate for driving sourcing strategies and joint improvement efforts by identifying world-class standards. However, cost estimating and target setting for a specific part demands a more detailed model. Such models add additional variables to the supplier-level model and/or use specific part-number estimates rather than facility averages.
For example, returning to the die-casting model, several of the key inputs to a detailed cost model are facility-specific, such as wage rates, equipment up time and material cost. However, estimates at a part-number level require additional part-specific information such as finished weight, machine-cycle times and material yield. A more complicated model might take into account drivers of part complexity such as wall thickness or number of inserts to calculate a complexity factor to be applied to the cost estimated from the tables.
Such information can be organized into a simple spreadsheet application with input screens for the primary variables.
“Cost tables” are created by calculating a range of scenarios using the model and organizing the results in tabular form. For example, a table for a given supplier (or simply for a “best practice” compendium) could show a range of part weights for column headings and machine cycle times for rows. A set of these tables could be developed showing the effect of different yield assumptions since part design often affects the yield. The output of such analysis is a simple look-up table that allows even an inexperienced buyer (or better still, designer) to estimate the cost of a part using only three variables: weight, cycle time and yield.
Creating a good total-cost-of-ownership model at the part-number level generally requires combining different cost tables. For example, another set of cost tables could provide part number estimates of transportation for the casting. Such a set of tables could use part weight as the column heading (as was done in the original castings model) and distance shipped for the rows. Separate tables could be used for each of a variety of “modes”: normal full-truck-load (TL) delivery, less-than-truck-load (LTL) delivery, and expedited air freight.
Cost tables simplify the model output and make costing knowledge available to everyone. As a result, even an inexperienced person can make use of the information. However, as expressed previously, understanding cost drivers – and operating to manipulate them – is far more powerful. For example, rather than simply estimating the cost of a casting, a far more powerful use occurs when the design engineer understands that increasing wall thickness by 2 millimeters adds an extra half pound that adds 50 cents to the cost.