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Inventory Management for Bus and Rail Public Transit Systems: Final Report (1995)

Chapter: V. THE EFFECT OF ORGANIZATIONAL STRUCTURE ON INVENTORY MANAGEMENT PERFORMANCE

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Suggested Citation:"V. THE EFFECT OF ORGANIZATIONAL STRUCTURE ON INVENTORY MANAGEMENT PERFORMANCE." Transportation Research Board. 1995. Inventory Management for Bus and Rail Public Transit Systems: Final Report. Washington, DC: The National Academies Press. doi: 10.17226/6352.
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Suggested Citation:"V. THE EFFECT OF ORGANIZATIONAL STRUCTURE ON INVENTORY MANAGEMENT PERFORMANCE." Transportation Research Board. 1995. Inventory Management for Bus and Rail Public Transit Systems: Final Report. Washington, DC: The National Academies Press. doi: 10.17226/6352.
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Suggested Citation:"V. THE EFFECT OF ORGANIZATIONAL STRUCTURE ON INVENTORY MANAGEMENT PERFORMANCE." Transportation Research Board. 1995. Inventory Management for Bus and Rail Public Transit Systems: Final Report. Washington, DC: The National Academies Press. doi: 10.17226/6352.
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Suggested Citation:"V. THE EFFECT OF ORGANIZATIONAL STRUCTURE ON INVENTORY MANAGEMENT PERFORMANCE." Transportation Research Board. 1995. Inventory Management for Bus and Rail Public Transit Systems: Final Report. Washington, DC: The National Academies Press. doi: 10.17226/6352.
×
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Suggested Citation:"V. THE EFFECT OF ORGANIZATIONAL STRUCTURE ON INVENTORY MANAGEMENT PERFORMANCE." Transportation Research Board. 1995. Inventory Management for Bus and Rail Public Transit Systems: Final Report. Washington, DC: The National Academies Press. doi: 10.17226/6352.
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Page 54
Suggested Citation:"V. THE EFFECT OF ORGANIZATIONAL STRUCTURE ON INVENTORY MANAGEMENT PERFORMANCE." Transportation Research Board. 1995. Inventory Management for Bus and Rail Public Transit Systems: Final Report. Washington, DC: The National Academies Press. doi: 10.17226/6352.
×
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Page 55
Suggested Citation:"V. THE EFFECT OF ORGANIZATIONAL STRUCTURE ON INVENTORY MANAGEMENT PERFORMANCE." Transportation Research Board. 1995. Inventory Management for Bus and Rail Public Transit Systems: Final Report. Washington, DC: The National Academies Press. doi: 10.17226/6352.
×
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Page 56
Suggested Citation:"V. THE EFFECT OF ORGANIZATIONAL STRUCTURE ON INVENTORY MANAGEMENT PERFORMANCE." Transportation Research Board. 1995. Inventory Management for Bus and Rail Public Transit Systems: Final Report. Washington, DC: The National Academies Press. doi: 10.17226/6352.
×
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Page 57
Suggested Citation:"V. THE EFFECT OF ORGANIZATIONAL STRUCTURE ON INVENTORY MANAGEMENT PERFORMANCE." Transportation Research Board. 1995. Inventory Management for Bus and Rail Public Transit Systems: Final Report. Washington, DC: The National Academies Press. doi: 10.17226/6352.
×
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Suggested Citation:"V. THE EFFECT OF ORGANIZATIONAL STRUCTURE ON INVENTORY MANAGEMENT PERFORMANCE." Transportation Research Board. 1995. Inventory Management for Bus and Rail Public Transit Systems: Final Report. Washington, DC: The National Academies Press. doi: 10.17226/6352.
×
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Page 59
Suggested Citation:"V. THE EFFECT OF ORGANIZATIONAL STRUCTURE ON INVENTORY MANAGEMENT PERFORMANCE." Transportation Research Board. 1995. Inventory Management for Bus and Rail Public Transit Systems: Final Report. Washington, DC: The National Academies Press. doi: 10.17226/6352.
×
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Page 60
Suggested Citation:"V. THE EFFECT OF ORGANIZATIONAL STRUCTURE ON INVENTORY MANAGEMENT PERFORMANCE." Transportation Research Board. 1995. Inventory Management for Bus and Rail Public Transit Systems: Final Report. Washington, DC: The National Academies Press. doi: 10.17226/6352.
×
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Page 61
Suggested Citation:"V. THE EFFECT OF ORGANIZATIONAL STRUCTURE ON INVENTORY MANAGEMENT PERFORMANCE." Transportation Research Board. 1995. Inventory Management for Bus and Rail Public Transit Systems: Final Report. Washington, DC: The National Academies Press. doi: 10.17226/6352.
×
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Suggested Citation:"V. THE EFFECT OF ORGANIZATIONAL STRUCTURE ON INVENTORY MANAGEMENT PERFORMANCE." Transportation Research Board. 1995. Inventory Management for Bus and Rail Public Transit Systems: Final Report. Washington, DC: The National Academies Press. doi: 10.17226/6352.
×
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Suggested Citation:"V. THE EFFECT OF ORGANIZATIONAL STRUCTURE ON INVENTORY MANAGEMENT PERFORMANCE." Transportation Research Board. 1995. Inventory Management for Bus and Rail Public Transit Systems: Final Report. Washington, DC: The National Academies Press. doi: 10.17226/6352.
×
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Suggested Citation:"V. THE EFFECT OF ORGANIZATIONAL STRUCTURE ON INVENTORY MANAGEMENT PERFORMANCE." Transportation Research Board. 1995. Inventory Management for Bus and Rail Public Transit Systems: Final Report. Washington, DC: The National Academies Press. doi: 10.17226/6352.
×
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Suggested Citation:"V. THE EFFECT OF ORGANIZATIONAL STRUCTURE ON INVENTORY MANAGEMENT PERFORMANCE." Transportation Research Board. 1995. Inventory Management for Bus and Rail Public Transit Systems: Final Report. Washington, DC: The National Academies Press. doi: 10.17226/6352.
×
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Suggested Citation:"V. THE EFFECT OF ORGANIZATIONAL STRUCTURE ON INVENTORY MANAGEMENT PERFORMANCE." Transportation Research Board. 1995. Inventory Management for Bus and Rail Public Transit Systems: Final Report. Washington, DC: The National Academies Press. doi: 10.17226/6352.
×
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Suggested Citation:"V. THE EFFECT OF ORGANIZATIONAL STRUCTURE ON INVENTORY MANAGEMENT PERFORMANCE." Transportation Research Board. 1995. Inventory Management for Bus and Rail Public Transit Systems: Final Report. Washington, DC: The National Academies Press. doi: 10.17226/6352.
×
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Suggested Citation:"V. THE EFFECT OF ORGANIZATIONAL STRUCTURE ON INVENTORY MANAGEMENT PERFORMANCE." Transportation Research Board. 1995. Inventory Management for Bus and Rail Public Transit Systems: Final Report. Washington, DC: The National Academies Press. doi: 10.17226/6352.
×
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Suggested Citation:"V. THE EFFECT OF ORGANIZATIONAL STRUCTURE ON INVENTORY MANAGEMENT PERFORMANCE." Transportation Research Board. 1995. Inventory Management for Bus and Rail Public Transit Systems: Final Report. Washington, DC: The National Academies Press. doi: 10.17226/6352.
×
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Suggested Citation:"V. THE EFFECT OF ORGANIZATIONAL STRUCTURE ON INVENTORY MANAGEMENT PERFORMANCE." Transportation Research Board. 1995. Inventory Management for Bus and Rail Public Transit Systems: Final Report. Washington, DC: The National Academies Press. doi: 10.17226/6352.
×
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Suggested Citation:"V. THE EFFECT OF ORGANIZATIONAL STRUCTURE ON INVENTORY MANAGEMENT PERFORMANCE." Transportation Research Board. 1995. Inventory Management for Bus and Rail Public Transit Systems: Final Report. Washington, DC: The National Academies Press. doi: 10.17226/6352.
×
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Suggested Citation:"V. THE EFFECT OF ORGANIZATIONAL STRUCTURE ON INVENTORY MANAGEMENT PERFORMANCE." Transportation Research Board. 1995. Inventory Management for Bus and Rail Public Transit Systems: Final Report. Washington, DC: The National Academies Press. doi: 10.17226/6352.
×
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Suggested Citation:"V. THE EFFECT OF ORGANIZATIONAL STRUCTURE ON INVENTORY MANAGEMENT PERFORMANCE." Transportation Research Board. 1995. Inventory Management for Bus and Rail Public Transit Systems: Final Report. Washington, DC: The National Academies Press. doi: 10.17226/6352.
×
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Suggested Citation:"V. THE EFFECT OF ORGANIZATIONAL STRUCTURE ON INVENTORY MANAGEMENT PERFORMANCE." Transportation Research Board. 1995. Inventory Management for Bus and Rail Public Transit Systems: Final Report. Washington, DC: The National Academies Press. doi: 10.17226/6352.
×
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Suggested Citation:"V. THE EFFECT OF ORGANIZATIONAL STRUCTURE ON INVENTORY MANAGEMENT PERFORMANCE." Transportation Research Board. 1995. Inventory Management for Bus and Rail Public Transit Systems: Final Report. Washington, DC: The National Academies Press. doi: 10.17226/6352.
×
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Suggested Citation:"V. THE EFFECT OF ORGANIZATIONAL STRUCTURE ON INVENTORY MANAGEMENT PERFORMANCE." Transportation Research Board. 1995. Inventory Management for Bus and Rail Public Transit Systems: Final Report. Washington, DC: The National Academies Press. doi: 10.17226/6352.
×
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Suggested Citation:"V. THE EFFECT OF ORGANIZATIONAL STRUCTURE ON INVENTORY MANAGEMENT PERFORMANCE." Transportation Research Board. 1995. Inventory Management for Bus and Rail Public Transit Systems: Final Report. Washington, DC: The National Academies Press. doi: 10.17226/6352.
×
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Suggested Citation:"V. THE EFFECT OF ORGANIZATIONAL STRUCTURE ON INVENTORY MANAGEMENT PERFORMANCE." Transportation Research Board. 1995. Inventory Management for Bus and Rail Public Transit Systems: Final Report. Washington, DC: The National Academies Press. doi: 10.17226/6352.
×
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Suggested Citation:"V. THE EFFECT OF ORGANIZATIONAL STRUCTURE ON INVENTORY MANAGEMENT PERFORMANCE." Transportation Research Board. 1995. Inventory Management for Bus and Rail Public Transit Systems: Final Report. Washington, DC: The National Academies Press. doi: 10.17226/6352.
×
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Suggested Citation:"V. THE EFFECT OF ORGANIZATIONAL STRUCTURE ON INVENTORY MANAGEMENT PERFORMANCE." Transportation Research Board. 1995. Inventory Management for Bus and Rail Public Transit Systems: Final Report. Washington, DC: The National Academies Press. doi: 10.17226/6352.
×
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Suggested Citation:"V. THE EFFECT OF ORGANIZATIONAL STRUCTURE ON INVENTORY MANAGEMENT PERFORMANCE." Transportation Research Board. 1995. Inventory Management for Bus and Rail Public Transit Systems: Final Report. Washington, DC: The National Academies Press. doi: 10.17226/6352.
×
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Suggested Citation:"V. THE EFFECT OF ORGANIZATIONAL STRUCTURE ON INVENTORY MANAGEMENT PERFORMANCE." Transportation Research Board. 1995. Inventory Management for Bus and Rail Public Transit Systems: Final Report. Washington, DC: The National Academies Press. doi: 10.17226/6352.
×
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Suggested Citation:"V. THE EFFECT OF ORGANIZATIONAL STRUCTURE ON INVENTORY MANAGEMENT PERFORMANCE." Transportation Research Board. 1995. Inventory Management for Bus and Rail Public Transit Systems: Final Report. Washington, DC: The National Academies Press. doi: 10.17226/6352.
×
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Suggested Citation:"V. THE EFFECT OF ORGANIZATIONAL STRUCTURE ON INVENTORY MANAGEMENT PERFORMANCE." Transportation Research Board. 1995. Inventory Management for Bus and Rail Public Transit Systems: Final Report. Washington, DC: The National Academies Press. doi: 10.17226/6352.
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V. THE EFFECT OF ORGANIZATIONAL STRUCTURE ON INVENTORY MANAGEMENT PERFORMANCE 5.1 ORGANIZATIONAL STRUCTURE AND INVENTORY MANAGEMENT DECISIONS The objective of this analysis is to examine the effects of a public transit agency's inventory management organizational structure and inventory management decisions upon the agency's inventory performance. In addition, selected agency and fleet characteristics were included in the analysis to determine if these factors, combined with organizational factors, have an effect on inventory performance. The organizational and inventory management decision factors, agency and fleet characteristics, and inventory performance indicators used in this analysis are summarized below in sections 5.1.1, 5.1.2, and 5.1.3. Section 5.2 of this chapter describes the approach and statistical techniques that were used in analyzing the effects of the factors and characteristics on inventory performance. Section 5.3 describes the organizational structure and inventory management decision factors in more detail and presents the results of the analysis of their effects on inventory performance indicators. Section 5.4 presents the general relationships between significant groups of factors and the performance indicators resulting from regression analysis. Section 5.5 summarizes the conclusions drawn from the analysis. 5.~.~ Organizational Structure and Inventory Management Decision Factors For the purpose of this analysis, a transit agency's organizational structure Includes the policies and management decisions that define how inventory is to be managed, as well as the personnel reporting relationships, responsibilities, and staffing levels. In particular, the effects of the following factors were examined in this analysis based on responses from the survey conducted during this study: . Staffing levels for managing inventory, both within and outside of the inventory management organization Organizational structure type (based on the five organizational structure types defined in Chapter m) and specific attributes, such as the responsible department and level, the existence of a fil11-time organizational head, the separation of the inventory planning function, etc. Setting inventory management goals for stocking levels and sentence · Methods used to replenish inventory matenal, including safety stock levels · Purchasing practices for inventor material 49

Storehouse network configuration and coverage Published procedures and catalog Physical inventory/cycle counting Dequencies Repair and stocking of re-usable components Use of systems and technology These factors and the specific attributes that were analyzed are described in detail in section 5.3 of this Chapter. S.~.2 Transit Agency and Fleet Characteristics As concluded in Chapter IV, transit agency and fleet characteristics alone have no significant effect on inventor perfonnance indicators, except for some differences between bus and rail Inventory. However, the following agency and feet characteristics were included in the regression analyses to determine if these characteristics have any effects in combination with specific inventory management structures and decisions: number of vehicles In Beet (bus, rail, and total) dollar value of inventor (bus, rail, and total) agency operating cost number of vehicle models (bus, rail, and total) inventor purchase dollars (bus, rally average annual miles (bus, rail) average vehicle age in years (bus, rail, and total) average vehicle age in percent of expected life expended (bus, rail, total) annual passenger miles (bus, rail) percent of vehicle manufactured In the United States (bus, rail, and total) (See Chapter ~ for more information on these factors.) 5.~.3 Inventory Performance Indicators The foldowing performance indicators, defined in Chapter IV of this report, are used to define inventory performance in this analysis: 2. 4. 5. 6. Bus Inventory Dollars per Vehicle Rail Inventory Dollars per Vehicle Annual Bus Inventory Turnover Annual Rail Inventory Turnover Bus Percent Demand Filled (Fill Rate) Rail Percent Demand Filled (Fill Rate) 50

Percent of Items Stocked Out per Week Average Days to Fib Bus Inventory Backorders Average Days to Fill Rail Inventory Backorders 1. 8. 9. 10. Percent of Items Out of Balance ~ I. Percent Obsolete Bus Inventory 12. Percent Obsolete Rail Inventory 13. Total Inventory Dollars per Person 14. Inventory Personnel Cost per Inventory Dollar IS. Total Inventory Transactions per Person 5.2 MET HODOLOGY FOR ANALYZING ORGANIZATIONAL EFFECTS The analysis summarized In this chapter examined the eject of each organizational structure and inventory management decision factor on the inventory performance indicators. The responses to the survey conducted during this study were used as data for this analysis. The inventory perfonnance indicators were derived Dom survey Section V data. Organizational and Inventory management decision factors were derived Dom sections m, IV, and V! of the survey. Transit agency and feet characteristics were denved Dom survey sections ~ and H. 5.2.! Preparing Survey Data for Analysis ~ perfonriing the above statistical analyses, the data was prepared as follows: "Yes/No" responses were coded. Factors whose categories were "yes" or "no" were quantified as "I" and "0" for inclusion in correlations and regressions. For example, survey responses as to whether an agency uses blar~cet purchase orders to replenish inventory were coded as ~ or 0 to represent "yes" or "no". This technique was also applied to each of the five orgaruzational types so that organizational type could be used In quantitative analyses. For example, if an agency's inventory management organization was type 3, then a vanable representing organizational type 3 was set to "I", and four vanables representing organizational types I, 2, 4, and 5 were set to "0". Missing responses were eliminated. Many survey respondents did not answer ad questions. As a result, some of the values of the orgaruzational and management decision factors were blank. The entire observation was excluded from analysis when the unanswered question was a variable In the analysis. For example, if a survey respondent did not specifier whether blanket orders were used In purchasing inventory material, all of the respondents' answers were omitted from analyses involving the use of blanket orders. Eliminating the entire observation ensured that the value of the performance indicator being examined related only to the values of the organ national and management decisions In the analysis. The observation was eliminated only for those analyses involving the missing response. Bus and rail responses were applied separately. Rail variables were excluded for bus inventory perfonnance indicators and vice versa. For example, the number of buses was not included in the regression analysis for rail inventory dollars per vehicle. Variables for the total fleet were used for 51

analyzing performance indicators representing overall inventory performance, but two yes/no variables were included to identify whether the property "has buses" and "has rail". For example, the total number of vehicles, as well as variables indicating whether the agency has bus and/or rail, were included in analyzing overall inventory accuracy (percent of items out of balance). Identical responses were eliminated. Variables were excluded when all ofthe survey responses were the same for the selected performance indicator. For example, all rail properties used a reorder point method to replenish inventory (response = "yes"). Therefore this variable is actually a constant in our survey and cannot be used to analyze the effect on inventory performance. As a result, this variable was excluded from the analysis of rail inventory performance indicators. 5.2.2 StatisticalAnalysis Methods Three primary statistical methods were used for this analysis, based on whether the organizational factors were numerical (e.g. number of storehouses) or divided into categories (e.g. reporting to maintenance, finance, administration, etc.). Correlation and regression were used for quantitative data and l-tests were used for category data. Section 4.3.2. 1 (Chapter IV) of this report describes correlations and section 4.3.2.2 describes t-tests. Regression analysis was used to examine the relationship between a performance indicator and a combination of organizational and inventory management decision factors. Regression analysis is a statistical process that defines a formula for predicting the value of a performance indicator using the values of organizational and management decision factors. For example, a regression equation can be derived to predict the value of annual inventory turnover using the number of buses, the number of individuals assigned to inventory management, and whether the authority uses an automated inventory system. Regression analysis will result in the equation that best utilizes the values in the survey to predict inventory turnover. Note that regression analysis does not imply that the number of buses, number of inventory management individuals, and the automated system causes inventory turnover to go up or down. The analysis simply defines the relationship between the variables. Two regression analysis methods were used in this study: (1) multiple linear regression and (2) step-wise linear regression. Multiple linear regression produces a formula for a line that uses Redefined multiple variables to predict the independent variable. All variables are included in the regression equation. Step-wise linear regression builds a linear equation by selecting variables one at a time from a Redefined set of variables. This technique selects the best variables for predicting the independent variable and does not necessarily include all variables in the equation. Both of these regression methods develop equations using the "least-squares" method. The least-squares method is a mathematical technique that produces a linear equation that minimizing the sum of the squares of the distance of survey data points from the regression line. The resulting regression equation is of the form: y = C + ClXl + C2X2 + C3X3 ... + CiXi Where: y is the independent variable (e.g. annual inventory turnover) 52

c is the constant for the equation (or the y-~ntercept of the line) x1 through ~ are the dependent variables (e.g. number of buses) c' through ~ are the coefficients for the dependent variables i is the total number of dependent variables In the equation Performing regression analysis involved the following steps: Eliminate redundant variables from the analysis (multi colineanty). In developing the regression equation, it is important to eliminate dependent variables that are highly correlated with each other. These variables wiD distort the analysis and could lead to incorrect conclusions. For example, a transit agency's total number of buses and the total bus passenger miles per year are highly correlated. These two variables represent redundant data and could distort the analysis, such as resulting in coefficients that mostly offset each other. Since these two variables represent almost the same data pattern, one of the variables is eliminated Dom the regression analysis. The technique used to identify highly correlated variables, or multi-colinearity, was to develop a correlation Manx. This matrix showed the correlation between each variables In the survey with each other variable. As a general rule of thumb, when vanables showed a correlation of over .70 (or less than -.70), one ofthe variables was eliminated from the regression analysis. Select the dependent vanables that best predict the independent vanable. Once multi~olinearity was addressed, the next task was to select the set of vanables Mom the 45 - 50 remaining that best predict the Independent vanable. Step-w~se regression was used for this task. Ste~w~se regression selects variables one at a tune to include or exclude Dom the regression equation until the best set of variables is found. The significance level can be set as a enters for including dependent variables. The higher the significance level, the fewer variables wid meet the criteria and be included in the equation. Step-w~se linear regression was run on the remaining survey data variables for each inventory perfonnance Indicator. The significance level for including variables In the equation was varied to observe the resulting effect on the variables included in the regression equation, the amount of variance in the independent variable explained by the equation, and the overall confidence level of the prediction. For example, a minimum 99°/O confidence level may result In an equation with few variables or may not produce an equation at all if no variables exceed the 99% minimum rate. Lowering the minimum rate to 95°/O may allow additional variables to be included and therefore increase the percent of variance in the independent variable that is explained by the equation. However, if the minimum rate is lowered too much, the confidence In the prediction resulting Tom the equation decreases, even though the equation explains a higher percent of the vanance. Furthermore, there is lower confidence that the dependent variables included in the equation actually have an effect. 53

Regressions were run until all variables with a 90% or greater confidence were included in the regression equation. ~ other words, there is at most a 10% probability the dependent variables that the regression selected Dom the survey data as significant, are really due to change and should have a coefficient of zero. The dependent vanables Dom this step-w~se regression analysis having a confidence of 90% or greater were selected for the next step. 3. Denve the regression equation. A multiple linear regression analysis was performed on the dependent variables identified in the previous step. This analysis resulted in a regression equation, a coefficient of determination (r23, an analysis of vanance, and other statistics. 4. Determine how well the equation predicts the independent variable. The coefficient of determination is the percent of variance in the independent variable explained by the regression equation, based on the survey data. This coefficient is a measure of how well the regression equation predicts the independent variable. For example, if a regression is run for annual inventory turnover with an rid of .85, the recession equation win explain 85% of the · · - var~ance In annua Inventory turnover. Determine the confidence level of the prediction. The F-statistic and the F-distnbution are used to determine the confidence level of the prediction resulting from the equation. The confidence level measures the confidence that the prediction resulting Dom the survey data's regression equation is not equal to zero (i.e., the null hypothesis). This is the confidence that all ofthe coefficients in the recession equation and the constant are not equal to zero. For example, if the confidence level is 92%, one can infer with 92% confidence that the predicted value of an inventory performance indicator is not equal to zero. 6. Determine the sigruficance of each dependent variable. Just as a confidence level can be calculated for the entire regression equation, a confidence level can also be calculated for each coefficient of each dependent variable. This confidence level is a measure of the probability that each dependent variable's coefficient is not equal to zero. S.3 THE EFFECT OF ORGANIZATION & INVENTORY MANAGEMENT DECISIONS This section summarizes findings regarding the effect of each organizational and inventory management factor on inventory management indicators. Only effects with a confidence level of 85% or higher for t-tests, and a correlation coefficient of .70 or higher (-.70 or lower) are identified as significant. Data groups must have at least three responses to be included in l-tests 54

and five for correlations. The following sections describe each organizational and inventory management factor in detail and present the findings associated with the factor. 5.3.! Invento~yStaff~ng Levels The effect of inventory management staffing levels was examined separately for (~) inventory organization personnel - total number of individuals within the inventory management organization, and (2) total inventory management personnel - total number of individuals within the transit agency with inventory management responsibility, regardless of whether they are within the inventory management organization. Inventory staffing levels were analyzed relative to the total number of vehicles in the transit agency and the agency's total inventory dollars. This allowed the effects of relative staffing levels to be compared across agencies of difference sizes. Based on this analysis, there is no correlation between relative staffing levels and any of the inventory perfonnance indicators. Furthermore, there is a high correlation between inventory organization employees and total employees involved with inventory management (r = .997). Therefore, it is not necessary to include both measures as an inventory performance indicator. Since the total number of employees involved with inventory provides a more comprehensive view of inventory management activities, it is used in inventory performance indicators "per person" (such as transactions per person or dollars per person). 5.3.2 Organizational Structure Type Five organizational structure types were defined in detail in Chapter III, ranging from no formal inventory management Unction to a dedicated inventory management group at the department level. In addition to examining the effects of these organizational structure types, selected organizational attributes were analyzed separately, such as the level to which the inventory organization reports, the departments to which the organization reports, the separation of the inventory planning functions, etc. The ejects of the organizational types and each attribute is presented separately. Organizational Type 1 - no formal inventory management filnction, inventory responsibility located in the maintenance department Organizational Type 2 -- formal inventory management function located in a department other than maintenance reporting to the sub-department level Organizational Type 3 - formal inventory management function located in the maintenance department 55

Organizational Type 4 ~ Organizational Type 5 - formal inventory management function located in a department other than maintenance reporting to the department level a dedicated department level inventory management group All rail transit properties that responded to the survey have organization Type 5, except for two which have Type 4 and one which has Type 3. Therefore, there were not enough rail property responses in organization types other than Type 5 to perform l-tests for each organization type. As a result, l-tests for rail inventory performance indicators were performed based on two categories: those that use organization Type 5 and those that do not use organization Type 5. formalization Type. The findings for the effects of organization type are: Agencies with Type ~ organization have less bus inventory dollars per vehicle than those with Type 2 and Type 5. Those with Type 4 also have less bus inventory dollars per vehicle than Type 5 organizations. Type ~ -- $3,175 Type 2 -- $7,612 (~36% confidence) Type ~ -- $3,175 Type 5 -- $6,408 (96% confidences Type 4 -- $4,403 Type 5 -- $6,408 (~% confidence) . Agencies with organization Type 5 have higher annual bus inventory turnover than those with Types 2, 3, or 4. Type 5 -- 2.61 Type 2 -- I.22 (91 °/O confidence) Type 3 -- I.61 (85% confidence) Type 4 -- 1.21 (94% confidence) Agencies with organization Type 5 have higher inventory fill rates than those with Types 1, 3, or 4. Type 5 -- 95.~% Type 2 -- 134.0% (95% confidence) Type 3 -- 136.2% (95% confidence) Type 4 -- 137.3% (89% confidence) Agencies with organization Type 4 have a lower stockout percentage than those with Type 5 organizations, 0.14% compared to 1.64% (90% confidence). Agencies with organization Type 3 take more days to fill a backorder for bus inventory than those with organization 2 or 5. Agencies without organization Type 5 also take more days to fill a backorder for rail inventow than those with a Type 5 organization. 56

Days to fill bus inventor backorder: Type 3 -- 21.2 days Days to fill rail inventory backorder: Not Type 5 -- 40.7 days . . . Type 2 -- 5.5 days Type 5 -- 7.8 days (99% confidence) (99°/O confidence) Type 5 -- 10.0 days (89% confidence) Agencies with organization Type 5 have a higher percent of obsolete bus inventory than those with organization 1 or 3. Agencies with organization Type 5 also have a higher percent of obsolete raid inventory than those without a Type 5 organization. Percent obsolete bus inventory: Type 5 -- 12.9% Percent obsolete rail inventory: Type 5 -- 8.5% Type 1 -- 6.2% Type 3 -- 5.8% (96% confidence) (98% confidence) Not Type 5 -- 1.3% (96% confidence) Agencies with no rail and organization Type 3 manage more inventory dollars per person than those with Type 1, 2, or 5. In addition, those with Type 4 manage more than those with Type I. Type 3 -- $135,664 Type 4 -- $106,869 Type 1 -- $57,51 1 Type 2 -- $84,570 Type 5 -- $88,569 Type 1 -- $57,51 1 (99% confidence) (94°/0 confidence) (94°/0 confidence) (92% confidence) Agencies with organization Types 1 or 2 process fewer inventory transactions per person per week than those with Types 3, 4, or S. Type 1 -- 33.2 Type2--61.2 Type 3 -- 130.9 Type 4 -- 183.7 Type 5 -- 206.8 Type 3 -- 130.9 Type 4 -- 183.7 Type 5 -- 206.8 57 (99% confidence) (97% confidence) (99% confidence) (96% confidence) (94% confidence) (99% confidence)

Note: Although all differences between organizational types were not significant at or above the 85% confidence level, as organization type progresses from 1 to 5, the number of inventory transactions processed per person per week increases. Full-time head of group-whether the top inventory management person was dedicated fur-time to inventory management responsibilities or had additional non-inventory related responsibilities. Agencies with a filll time head of the inventory management group: have higher annual bus inventory turnover-2.02 vs. 1.37 · take less days to fill bus inventory backorders-13.2 vs. 20.8 · process more transactions/week per person ~ 171.8 vs. 97.8 · manage more inventory dollars/person-$198,730 vs. $95,462 However, these agencies also: . (93% confidence) (8S% confidence) (96% confidence) (99% confidence) have a higher percentage of obsolete bus inventory - 9.4% vs. 4.3% (99% confidence) have a higher percentage of items out of balance - 7.3% vs. 4.4% (92% confidence) Separate planning function-whether the inventory management organization included personnel dedicated to inventory planning functions with no responsibility for storekeeping or other inventory functions. Agencies with a separate planning Unction: have higher annual rail inventor turnover-0.79 vs. 0.50 (85% confidence) have higher Inventor ! rates-94.0% vs. 90.5°/0 (88% confidence) manage more inventory dolIars/person -- $244,233 vs. $131,296 (97% confidence) However, these agencies also: . have higher bus Inventory dolIars/vehicle-$7,106 vs. $4,484 (99% confidence) have a higher percentage of obsolete bus inventory-12.5% vs. 5.6% (98% confidence) have a higher percentage of obsolete rail inventory-9.2% vs. 2.3% (90% confidence) Inclusion of purchasing-whether personnel responsible for executing the purchase of inventory matenal were Included In the same organization as inventory management personnel. Agencies that include purchasing in the same organization as inventory management: have lower bus inventory dolDars/vehicle ~ $4,230 vs. $5,424 (94% confidence) have a lower percentage of stockouts-0.33% vs. 2.32% (95% confidence) Centralized versus decentralized -- whether the inventory organization was centrally managed or whether multiple inventory organizations existed within the transit agency. 58

· Centralized inventor management organizations manage more inventory dollars per ~~~~ ~- (96% confidence). person than decentralized organizations-5159,070 vs. 511(),4Z7 Department reporting to-this attribute was examined two ways: (~) whether or not the inventory organization reports directly to the maintenance department (yes or no), and (2) the specific department to which the inventory management organization reports (e.g. maintenance, operations, a~ninistration, finance, etc.~. Agencies that report to the maintenance department have the following findings versus those Cat do not report to maintenance: lower bus inventory dolIa~/vehicle-$4,535 vs. $5,756 lower percentage of obsolete bus inventory-6.0% vs. 9.~% lower inventory personnel costh~nvento~y dodar-$0.25 vs. $0.38 However, these agencies also: take more days to fib bus inventory backorders-20.9 VS. 9.0 manage fewer Inventor dolIars/Derson-$124 457 vs. $193 593 process fewer transactionsJweek per person-~ 14.0 VS. 165.5 , ~_ , (86% confidence) (92% confidence) (94% confidence) (99% confidence) (94% confidence) (SS°/O confidence) En addition, the foDow~ng findings relate to the specific departments responsible for inventory management: . . Agencies with inventory management reporting to the finance department have a higher bus inventory fill rate than those reporting to maintenance-96.0% versus 86.0% (99% confidence). Agencies with inventory management reporting to the finance department process fewer transactions per week per person than those reporting to all other departments: Reporting to finance: 73.3 reporting to maintenance - I 14.0 (82% confidence) reporting to purchasing-223.5 (88% confidence) reporting to materials-1S5.6 (90% confidence) reporting to administration - 164.1 (90°/O confidence) Level reporting to-the level within the transit agency to which the inventory organization reports (e.g. board, executive, department, or sub-department). . Agencies with inventory organizations reporting to the sub-department level have lower bus inventory dollars per vehicle than those reporting to the department level or executive level: 59

Reporting to sub-dept.: $2,028 reporting to dept. - $5,298 repotting to exec. - $S,095 . . . . (99+% confidence) (99+% confidence) Agencies with Inventory organizations reporting to the executive level have lower rail inventory dollars per vehicle than those reporting to the department level-$19,124 versus $70,571 (94% confidence). Agencies with Inventory organ cations reporting to the sub-depa~ent level have higher annual bus inventory turnover than those reporting to the department level or eXe~ltiV~ level: Reporting to sub-dept. - 2.71 reporting to dept. - 1.71 reporting to exec. - 1.59 (94% confidence) (96% confidence) Agencies with Inventory organizations reporting to the executive level take fewer days to fill inventor backorders (for both bus and rail! than those reporting to the department level. Furthem~ore, those reporting to the department level take fewer days to fi!! bus inventory backorders than those reporting to the sub-department level (note: no rail property organizations report to the sub-department level): Days to fill bus backorders: reporting to executive level reporting to department level reporting to sub-dept. level executive vs. department- 9.0 days 18.7 days 27.0 days 97% confidence executive vs. sub-department 99% confidence department vs. sub-department ~ 90% confidence Days to fill rail backorders: reporting to executive level - reporting to department level executive vs. department- 12.8 days 50.5 days 97°/O confidence Agencies with inventory organizations reporting to the sub-department level manage fewer inventory dollars per person than those reporting to the department and executive level: Reporting to sub-dept. -- $40, ~ 80 60 reporting to dept. -- $146,325 (99+% confidence) reporting to exec. -- $~82,175 (99~% confidence)

5.3.3 Inventory Management Goals The effects of setting target inventory stocking levels (inventory value) and target service levels Inventory availability) was examined. Survey responses yielded information on whether or not transit agencies set goals in these nvo areas. (The survey results were not sufficient to examine the effect of the actual value of the targets.) Set target inventory dollar levels: Agencies that set target inventory dollar levels: · have higher annual bus inventory turnover-2.00 vs. 1.38 (93% confidence) · have a higher percentage of items out of balance-7.0% vs. 4.5% (87% confidence) · spend more on inventory personnel/~nventory dollar -- $.36 vs. $.25 (92% confidence) Set target inventory service levels (availability): Agencies that set target inventory seance levels: . have higher annual bus inventory turnover-2.51 vs. 1.42 (96% confidence) · have higher bus inventory fill rates-94.2% vs. 90.4% (93% confidence) manage more inventory dollars/person-$206,771 vs. $12S,305 (95% confidence) · have a higher percentage of obsolete bus inventory - 10.5% vs. 6.0% (92% confidence) S.3.4 Inventory Replenishment Methods The mix of methods used for determining when to order inventory items and how many items to order were examined. in addition, the effect of safety stock levels was ar alyzed. Mix of replen~shrnent methods used: The following replenishment methods were analyzed: Use of reorder point replenishment methods whether replenishment is triggered when stocking levels reach a predetermined level, as with min/max or reorder point/econom~c order quantity methods. Use of fixed period replenishment methods-whether replenishment is triggered by fixed periods oft~me or time intervals, as with weekly orders and seasonal orders. Use of maintenance forecasts and projections whether replenishment is based on projected maintenance activity, projects, campaigns, etc. 61

The analysis consisted of examining all combinations of the above methods. None of the survey respondents used fixed period methods alone, in combination with a reorder point method, or in combination with maintenance forecasts. Therefore the combinations analyzed are: reorder point only, maintenance forecasts ondy, reorder points in combination with maintenance forecasts, and a combination of aD three methods. The findings were as follows: . Agencies that use maintenance forecasts only have lower bus inventory dollars per vehicle than those using all other combinations: Forecasts only-$1,569 reorder point only $5,727 reorder point & forecasts-$5,03 1 ad three - $5,537 (99% confidence) (97% confidence) (98% confidence) Agencies that use maintenance forecasts only have lower annual bus inventory turnover than those that use a combination of reorder points and maintenance forecasts, and those that use ad three methods: Forecasts only ~ 0.97 reorder point & forecasts-1.76 (99+°/0 confidence) ad three - 2.73 (87% confidence) Agencies that use maintenance forecasts only have lower percent obsolete bus inventory than those that use a combination of reorder points and maintenance forecasts, and those that use all three methods. In addition, those that use reorder point only have a lower percent than those that use a combination of reorder points and maintenance forecasts. Forecasts only ~ 3.5°/0 Reorder points only-6.0% reorder point & forecasts - 1 1.0% (97% confidence) all three-10.2% (93°/0 confidence) reorder point & forecasts-1 1.0% (8S% confidence) Agencies that use reorder points onIv take more days to bus inventory backorders than those that use a] other combinations. In addition, those that use a combination of reorder points and maintenance forecasts take more days than those using maintenance forecasts only and those using all three methods: Reorder points only ~ 24.8 forecasts only ~ 8.5 reorder point & forecast-14.6 all three ~ 8.5 Reorder point & forecast -- 14.6 forecasts only ~ 8.5 days all three ~ 8.5 62 (99°/0 confidence) (90°/0 confidence) (99°/0 confidence) (94% confidence) (87% confidence)

. . Agencies that use maintenance forecasts only manage fewer inventory dollars per person than those using all other combinations: Forecasts only ~ $42,215 reorder point only ~ $135,840 (97% confidence) reorder point & forecasts-$151,758 (98% confidence) all three: $291,141 (95% confidence) Agencies that use a combination of reorder points and maintenance forecasts process more inventor transactions per week per person than those using reorder points only and those using all three methods: Reorder point & forecast -- 164.3 reorder point only-101.0 (95°/0 confidence) all three-1 14.4 (88% confidence) Number of different methods used ~ the total number of methods used to trigger inventory replenishment. (Note: Many survey respondents indicated that multiple methods were used depending on the material and its planned use.) There was no correlation between the number of different replenishment methods used and any of We inventor performance indicators. Target safety stock percentage ~ the average percent of units of an item that is set aside as "safety stock" to cover uncertainties while the item is being replenished. Safety stock reduces the probability that an item will stockout due to such uncertainties as fluctuations in vendor lead time, unanticipated demand for a part, goods damaged In shipment, etc. There was no correlation between the target safety stock percentage and any of the inventory performance indicators. However, the survey respondents were grouped as follows: No safer stock (0°/0) Less than 10% 10% or greater, but less than 20% 20% or greater, but less than 40°/0 (bus) or 50°/0 (rail) Conducting l-tests on inventory performance indicators between these groups led to the following findings' all relating to the 20% or Neater group. Agencies with target safety stock levels of 20% or greater: · have higher rail inventory dollars per vehicle ($67,092) than those with less than 20% ($19,181)-91% confidence. . have lower annual rail inventory turnover (0.45) than those with less than 20% (0.86)- 81% confidence. 63

. . . take more days to fill bus inventory backorders (29.3 days) than those with less than 20% (12.5 days) and those with less than 10% (9.8 days) -- 91% and 95% confidence, respectively. have higher percent obsolete bus inventory (12.6%) than those with less than 20% (5.2%) and those with less than 10% (7.~%) ~ 97% and 87% confidence, respectively. have a higher percentage of items stocked out (4.5%) than those with less than 20% (0.7%), those with less than 10% (0.~%), and those with no safety stock (0.2%)-87%, 86%, and 91% confidence, respectively. 5.3.5 Inventory Purchasing Practices: The eject using the following factors regarding the purchase of inventory material was examined: Direct purchasing authority-whether inventory management personnel have authority (within dollar limits) to purchase inventory material directly without involving purchasing personnel. Agencies with direct purchasing authonty: - manage fewer inventory dollars/person ~ $126,295 vs. $20l,} 19 (95% confidence) spend less on inventory personne~nvento~y dollar -- $.27 vs. $.38 (87% confidence) Use of blanket purchase orders -- whether the transit agency establishes blanket purchase orders for selected inventory items, and allows inventory management personnel to purchase material through releases against the blanket orders. Agencies that use blanket purchase orders to purchase inventory material: . have higher annual rail inventory turnover -- 0.75 vs. 0.53 · manage more inventory dollars/person-$222,513 vs. $96,465 have higher bus inventory dolIars/vehicle-$6,050 vs. $4,247 · have higher rail inventory dollars/vehicle-$41,989 vs. $21,031 (~% confidence) (99+% confidence) (97% confidence) (87% confidence) Percent of inventory material purchased using blanket purchase orders-the percent of inventory part numbers that are covered by blanket orders (if the agency uses blanket purchase orders to purchase inventory materials. There was only one finding relating to this factor: · The percentage of inventory items covered by a blanicet order is negatively correlated with the rail inventory fill rate. The correlation coefficient is -.7l, and the coefficient of 64

detemiinat~on is .50. An other words, 50% of an agencies variance In rail inventory fill rate can be explained by variance In the percent of Inventory items covered by blanket purchase orders. The higher the percentage of items covered, the lower the rail inventory fib rate. 5.3.6 Storehouse Configuration and Coverage The eject of the following attributes of the agency's storehouse configuration and coverage policies were examined: Number of storehouses-the number of storehouses used to stock inventory material was examined in two ways: (1) whether the agency used a single storehouse or multiple storehouses to store inventory matenal, and (2) the total number of storehouses used by the agency. All rail properties responding to the survey have multiple storehouses, so this factor could not be used to analyze rail inventory performance indicators. In addition, the total number of storehouses has no correlation with any inventory performance indicators (rail, bus, or total). The only findings are that agencies with multiple storehouses: · have a higher percentage of obsolete bus inventory - 9.0% vs. 6.~% (~35% confidence) · manage more Inventory dollars/person - $192,563 vs. $} 10,947 (99% confidences Storehouse network configuration ~ the configuration of the storehouse network such as one central storehouse, a central storehouse supplying separate smaller storehouses, multiple independent storehouses, or "other'' (usually a combination or hybrid of the other configurations). The findings relating to the storehouse configuration were: Agencies with "other" storehouse configurations have a higher bus inventor fill rate (98.9%) than those with one central storehouse (88.6%) or those with a central and separate satellite storehouses (87.8%)-99+% and 99% confidence, respectively. Agencies with independent storehouses have a lower percent stockout (0.3%) than those with a central and separate satellite storehouses (1.7%) - 86% confidence. Agencies with a central storehouse supplying satellite storehouses have a higher percent of items out of balance than all other configurations. In addition, those with one central storehouse have a higher percent than those with independent or"other'' configurations: Central/satellite~ 16.0% central only-5.6% (94% confidence) independent-1.5% (99% confidence) "other'' ~ 2.2% (98% confidence) 65

Central only 5.6% . independent-1.5% "other,' - 2.2% (99+°/0 confidence) (98% confidence) Agencies with a central storehouse supplying satellite storehouses manages more inventory dollars per person ($227,401) than those with one central storehouse ($}lO,lSS) and those with "other'' configurations ($122,202) 99% and 94% confidence, respectively. Percent of secured storehouses ~ the percentage of storehouses that were locked with access limited to designated individuals. The survey responses were arranged into three groups so that there would be enough responses In each group to perfonn t-tests: unsecured storehouses (0%), less than 100°/0 secured, and 100% secured. The findings based on these three groups were: . . Agencies with 100% secured storehouses have higher ar~nual bus inventory turnover (~.84) than those with unsecured storehouses (~.26) ~ 85% confidence. Agencies with less than 100% secured storehouses manage more inventory dollars per person ($252,648) than those with unsecured storehouses ($144,365) and those with 100% secured storehouses ($140,723) 87% and 90% confidence, respectively. Percent of storekeeper coverage ~ the percentage of time that storehouses are attended by storekeepers or other personnel. The survey responses were grouped as follows: 0% coverage Less than 50% coverage 50% or greater, but less than 75% 75% or greater, but less than 100% IW/o coverage The findings were: . . less than 100% ~ 2.24 Agencies with 100°/O storehouse coverage have higher bus dollars per vehicle ($6,032) than those with less than 100% ($4,458) and those with less than 75°/O ($4,562) ~ 87°/O and 88% confidence, respectively. Agencies with less than 100°/0 coverage have higher annual bus inventory turnover than those with less than 75%, less than 50%, and 0%: less chart 75% - 1.48 less than 50% - 1.41 0%-1.27 66 (85% confidence) (86% confidence) (93% confidence)

. Agencies with 0% storehouse coverage have lower bus inventory fill rates (83.8%) than those with 100% (92.9%) and less than 100% coverage (92.7%) - 90% and 89% confidence, respectively. Department responsible for stores-the department responsible for stores coverage (e.g. maintenance, inventory management, operations, or other). The findings were: . Agencies with maintenance responsibility for stores process fewer transactions per week per employee (91.~) than those with stores reporting to an inventory management organization (163.1) - 96% confidence. Agencies with maintenance responsibility for stores manage less inventory dollars per employee ($107,359) than those with stores reporting to an inventor management organization ($170,919) - 95% confidence. Agencies with maintenance responsibility for stores manage take more days to fill bus inventory backorders (26.3) than those with stores reporting to an inventor management organization (14.0)-97% confidence. Agencies with maintenance responsibility for stores have less percent obsolete bus inventory (4.3%) than those with stores reporting to an inventory management orgariization (10.9%) - 98% confidence. 5.3.7 Published Procedures and Catalog: The eject of the existence of (~) written inventory management policies and procedures, and (2) a published catalog of Inventory material were examined. Written policies and procedures: Agencies with written policies and procedures: manage more inventory dollars/person - $210,352 vs. $100,948 (99+% confidence) have higher percent obsolete bus inventory-9. I% vs. 5.9% (90% confidence) Publish an inventory material catalog: Agencies that publish a material catalog: . · manage more inventory dolIars/person-$186,000 vs. $122,173 (94% confidence) have higher percent obsolete bus inventory-8.7% vs. 5.9% (87% confidence) 67

5.3.S Physical Inventory/Cycle Counting: The eject of regularly counting Inventory items was examined both for (~) the frequency of a complete physical inventory (counting all items), and (2) the Dequency of cycle counting (counting selected items based on a schedule). . There is no significant difference in the percent of items that are out of balance for transit agencies that conduct a complete annual physical inventory, semi-annual physical inventory, or never conduct a complete physics inventory: annual - 7.~% out of balance sem~-annual - S.2% out of balance never- 7.4°/0 out of balance There is no difference in annual and sem~-annual (94% confidence), annual and never (94% confidence), and sem~-annual and never (90% confidence). . Transit agencies that conduct quarterly cycle counts have a significantly lower percent of items out of balance than those that conduct cycle counts daily, weekly, and monthly: daily- 9.2% out of balance weekly- 5.9% out of balance monthly- 5.2% out of balance quarterly- 1.8% out of balance The quarterly out of balance percentage is lower than dally (95% confidence), weekly (95% confidence), and monthly (96% confidence). 5.3.9 Repair and Stocking of Components: The eject of a transit agency's handling of components (e.g. engines, transmissions, starters, pilot motors) was examined, specifically, whether an agency: (1) repairs or rebuilds components, and (2) stocks spare components in inventory. Repair/rebuild components: Agencies that repair and rebuild components: · manage more inventory doliarstperson -- $165,275 vs. $67,383 (99+% confidence) · process more transactions per week/person-147.8 vs. 81.4 (88% confidence) · take more days to fill bus inventory backorders-17.6 vs. 9.3 (90% confidence) 68

Stock repa~red/rebuilt components: Agencies that stock rep abed and rebuilt components: · have higher bus inventory dollars/vehicle - $5,300 vs. $3,895 (93% confidences 5.3.10 Use of Systems and Technology: The effect of an agency's use of automated systems and technology In manag ng inventory was examined as follows: Use of an automated versus a manual system-whether an agency uses an automated computer system to track and manage inventory or a manual system (e.g. carded, signout sheets, etc.). . . Transit agencies with automated inventory systems process more transactions per week per inventory person (based on total persons involved with inventory management) than agencies with manual systems: 149.6 compared to 73. ~ (85% confidence). Transit agencies that use a combination of both automated and manual systems for inventor management have a higher percent of inventory items out of balance (12.6%) than agencies that use automated (5.2%) and manual (4.9%) systems solely. (93% and 92% confidence, respectively) · Transit agencies that use an automated inventory system solely have higher annual inventory turnover than those that use a combination of automated and manual systems. This is true both for bus and rail properties: Bus~nvento~tumover: automated-2.26 both-1.19 (99+%confidence) Rail inventory turnover: automated - 0.98 both-0.45 (96% confidence) Bus properties that use manual systems solely have lower dollar Inventory levels per vehicle ($1,877) than those using an automated system solely ($S,232) or a combination of both ($5,426~. (99~% confidence for each difference) Use of bar code-whether an agency uses bar code capabilities for inventory tracking and 2nanagement. Bus properties using bar code have a higher average fill rate, 95.2% compared 91.0% for bus properties not using bar code. (94% confidence) Use of other technology - whether an agency uses other technology, such as light pens, wands, key cards, etc. in tracking and managing inventory. 69

· Rail properties using "other technology'' have a higher annual inventory turnover rate, 0.87 compared to 0.53 for raid properties not using "other technology''. (88% confidence) . Transit agencies using "other technology,' control more inventory dolDars per inventory organization employee, $369,717 compared to $164,368 (93% confidence) and more dollars per total employee involved in inventory management, $286,]59 compared to $129,463 (93% confidence). However, this result may be due to a higher percent of rail properties (57%) using other technology than bus properties (16%~. (Rail properties have a significantly higher inventory than bus properties, as noted in chapter m.' Infonnation available to stores -- the level of information available to store personnel, as defined by the number of different reports/screens that stores personnel can access. There is no correlation between the amount of information available to stores personnel and the inventory performance indicators. 5.4 RELATIONSElIPS BETWEEN INVENTORY ORGANIZATION MANAGEMENT DECISIONS AND INVENTORY PERFORMANCE This section describes the quantitative relationships between inventory performance indicators and inventory organization and management decision factors. Selected agency and Deet characteristics were also included in the analysis to determine if these characteristics are significant when combined with organization and decision factors. The relationships were derived using regression analysis and are expressed as first order (linear) equations. The regression analysis process is described in section 5.2.2 ofthis chapter. The sections below present a regression equation for each inventory performance indicator. The sections also identify the inventory organization and management decision factors, and agency and fleet characteristics that are statistically significant in projecting the value of the performance indicator. Specifically3 each section contains the: ~ constant for the regression equation ~ the constant is the starting point for predicting the value of the inventory perforTnance indicator using the regression equation. factors that were identified as the best predictors-these were factors that were selected for the regression equation, based on the step-wise regression analysis ant! the confidence level of their coefficients. coefficient for each factor -- each factor in the equation is multiplied by its coefficient and then summed along with the constant to calculate the predicted value of the inventory performance indicator. confidence level for each coefficient ~ indicates the Resee of confidence that the coefficients are significant (not equal to zero). For example, if a coefficient has a confidence level of 95%, there is a 95% probability that the coefficient value denved Dom the survey sample is 70

representative of the population. Conversely, there is only a 5% probability that the value denved Tom the survey sample was due to chance, and that the value is really zero for the population. Only coefficients with a high degree of confidence (e.g. greater than or equal to 90%) are accepted and included in the equation. (Note: Some coefficients for rail inventory perfonnance indicators are included at a lower confidence level due to the relatively small sample of rail properties in the survey - 14 responses). coefficient of determination if! ~ defies the amount of variance in the inventor perforce indicator that is explained by the factors in the regression equation, based on the survey data. adjusted coefficient of determination ~ the coefficient of determination adjusted for the degrees of freedom in the sample data. It resects the percent of variance explained when applying the equation to the population of transit agencies, rather than the survey sample. It provides an unbiased estimate ofthe population coefficient. confidence level for the regression equation ~ indicates the degree of confidence that the value predicted by the regression equation is significant (not equal to zero). In general, the regression equations were denved to balance the significance of the individual coefficients with the amount of variance explained. For example, factors with a lower confidence level can be added to the equation. The result will be a greater amount of variance explained (higher coefficient of detenn~nation), but a lower confidence level for the prediction and the individual coefficients. 5.4.~. Bus Inventory Dollars per Vehicle The following table summarizes the regression equation for bus inventory dollars per vehicle based on the factors that were determined to be the best predictors: Factor Coefficient Confidence Level Constant 2767 99+% Bus inventory dollars .000361 99+% Organization type 2 7715 994% Use noun inventory system -6007 99/ Repontoma~ntenance 1951 97% Decentralized organization -2992 95% Separateinventory planning 2731 98% Annual bus maters purchases -.000056 96% The coefficient of determination (r2) for the regression equation is 55.3%, therefore 55.3% of the variance in bus inventory dollars per vehicle is explained by the above factors, based on the survey. The coefficient, adjusted to reflect the population oftransit agencies rather than the survey sample, was 48.1%. The confidence level for the regression equation is 99 a%. 71

5.4.2. Rail Inventory Dollars per Vehicle The factors selected to predict rail inventory doLars per vehicle were: Factor Constant Avg. vehicle age (years) % coverage of storehouses by storekeeper Coefficient of determination (rib: Adjusted r2: Regression confidence level: 5.4.3. Annual Bus Inventory Turnover Coefficient Confidence Level 55623 - 649 538 61.2% 52.6% 99o/o The factors selected to predict annual bus inventory turnover were: Factor Constant Separate inventory planning Organization type 5 Cost of inventory personnel Avg. annual bus miles per vehicle Stock components in inventory Percent of inventor items on blanket POs Purchasing included in inventory organization Total bus inventory dollars Coefficient of determination bred: Adjusted r2: Recession confidence level: 85.6% 80.2% 99+o/o 5.4.4. Annual Rail Inventory Turnover Coefficient 3.411 -3.1 18 4.736 0.00000005 -0.00001 12 -1.104 0.022 0.506 0 00000005 The factors selected to predict annual raid inventory turnover were: Factor Constant Cost of inventory personnel Number of rail vehicle models Tote number of replenishment methods used 91% 99o/o 86% Confidence Level 99+o/o 99+o/o 99+o/o 99+o/o 99+o/o 99+o/o 99+o/o 92% 91% Coefficient Confidence Level -0.59 0.00000003 0.041 0.18 72 99+o/o 99+o/o 99+o/o 99+o/o

% coverage of storehouses by storekeeper Have direct purchase authority for inventory Set target inventory levels Coefficient of determination (ray: Adjusted r2: Recession confidence level: 99.9o/o 99.6% 99+o/o 5.4.~. Bus Percent Demand Filled (Fill Rate) The factors selected to predict bus percent fib rate were: Factor Constant Percent safety stock % coverage of storehouses by storekeeper Have direct purchase authority for inventory Total employees involved in inventory mat. Publish a catalog of inventory material Coefficient of determination (r31: Adjusted r2: Regression confidence level: 27.4% 19.5% 99o/o 5.4.6. Rail Percent Demand Filled (Fill Rate) 0.0058 0.19 0.10 99+o/o 99+o/o 98% Coefficient Confidence Level 77.8 -0.27 0.14 13.76 0.13 -8.23 99+o/o 9so/o 98% 99o/o 94o/o 91% Only two factors were selected to predict the rail percent fill rate. These were the only factors with over 90°/0 confidence that the coefficients were not equal to zero. The next closest factor was the percent of secured storehouses with only 31% confidence that the coefficient was not equal to zero. Factor Constant Percent of inventory items on blanket POs Number of rail vehicle models Coefficient of determination (rib: Adjusted A: Regression confidence level: 93.9o/o 91.4% 99+o/o Coefficient Confidence Level 87.7 -0.50 1.14 73 99+o/o 99~/o 99o/o

5.4.7. Percent of Items Stocked Out per Week There were two recession equations that can be used to predict the percent of items stocked out per week. En the first equation, two factors were selected as significant, however there is only a 43% confidence that the constant is not equal to zero. In the second equation, only one factor was selected as significant. Factor Coefficient Confidence Level Constant 0.36 43°/0 Total employees involved with inventory management. 0.024 91% Organization type2 6.31 99~°/0 Coefficient of determination bray: 28.~% Adjusted A: 25.0% Recession confidence level: 99+% Factor Coefficient Confidence Level Constant 0.94 91°/0 Orgariization type 2 5.78 99+°/O Coefficient of determination brig: Adjusted r2: Recession confidence level: 23.0% 20.9% 99+o/o 5.4.~. Average Days to Fill Bus Inventory Backorders Only one factor was selected to predict the average days to fill bus inventory backorders. This was the only factor with over 90% confidence that the coefficient was not equal to zero. The next closest factor was the use of an automated inventor system with only 67% confidence that the coefficient was not equal to zero. Factor l Coefficient Confidence Level Constant 24.8 99+% Use maintenance forecasts for replenishment -I I.2 96% Coefficient of determination (rib: Adjusted A: Recession confidence level: X.5% 6.6% 96% 74

5.4.9. Average Days to Fill Rail Inventory Backorders Only one factor was selected to predict the average days to fill rail inventory backorders. This was the only factor with over 90% confidence that the coefficient was not equal to zero. The next closest factor was whether the agency set target service levels with 83% confidence that the coefficient was not equal to zero. Factor Constant % coverage of storehouses by storekeeper Coefficient of determination arch: Adjusted r2: Regression confidence level: 5.4.10. Percent of Items Out of Balance 59.6°/o 49.S% 93o/o Coefficient Confidence Level 82.8 -0.71 97o/o 93o/o The best regression equation for predicting the percent of items out of balance has a 68% confidence that the constant is not equal to zero. To derive an equation with a confidence level for the constant that is greater than 90%, all factors except "inventory orgaruzation reports to maintenance" must be eliminated Dom the equation. Factor Constant Inventory organization reports to maintenance Have multiple storehouses Have buses Total number of replenishment methods used Stock components in inventory Set target inventory levels Coefficient of determination bred: Adjusted r2: Regression confidence level: 5.4.~. Percent Obsolete Bus Inventory 23.7% 13.oo/o 94o/o 9.03 -6.51 10.93 14.97 -2.35 -9.60 -5.48 The factors selected to predict percent obsolete bus inventory were: 75 Coefficient Confidence Level 68% 96% 99o/o 94o/o 94o/o 94o/o 91%

Factor Coefficient Confidence Level Constant 5.91 gg+o/O Decentralized orgaruzation 54.5 p9+o/O Use"other'' technology 21.8 99+°/0 Total bus inventory dollars 0.0000013 99°/0 Total employees involved with inventory mat. -0.22 ~97°/O Organization type 5 8.5 99°/O Total bus material purchases -0.00000022 99°/O Orgariization type 4 a. ~ggo/O Coefficient of determination arty: Adjusted r2: Regression confidence level: 5.4.12. Percent Obsolete Rail Inventory 86.7% 83.5o/o 99+o/o Only two factors were selected to predict the percent obsolete rail Inventor. These were the only factors with over 90% confidence that the coefficients were not equal to zero. The next closest factor was the amount of infonnation (number of reports/screens) available in the storehouse with only 58% confidence that the coefficient was not equad to zero. Factor Coefficient Confidence Level Constant 3.10 98% Total rail Inventory dollars 0.00000047 99+% Use "other'' technology -3.9S 94% Coefficient of determination arty: 91.9°/0 Adjusted r2: 89.2% Regression confidence level: 99+°/0 S.4.13. Total Inventory Dollars per Person The factors selected to predict total inventory dollars per person were: Factor Coefficient Confidence Level Constant 9~075 gg+o/O Have written policies and procedures 97274 99% Have rail vehicles 257481 99+% Use fixed period replen~shrnent method 210105 99+% 76

Set target inventory levels -74418 96% Coefficient of determination (r21: Adjusted r2: Regression confidence level: 57.5o/o 54.9o/o 99+o/o 5.4.14. Inventory Personnel Cost per Inventory Dollar The factors selected to predict inventory personnel cost per inventory dollar were: Factor Coefficient Confidence Level Constant 0.13 99% Average feet age (years) 0.00043 99~% Use manual inventory system 0.88 99+% % coverage of storehouses by storekeeper 0.0028 99+% Organization typed 0.13 95% Separate inventory planning -0.085 90% Use fixeUpenod method 0.18 98% Use blanket POs for inventory -.085 93% Coefficient of determination bras: Adjusted A: Regression confidence level: 78.4% 73.2% 99+o/o S.4.~. Total Inventory Transactions per Person No regression equation that could be denved to predict the total inventory transactions per week per person that contains a coefficient with a confidence greater than 90%. The highest confidence was for the factor "organization type 4" at 65%. As a result, 65% was also the highest confidence for the entire regression equation. 5~5 CONCLUSIONS The conclusions presented in this section are general conclusions implied by the findings outlines in the previous sections. Conclusions are Toupee by the organizational and inventory management decisions examined in this study. Conclusions regarding organizational structure: Organization type 5 provides some service level benefits over other organization types, such as higher fill rates and fewer days to fill back orders. However, these gains appear 77

. . . to be at the expense of higher inventory levels (e.g. dollars per vehicle) leading to highe percent of obsolete items. This is an illustration of the traditional trade-offs between the conDicting goals of increasing service level while lowering Inventory investment. The higher turnover experienced by organization type 5 is a by-product of the higher service levels, and appears to imply that service level increases more than compensate for the higher inventory levels. The more evolved inventory organization structures appear to process inventory transactions more efficiently. However, this transaction efficiency does not necessarily translate into efficiency In managing inventory dollars. In general, the organization types represent an evolution from no formal inventory organization (type 1) to a dedicated materials management group at the department level (type 5). The average number of inventory transactions per person increases from type 1 through type 5 (although not all differences have high confidence levels). However, the inventory dollars managed per person does not follow this pattern. Organization types 3 and 4 manage more dollars per person than the other types. Other differences between organizational type were isolated and inconclusive. For example, agencies with type 4 organization have a lower stockout rate than those with type 5. However, this was not a consistent pattern and no significant differences in stockout rate were found between any other combination of organization types. · The existence of a full-time head and a separate inventory planning function appear to be key organizational attributes leading to the service level gains of inventory organizations. In addition, these attributes appear to contribute to more efficient management of inventory dollars. Inventory organizations with these attributes take less time to fill backorders and have a higher fill rate, respectively, and organizations with either or both attributes have higher annual tumover. These attributes appear to allow organizations to manage more inventory transactions and dollars per person. These attributes are also consistent with the higher inventory levels and percent of obsolete items observed in organizations with higher service performance. Including purchasing in the same organization as inventory management appears to lead to lower inventory levels and stockout rates. A centralized organization manages significantly more dollars per person than a decentralized organization. 78

. Inventory organizations that report to the maintenance department have better performance relating to inventory levels, but manage inventor th lower efficiency. These organizations have lower inventory dollars per vehicle and a lower percent obsolete items. However, inventory organizations reporting to maintenance manage less inventor dolBars per person and process fewer inventory transactions per person. Agencies with Inventory orgaruzations reporting to the finance department have a higher service level than those reporting to maintenance, but process fewer transactions per person than those reporting to any other department. In general, the higher in the agency that the Inventory orgariizations reports, the faster the orgaruzation responds to user needs. Organizations reporting to the executive level take significantly fewer days to fill backorders than those that report to the departmental level, and those reporting to the departmental level take significantly fewer days than those reporting to the sum department level. Sub-departmental orgaruzations have lower Inventory dollars per vehicle and a higher turnover rate than other orgaruzations. However, these organizations manage significantly fewer inventory dollars per person than orgar~ations reporting to higher levels. Conclusions regarding setline goals: · Agencies that set target service levels achieve a higher level of service than organizations that do not set target seance levels. Inventory organ zations that set target service levels have a higher fig rate and higher annual inventory turnover. These agencies also manage more inventor dollars per person and have a higher percent obsolete inventory. Agencies that set target inventory levels have higher annual inventory turnover, but do not achieve lower inventory levels. There is no significant difference in inventory levels between those orgariizations that set target inventory levels and those that do not. Conclusions regarding replenishment methods: . No replenishment method or combination of replenishment methods resulted in higher inventory service levels, however the use of maintenance forecasts only appears to lead to lower inventory levels. 79

. The lower inventory levels for organizations using only maintenance forecasts are accompanied by lower inventory turnover, a lower percent obsolete items, and fewer Inventory dolDars managed per person. Several detrimental effects on inventory perfonnance are associated with having a safety stock percentage of 20% or greater, Including a higher percent of stockouts. Organizations with 20% or greater safety stock have higher inventory levels and lower turnover for rail inventory, take more tune to fill backorders, and have a higher percent obsolete items. In particular, since these orgaruzations have a higher percent stockouts, the high safety stock levels do not accomplish the primacy goal of safety stock (to reduce stockouts). Conclusions regarding E~urchasine practices: . The use of blanket orders and direct purchasing authority to replenish inventory material ho minimal effect on inventory performance. There was no general pattern on which to draw conclusions. Only isolated effects were observed relating to these variables. Using blanket orders resulted in higher inventory dollars per vehicle for both bus and raid inventones, and the more rail inventory material covered by blanket orders, the lower the service level (fill rate from inventory. Organizations using direct purchasing authority manage less inventory dollars per person, although the opposite is true for those using blanket orders. Conclusions regarding storehouse configuration and coverage: . . There was no storehouse configuration that consistently resulted In higher inventory performance, only a Sew isolated effects. Agencies with a hybrid configuration (a mixture of those tested in the surveys have a higher inventory fill rate, and independent storehouses have lower stockout rates. A central storehouse with satellites manages more inventor dollars per person, but also has a higher percent of items out of balance. Whether a storehouse was secured or not had no significant effect on inventory accuracy. Again, there were only a few isolated effects on inventory perfonnance relating to secured storehouses. Agencies with secured storehouses have higher inventory turnover, and those with less than 100°/0 (but more than 0°/0) manage more inventory doldars per person. Unmanned storehouses contribute to lower leads to higher inventory levels. 80 inventory service levels, but 100% coverage

The two ends of the spectrum, no coverage and 100% coverage, adversely effect inventory performance in one of the major conflicting goals of inventory management. Storehouses with no storekeepers contribute to lower inventory fib rates and storehouses that are 100% covered contribute to higher inventory dollars per vehicle, respectively. Storehouses with 75% or greater coverage (but less than 100%), have the highest annual inventory turnover. Storehouses run by maintenance are run less efficiently than those not run by maintenance. Maintenance run storehouses process fewer transactions per person and manage fewer inventory doDars per person. These storehouses also take longer to fib inventory backorders, but have a lower percent obsolete items. Conclusions regarding published procedures and catalog: . Written policies and procedures have minimal eject on inventory performance, but assist in the efficiency of managing inventory. Written policies and procedures and a published inventory material catalog both allow organizations to manage more inventory dollars per person, but contribute to a higher percent obsolete items. Conclusions regarding Physical and cycle counting: . . Conducting a complete physical inventory has no effect on inventory accuracy, regardless of the frequency. The percent of Steno out of balance was the statistically the same for agencies that conducted annual and semiannual physical inventories, as well as those that never conducted complete physical inventory. Quarterly cycle counting appears to result in significantly higher inventory accuracy than other frequencies. Those agencies that conducted quarterly cycle counts had a lower percent of items out of balance than those counting at other frequencies (daily, weeldy, monthly). Conclusions regarding repairing and stocking components: · Agencies that repair and rebuild components are able to absorb the additional inventory activity and dollars with increased staff responsibility. Agencies that stock repaired and rebuilt components have significantly higher inventory levels than those that do not. The additional inventory transactions and inventory dollars 81

resulting from components lead to processing more inventory transactions and managing more inventory dollars per person rather than to higher staffing levels. Conclusions regarding use of systems and technology: . . Summary: Automated inventory systems and other technology allow orgaruzations to operate more efficiently and turnover inventory more open, but manual systems contribute to lower inventory levels. Agencies with automated inventory systems and/or "other'' technology process more inventory transactions per person, manage more inventory dollars per person, and have higher turnover for both bus and rail inventory. Agencies with manual system have lower inventory dollars per vehicle, and those with both have higher percent of items out of balance. Bar code technologr contributes to higher inventory seance levels. Agencies using bar code technology for inventory have higher inventory fig rates than those that do not use bar code. In general, no inventory orgaruzation factor, decision factor, agency or fleet characteristic has a comprehensive and consistent effect on inventor performance. Those factors that tend to favor higher service levels often do so at the expense of higher inventor levels and percent obsolete items. In addition, none of the factors affect more than one service level perfonnance indicator (e.g. inventory fill rates, days to fig back orders). Those that eject the efficiency of managing inventory seldom have more than minimal ejects on performance factors relating to inventor investment or service level. This mixture of ejects is further supported by the regression analysis and resulting equations. In 15 equations (one for each inventory performance indicators, 32 separate vanables were selected as significant. Four of these vanables appear in three equations, and one appears In five equations. The other 27 vanables appear In two equations at most. The variable that does appear In five equations, (percent of storehouses covered by a storekeeper) has a positive eject In three equations and a negative effect In two. The survey process yields valuable infonnation regarding the inter-relationships between Inventory management and organizational decision factors. However, the conclusions based on this information are preliminary at best. There are some inherent shortcomings in any survey process and the related statistical analysis. Most of the statistical tests applied during this study test a pair or group of factors and assume that "other things are equal". In addition, there was no way to verify the accuracy of survey responses beyond the application of common sense tests to the range of response values, comparing related responses in difference sections of the survey, and comparing an individual response to the nonnal range of responses. Finally, a survey cannot effectively capture information regarding the competence and attitude of the individuals actually involved with inventory management 82

at transit agencies. Therefore, the conclusions should be developed into hypotheses for more detailed testing in a more controlled environment where individual effects can be more effectively isolated and quantified. 83

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