Modeling a Management Accounting System for Lean ...

Modeling a Management Accounting System for Lean Manufacturing Firms by

Rosemary R. Fullerton Associate Professor Jon M. Huntsman School of Business Utah State University Logan, Utah 84322-3540 Phone: 435-797-2332 E-mail: [email protected] and Frances A. Kennedy Associate Professor Clemson University Clemson, SC 29634-1303 Phone: 864-656-4712 E-mail: [email protected]

Keywords: Lean accounting, Value Stream Costing, Lean manufacturing, survey analysis, structural equation modeling

Modeling a Management Accounting System for Lean Manufacturing Firms Abstract Lean thinking is rapidly becoming the dominant paradigm in manufacturing. Traditional management accounting fails to provide relevant information to decision makers in a Lean organization. A relatively new management accounting approach better suited to supporting lean initiatives is emerging. Popularly termed Lean accounting (LA), the information generated by this system is portended to be simpler to prepare, easier for shop-floor decision makers to understand, and more useful for decision making. Although the advantages of this emerging management accounting paradigm are becoming more widely understood, a clear definition and theoretical basis is lacking. The purpose of this exploratory study is to identify some of the most critical characteristics of firms that have changed their accounting systems to support their lean production The SEM results from 259 U.S. survey respondents suggest that companies accounting for Lean operations with value stream costing (VSC) have the following characteristics: 1) top management that is highly supportive of change and lean initiatives; 2) workers who are trained in quality issues and empowered to make decisions; 3) considerable use of lean manufacturing tools such as reduced setup times, cellular manufacturing, 5S, Kanban, and Kaizen events; 4) information that is visual, aligned with strategic objectives, and readily available on the shop floor; 5) elimination of inventory tracking and allocation of labor and overhead; and 6) a simplified and streamlined management accounting system that is aligned with strategic goals. The analyses of these results suggest that accountants are beginning to understand the different types of informational needs of Lean environments.

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Modeling a Management Accounting System for Lean Manufacturing Firms 1.

Introduction In the rapidly changing and highly competitive market of the past two decades,

manufacturing firms have been pressured to improve quality, flexibility, and customer response time. They have responded by implementing a variety of practices, including quality circles, statistical process control (SPC), Theory of Constraints (TOC), Just-in-Time inventory management (JIT), Total Quality Management (TQM), Six Sigma, and Total Preventive Maintenance (TPM). Each of these has made a marginal contribution to the mindset of continuous process improvement. Rather than evaluated as separate initiatives, more recently these techniques have been recognized as a set of tools that work together in support of what is rapidly becoming the dominant paradigm in manufacturing – Lean thinking. All types of business organizations are embracing Lean strategies, as they reorganize into cells and value streams. Many decisions previously made by managers are now made by those teams close to the work processes. Lean thinking changes an organization, transforming the traditional top-down, project-driven improvement led by middle managers into continuous improvement conducted throughout the company by locally empowered teams. It follows that the information and performance measures provided to these lean decision-makers should also reflect a more customer-focused organization. For more than two decades, many assertions have been extended that the informational needs of world-class manufacturing environments are changing (see Johnson and Kaplan 1987; Kaplan, 1983). Yet, the accounting profession remains slow to respond. Traditional standard costing systems continue to proliferate, even in lean manufacturing environments (see Baker et al., 1994; Cobb, 1992; Fullerton and McWatters, 2004). As suggested by practitioners and 2

academics alike, firms competing in a global arena and adopting sophisticated manufacturing techniques require complementary management accounting systems (MAS) (Sillince and Sykes, 1995; Welfe and Keltyka, 2000; Fullerton and Wempe, 2009). A relatively new management accounting approach potentially better suited to providing information supportive of lean initiatives is beginning to capture the attention of Lean practitioners across the globe. It is popularly termed Lean accounting (LA). This term has a double meaning: first, it refers to implementing lean practices into the accounting process. Second, it indicates changing the traditional MAS to one that is more relevant for a Lean environment. This is sometimes distinguished as ―accounting for lean.‖ This LA perspective is much more critical to a firm‘s ultimate success with lean implementation; yet, it has received a relatively low adoption rate partly because of the implementation challenges. Firms appropriately accounting for lean operations must make dramatic changes to their MAS and record actual material and conversion (labor and overhead) costs for each value stream1. This new accounting approach is often referred to as ―value stream costing‖ (VSC). Those using VSC no longer track labor or allocate overhead costs to individual products, contrary to the traditional standard costing system. VSC eliminates wasteful transaction recording and reporting. Accounting reports built around this system are simpler to prepare, easier for shop-floor personnel to understand, and appear to be more useful for decision making (Kennedy and Maskell, 2006). Although the advantages of this emerging management accounting paradigm are becoming more widely understood, a clear definition and theoretical basis is lacking, which may contribute to its heretofore relatively low overall adoption rate. This exploratory study uses

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Value streams are generally defined as all of the activities incurred to create value for the customer (e.g., designing, ordering, producing, delivering, and servicing). Value streams are typically organized per similar process and/or product families.

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structural equation modeling (SEM) to examine the relationships in manufacturing firms that lead to the adoption of a new, leaner accounting system. The primary purpose of this study is to identify some of the most critical characteristics of firms that are accounting for their lean operations through VSC, as opposed to traditional accounting methods. Grounded in contingency theory and prior work with horizontal organizations (H.O.), this study responds to the calls by van der Merwe and Thomson (2007) to provide fact-based, empirical research in support of LA, and by Chapman (2008) to explore lateral management accounting mechanisms that are consistent with horizontal organizations. This is the first known cross-sectional survey study to specifically examine issues related to innovative accounting practices that support Lean organizations. The results show that firms adopting VSC have simplified their financial reporting, streamlined their closing processes, and reduced their tracking of inventories. Lean accounting systems appear to operate in an environment where they have the support of top management, many lean manufacturing practices have been implemented, workers are trained in lean and empowered, and more visual performance measures are used on the shop floor. The remainder of this paper is organized as follows. Section 2 discusses the literature related to accounting for Lean operations and develops the hypotheses. Section 3 outlines the research study, and Section 4 discusses the results. Finally, Section 5 provides a summary of the study and suggestions for future research.

2.

Literature Support and Hypotheses Development

2.1.

The Lean Manufacturing Environment Lean thinking is proving to be the most important strategy for achieving world class

performance. Womack et al. first coined the term ―Lean production‖ in their seminal book, The Machine that Changed the World (1991). However, the origin of the Lean philosophy is 4

generally attributed to Toyota, whose production system was originally referred to as just-in-time (JIT), 2 but is now commonly called the Toyota Production System (TPS). The Lean philosophy emphasizes excellence through the elimination of waste and the focus on continuous improvement. Ross Robson, former Executive Director of the Shingo Prize,3 defined Lean production as: The organization and empowerment of leaders and associates to maximize customer value through the identification and elimination of waste throughout the entire business value stream by way of process flow and on demand response to the customer. Lean strives to ensure customer value and sustained profitability through the relentless pursuit of perfection in terms of quality, cost, and delivery in product design, manufacturing, logistics, supply chain and all administrative functions (Solomon and Fullerton, 2007, 35). Referring to JIT/TPS, Schonberger (1987, 5) called Lean ―the most important productivity enhancing management innovation since the turn of the century.‖ Rinehart et al. (1997, 2) referred to Lean production as ―the standard manufacturing mode of the 21st century.‖ A 2006 Aberdeen study (Aberdeen Group) states that ―Lean manufacturing processes have revolutionized the way many leading enterprises deliver products to their customers,‖ and the results of firms implementing Lean in a wide-variety of industries have been stunning. Nearly 90 percent of the 300 manufacturing firms that responded to their survey indicated that they had either adopted Lean (over 50 percent), or were committed to implementing it in the near future. While this seems like an inordinately high number, it does provide some evidence that Lean production is becoming mainstream. Further, the Economist (2006) reported that Lean strategies have made a significant contribution to increased U.S. manufacturing productivity in the last five 2

The definition of JIT has appeared to go full circle. At first, most referred to it as simply an inventory management system. It was then more broadly defined as equivalent to the Toyota production system. More recently, the broad philosophy is referred to as Lean, and most recognize JIT as an inventory management system within a Lean business system. 3 The Shingo Prize is a widely recognized annual award given to North American firms who have excelled in implementing Lean manufacturing. Business Week (2000) deemed it the ―Nobel Prize of Manufacturing.‖ The Prize is administered by Utah State University.

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years. ―American manufacturers are increasingly looking to Lean thinking to improve productivity, reduce costs, enhance flexibility, create better value for their customers, and raise profits, cash flow, and stock price‖ (Maskell and Kennedy, 2007, 59-60). There appears to be substantial and growing evidence that Lean should not be discounted as the consultant‘s new ―flavor of the month.‖ 2.2.

Lean as a Total Business Strategy Lean is most well-known as a manufacturing system, but to be successful it has to be

applied much more broadly as a complete business system (Grasso, 2005; Kennedy and Widener, 2008; Womack and Jones, 1996). The essence of the Lean management philosophy is that ―all business processes and functions integrate into a unified, coherent system whose single purpose is to continue to provide better value to customers through continuous improvement and elimination of waste using Lean principles and tools‖ (Grasso, 2005). Since all business processes are interrelated, Lean cannot operate in isolation to realize its potential (Maskell and Kennedy, 2007). The 2006 Aberdeen study reported that there was a large performance gap between those manufacturing firms that had applied Lean practices solely on the shop floor as opposed to those that had developed a Lean culture throughout the organization. Liker (2004) indicated that while many U.S. companies have embraced Lean tools, they have not understood how they interrelate together as a system. Prior research in operations management and organizational behavior has largely focused on isolated practices, such as JIT, TQM, and HRM. Numerous studies have examined specific advanced manufacturing practices, such as quality and continuous improvement (e.g., Flynn, Schroeder and Sakakibara, 1995; McLaughlin, 1997; Shah and Ward, 2003), TPM (e.g., Cua, McKone and Schroeder, 2001; Fullerton, McWatters and Fawson, 2003; Shah and Ward, 2003,

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2007), and various other Lean production techniques (e.g., Cua, et al., 2001; Shah and Ward, 2003). Shah and Ward expanded the research findings by first grouping production tools into clusters to aid in defining constructs (2003) and then determining the underlying constructs that define Lean production (2007). Research examining the larger Lean system perspective, however, is scarce. Chenhall (2008) summarizes the shift from vertical to horizontal structures and integrates views from operations and HRM perspectives. The H.O. is an integrated system of manufacturing, HRM, and organizational behavior. Generally, it is described as an organization with a customeroriented focus, continuous improvement, flattened structures, empowerment of team-based employees, and a supportive environment. This description aligns with characteristics attributed to those within a Lean organization. Chenhall argues that the key in H.O.‘s is to reconstruct the organizational structure to reflect the manner in which work is performed in companies engaged in advanced manufacturing practices. This requires movement away from vertically managed structures to a ―lateral integration of strategies, processes, structures and people to deliver value to customers (Chenhall, 2008, 518).‖ This study is grounded in a contingency framework, which suggests that particular strategies fit with complimentary contextual variables. This is an appropriate theoretical basis for this study, which examines the interconnected relationships in firms using Lean thinking as an operating strategy. Implicit in the theory is that practices not consistent with the system will diminish the system‘s effectiveness. Gerdin and Greve (2004) developed a typology to classify contingency studies as either Cartesian or Configuration, depending on whether the organization‘s movement towards a new state is in small incremental movements (Cartesian) or if it requires a leap from one state to another due to fewer possible states (Configuration).

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Within this schema, this study has a Configuration fit because it examines several variables together, produces few system states, contains dramatic changes and produces large changes in performance. The research model in this study examines the interconnected relationships of a lean business enterprise that encourage the adoption of VSC to support their lean initiatives. It is expected that those using VSC will have strong top management support in their lean efforts. These firms will have adopted many of the lean manufacturing tools and empowered their workers through training and problem-solving opportunities. Lean production is supported with visual performance measures, and the lean culture has extended into the accounting processes. Further, these lean firms are organized into value streams and account for their operations through visual measures and actual value stream costs, eliminating the need for extensive inventory tracking and overhead allocations. Figure 1 is a representation of the model and depicts the hypotheses that are tested in this study. [Insert Figure 1 about here.]

2.3.

Hypotheses Development—Lean Manufacturing 2.3.1

Top Management Support

Womack and Jones (1996) argue that lean manufacturing is not a set of techniques, but a complete business system. The move to lean requires leadership that uses the techniques, inspires the work force, and establishes the infrastructure that motivates correct actions. The role of an organizational leader is to raise employees‘ awareness about what is important (Bass, 2000). The business literature stresses the need for strong leadership in order for any management intervention to succeed (Burton and Boeder, 2003).

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To have a successful integrative, cross-functional H.O. environment, leaders must provide the structure and support that affect teams‘ abilities to perform and achieve their goals (Guzzo & Dickson, 1996; Levine & Moreland, 1990; Sundstrom, DeMeuse & Futrell, 1990; Tata & Prasad, 2004). These resources include making time available for team meetings and providing access to information, equipment, and facilities (Lawler 1992; Stocks & Harrell, 1995). Training is also necessary to motivate effective improvement efforts and cultivate appropriate recognition systems (Cannon-Bowers et al., 1995). Employees must also be ‗empowered‘ in order to provide the information that enables them to participate in decisionmaking that affects organizational outcomes (Bowen and Lawler, 1992). The following hypothesis is proposed: H1:

Organizations that have the support of top management for change programs and lean initiatives are more likely to have empowered employees.

Prior research provides ample support for the notion that top management commitment and operational-level employee involvement are crucial elements in the successful implementation of new business practices. For example, based on an extensive review of 105 prior JIT studies, Ramarapu et al. (1995) conclude that the critical factors for successful JIT adoption include (among other things) management commitment and employee participation. Similarly, an extensive stream of prior literature suggests that the role of leadership is a strong predictor of the effects on performance from change interventions such as TPM, TQM, and JIT on performance (e.g., Cua et al., 2001; Flynn et al., 1995; McLachlin, 1997; Samson and Terziovski, 1999; Ishikawa, 1985; Deming, 1986; Powell, 1995; Ahire and O‘Shaughnessy, 1998; Kaynak, 2003). Parks (2003) maintains that the successful implementation of the lean philosophy requires that a CEO embrace the philosophy at a deep level. To succeed, any major

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change in an organization‘s strategy and culture must first be accompanied by a concentrated effort from top management (Kaynak, 2003). This leads to our second hypothesis: H2:

Organizations that have the support of top management for change programs and lean initiatives are more likely to adopt lean manufacturing practices.

2.3.2. Employee Empowerment One of the pillars of the Toyota Production System is respect for people (Liker, 2004). This suggests that Lean environments should utilize the resources of their people, listen to their feedback, and include them in decision-making. It is critical that employees are informed about their firm‘s strategic initiatives and have the ability to make operating decisions and adjustments to their own work (Fullerton and McWatters, 2002). Lean practices, such as JIT and TQM, center around providing the tools and information for employees to troubleshoot problems, improve the process flow, and make quality decisions (McLachlin, 1997; Patterson et al., 2004; Ugboro and Obeng, 2000). Ittner and Larcker (1995, 6) stated that the ―primary role of MAS in a TQM environment is providing empowered workers with information for problem solving and continuous improvement activities.‖ In a JIT environment, according to Banker et al. (1993), workers are put in control of production operations that require their involvement in solving production problems. In many cases, the employees in lean environments are the generators of the information that enables them to monitor their production processes (Kennedy and Widener, 2008). Employee empowerment is an important indicator of an organic, flexible structure in which lean firms operate (Lui et al., 2006). Kennedy and Widener (2008) found that prior to the adoption of Lean initiatives, workers were only trained in one part of the production process. After Lean implementation, they were cross trained and empowered to perform all of the manufacturing processes within their cell. Woolson and Husar (1998) determined that in the 10

initial stages of Lean, it was important to train workers in team skills and continuous improvement practices. In their cross-sectional survey study, Fullerton and McWatters (2003) found that employee empowerment had a significant relationship with the implementation of JIT. Thus, the following hypothesis is proposed: H3:

Firms that have adopted more lean manufacturing practices are more likely to have empowered employees

2.3.3. Visual Management The priorities and structures of a lean organization differ from those of a traditional organization. Rasch (1998) states that Lean requires cooperation among everyone involved in production. This level of cooperation necessitates immediate communication among those involved in the task. Using visual boards, shop floor workers can readily identify production needs and problems, and communicate to technical people when help is required. Williams and Sackett (2003) demonstrate how visual data that is dynamically updated with both historic and real time actual data facilitate communication and prompt corrective actions where needed. One of Meyer‘s (1994) four good performance measurement principles is to allow an empowered team to design its own measurement system. Parry and Turner (2006) that it is important that value stream teams are empowered to design their own visual performance boards that are simple and supportive of business goals. Banker et al. (1993) argue that performance feedback is necessary for empowered workers to relate their behavior and decisions to outcomes. Common in lean manufacturing environments are visual boards to facilitate the communication of this feedback. Rather than wait for managers to communicate work instructions based on the monthly accounting reports, empowered workers use visual systems to control their processes and monitor their performance. This leads to the fourth hypothesis, which links employee empowerment with visual management: 11

H4:

Firms that have empowered employees are more likely to be using visual management controls.

Visualization is critical in advanced manufacturing technologies. Visual signals are used to provide information (e.g., metric boards), and instructions (e.g., standard operation procedures), signal needs and alert issues (e.g., andon lights), and demonstrate control and location (e.g., signage and stripes) (Galsworth 1997). Greif (1991, p. 21) suggests that the objective is to create a visual mode of organization: ―a visual workplace is a work environment that is self-explaining, self-ordering, self-regulating and self-improving – where what is supposed to happen does happen, on time, every time, day or night.‖ Continuous improvement initiatives require that processes be measured using quality metrics, SPC charts, and other visible means of measuring and communicating information. Once an improvement has been implemented, close monitoring of process and output measures is critical to signal when an adjustment needs to be made. ―A key driver of TPS is that every person involved must be able to see and fully understand the different aspects of the process and its status at any time‖ (Parry and Turner, 2006). Charts and information controls containing performance metrics have been shown to be strongly associated with quality performance (Flynn et al, 1994; Shah and Ward, 2003). Banker et al. (1993) find that posting of defect charts is associated with JIT initiatives, and Parry and Turner (2006, 84) conclude that visual boards used in lean environments are a ―dynamic measurement system as they provide instant feedback.‖ This leads to the fifth hypothesis: H5:

2.4.

Firms that are using more lean manufacturing practices are more likely to be using visual performance measures.

Accounting and the Lean Enterprise 2.4.1. Traditional Accounting and Lean Manufacturing Practices

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Traditional management accounting systems are transaction control systems that collect data and information and aggregate that information into periodic summary reports for floor managers. The purpose of this reporting practice is to ‗tell the story‘ of what occurred during that period (i.e., whether or not actual costs were aligned with budgeted costs) and to alert managers through variance analysis when a cost is out of control. Usually, traditional management accounting reporting includes standard costing information based on fully absorbed overhead, manufacturing spending, and volume overhead variances. In order to prepare the departmental expense statements, detailed tracking of labor and inventory must be done for allocating overheads and determining actual product costs for variance analysis. The targeted information customers are production managers, the primary decision-makers in a traditionally managed hierarchical system. Basically, traditional accounting reporting mirrors the structure and priorities of a vertically managed structure with functional departments and an emphasis on efficiency and cost per unit. Lean thinking, however, creates major changes in an organization‘s way of doing business that require parallel changes to the information system that aids in decision making. The appropriate design of a MAS is dependent upon the environment in which it operates (Kaplan, 1983; Johnson and Kaplan, 1987; Chenhall, 2003; Abdel-Maksoud et al., 2005). Considerable literature asserts that traditional MAS do not support Lean environments (e.g., Green et al., 1992; Hedin and Russell, 1992; Kaplan 1983, 1984; Johnson 1992; Sillince and Sykes, 1995; Solomon and Fullerton, 2007). The classical model of cost accounting is ―seriously deficient for the new manufacturing environment and encourages inappropriate behavior‖ (Howell and Soucy 1987, 43). Goldratt (1983) called cost accounting the ―number one enemy of productivity,‖ and Ohno, credited as the father of JIT, declared that cost accountants should be banished from the plant in

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order to successfully implement and operate JIT (Mackey 1991). Traditional accounting systems impede Lean transformations by: (1) encouraging firms to build more inventory; (2) focusing on the ―accuracy‖ of product costs rather than customer value; (3) using volume and efficiency variances to discourage the creation of excess capacity; and (4) building a complex system of data collection and reporting that is difficult to understand (Maskell and Kennedy, 2007). Lean requires that a smooth flow of product is maintained throughout the operations, and the production focus is on the identification and elimination of waste. In this environment, employees must be given the right information to help identify and eliminate waste and obstacles to flow in a timely manner (Maskell and Baggaley, 2004). Even though practitioners and academics alike have warned for several years that the current accounting systems are outdated for the new manufacturing environment, most research finds that companies continue to rely on conventional accounting information systems, even in sophisticated manufacturing environments (see Fullerton and McWatters, 2004). In fact, in this survey, 77 percent of the respondents, who all demonstrated a strong interest in Lean thinking by attending Lean Accounting Summits, were still relying extensively on a standard costing system. Firms that embrace a Lean culture should better understand the waste and useless information gleaned from a traditional MAS. For this reason, we propose the following hypothesis: H6:

Firms that have adopted more lean manufacturing practices are LESS likely to be using traditional management accounting practices.

2.4.2. Simplified Accounting and Lean Manufacturing Practices Lean accounting is a ―radically different way‖ to manage a business that is built upon Lean principles. It provides ―excellent and timely management information, and eliminates the need for the complex MAS that weigh down most companies‖ (Maskell and Baggaley, 2004, 13). In their in-depth case study, Kennedy and Widener (2008) found that Lean accounting 14

practices help to mediate the relationship between Lean manufacturing initiatives and control systems. A group of leading lean accounting practitioners and academics developed a set of five mission statements that emphasize the need for clear, timely, and understandable information that highlights the priorities of lean thinking, such as creating customer value while eliminating waste (Fiume et al., 2007). Responsibility accounting (RA) guides how information is summarized and at what level it is aggregated. The levels of RA are referred to as responsibility centers (RC). In the theory of ‗relational framing,‘ how individual employees envision their ―fit‖ within the organization guides their behavior – as either competitive or cooperative (Tetlock and McGraw, 2005). Rowe et al., (2008) find that RA can influence the employee‘s behavior by either encouraging the employee to cooperate as a team member responsible for the RC outcome; or by motivating the employee to singularly compete for the RC outcome. The research concludes that for proper behavioral motivation, the structure of accounting reporting should be congruent with organizational goals. There are four boundaries to manage within ‗relational framing‘—organizational, communication, spatial, and temporal (Rowe et al., 2008). The first two of these are critically important in the lean organization. The organizational boundary refers to whether work is performed by cross-functional teams or by functional units. The impact for RA is the level of aggregation and target of reports. In a lean organization, the customers of the information are primarily cross-functional teams. Reporting mechanisms consistent with this customer orientation encourage cooperation. The communication boundary considers the manner and frequency with which information is shared. Information should be timely, clear, and understandable to the user in order to aid in decision-making. Cardineals (2008) determined that

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graphical cost accounting data was better at informing users with limited accounting knowledge than tabulated numbers. The considerable range of financial background in the cross-functional, value stream environments of lean manufacturing companies would suggest that accounting information provided to value stream members should be in very clear, simple, and easy-tounderstand formats. Womack and Jones (1996) indicated that they coined the term Lean production to represent the TPS mantra of doing ―more and more with less and less.‖ Traditional accounting is known for its complex reporting system. Lean thinking would simplify accounting through the elimination of non-value added activities. Reid and Smith (2000) find that the complexity of a MAS is largely determined by the level of control managers exercise over their employees—the less control, the simpler the MAS. Unfortunately, when complex problems arise, the accounting function‘s solution is to generally create a larger data warehouse and prepare more extensive reports. Lean thinking, on the other hand, would encourage drilling down to the root problems and creating simpler, more directed reports with solutions to which everyone has access. Cunningham and Fiume (2003) support this concept, stressing that the accountant‘s responsibility is to provide accounting information that is simple, timely, and easy to understand—that supports the company‘s strategy and motivates the right behaviors. In their indepth, cross sectional study, Young and Selto (1993) found that although information related to critical success factors was well designed and available, this information was not provided at the shop-floor level where it could affect operating decisions. Performance measures should be displayed visually, with charts and graphs for key measures posted and maintained on display boards in the production cells where the work is performed (Cunningham and Fiume, 2003; Maskell and Kennedy, 2007).

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An agreed upon key principle of Lean is eliminating non-value added activities. As indicated previously, accounting should not be exempted from this guideline. Anecdotal observations confirm that accountants often find monthly end-of-the-month closings highly stressful and lingering longer than desirable. In addition, each day of extended time in preparing the month-end reports dilutes the relevance of the information. Cunningham and Fiume (2003) describe how they applied the principles of Lean accounting to overcome many of these problems by eliminating transactions, streamlining the closing process, and making reports more understandable. This further leads to reduced costs, increased quality and efficiency, and better aligned corporate strategies throughout the business functions. The following hypothesis is proposed: H7: Firms that use more visual performance measures are more likely to have simplified their MAS. 2.4.3. Accounting for Value Stream Performance In a traditionally managed, hierarchical organization, the reporting information is directed to the departmental managers of RC‘s, usually defined by process or function. However, a lean organization is typically structured into cross-functional value stream teams who make decisions regarding process, continuous improvement, and customer value. These value stream teams become the RC for the lean organization. Womack and Jones (2003) recommend that value stream managers should have complete responsibility for the activities and costs of the value stream, and are the link to creating customer value. It follows that the reporting mechanism in these organizations should be aggregated to the value stream level and provided to the crossfunctional team in a timely manner. This is consistent with the theory of relational framing. The organizational boundary needs to be managed to provide a strategic fit with the structure and the goals of the company (Tetlock and McGraw, 2005). A lean organization is organized around 17

cross-functional value stream teams responsible for operating decisions. VSC supports this team structure by assigning and reporting actual costs at the value stream level. The individuals on the value stream team identify as a unit; VSC reinforces this identity and fosters cooperative behavior among team members to reach their goals. The standard costing system was developed for a different period when direct labor (DL) was generally the largest portion of manufacturing costs and material was the smallest. In today‘s typical manufacturing firm, materials constitute over half of the costs, labor averages 10 to 15 percent of product costs, and overhead is approximately three times DL cost (Banker et al., 1993). Yet, most firms continue to make decisions with standard product costs that include overhead values allocated via an outdated DL ratio. This creates distorted product costs and complex inventory tracking systems. The following accounting features impair the success of Lean initiatives: reliance on standards; emphasis on variance and efficiency computations; preoccupation with DL; extensive inventory tracking; and overhead allocations based on DL (Green et al., 1992). The use of traditional labor and machine utilization as volume and efficiency criteria encourages overproduction and excess inventories (Haldane, 1998; Johnson 1992; Fullerton and McWatters, 2004; Wisner and Fawcett, 1991). Firms would more effectively account for Lean by using basically a ―materials only‖ cost system. This approach encourages streamlining transactions by reducing labor tracking. Labor is treated as a fixed, period cost. Complex, detail allocations are avoided, with overhead allocated only at the product family level (Kennedy and Widener, 2008; Solomon and Fullerton, 2007). In VSC, individual product costs are no longer calculated; rather product decisions and profitability margins are evaluated per the overall value streams. Further, as companies become more ‗Lean‘, the value of

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their inventories becomes less material and correspondingly less important, considerably reducing the need to track inventory. With this in mind, the following hypothesis is proposed: H8:

Firms that use more traditional management accounting practices are less likely to use VSC.

Overemphasis on financial reporting in traditional environments has encouraged firms to manage by the numbers, rather than managing and measuring value-creating activities (McNair et al., 1989). This usually means monthly reporting statements in the form of financial statements and variance analyses targeted to functional managers. In a flexible environment, monthly reports are no longer conducive to decision making. Lean organizations are designed to respond quickly to changing demands, and use visual management controls to provide information, signal a need, and control production processes (Galsworth, 1997). The fundamental concept is that workers need to assess how well their processes are working (e.g., on-time delivery, product quality) so that they can immediately respond to changing customer needs. They depend on visual queues for direction. Having appropriate information readily available, up-to-date, and visible is essential for the success of a flexible manufacturing entity. Managing the communication boundary of relational framing means that a firm must be willing to make substantial changes in its accounting system to support Lean initiatives. As the primary source of decision making, accounting plays a prominent role in a Lean transformation and is absolutely key to its implementation success. Both process and performance information in Lean operations should be available to team members on a frequent basis. VSC performance metrics are normally provided on a weekly basis, made available to all team members, and often displayed on metric boards (Kennedy and Widener, 2008). It follows that those firms using more visual performance measures to help manage their Lean processes are more likely to recognize the need for an improved accounting system. This leads to our next hypothesis: 19

H9:

Firms that use more visual performance measures are more likely to use VSC.

Oftentimes accountants are the most resistant members of the workforce to get involved with Lean initiatives. They too often remain rooted in their traditional methods, speaking their own language, and working exclusively in their ―ivory towers.‖ There is a reason that financial executives see their personal work efforts in a more positive light than do their non-financial peers (Cunningham and Fiume, 2003). While their knowledge of accounting procedures is excellent, the management accountants‘ exposure to change initiatives in the plant is generally limited. However, as accountants move away from spending the majority of their time as number crunchers to business partners in lean organizations, they will become more involved with ―leaning‖ their own processes. This new education in lean initiatives should provide a greater understanding of the deficiencies of the traditional MAS in reporting the benefits from lean. Those firms using lean principles to streamline their accounting processes and strategically align their performance measures should more readily recognize the benefits from adopting VSC as a valuable decision-making approach. VSC is a more effective system because of its simplicity and relevance. Not only does it provide more useful information for lean decision makers, it also conveys the continuous improvement and reduction of waste principles embodied in Lean thinking—supporting a strategically ―fit‖ application of relational framing. Our last hypothesis is as follows: H10: Firms that have simplified their MAS are more likely to have adopted VSC.

3. Methodology 3.1.

Survey Instrument and Data Collection To test the research hypotheses, a detailed survey instrument was designed to collect

specific information about the manufacturing operations, organizational culture, top management 20

leadership, performance measures used, management accounting control system in place, financial and operational performance changes, and general demographics of U.S. manufacturing firms. Only a portion of the 125 survey questions are applicable to the relationships examined in this research project. The majority of the survey questions were either categorical or interval Likert scales. (See the Appendix for a description of the questions used in this study.) To evaluate the survey instrument for readability, completeness, and clarity, a limited pretest was conducted by soliciting feedback from several colleagues, as well as four manufacturing managers working in firms that were in the process of implementing Lean. Appropriate changes were made in response to their feedback. Because of the limited number of firms that have actually changed their accounting systems in support of Lean initiatives, collecting data from firms that have implemented lean accounting practices is particularly difficult. However, the interest in re-designing a more relevant management accounting system is becoming more widespread, which encouraged the formation in 2005 of the first annual Lean Accounting Summit (LAS), a conference venue that focused on the various aspects of appropriately accounting for lean operations. The LAS had just over 250 attendees in 2005; through the next three years (2006-2008), the annual LAS grew each year to over double its size in 2008. The Summit attendees were invited to leave their contact information for future professional exchanges on the LAS website. The researchers were given permission (actually encouraged) by the Summit organizers to utilize the attendees‘ information list for this research project. Thus, the sample respondents for this study were drawn from the participants at the annual LASs from 2005-2008. There were a total of 1,389 names available on the lists covering the four Summits. However, over one-third of these were either duplications of people who

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attended more than one Summit or attendees from the same plant. Those duplications were eliminated from the sample.4 Another significant number of potential contacts were eliminated due to the following reasons: 1) they were employees of non-manufacturing entities; 2) they were employees of international firms (which is outside the scope); or 3) the contact information was incorrect. After adjusting for all of the above reasons, the applicable sample size was 476. Respondents were contacted a maximum of four times (three were by e-mail and the last contact was by mail) and asked to complete a detailed, 15 minute on-line survey reflecting operations at their facility. Two-hundred sixty-five responses were received from U.S. managers who attended one of the LASs from 2005 through 2008. Six responses were largely incomplete and eliminated from the testing, leaving a relevant sample response rate of 54 percent. The 15 duplicate responses from the same plant were averaged together, leaving a test sample of 244. The large majority of the respondents had accounting and finance backgrounds, with titles of controller, CFO, and VP of finance. 3.2.

Reliability and validity tests 3.2.1. Exploratory factor analysis In order to develop a parsimonious representation for the various constructs in the survey,

an initial principal-components-based exploratory factor analysis was conducted for each set of questions from the individual sections of the survey instrument. Those elements that loaded greater than 0.50 on more than one construct or elements that loaded onto a factor that did not make logical sense were eliminated from consideration. After all of the survey instrument 4

Although it would be helpful to have multiple responses from the same plant, it was considered not very practical, and even detrimental to obtaining responses. In fact, when this occurred accidentally, some complaints were received from contacts saying that either they or a colleague had responded previously. Fifteen responses were received from duplicate plants. The answers from those duplications were averaged together. Summit attendees from the same firm were contacted and responses used as long as they represented different manufacturing plants.

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constructs were defined, another factor analysis was performed to verify the initial constructs that are used in this study. Only those elements from the results of the initial factor analysis related to the six constructs used in this study were included. Using the principal components method, the same six constructs emerged with eigenvalues greater than 1.0, accounting for 62% of the total variance in the data. These factors were in general alignment with a priori expectations. The VARIMAX rotation resulted in the following factors: TMGT:

The extent to which top management is supportive of change and lean production strategies.

LMFG:

The extent to which the facility has implemented various Lean manufacturing tools such as cells, a Kanban system, one-piece flow, 5S, and Kaizen.

EMPR:

A participative organizational culture where employees are cross-trained and responsible for decision-making and quality output.

VLPM:

The availability and visibility of strategically aligned performance measures on the shop floor.

TRMA:

The importance of tracking inventories and assigning labor and overhead costs to those inventories.

SMAS

The efforts made in the accounting system to simplify and align it with strategic initiatives.

A description of the specific survey questions that support these factors is found in the Appendix. For the results from the factor analysis, refer to Table 1. Note that the positive anchor of the 5point Likert scaled questions for independent variables TMGT and LMFG is ―5‖ and for independent variables EMPR, VLPM, TRMA, and SMAS, the positive anchor is ―1‖. The dependent variable, VSC, was a single five-point Likert scaled question that asked respondents to assess the extent to which they used value stream costing from ―not at all‖ to a ―great deal.‖ [Insert Table 1 about here.]

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The factor solutions for the defined constructs support the construct validity of the survey instrument. Multiple-question loadings for each factor in excess of 0.50 demonstrate convergent validity (see Bagozzi and Yi, 1988). In addition, discriminant validity is supported, since none of the questions in the factor analyses have loadings in excess of 0.40 on more than one factor. Cronbach‘s alpha (1951) is used as the coefficient of reliability for testing the internal consistency of the constructs. Table 2 shows the correlation coefficients for the factors and the alpha coefficients, which all exceed the acceptable standard of 0.70 for established constructs (Nunnally and Bernstein, 1994). [Insert Table 2 about here.] There is a concern that the data may suffer from common method variance since selfreported data is used exclusively in this study (Campbell and Fiske 1959; Podsakoff and Organ 1986). The exploratory factor analysis helps determine the extent of such concerns. If the majority of variance is explained by the first factor, then there is significant bias (Podsakoff and Organ 1986). In this analysis, only 16.1% of the variance is explained by the first factor and the balance of the variance is explained by the remaining variables (12.6%, 12.3%, 7.8%, 7.2%, 5.9 %,). Overall, these tests support the validity of the measures representing the constructs used in this study. 3.2.2. Confirmatory factory analysis The measurement model using the scales resulting from the exploratory factor analysis was evaluated per a confirmatory factor analysis (CFA) (Gerbing and Anderson, 1988). Schumacker and Lomax (1996, p. 72) recommend a two-step modeling approach proposed by James et al. (1982) that first evaluates the measurement model to assure its fit and then examines the full structural model. The measurement model provides an assessment of convergent and

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discriminant validity, and the structural model provides an assessment of predictive validity. Jöreskog and Sörbom (1993, p. 113) indicate that the measurement model must be tested independently before testing the structural model in order to determine whether the chosen indicators for a construct measure that construct. The maximum likelihood (ML) approach in AMOS 7 was used to test the measurement model and full structural model. Among the 244 responses, most measures have a full response, with no more than four responses missing for any single measure. AMOS does not evaluate missing data, but provides a theoretical approach to random missing data that is ―efficient and consistent, and asymptotically unbiased‖ (Byrne, 2001, 292). Where covariances were suggested by AMOS and justified theoretically, they were included between error terms of the same construct (see Baines and Langfield-Smith, 2003; Fullerton and Wempe, 2009; Jaworski and Young, 1992; Shields et al., 2000). All of the structural models are over-identified and recursive. The measurement model fit (as defined by Hair et al., 1998) was evaluated using a number of fit indices, including: Χ2 and the ratio of X2 to degrees of freedom; Root Mean Square Error of Approximation (RMSEA); incremental fit index (IFI) (Bollen, 1989); TuckerLewis Index (TLI) (Tucker and Lewis, 1973); Comparative Fit Index (CFI) (Bentler, 1990), and Akaike information criterion (AIC) (Akaike, 1987). Small p-values for the Χ2 would indicate that the hypothesized structure is not confirmed by the sample data (Hughes et al., 1986). However, Jöreskog and Sörbom (1989) note that this statistic should be interpreted with caution, and that other measures of fit should be considered, such as the ratio of Χ2 to degrees of freedom. RMSEA is one of the most informative criteria in assessing model fit (Byrne, 2001), with a builtin correction for model complexity (Kline 2005, p. 137). A RMSEA value of less than .08 is reasonable, although many view a value of .05 or less as indicating a good fit (Browne and

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Cudeck, 1993; Byrne, 2001; Kline, 2005). The other ratios (TLI, CFI, and IFI) are evaluated for their closeness to 1.0, and are preferred fit indices for small sample sizes (Shah and Goldstein, 2006). In addition, the AIC, which compares the hypothesized sample model to a hypothetical random sample (saturated) model, was also used to measure model parsimony (Kline, 2005, p. 142). The AIC of the hypothesized model should be less than that of the saturated model, since the model with the smallest AIC is the one most likely to replicate (Byrne, 2001; Hu and Bentler, 1995; Kline, 2005). The measurement model has good fit indices, as shown in Table 3. [Insert Table 3 about here.] Convergent validity is evident when multiple attempts at measuring the same constructs produce the same results (Bagozzi et al., 1991). Convergent validity was evaluated with the fitted residual matrix and the standardized coefficients of the construct indicators. None of the standardized residuals in the fitted residual matrix were large enough (> │2.58│) to demonstrate potential areas of model misfit per Jöreskog and Sörbom (1988). In addition, as indicated in Table 3, all of the standardized coefficients are highly significant at p < 0.001, again indicating convergent validity. Discriminant validity is concerned with assuring that the measures of the individual constructs are discrete (Bagozzi et al., 1991). Crocker and Algina (1986) indicate that discriminant validity is shown when the correlations of individual factors do not exceed the reliability coefficients. All of the correlation coefficients shown in Table 2 are less than the reliability coefficients. Multivariate multicollinearity in the measurement model was assessed by examining tolerance factors and variance inflation factors. None of the variance inflation factors exceeded 2.0 and the tolerance statistics were all under 1.0 (not reported), indicating multicollinearity is not a concern.

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4. Research Results 4.1.

Descriptive Statistics The survey respondents were asked to indicate whether or not (―yes or no‖) they had

formally implemented Lean accounting at their facility. The results show that 119 of the 244 plants have some form of Lean accounting in place, and all of those plants indicated that they have formally implemented Lean manufacturing.5 Figure 2 depicts the implementations of Lean practices (Lean accounting, Lean accounting, JIT, TQM, and TPM) for all respondent firms. [Insert Figure 2 about here.] Table 4 presents descriptive statistics for the test model variables for the full sample, the plants indicating they had adopted Lean accounting, and the plants that had not adopted Lean accounting. The mean ratios for the Lean accounting plants versus the non-Lean accounting plants are all in the directions expected. Interestingly, the ANOVAs show that the means for all of the variables are significantly different for the LA plants versus the non-LA plants. Also, note that the two variables that would most likely be elements of a Lean accounting system (SMAS and VSC) have extremely strong significant differences between lean accounting and non-lean accounting respondents. VSC is the proxy for the dependent adoption of LA variable. [Insert Table 4 about here.] 4.2.

Fitness of the structural equation model Before the path coefficients can be assessed, the fitness of the structural model must be

evaluated. As shown in Table 5, the goodness-of-fit statistics generally indicate a good fit to the data. Although the Χ2 is significant, the Χ2 ratio is less than two, indicating an acceptable fit

5

Note that this sample does not approximate a representation of the percentage of LA adopters in the general population, since the sample was taken from attendees at Lean Accounting Summits, where the interest in LA and percentage of adoption would be much higher.

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(Kline, 2005). Each of the remaining model fit indices shown in Table 5 (IFI, TLI, and CFI) exceeds the acceptable fit level of 0.90, and the RMSEA does not exceed the acceptable fit measure of 0.08 (Browne and Cudeck, 1993). AMOS calculates a 90% confidence interval for the population parameter estimated by the RMSEA. The low to high range for the model‘s RMSEA is 0.033 to 0.047, which indicates that the model has close approximate fit in the population (see Byrne, 2001; Kline, 2005). In addition, the probability value that the model is a close fit is convincing at 0.993. Jöreskog and Sörbom (1996) suggest that the p-value for this test should be > 0.50. Further, parsimony is demonstrated by an AIC that is lower than that for the saturated model. [Insert Table 5 about here.] 4.3.

Test results of the structural equation model As shown on Table 5, all of the structural paths are supported and are in the expected

direction as hypothesized. This suggests that the variables shown in the structural model represent the elements that are likely to be in place for firms that have taken the dramatic step of changing their accounting systems to support their lean environments. Top management support is important for an empowered culture that has implemented many of the lean manufacturing tools, such as cellular manufacturing, Kanban, 5S, and one-piece flow (H1 and H2). The results further show that the higher the level of lean manufacturing practices in place, the more the workers are trained in lean and empowered (H3). Those firms that are more apt to manage with readily available, visual, strategically-aligned performance measures are those firms that also have developed more of a lean, empowered environment (H4 and H5). Further, the more lean production practices used by these firms, the less likely they are to be using traditional inventory tracking and costing methods (H6). Visual management should also enable companies to

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streamline and strategically align their MAS, which is confirmed by the supporting results of H7. Those firms that have organized into value streams and have adapted their accounting reporting per the results of those value streams are more likely to have simplified their accounting operations, established visual management controls on the shop floor, and reduced their efforts in inventory tracking and overhead allocations (H10, H9, and H8, respectively). The structural model provides an empirical understanding of the characteristics of firms who are Lean accounting adopters, and as such are using VSC6. There has been much conjecture by practitioners and consultants as to what elements must be in place for firms to not only understand the need for accounting change, but to actually make the necessary transitions for providing appropriate decision-making information that encourages and sustains lean implementations. However, to date there have been no robust empirical studies of which we are aware that provide insight into the characteristics of those firms. This unique database evaluates responses from firms that are highly interested in adapting their accounting systems to report lean improvements and who have made progress in lean practices both on the shop floor and in their MAS. These results mainly provide an empirical confirmation of the relationships that have been anecdotally encouraged in various practitioner outlets. All of the results were anticipated a priori. The relationships among top management support, lean manufacturing practices, and an empowered environment have been examined in other empirical studies. However, understanding their relationships to visual management controls and streamlined accounting

6

The model was also run using the dichotomous measure Lean Accounting as the dependent variable with qualitatively similar results. However, using a dichotomous variable with AMOS in a SEM is statistically problematic. Although VSC is a categorical variable, it has 5 categories. The results from a categorical variable are less worrisome when a variable has four or more categories (Byrne, 2001: Bentler and Chou, 1987; Green et al., 1997).

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processes has not been well researched. The results here suggest that these are important precursors to the adoption of VSC. One of the main objectives of this study is to gain a better grasp through empirical evidence of what lean accounting/VSC represents, since no theoretical definition is available. The results confirm that firms who have invested their efforts into implementing a management costing system tied to their value streams are more likely to have streamlined and simplified their accounting processes and developed a strategically-aligned accounting system. These characteristics could and probably should be emulated by all firms, because they represent a system-wide focus on continuous improvement and waste elimination. Further, the results suggest that firms using VSC are turning off their traditional overhead allocation schemes and burdensome inventory tracking systems. This represents a major departure from the traditional MAS found in the majority of manufacturing firms. The time savings resulting from the elimination of hundreds of inventory-tracking transactions would require a firm to have minimal and/or stable inventories. 5.

Conclusion This research provides some of the first empirical evidence of the environmental

characteristics associated with VSC. The results suggest that companies that have implemented a Lean accounting approach have simplified their accounting processes and eliminated their inventory tracking. They are supported directly or indirectly by an encouraging top management, the implementation of a greater number of Lean practices, more quality-focused, empowered work teams, and a more visual performance management system. It appears that as Lean processes mature in the plant, the organizational culture promotes Lean thinking as a working model throughout all business functions, including accounting.

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There is much that needs to be learned and clarified about this emerging management accounting measurement system. There are a few anecdotal examples of company successes from changing their accounting systems in support of their Lean environment, but fact-based research is lacking. While Lean manufacturing has been around for almost three decades and is growing in importance in the manufacturing world, implementing a corresponding measurement system that is appropriate for this environment has been slow to emerge. The traditional standard costing system continues its popularity, even though its deficiencies have been thoroughly discussed by practitioners and academics alike for over two decades. Field studies have indicated that accounting departments are much more optimistic about the accounting improvements they have made than are production managers (Carnes and Hedin, 2005). Accountants need to mingle more on the shop floor and gain a better understanding of production‘s informational needs for sound decision making. The current traditional accounting system is so entrenched that until strong evidence is provided of the value of change, not much will happen. Hopefully, research projects such as this will provide momentum for further research and discussion about the necessity of having MAS that match and support the strategic thinking and initiatives of forward-looking manufacturing firms. 5.1.

Limitations of the Research The most limiting aspect of this research is the lack of a random sample, which reduces

the generalizability and applicability of the findings. It is very difficult to find a sizable sampling of firms that have adapted their MAS to match their lean production, so the researchers depended on a single venue for selecting their respondents. It is assumed that all of the respondents had a vested interest in Lean thinking, and particularly the principles of Lean accounting by their

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attendance at the Lean Accounting Summits. As in all survey research, a necessary assumption in data collection is that the respondents had sufficient knowledge to answer the items, and that they answered the questions conscientiously and truthfully. 5.2.

Future Research This study examines various aspects of the environment and characteristics that may

encourage manufacturing firms to be willing to take the rather dramatic steps to change their accounting systems in support of other change initiatives occurring throughout their operations. In-depth case studies are needed to identify these characteristics more specifically. Long-term analyses would be helpful to evaluate the sustainable success of changes in accounting measures. It would also be interesting to find out if changing the MAS leads to better operation or financially performance. Further, survey studies that have a larger cross-sectional random sample may provide a clearer understanding of the results found in this study. Examination of accounting systems other than VSC that may support lean practices such as time-driven ABC, throughput accounting, and GPK is another avenue for study. It is critical that research continues to search for ways to improve our MAS, so they provide more useful information to the decision makers of world-class firms operating in the highly competitive global markets of today.

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APPENDIX Survey Questions that Support the Variables used in this Research TMGTc How supportive is top management in:  Initiating change programs?  Implementing lean manufacturing practices?  Providing training for new production strategies? LMFGa To what extent has your facility implemented the following:  Standardization  Manufacturing cells  Reduced setup times  Kanban system  One-piece flow  Reduced lot sizes  Reduced buffer inventories  5S  Kaizen (continuous improvement) EMPRb Please indicate the level of agreement or disagreement to the following statements:  Shop-floor workers participate in quality decisions  Management is committed to quality-related training  Resources for training are readily available  All employees are encouraged to make suggestions for problem solving  Employees are recognized for superior quality performance  We have a great deal of employee involvement-type programs b VLPM Indicate your agreement to the following statements related to your management accounting system:  Many performance measures are collected on the shop floor.  Performance metrics are aligned with operational goals  Visual boards are used to share information.  Information on quality performance is readily available.  Charts showing defect rates are posted on the shop floor.  We have created a visual mode of organization  Information on productivity is readily available.  Quality data is displayed at work stations. b TRMA Indicate your agreement to the following statements related to your management accounting system:  Tracking inventories is an important accounting function.*  Assigning accurate overhead costs to product is critical.*  Assigning labor costs to inventory is critical.*

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SMASb Indicate your agreement to the following statements related to your management accounting system:  Our accounting system has been simplified in the past 3 years.  Our accounting closing process has been streamlined.  Our management accounting system supports our strategic initiatives.  Our accounting information system facilitates strategic decision making. VSCa Indicate the extent to which your facility uses value stream costing. a

Possible responses: Not at all = 1; Little = 2; Some = 3; Considerable = 4; Great Deal = 5 Possible responses: Strongly agree = 1…2…3….4…..Strongly disagree = 5. c Possible responses: Indifferent = 1……2….Encouraging = 3……4…..Highly Supportive =5 b

*These questions were reverse coded. Note that responses for a and c questions have a positive anchor at ―5‖ and responses for b questions have a positive anchor at ―1‖.

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42

Figure 1

Lean Accounting SEM Model

SMAS

EMPR H1

H10

H7

H4

H9

H3 TMGT

H8

H5

H2

LMFG

VSC

VLPM

H6

TRMA

TMGT = Top management support in lean and change initiatives EMPR = A culture where employees are cross-trained and empowered LMFG = Implementation of lean manufacturing practices VLPM = The visibility and strategic alignment of performance measures TRMA = The tracking of labor, overhead, and inventories SMAS = The simplification and strategic alignment of the accounting system VSC = The extent of use of value stream costing

43

Figure 2

Adoptions of Lean Practices # of Respondents

300 17

250 200 150

112

125

129

108

100 50

Non-Adopters 119

227

132

115

136

LA

LM

JIT

TQM

TPM

0

Adopters

Lean Practices

LM = lean manufacturing LA = lean accounting JIT = just-in-time TQM = total quality management TPM = total productive maintenance

44

TABLE 1 Factor Analysis: Factor Loadings for Explanatory Variables

LMFG-standardization

Factor 1 LMFG 0.665

LMFG-cells

0.738

LMFG-reduced setup

0.679

LMFG-Kanban

0.720

LMFG-one-piece flow

0.740

LMFG-reduced lot size

0.780

LMFG-reduced inventory

0.655

LMFG-5S

0.712

LMFG-Kaizen

0.669

Factor 2 EMPR

EMPR-cross-train

0.641

EMPR-quality decisions

0.756

EMPR-quality training

0.733

EMPR-training resources

0.667

EMPR-empl suggestions

0.730

EMPR-quality recognition

0.697

EMPR-involvement

0.761

Factor 3 TMGT

TMGT-change

0.827

TMGT-lean support

0.795

TMGT-new strategies

0.768

Factor 4 SMAS

SMAS-MAS simplified

0.762

SMAS-close streamlined

0.721

SMAS-support strategies

0.774

SMAS-decision making

0.756

Factor 5 VLPM

VLPM-collect shop floor

0.633

VLPM-aligned measures

0.639

VLPM-visual boards

0.659

VLPM-quality info

0.655

VLPM-defect charts

0.735

VLPM-visual organization

0.558

VLPM-productivity info

0.700

VLPM-data work stations

0.673

Factor 6 TRMA

TRMA-track inventories

0.723

TRMA-assign OH

0.803

TRMA-assign labor

0.783

Notes: n = 244; all loadings in excess of 0.40 are shown; Kaiser-Meyer-Olkin measure of sampling adequacy is very good (0.90) and the Bartlett test of Sphericity is highly significant (p = 0.000).

TABLE 2 Pearson correlation table for independent variables

1. TMGT

# of Measures 3

2. LMFG

9

.48**

1.00

3. EMPR

7

-.50**

-.44**

4. VLPM

8

-42**

-.51**

.48**

1.00

5. TRMA

3

-.12

-.32*

.09

.08

1.00

6. SMAS

4

-.32**

-.37**

.31**

.36**

.14*

1.00

7. VSC

1

.22**

.34**

-.39**

-.35**

-.18**

Notes: .

1

2

3

4

5

6

7

1.00 1.00

-.17*

1.00

8 Mean~ 3.959

9 S.D. 0.95

10 Cr. Α 0.90

3.758

0.89

0.91

2.474

0.70

0.87

2.589

0.74

0.86

3.033

0.90

0.82

2.806

0.93

0.71

2.440

1.21

N/A

n = 244 ** significant at the .01 level; *significant at the .05 level (2-tailed) ~ All measures are a Likert scale from 1-5. Variables 1, 2, and 7 have a positive anchor of ―5‖ and variables 3, 4, 5, and 6 have a positive anchor of ―1‖ on the Likert scale.

TMGT = Top management support in lean and change initiatives EMPR = A culture where employees are cross-trained and empowered LMFG = Implementation of lean manufacturing practices VLPM = The visibility and strategic alignment of performance measures TRMA = The tracking of labor, overhead, and inventories SMAS = The simplification and strategic alignment of the accounting system VSC = The extent of use of value stream costing

Table 3 Results from Confirmatory Factor Analysis Summary Data for Individual Construct Indicators Construct Standardized t-values Indicators Coefficients (all significant to p (loadings)