Total cost of ownership models: an exploratory study.
The concepts of total cost (Cavinato 1991, 1992), life cycle costing (Jackson and Ostrom 1980), product life cycle costs (Shields and Young 1991), and total cost of ownership (Ellram and Siferd 1993, 1998; Ellram 1993, 1994, 1995) are all related. These concepts all suggest that supply managers adopt a long-term perspective, not a short-term, initial-price perspective, for the accurate valuation of buying situations. Three ideas support all of these procurement valuation constructs. First, cost must be examined from a long-term perspective and should include elements other than initial purchase price. Second, supply managers must consider the impact of other business functions on the valuation of a specific purchase. Finally, to value a purchase situation accurately, a supply manager must understand, and measure, the cost impact of all the activities associated with the purchase.
Ellram and Siferd (1993), in quoting the importance of considering cost beyond initial purchase price, suggested purchasing management books dating from 1928 have expressed this notion. Further, Cavinato (1992) suggested that firms in the 1940s first implemented interfunctional total cost analysis as they sought to understand the cost influence of one logistics function on another, transportation and inventory, for example. Eliram and Siferd (1993) proposed the concept of total cost of ownership (TCO) as an integrating concept. They defined TCO from the perspective of the flow of activities related to the purchase of a good or service and the costs associated with those activities.
The purpose of this article is to report on a study that examines the nature of total cost of ownership. While still an exploratory study, in the sense that no hypotheses are tested, this research presents purchasing managers' updated perceptions of the nature of total cost of ownership and its usage in practice. Further, the research examines the issue of the nature of TCO models and whether it is feasible to have a single TCO model for a firm or whether multiple TCO models are required for specific firms. The research generated, and categorized, a wide range of TCO cost drivers that supply managers currently use in TCO evaluations.
The article begins with a literature review of total cost of ownership and related constructs. The study procedure is described, findings are reported in detail, and their significance for practicing supply managers is discussed. Finally, the limitations of the study ate outlined.
As shown above, the total cost concept is not new. However, as Milligan (1999) noted, accurate total cost measurement is elusive, because most organizations either don't understand the calculations or don't have, or won't share, the data necessary for such calculations. Meckbach (1998) reported that although "information technology managers are aware that the annual cost of administering a [personal computer] can exceed its purchase price ... total cost of ownership (TCO) is not a major factor in purchasing decisions ..."
Meckbach's source attributed this to the fact that TCO is hard to measure and, to date, "TCO estimates [for personal computers] often fail to account for factors such as user productivity, business benefits, and user satisfaction."
Avery (1999) provided a different, more philosophical reason for the emphasis on price in supply management: she suggested that many companies concentrate on direct costs, mostly purchase price, because they feel indirect costs will decline as direct costs decline. However, Avery noted some organizations have successfully reduced total costs using project teams focused specifically on total overall cost reductions, including indirect costs. In other words, she suggested that indirect costs will not fall by themselves, or because of reduced direct costs, but because of management's focus on controlling indirect costs.
Academic research defines several cost concepts incorporating both direct and indirect costs, including total cost (Cavinato 1991), total cost of ownership (Ellram 1993), product life cycle costs (Shields and Young 1991), or life cycle costing (Jackson and Ostrom 1980). However, whatever the name given the total overall cost concept, all of this research stresses the need for management to concentrate on reducing indirect costs by design.
Ellram (1995) was very explicit: she stated clearly that effective implementation of total cost of ownership modeling depends heavily on the use of activity-based costing methodologies. Porter (1993) made basically the same argument, explicitly suggesting the need to focus on cost drivers and activity-based management systems. Tyndall (1988) also provided examples of total cost measurement, concentrating on logistics cost management in the distribution chain. Bennett (1996) offered a similar case study model using activity-based management concepts to measure and reduce overall costs. Finally, according to Ersten (1998), a market exists for TCO software systems. Ersten noted that several firms serve this market with software and systems for either purchasing or other corporate functions, such as information technology.
Ellram (1993, 1994, 1995) and Ellram and Siferd (1993, 1998) conducted the primary research examining TCO. Their work defined the components of TCO in two ways. Ellram and Siferd (1993) suggested six categories: quality, management, delivery, service, communications, and price. However, this categorization examines only those purchasing activities that contribute directly to total cost of ownership (TCO). Ellram and Siferd did look at both formal activities and support activities, also suggesting some cost drivers for these activities.
Ellram (1993) suggested a transaction-sequence cost component structure, involving pre-transaction cost components, transaction cost components, and post-transaction cost components. This typology is more general and considers direct and indirect costs. Pre-transaction cost components include costs related to activities such as identifying a need and investigating a source. Transaction cost components include price, delivery, tariffs, inspection, etc. Post-transaction cost components include cost categories such as field quality problems and cost of repair parts. According to Tibben-Lembke (1998), post-transaction costs include reverse logistics.
However, there is limited empirical research on the use of total cost of ownership modeling in supply management. Ellram and Siferd (1993) provided some empirical evidence: 18 percent of their sample of 103 purchasing personnel reported the use of a formal TCO system, whereas 25 percent reported no TCO use, with the remaining 58 percent reporting an informal approach to TCO.
Ellram's 1994 research extended the definition of TCO models. She noted two basic models: the standard model, generally in writing and repetitively applied to a variety of purchasing situations; and the unique model, developed for a specific item or purchase situation. Ellram used a small set of in-depth interviews to collect data. She found evidence of both types of models and provided examples. She noted that effective use of TCO is difficult and found no standard implementation procedure.
Ellram and Siferd (1998) also conducted case-based research focusing on 11 companies in 10 industries. They found TCO helped in supplier selection, communication, ongoing supplier management, continuous improvement initiatives, and creating organizational understanding of indirect costs. In addition, Ellram and Siferd reported most firms that use TCO modeling focus on important buying activities. However, they found each firm seems to have its own definition for an important buying activity. Ellram and Siferd reported several reasons more firms don't use TCO. Their list includes complexity of TCO measurement, organizational culture, and arguments regarding the relevance and proper use of TCO data.
The research by Ellram and Ellram and Siferd suggests TCO is a little used concept. However, their findings suggest that when firms use TCO modeling, it may be through either formal or informal mechanisms. Further, Ellram and Siferd's research suggests significant bafflers to the use of TCO modeling, including the nature of a firm's costing system and data reporting.
Other authors have also examined total cost of ownership or related concepts. Cavinato (1991, 1992) used the total cost concept to examine cost structures across the supply chain. He examined such cost indicators as labor rate, process effectiveness, capital cost, tax rates, and depreciation. Cavinato suggested comparing supply chain entities based on these cost indicators can provide a basis for assigning specific supply chain processes. He argued that firms can reduce their total supply chain costs by assigning specific supply chain processes to those firms in the supply chain whose cost structures are optimally configured to support the assigned processes.
Jackson and Ostrom (1980) did an early empirical study of life cycle costing. In the early 1960s, the Department of Defense developed life cycle costing to improve its procurement processes (Shields and Young 1991). Jackson and Ostrom indicated 25 percent of their 107 respondents used life cycle costing. This is similar to Ellram and Siferd's 1993 finding that 18 percent of their sample used formal systems for measuring total cost of ownership. The similarity in these results from two projects conducted 13 years apart suggests the field of supply management has made little progress in addressing the impact of indirect costs on sourcing decisions.
Degraeve and Roodhooft (1999b) provided a case study illustration of the application of total cost of ownership to supplier selection. They divided purchasing activities into three hierarchical levels. A firm performs supplier-level activities, e.g., a quality audit, only if it uses a specific supplier. A firm performs ordering-level activities, e.g., receiving, invoicing, transportation, each time it places an order. A firm performs unit-level activities, e.g., a production shutdown due to defects in purchased materials, based on the attributes of the specific items received in a specific order. Using this activity hierarchy, Degraeve and Roodhooft developed a mathematical programming optimization model to select suppliers based on total cost of ownership minimization. To test the model, they applied it to supplier selection decisions for heating elements and ball bearings faced by a "large multinational Belgian steel producer with annual purchases of over $1.5 billion." Degraeve and Roodhooft reported expe cted cost savings of 8 percent for heating elements and more than 10 percent for ball bearings.
Geiger (1999) discussed the challenges of selecting appropriate cost drivers for cost management systems. He defined a cost driver as" ... another measure that is used to proportionally distribute the cost of activities to cost objects." Geiger stated in any situation, typically there are many possible choices of cost drivers. Each potential cost driver may differ from others in how it distributes (or assigns) costs; the cost measurements it requires; the cost to make the measurements; and how it will affect managers' behavioral response to the cost management system.
Geiger explained cost driver selection "can generate powerful managerial responses to reduce drivers because they appear to be causing" the cost on which the firm evaluates managers. In other words, effective selection of cost drivers can focus management attention on the process attributes that create cost. Geiger also reported effective cost drivers must be measurable, with reasonable measurement costs.
In summary, the TCO concept, and the very similar concept of life cycle costing, has been discussed, and examined empirically, in the literature, but with limited scope. The scholarly literature on total cost of ownership consists mostly of case study and anecdotal data. In particular, little empirical research has been done on the cost drivers organizations use for TCO modeling. Cavinato (1991, 1992) suggested a supply chain, or value chain, approach to total cost of ownership measurement; currently, little research exists on this approach.
The purpose of this research is to provide empirical data on this complex topic. Although the investigation focuses on individual firms, not supply chains, the study devotes some attention to supply chain implications. Some data on TCO usage were collected. However, the main focus of the study was to examine the complexity of TCO modeling. The research design focused on three issues: whether standard models are feasible or specialized models are required, development of more specific information on cost drivers, and development of an improved taxonomy for cost drivers.
A questionnaire was developed to examine supply managers' perspectives on total cost of ownership; a single-wave mail survey was used to elicit responses. The questionnaire was mailed to members of the Institute for Supply Management[TM] (ISM) (formerly the National Association of Purchasing Management (NAPM)). The data collection instrument included questions on the valuation logic supply managers use to assess specific purchases and on the use of total cost of ownership as a value mechanism.
One hundred forty-six respondents, of the 990 who received the survey, returned the questionnaire, representing a 15 percent response rate. Of these respondents, 122, or 84 percent, reported manufacturing as their primary business. The remainder of the respondents represent a variety of service businesses and government agencies. Forty-two percent of the sample reported total annual revenue between $35 million and $10 billion. Forty-five percent of the sample reported annual purchase volume between $10 million and $250 million. Sixty percent of the sample reported total employment in their firm or division between 100 and 5,000. Forty-nine percent of the sample reported total purchasing employment between two and 10. Table I provides the demographics of the sample.
Clearly, the sample of respondents is concentrated on medium-sized, manufacturing organizations with limited purchasing management staffing. Also, the response rate of 15 percent is moderate at best. Non-response bias was ascertained using a procedure suggested by Armstrong and Overton (1977). Using the variables total employment and total revenue, there were no statistically significant differences between first- and fourth-quartile respondent means. This suggests limited or very low non-response bias.
Data Collection Instrument
The questionnaire asked respondents to answer a number of open- and closed-ended questions. The survey form defined TCO as follows:
"We take a total overall cost, or total cost of ownership, approach (sometimes referred to as value in use) to valuing the purchase. In other words, we examine all the direct and indirect costs over the life of the product/service."
This operational definition of total cost of ownership was used in the questionnaire to provide, for measurement purposes, a fundamental and explicit way for respondents to think about TCO without introducing all the technical nuances presented in the literature. The term "value in use" was included since it has been used in the marketing literature and there is a possibility this terminology would be familiar to some of the sample.
The questionnaire first asked respondents to report the percentage of their firm's expenditures on purchased goods and services based on what the respondent considers a total cost of ownership valuation model. In addition, the survey asked respondents to check off the categories of goods and services their firm purchases for which there are examples of TCO valuation.
In its second section, the questionnaire asked respondents to evaluate their firm's or division's overall efforts concerning TCO purchasing. In answering this question, respondents used a one (Very Poor) to five (Excellent) scale. The survey asked respondents to use this same scale to assess their firm's or division's success in identifying key TCO cost drivers. The questionnaire asked respondents to think about one or more purchase situations with which they were familiar that use TCO. Based on their recollection, respondents listed or described the key cost drivers used. This was an open-ended question; respondents used their own words to describe one or more cost drivers their firm uses in a TGO model.
Following the open-ended question, the survey asked respondents two questions on cost drivers and TGO modeling. The first question asked if they thought specific TCO drivers vary from commodity to commodity, using a one (no variation) to five (high variation) scale. The second question asked respondents whether, if they were to design a TCO model for their purchasing responsibilities, they would create one model or multiple models.
The questionnaire's next section focused on the issue of standard versus multiple TCO models. Specifically, the survey asked respondents to assess this statement:
There is a core set of cost drivers that apply to every commodity or commodity category. If this statement is true, a company could have multiple TCO models for different commodities, but every TCO model would include some of the same cost drivers. Based on the TCO experiences of your firm or division, do you think this is an accurate description of the nature of cost drivers and TCO in your firm or division?
So respondents could indicate their agreement with this statement, the question included a one (Definitely Yes) to five (Definitely No) scale.
The survey included a second item dealing with standard versus multiple TCO models. The instrument again provided a one (Definitely Yes) to five (Definitely No) scale for respondents to use to show their opinion with this statement:
There are cost drivers (other than those in [the previous question]) that apply only to specific commodities or commodity categories. If this statement is true, a company could develop a modular TCO model in which, for different commodities or commodity categories, specific cost drivers could be connected or disconnected as required. Based on the TCO experiences of your firm or division, do you think this is an accurate description of the nature of cost drivers and TCO models in your firm?
Finally, the questionnaire asked respondents to consider the involvement of other functions in the development and ongoing use of TCO. Respondents could, using a zero (No Involvement) to five (Heavy Involvement) scale, indicate the involvement, in TCO modeling and cost management, of Purchasing, Design Engineering, Manufacturing, Marketing, Accounting, Information Technology, and Logistics.
Thirteen of the survey's items required respondents to provide numerical answers or use a numerical scale. Table II provides summary statistics for these responses.
One hundred thirty-seven of the 146 respondents answered Question 1, on the percentage of purchases made with total cost of ownership valuation. As shown by the standard deviation provided in Table II, there was wide variation in the responses to this item. Table III shows the range of responses to this item.
Table III suggests most respondents' firms do some TCO valuation. Further, these results indicate some firms extensively use TCO concepts in supply management.
TCO Light vs. Heavy Users. Data on TCO usage were examined further to compare characteristics of respondents reporting minimal use of TCO to respondents reporting extensive use of TCO. A frequency analysis of responses to TCO usage was performed. Responses were divided approximately into equal thirds and categorized as high, medium, or low TCO. Forty-eight respondents reported less than 15 percent of purchases under TCO and were categorized as minimal users of TCO. Fifty respondents reported 15 to 49 percent of purchases under TCO and were categorized as moderate users of TCO. Fifty respondents reported more than 50 percent of their purchases under TCO and were categorized as extensive users of TCO. Nine surveys did not respond to the question on TCO usage.
Using cross-tabulation and chi-square analysis, the three groups were examined by product type (capital goods, packaging, raw materials, services, subassemblies, MRO, manufactured parts, other). Statistically significant differences in products/services acquired under TCO valuation were identified across the three usage categories for three types of items: subassemblies ([[PI].sup.2] = 9.246, p # 0.01), manufactured parts ([[PI].sup.2] = 22.658, p # 0.000), and MRO items ([[PI].sup.2] = 9.732, p # 0.008).
For all three of these items, respondents reporting low usage of TCO were much less apt to use TCO for the specified product type. For example, none of the low TCO users reported using TCO for valuation in the purchase of subassemblies, manufactured parts, and MRO items. However, 30 percent of the extensive TCO users reported using TCO valuation for the purchase of subassemblies, manufactured parts, and MRO, while 28 percent of moderate TCO users reported using TCO valuation for the purchase of subassemblies, manufactured parts, and MRO items.
Conversely, 55 percent of respondents reporting minimal TCO usage reported using TCO valuation for the purchase of capital goods, while 57 percent of extensive users of TCO and 62 percent of moderate TCO users reported using TCO valuation for the purchase of capital goods. For the purchase of services, similar percentages of minimal, moderate, and extensive users of TCO all reported the use of TCO valuation.
This finding suggests that in employing TCO valuation logic, purchasing managers are likely to first apply it to nonroutine purchases, such as capital goods. The use of TCO valuation logic for routine purchases such as MRO items, subassemblies, and manufactured parts comes, if at all, much later.
Items or Services Purchased under TCO Valuation
A total of 116 respondents answered Question 2 by indicating the types of items, including services, purchased under TCO valuation logic. Table IV shows the range of responses to this item. Many respondents indicated more than one category. The "Other" category included such purchases as "paper/forms," "fleet," "animal tissue," "specialty manufactured products and chemicals," "prototypes for R&D," "finished goods (OEM)," "laboratory supplies," and "computer equipment and software." The demographic profile of the sample accounts for purchase categories such as "Capital Goods," "Raw Materials," and "Manufactured Parts" reported most frequently as items purchased with TCO valuation.
Respondents' Ratings of Their Firm's or Division's Efforts in TCO Implementation
One hundred fifteen of the 146 respondents gave their overall rating of their firm's or division's "efforts in the area of TCO purchasing." As indicated in Table II, the standard deviation of responses to this item was low. Table V shows the range of responses to this item.
These results suggest that while a few firms believe they are successful in applying TCO valuation to strategic sourcing, many firms believe they are struggling in their attempts to use TCO valuation logic in supply management, or at best doing an average job.
TCO Cost Drivers
As described above, the data collection instrument included three questions dealing with TCO cost drivers. One hundred fifteen respondents rated their firm's or division's identification of its key TCO cost drivers. Table VI provides the range of responses to this item.
These results suggest, given the challenging nature of TCO cost driver identification, firms are unsure of their ability to effectively identify the critical cost drivers for estimating total cost of ownership. This finding is consistent with results from similar questions raised in connection with activity-based costing (Cokins 1996; Innes and Mitchell 1998).
One hundred fifteen respondents answered the survey's question on variation in the specific TCO cost drivers their firms use for TCO valuation of different commodities. As indicated in Table II, the standard deviation of responses to this item was somewhat large. Table VII gives the range of responses to this item.
These results support existing conceptual research in total cost of ownership. The literature clearly suggests the need for different cost drivers to accurately estimate total cost of ownership for different commodities (Ellram 1994; Degraeve and Roodhooft 1999a).
Cost Driver Categories
The third TCO cost driver question was an open-ended question asking respondents to think about a TCO purchasing situation and identify and describe the key cost drivers. A total of 73 responses generated a list totaling 237 cost drivers, with individual respondents providing between one and six cost drivers.
In an attempt to categorize these cost drivers, content analysis was performed. Based on the 237 cost drivers respondents listed, the authors created 13 cost driver categories. The 13 categories are the authors' best estimation of the relationships between the 237 cost drivers respondents identified. One can think of this categorization process as much like naming factors in an exploratory factor analysis. Duplicate elimination reduced to 135 the cost drivers the respondents named. Table VIII lists these 135 cost drivers, separated into 13 categories.
Operations Cost. The authors categorized 12 cost drivers as Operations Cost drivers. In the Operations category, most respondents mentioned cost, speed, or both.
Quality. The authors categorized 14 cost drivers as Quality cost drivers, which typically relate to the consequences of poor quality. At least one respondent mentioned a Quality cost driver, customer downtime, related directly to quality's impact on the customer and their operation.
Customer-Related. Three cost drivers were categorized as Customer-Related cost drivers, which refer to user satisfaction, customer perceptions, and customer specifications. Obviously, these need to be measured from the customers' point of view and represent respondents from organizations with very customer-focused strategies.
Logistics. There are 17 Logistics category cost drivers, covering almost the entire logistical process, including freight (service quality, costs, rate stability), packaging, materials handling, warehousing, tariffs and duties, availability, and logistics customer service.
Technological Advantage. The authors categorized seven respondent-specified cost drivers as Technological Advantage cost drivers. These seven cost drivers all involve the ability of technology to affect the buyer's cost structure. Some of the Technological Advantage cost drivers relate to the supplier's ability to deal with changing technology.
Initial Price. Four respondent-specified cost drivers were categorized as Initial Price cost drivers. One respondent identified long-term price stability as a key TCO cost driver. In this context, long-term price stability probably refers to a supplier's ability to maintain an initial quoted price over a longer time period.
Opportunity Cost. The authors put two respondent-supplied cost drivers in an Opportunity Cost category: the cost of money, for obvious reasons, and overhead. One can only assume a respondent reported overhead as a cost driver because of the limiting effect increasing overhead can have on resource availability for other activities.
Supplier Reliability and Capability. The authors put 11 respondent-reported cost drivers in a Supplier Reliability and Capability category. Such cost drivers obviously include attributes like trust, partnering costs, and teaming costs; however, respondents often reported factors such as familiarity, suppliers' ability to grow, supplier service, R&D capability, and payment terms.
Maintenance. Twelve respondent-reported cost drivers were categorized as Maintenance cost drivers, related to the preservation of the assets required for operation.
Inventory Cost. Inventory Cost normally falls in the Logistics category. However, with five respondents all mentioning different inventory attributes, the authors established a separate category. Four of the Inventory-related cost drivers are routine, post-transaction inventory issues. Interestingly, one respondent reported as a cost driver a supplier's influence on the customer's ability to design or procure an item in a way that permits the buyer to meet inventory reduction objectives.
Transaction Cost. The Transaction Cost category of cost drivers relates to the costs involved in the actual procurement. As shown in Table III, respondents identified a broad range of transaction-related costs as cost drivers for TCO valuation. The authors put six of the respondent-supplied cost drivers in a Transaction Cost category.
Life Cycle. Life Cycle costs suggest a longer-term orientation in supply management. Many respondents suggested they used life cycle costing procedures to examine cost over time. Whatever terminology the respondent used -- life of the product, cost savings over the life of the product, or life cycle stability--the issue is the same: computing costs over time. The authors categorized nine respondent-supplied cost drivers as Life Cycle cost drivers.
Miscellaneous. Thirty-two of the 135 respondent-specified cost drivers did not seem to fit any of the other 12 established categories. Therefore, the authors created a Miscellaneous category. These Miscellaneous cost drivers provide considerable support for the argument that there will be situations in which accurate TCO estimation will depend on the identification of unique cost drivers. For example, disposal costs are relevant only for items that require disposal. Further, environmental issues are less important in many procurement situations, but more important in others.
Total Cost of Ownership Models
Multiple Models Needed. As described above, the data collection instrument included three questions on developing models to estimate total cost of ownership. One hundred fourteen respondents answered the question on the need for multiple models to accurately assess TCO for a variety of commodities. Ninety-six respondents, or 65.8 percent of the sample, believed multiple models are necessary to accurately estimate total cost of ownership for a variety of commodities or commodity categories. A total of 12.3 percent of the sample agreed it's possible to derive a general TCO model that applies to all commodities or commodity categories.
As expected, most of the cost drivers identified by respondents are quantifiable attributes. In fact, entire categories of cost drivers (see Table VIII) are given over to indirect costs associated with supplier relationships and performance. However, respondents identified a number of qualitative drivers of total cost of ownership. In the "Technological Advantage" category of cost drivers, respondents identified as cost drivers such difficult-to-measure attributes as "Suitability for Intended Use," "Flexibility for New Use," "Changing Technology," "Long-Term Advantage," and "Supplier Ability to Change Technology." In the "Supplier Reliability and Capability" category of cost drivers, respondents identified as cost drivers such qualitative attributes as "Trust," "Supplier Capabilities," "Supplier R&D Capability," "Supplier Ability to Grow," "Supplier Support," and "Familiarity with Supplier."
Clearly, this suggests that effective implementation of TCO valuation requires procurement managers to look beyond transaction costs and operating costs in their efforts to identify appropriate cost drivers. In implementing TCO, supply managers must be prepared to consider the behavioral aspects of supply chain process performance.
Core Drivers. One hundred fourteen respondents answered the survey item on the existence of a core set of cost drivers that could be applied in every TCO model for any commodity or commodity category. Table IX provides the range of responses to this item.
These results suggest that respondents agree with the proposition that while supply managers need multiple TCO models to estimate accurately the total cost of ownership for a variety of commodities, they would use some cost drivers for most, or all, of the TCO models.
Modular Drivers. One hundred twelve respondents answered the question on the existence of a set of cost drivers, other than the core drivers that apply to all commodities, that only apply to some specific commodities. Table X gives the range of responses to this item.
These findings suggest the validity of a modular structure for TCO models. In this modular structure, supply managers would use the core drivers for every TCO model for any commodity or commodity category.
However, supply managers would also create a set of modular drivers for specific TCO models related to a commodity or commodity category. This set of modular drivers would consist of all the additional cost drivers necessary to tailor the core cost drivers to estimate accurately the total cost of ownership for any commodity or set of commodities.
Functional Involvement in Total Cost of Ownership Valuation Mechanisms
As described above, the survey asked respondents to indicate the involvement of other functional areas in the development and ongoing use of total cost of ownership valuation in supply management. Table II provides the average response, on the zero (No Involvement) to five (Heavy Involvement) scale, and the standard deviation of responses for each functional area. Table XI provides the range of responses to this item for each corporate function. However, since for each business function different numbers of respondents answered this question, the percentages are not directly comparable.
As expected, and with solid justification, respondents perceived purchasing managers as having a major role in TCO valuation. Respondents also indicated, again not surprisingly, that design engineering has an important role in TCO. However, nearly 63 percent of respondents indicated their perception that the contribution logistics managers make to TCO valuation is "Moderate," "Minor," "Slight," or "None." Further, 84 percent of respondents indicated that marketing makes only a "Moderate," "Minor," "Slight," or no contribution to TCO valuation; 75 percent of respondents indicated accounting plays little role in TCO valuation; and nearly 72 percent of respondents indicated information technology managers play only a minor role in TCO valuation.
Clearly, as firms attempt to make their supply chains market-responsive, marketing managers must assume a larger role in TCO valuation. Further, as firms pursue market-responsive supply chains, boundary-spanning functions such as logistics and information technology must also make a significant contribution to TCO valuation, especially given the impact that supplier performance can have on the buying firm's costs in areas such as logistics, manufacturing, and information technology.
This research supports the notion that total cost of ownership valuation is a difficult process, but is likely to be worthwhile for firms that apply it well. The data, while not necessarily an outstanding sample of the population from which it was drawn, indicate firms are making significant efforts at TCO valuation.
An important finding is the staggering number of cost drivers that companies can and do use when attempting to implement TCO. This research suggests that a standard TCO model will not exist, but that some cost drivers are more universal than others and will appear in many TCO valuation models. This is, however, consistent with the work of Ellram (1994) who found support for both standard and unique TCO models. It is suspected, as companies continue to refine their TCO models, these will evolve into various sets of models, some of which will be standardized, some slightly modified, and some unique.
This research also suggests that a completely precise categorization scheme for TCO cost drivers is likely to be elusive. The three-category schema developed by Eliram (1994) is a good start. However, the large number of cost drivers identified by respondents, and the subjective nature of many of these cost drivers, suggests a more complex categorization taxonomy may be useful.
Further case-based research is probably necessary as TCO valuation continues to grow. An examination of many cases will provide the reader with insight into how the process is developing over time and the nature of the cost drivers firms use in different situations.
LIMITATIONS OF THE RESEARCH
As with any research, the limitations of this investigation must be considered. This is a cross-sectional study, based on a single respondent in each firm. Thus, the responses are based on the memories and perceptions of a single individual and may not represent all TCO purchasing activity in a given firm.
The sample frame was drawn from the membership of ISM and may not represent the total population of purchasing professionals. For example, in this sample, only 12.8 percent of respondents are employed in firms with less than 101 employees; whereas the ISM 1999 Profile of Purchasing and Supply Management Professionals shows 20 percent of ISM membership is employed in firms of this size, and the 1992 Economic Census of the U.S. Department of Commerce shows 38.3 percent of the general economy is employed in firms of this size. Thus, a typical survey respondent is employed by a larger firm.
The moderate response rate of 15 percent is another limitation of this research. This response rate is attributed to the difficulty of the questionnaire; that begs the question of non-response bias. While a procedure (Armstrong and Overton 1976) indicated limited potential non-response bias, that procedure is not above criticism. Therefore, confirmation of these findings on different samples is necessary before projecting these results to the general corporate population.
Table 1 RESPONDENT DEMOGRAPHICS A. Purchase Volume Respondent Percentage of Purchase Volume Frequency Respondents Under $1 million 3 2.4 $1 million to $9.99 million 23 18.3 $10 million to $49.99 million 39 30.9 $50 million to $249.99 million 35 27.8 $250 million to $999.99 million 13 10.3 $1 billion and over 13 10.3 TOTAL 126 (18 missing) 100 B. Purchase Employment Number Purchasing Respondent Percentage of Employees Frequency Respondents 1 9 6.3 2-5 49 34.3 6-10 33 23.1 11-20 11 7.7 21-50 21 14.7 over 50 20 13.9 TOTAL 143 (3 missing) 100 C. Total Revenue Respondent Percentage of Company Revenue Frequency Respondents Under $5 million 2 2.1 $5 million to $99.99 million 36 37.9 $100 million to $999.99 million 30 31.6 $1 billion to $9.99 billion 19 20.0 $10 billion and over 8 8.4 TOTAL 95 (51 missing) 100 D. Total Employment Respondent Percentage of Number of Employees Frequency Respondents Under 100 19 13.1 100 to 999 63 43.4 1,000 to 4,999 33 22.8 5,000 to 9,999 10 6.9 10,000 to 24,999 7 4.8 25,000 and over 13 9.0 TOTAL 145 (1 missing) 100 Table II SUMMARY STATISTIC FOR SURVEY ITEMS Valid Response Mean Issue Responses Range Response Percent of purchases made under TCO valuation 137 0 to 100 35.76% Rate firm's overall efforts in TCO purchasing 115 1 to 5 3.03 Rate firm's efforts at identifying key cost drivers 115 1 to 5 2.95 Do the specific cost drivers your firm uses vary from commodity to commodity? 115 1 to 5 3.12 Is there a set of core cost drivers that apply to every commodity or commodity category? 114 1 to 5 2.24 Are there cost drivers that apply only to specific commodities or commodity categories? 112 1 to 5 2.32 In your firm, is purchasing involved in the development and ongoing use of TCO? 114 1 to 5 3.92 In your firm, is design engineering involved in the development and ongoing use of TCO? 109 1 to 5 3.05 In your firm, is manufacturing involved in the development and ongoing use of TCO? 100 1 to 5 2.89 In your firm, is marketing involved in the development and ongoing use of TCO? 106 1 to 5 1.99 In your firm, is accounting involved in the development and ongoing use of TCO? 112 1 to 5 2.18 In your firm, is information technology involved in the development and ongoing use of TCO? 109 1 to 5 2.42 In your firm, is logistics involved in the development and ongoing use of TCO? 107 1 to 5 2.61 [sigma] of Issue Responses Percent of purchases made under TCO valuation 30.7% Rate firm's overall efforts in TCO purchasing 0.94 Rate firm's efforts at identifying key cost drivers 1.04 Do the specific cost drivers your firm uses vary from commodity to commodity? 1.27 Is there a set of core cost drivers that apply to every commodity or commodity category? 0.92 Are there cost drivers that apply only to specific commodities or commodity categories? 0.96 In your firm, is purchasing involved in the development and ongoing use of TCO? 1.19 In your firm, is design engineering involved in the development and ongoing use of TCO? 1.48 In your firm, is manufacturing involved in the development and ongoing use of TCO? 1.39 In your firm, is marketing involved in the development and ongoing use of TCO? 1.42 In your firm, is accounting involved in the development and ongoing use of TCO? 1.58 In your firm, is information technology involved in the development and ongoing use of TCO? 1.54 In your firm, is logistics involved in the development and ongoing use of TCO? 1.59 Table III PERCENT OF PURCHASES UNDER TCO Percent of Purchases Number of Responses % of Responses <5% 26 19.0 5% - 20% 36 26.3 21% - 40% 23 16.8 41% - 60% 19 13.9 61% - 80% 22 16.1 81% - 100% 11 7.9 Total 137 (9 missing) 100 Table IV TYPES OF PRODUCTS/SERVICES PURCHASED UNDER TCO VALUATION Type of Good/Service Number of Responses % of Responses Capital Goods 79 28.8 Raw Materials 38 13.9 Subassemblies 25 9.1 Manufactured Parts 46 16.8 Packaging 22 8.0 Services 29 10.6 MRO 26 9.5 Other 8 3.3 Total 274 (18 missing) 100 Table V RESPONDENTS' RATINGS OF THEIR FIRM'S OR DIVISION'S OVERALL EFFORTS IN TCO PURCHASING Respondent Rating Number of Responses % of Responses Excellent 2 1.7 Good 39 33.9 Average 42 36.5 Somewhat Poor 25 21.7 Very Poor 7 6.2 Total 115 (31 missing) 100 Table VI HOW WELL DID YOUR FIRM OR DIVISION IDENTIFY ITS KEY TCO COST DRIVERS? Respondent Rating Number of Responses % of Responses Excellent 7 6.1 Good 29 25.2 Average 39 33.9 Somewhat Poor 31 27.0 Very Poor 9 7.8 Total 115 (31 missing) 100 Table VII DO THE SPECIFIC TCO DRIVERS YOUR FIRM USES VARY FROM COMMODITY TO COMMODITY? Degree of Variation Reported Number of Responses % of Responses High Variation 15 13.0 Major Variation 32 27.8 Moderate Variation 38 33.0 Minor Variation 17 14.8 Slight Variation 8 7.1 No Variation 5 4.3 Total 115 (31 missing) 100 Table VIII CATEGORIZATION OF IDENTIFIED TOTAL COST OF OWNERSHIP COST DRIVERS Operations Cost * Manufacturing * Machine Efficiency * Production to Schedule * Labor Savings * Assembly Cost * Operating Supplies * Long-Term Operating Costs * Capacity Utilization * Increase in Production Output * Equipment Speed * Cost in Use * Line Speed Quality * Durability * Replacement * Field Failure * Customer Downtime * Inspection * Cost of Quality * Calibration Cost * Rework * Scrap * Customer Returns * Rejection Cost * Quality Improvement * Unplanned Downtime * Out-of-Service Costs Logistics * Freight * Packaging * Customer Service * Availability * Handling * Instability in Freight Rates * Outbound Cost * Tariffs * Leadtime * On-Time Delivery * Supplier-Managed Inventory * Time to Schedule * Warehousing * Duties * Area of the Country Customer Must Order From * Import Fees * Entry and Harbor Maintenance Fees Technological Advantage * Design Obsolescence * Suitability for Intended Use * Flexibility for New Use * Technology * Changing Technology * Long-Term Advantage * Supplier Ability to Change Technology Supplier Reliability and Capability * Partnering Costs * Team Costs * Trust * Supplier Capabilities * Payment Terms * Supplier R&D Capability * Supplier Ability to Grow * Supplier Support * Service by Supplier * Stocking at Supplier (Quantity Availability) * Familiarity with Supplier Maintenance * Supplies * Training * Downtime * Costs * Labor * Repair Costs * Parts * Spare Parts * Long-Term Maintenance Costs * Repair Frequency * Reliability * Preventive Maintenance Schedule Inventory Cost * Safety Stock * Design/Procurement for Inventory Reduction * Storage * Perishability * Turnover Transaction Cost * Administration of Post-Purchase Agreements * Ease of Transaction * Supplier Conversion Cost (Cost to Change Supplier) * Small Orders * Procurement * Transactional Activity * Long-Term Savings Life Cycle * Long-Term Usage * Projected Life Cycle * Life of Product * Life Cycle Stability * Cost Savings over Life of Product * Useful Life * Redesign Cost * Life Cycle Obsolescence Cost Initial Price * Unit Cost * Initial Purchase Price * Long-Term Price Stability * Initial Capital Expenditure Customer-Related * User Satisfaction * Customer Perceptions * Customer Specifications Opportunity Cost * Cost of Money * Overhead Miscellaneous * Taxes * Value Chain * Warranty * Product Design * Availability from a Supplier * Disposal Costs * Liability and Indemnification * Obsolescence Cost * Salary, Benefits * Indirect Labor * Product Use * Depreciation * Lease or Buy * Supplier Cost Drivers (From Requisition to Receipt) * Safety * Support Costs * Utility Costs * Installation * Ease of Operation * Noise Level * Technical Support * Validation/Registration Cost * Overall Competition * Service Costs * Disposal Value * Currency Exchange Rates * Direct Labor * Total Installed Price * Lease Rate Factors * Flexibility of the Supplier * Tooling and Fixtures * Environmental Issues Table IX CORE SET OF COST DRIVERS THAT APPLY TO EVERY COMMODITY OR COMMODITY CATEGORY Respondent Opinion Number of Responses % of Responses Definitely Yes 23 20.2 Mostly Yes 54 47.4 Neither Yes nor No 26 22.8 Mostly No 9 7.9 Definitely No 2 1.7 Total 114 (32 missing) 100 Table X SET OF COST DRIVERS RELEVANT ONLY TO SPECIFIC COMMODITIES OR COMMODITY CATEGORIES Respondent Opinion Number of Responses % of Responses Definitely Yes 24 21.4 Mostly Yes 41 36.6 Neither Yes nor No 36 32.1 Mostly No 9 8.1 Definitely No 2 1.8 Total 112 (34 missing) Table XI INVOLVEMENT OF DIFFERENT BUSINESS FUNCTIONS IN TCO VALUATION Purchasing Manufacturing Logistics # % # % # % Heavy 46 40.4 8 8.0 10 9.3 Major 35 30.7 31 31.0 30 28.0 Moderate 19 16.7 30 30.0 22 20.6 Minor 6 5.3 13 13.0 13 12.1 Slight 8 7.0 9 9.0 17 15.9 None 0 0.0 9 9.0 15 14.0 Total 114 100 100 100 107 100 Information Marketing Accounting Tech. # % # % # % Heavy 5 4.7 9 8.0 8 7.3 Major 12 11.3 19 17.0 23 21.1 Moderate 22 20.8 18 16.1 26 23.9 Minor 22 20.8 24 21.4 19 17.4 Slight 28 26.4 21 18.8 16 14.7 None 17 16.0 21 18.8 17 15.6 Total 106 100 112 100 109 100 Design Engineering # % Heavy 20 18.3 Major 28 25.7 Moderate 25 22.9 Minor 15 13.8 Slight 15 13.8 None 6 5.5 Total 109 100
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Bruce G. Ferrin is assistant professor of logistics at Western Michigan University in Kalamazoo, Michigan.
Richard E. Plank is professor of marketing at Western Michigan University in Kalamazoo, Michigan.
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|Author:||Ferrin, Bruce G.; Plank, Richard E.|
|Publication:||Journal of Supply Chain Management|
|Article Type:||Statistical Data Included|
|Date:||Jun 22, 2002|
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