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An Econometric Analysis of Inventory Turnover Performance in Retail Services

An Econometric Analysis of Inventory Turnover Performance in Retail Services. Vishal Gaur Stern School of Business, New York University Marshall Fisher The Wharton School, University of Pennsylvania Ananth Raman Harvard Business School, Harvard University

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An Econometric Analysis of Inventory Turnover Performance in Retail Services

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  1. An Econometric Analysis of Inventory Turnover Performance in Retail Services Vishal Gaur Stern School of Business, New York University Marshall Fisher The Wharton School, University of Pennsylvania Ananth Raman Harvard Business School, Harvard University School of Management, Boston University, March 24, 2005

  2. Research Papers • Gaur, Fisher and Raman (2005), “An Econometric Analysis of Inventory Turnover Performance in Retail Services” • Benchmarking of inventory productivity • Gaur, Fisher and Raman (2004), “Inventory Productivity and Financial Performance in U.S. Retail Services” • External validation of the benchmarking methodology by correlating performance relative to the inventory productivity benchmark with long-run stock returns

  3. Importance of Inventory Management in Retailing • $307 billion of investment in inventory in the U.S. retailing industry in 2004 ($469 billion including motor vehicles and spare parts). • Inventory represents 36% of total assets and 53% of current assets of retailing firms. • Inventory turnover • Routinely used for productivity comparisons by retailers, manufacturers, consultants and analysts. • Benefits of high inventory turnover • Lower working capital requirement • Lower inventory holding and obsolescence costs • Greater ability to respond to market dynamics

  4. Variation in Inventory Turnover • Within-firms variation • Range of inventory turnover of commonly known firms in 1985-2000:Best Buy Co. Inc. 2.8 – 8.5Circuit City Stores, Inc. 4.0 – 5.6The Gap, Inc. 3.6 – 6.3Radio Shack Corp. 1.1 – 3.1Wal-Mart Stores, Inc. 4.9 – 7.2 • Across-firms variation • Range of inventory turnover of supermarket chains during the year 2000: 4.7 to 19.5.

  5. Time-Series Plot of Annual Inventory Turnover of Four Consumer Electronics Retailers for 1987-2000

  6. Research Questions • Explain variation in inventory turnover using covariates: gross margin, capital intensity and deviation of sales from forecast. • Characterize the “earns versus turns” tradeoff. • Determine time-trends in inventory productivity. • Provide methodology for benchmarking inventory productivity. • Understand how firms make aggregate-level inventory decisions.

  7. Literature Review • Impact of operational improvements on operational and financial performance • Balakrishnan, Linsmeier, Venkatachalam (1996), Billesbach and Hayen (1994), Chang and Lee (1995), Huson and Nanda (1995), Hopp and Spearman (1996). • Hendricks and Singhal (1996, 1997, 2001). • Time-series analysis of inventory turnover • Aggregate-level data for US manufacturing industry: Rajagopalan and Malhotra (2001) • Firm-level data for US manufacturing industry: Chen, Frank and Wu (2004)

  8. Literature Review (contd.) • Impact of variety on performance • Kekre and Srinivasan (1990) • Pashigian (1988) • Fisher and Ittner (1999), Randall and Ulrich (2001) • Impact of EDI, CRP and VMI on performance • Cachon and Fisher (1997), Clark and Hammond (1997) • Case studies: Barilla SpA (Hammond 1994), H. E. Butt Grocery Co. (McFarlan 1997), Wal-Mart Stores, Inc. (Bradley, et al. 1996), etc.

  9. Description of Data • Data: • Obtained from S&P’s Compustat database • 311 firms across 10 retailing segments for years 1985-2000. • 3407 observations across firms and years; 11 annual observations per firm. • Preparation: • At least five consecutive years of observation for each firm • Causes of missing data: new entry, mergers, acquisitions, liquidations. • Missing data other than at the beginning or the end of the period • Bankruptcy and reorganization • Inventory valuation method • FIFO, LIFO, Average cost method, Retail method.

  10. Variables • Inventory Turnover • Gross Margin • Capital Intensity • Sales Surprise

  11. Modeling Assumptions • Focus on year-to-year variation within firms. • Control for firm characteristics exogenous to the model, such as differences in accounting policies, location strategy, management, etc. using firm-specific fixed effects. • Effects of aggregate industry characteristics, such as competition, and economic conditions are controlled for using time-specific fixed effects.

  12. Hypothesis 1: Inventory turnover is negatively correlated with gross margin. • Gross margin directly affects inventory turnover through service level • Increase in GM Higher optimal inventory level Higher average inventory level Lower inventory turnover.

  13. Hypothesis 1 (contd.) • Gross margin is indirectly related to inventory turnover through product variety and length of product lifecycle. • Gross margin increases with increase in variety.Increase in variety  Increase in consumer utility  Higher price  Higher gross margin. • Lancaster (1990), Dixit and Stiglitz (1977), Kotler (1986), Nagle (1987), Lazear (1986), Pashigian (1988). • Inventory turnover decreases with increase in variety.Increase in variety  Increase in demand uncertainty Higher safety stock requirement Decrease in inventory turnover • Benetton SpA (Heskett and Signorelli 1989), Hewlett-Packard (Feitzinger and Lee 1997), Swaminathan and Tayur (1998), Zipkin (2000), van Ryzin and Mahajan (1999).

  14. Hypothesis 1 andthe “earns versus turns” tradeoff • Multiplicative models used in managerial practice • Du Pont Model, Strategic profit model (Levy and Weitz, 2001) • Gross Margin Return on Inventory (GMROI) GMROI = GM  IT • These models do not explain why GM and IT should be correlated with each other!

  15. Hypothesis 2: Inventory turnover is positively correlated with capital intensity. • Factors that increase capital intensity increase inventory turnover • Adding a new warehouse • Reduction in safety stock, flexibility to re-balance store inventory in season: Eppen and Schrage (1981), Jackson (1988). • Introducing information technology systems • Continuous replenishment process: Clark and Hammond (1997), Cachon and Fisher (1997). • Benefits of sharing information: Gavirneni et al. (1999), Lee et al. (2000), Cachon and Fisher (2000). • Case studies: Campbell Soup, Barilla Spa, H.E.B., Wal-Mart Stores.

  16. Hypothesis 3: Inventory turnover is positively correlated with sales surprise. • Sales higher than forecast Less inventory at the end of the period Less average inventory during the period Higher inventory turnover. • Computation of sales forecast • Holt’s Linear Exponential Smoothing model • Smoothing parameters chosen from a range of values. • Lower prediction error and less biased forecasts than Simple Exponential Smoothing or Double Exponential Smoothing.

  17. Model Specification • where • s denotes segment index, i the firm index, and t the year index. • Fi : firm-specific fixed effects.Control for differences in the intercept between firms, such as between their managerial efficiency, location, accounting policies, marketing, etc. • ct : year-specific fixed effects.Control for differences in economic conditions over time. • b1s, b2s, b3s: segment-wise coefficients.b1s 0 for hypothesis 1, b2s > 0 for hypothesis 2, b3s > 0 for hypothesis 3. • sit denotes the error term.

  18. Alternative Model Specifications • Coefficients pooled across segments • Intercept pooled across firms • Interaction effects • Separate year-wise fixed effects for each segment • Separate coefficients for each segment and each year • Inventory as dependent variable

  19. Summary of Data

  20. Overall Fit Statistics • Model explains 66.7% of the within-firm variation and 97.2% of the total variation (within and across firms) in log(IT). • Intercept of the regression line varies across firms and across years. • The coefficients of gross margin, capital intensity and sales surprise are statistically significant. They differ by segment.

  21. Estimated Prediction Error The model explains 97.2% of the total variation and 66.7% of the within-firm variation in log(Inventory Turnover).

  22. Coefficients’ Estimates • Coefficients marked * are not significant, coefficients marked ** have p<0.02, all other coefficients have p<0.001.

  23. Tradeoff curve model specifies the tradeoff between IT, GM and CI, and corrects for the effect of sales surprise. Adjusted Inventory Turnover (AIT) equals the residual from the model and shows the distance of a firm from its tradeoff curve (benchmark). Application to Benchmarking

  24. Example 1: Comparison of Four Consumer Electronics Retailers

  25. Example 1: Values of Adjusted Inventory Turns for different gross margins for the four consumer electronics retailers Note: Figures are drawn using the average values of CI and setting SS = 1.

  26. Example 2: Comparison across years within a firm - Ruddick Corp. IT is decreasing with time, but AIT is increasing with time. Gross Margin and Capital Intensity are increasing with time. Inventory Turnover Adjusted Inventory Turnover

  27. Time Trends in CI, IT, GM • Capital intensity has increased with time, Inventory turnover has decreased with time, and Gross Margin shows no trend with time. • Computation of unadjusted time trends: yit = ai + bt + error termHere, ai is the firm-specific intercept, and b is the slope w.r.t. time.

  28. Time Trend in Inventory Productivity Estimated from Year-wise Fixed Effects • The values of year-wise fixed effects, ct, show the time trend in inventory productivity by adjusting for changes in GM, CI and SS, and for differences across firms. This trend is downward sloping. Error-bars around the estimates show intervals of ± 2 standard deviation.

  29. Histogram of Firm-wise Time Trends Estimated from Year-wise Fixed Effects 167 firmswith –ve trends 144 firmswith +ve trend

  30. Summary • Model to evaluate inventory productivity in retailing • Results differ from the Du Pont model • Adjusted Inventory Turnover • Estimate the effect of sales surprise on inventory turnover • Separate the effects of covariates, investment in capital intensity and time-trends in inventory productivity • Time-trend differs significantly across firms.

  31. Inventory Productivity and Financial Performance in the U.S. Retail Sector

  32. Research Questions • Is superior IT performance or AIT performance correlated with financial performance (stock returns; incidence of bankruptcy)? • Does the financial market provide external validation for AIT as a performance metric?

  33. Research Methodologies • Event-study • Analyze a firm’s stock returns following a change in inventory turns • Issues: • Separating material changes from random variation in inventory turns • Defining the time window in which the event can be said to have taken place • Contemporaneous correlation with long-run stock returns • Issues: • Survival bias – only firms that survived over the long time period can be used • Hard to make a causal argument: did better inventory turns precede higher stock returns? • Results could be confounded by missing intermediate variables that are correlated with both inventory turns and stock returns (e.g., risk measures and factor-mimicking variables) • Long-run event-study • Construct portfolios of firms based on AIT at the end of each year using historical data • Analyze the results of investments in these portfolios over the subsequent year • Conduct analysis over a long time-horizon by rebalancing the portfolio every so often • References: Carhart (1997), Cochrane (2001), Gompers et al. (2003), Jegadeesh and Titman (1993). 

  34. Data Description • Time period: 1984-2003 • Source: • Annual financial statements: S&P’s Compustat database • Monthly stock returns: CRSP

  35. Data Description - 2 • IT = [cost of goods sold]/[inventory]GM = [sales – cost of goods sold]/[sales]CI = [gross fixed assets]/[inventory + gross fixed assets] • Annual closing values are used for all balance-sheet items • No observations are omitted from the dataset to avoid survival bias • Large differences between median and average values of performance variables

  36. Assignment of firms to portfolios • Let i = firm index, s = segment index, t = calendar year index. • If fiscal year-end date for fiscal year 1995 for a firm is June 30, 1996, then data for fiscal year 1995 are assigned to calendar year 1996. • For portfolios formed in year t, stock returns are assessed for year t+1. • Using AIT • Regression done in each year: • log(ITsit) = as + b1*log(GMsit) + b2*log(CIsit) + esit • Firms are ranked into 10 decile portfolios based on the values of studentized residuals [= esit / std. err.(esit)] • Remarks: • Cross-sectional regression because (i) we require comparisons across firms in each year to rank firms; (ii) we cannot use entire time period to estimate the coefficients of the model. • Using IT • Regression done in each year: • log(ITsit) = as + esit • Firms are ranked into 10 decile portfolios based on the values of studentized residuals [= esit / std. err.(esit)] • Remarks: • A linear model may be used instead of a log-model. We use a log-model for consistency. • In both models, comparisons across firms can be confounded by missing variables, for example, differences in accounting practices, location of stores, management differences, etc.

  37. Characteristics of Decile Portfolios • Portfolio 1: lowest decile; Portfolio 10: highest decile. • Portfolios are uniform in composition with respect to retail segments and sizes of firms. • 3163 annual observations are used in the final analysis; remaining 985 observations had missing stock returns data. [Stock returns are computed over the calendar year following the formation of portfolio.]

  38. Examples of portfolio ranks of large firms

  39. Comparison of returns on highest and lowest ranked portfolios Annual returns on a $1 investment in portfolios formed using AIT • Portfolios 1-3: formed using the lowest ranked 30% of the firms • Portfolios 8-10: formed using the highest ranked 30% of the firms • Portfolios are rebalanced every year • Firms that undergo bankruptcy or liquidation in a year are assigned zero returns that year Annual returns on a $1 investment in portfolios formed using IT

  40. Comparison of total returns on all decile portfolios Lowest ranked decile portfolio Highest ranked decile portfolio AIT: Total returns over 20 years for portfolio 8-10 are 1208%, while for portfolio 1-3 are 98%. IT: Total returns over 20 years for portfolio 8-10 are 893%, while for portfolio 1-3 are 121%.

  41. Performance-attribution regressions for decile portfolios • Four-factor model (Carhart 1997) to explain differences in returns:Rit = ai + b1i*RMRFt + b2i*SMBt + b3i*HMLt + b4i*Momentumt + eit whereRit = excess return on portfolio i in month t,RMRFt = value-weighted market return minus the riskfree rateSMBt, HMLt, Momentumt = month t returns on zero-investment factor- mimicking portfolios to capture size, book-to-market and momentum effects (Fama and French 1993; Jegadeesh and Titman 1993) • ai= estimated intercept, interpreted as the abnormal return in excess of that achieved by passive investments in the factors.

  42. Results of performance-attribution regressions - Summary • Using AIT • Estimate of the intercept, , increases as portfolio rank increases. • Low ranked portfolios have significantly negative intercept, showing below-average returns. • Five out of ten portfolios have statistically significant intercept (p=0.10) • Abnormal return on a zero investment portfolio (buy top 30% and short-sell bottom 30% firms at the beginning of each year) = 0.9 bp/month = 11.25% per year. (p<0.01) • Using IT • Estimate of the intercept, , has a less evident trend as portfolio rank increases. • Two out of ten portfolios have statistically significant intercept (p=0.10) • Abnormal return on a zero investment portfolio is not statistically significant. • All regressions yield significant F-statistics (p<0.01) with R2 ranging between 36.5% and 61.2%.

  43. Results of performance-attribution regressions - Details (High – Low): Zero investment portfolio formed by investing $1 in the top 30% firms, and short selling $1 in the bottom 30% firms in each year.

  44. Inventory productivity and the value of the firm • Valuation measure: Tobin’s Q • Ratio of market value to book value of a firm. • Market value = (Book value of assets + Market value of common stock – Book value of common stock – Deferred taxes). • Regression to estimate whether variation in inventory productivity is associated with differences in firm value:Qit = at + bt*Xit + ct*Wit + eit wherei = firm indext = year indexQit = industry-adjusted Tobin’s Q (firm Q minus median Q for the retail segment)Xit = inventory productivity measure for firm i in year t (studentized residuals from AIT or from IT)Wit = log(Book Value of assets); known to be correlated with Qit (Shin and Stulz 2000).

  45. Inventory productivity and the value of the firm – Regression results • Regressions done • for portfolio rankings obtained from AIT as well as from IT • first using all portfolios, then using the portfolios of top 30% and the bottom 30% firms, with a dummy variable for the top 30% firms. • Coefficients are significantly negative in 9/10 years using AIT, and 5/10 years using IT • Firms with stronger AIT (or IT) outperform those with weaker AIT (or IT).

  46. Summary • Validation that AIT provides a better performance metric than IT for the retail sector • Consistent positive correlation with stock returns, risk-adjusted stock returns and value of the firm • Portfolio based on stronger AIT yielded 1208% total returns, while that based on weaker AIT yielded 98% total returns over 20 years. • Interpretation from financial perspective • Results need not constitute new evidence of market inefficiency • Inventory productivity may be correlated with other variables known to predict stock returns, e.g., business cycles • Is there sufficient reason to think that the stock market does not fully factor in the impact of superior inventory productivity? • Limitations • Robustness of results with respect to changes in dataset • Sensitivity of results to outliers due to large variations in the values of performance variables • Changes in portfolios over time • Causal variables

  47. Further Research • Omitted variables: variety, lifecycle length, components of capital investment. • Within-firm analysis using product or store level data. • Firm level analysis using disaggregated data • Augmented data from I/B/E/S. • Other variables, e.g. firm size, accounts payable. • Case studies: how do firms make aggregate inventory and margin decisions? • Explain differences in the coefficients of benchmarking model across segments. • Manufacturing and distribution sectors

  48. Systematic differences in fixed firm effects • Across segments • Within each segment, firms with lower gross margin have higher intercepts than firms with higher gross margin.

  49. Regression Across Firms log Inventory Turns Estimated line for a cross-sectional model with a single observation per firm. Slope = -0.40 Estimated regression lines for different firms in the apparel industry Slope = -0.15 log Gross Margin Fixed Firm Effect = Segment – 0.25 log (Average Gross Margin)

  50. Thank you!

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