The effects of overproduction on future firm performance and inventory write‐downs

Abstract

This study aims to examine the effects of current overproduction on future operating efficiency and inventory write-downs. Using electronic manufacturing companies listed on NYSE/AMEX/NASDAQ during the period of 2003-2013, this study: (i) employs the interaction term between relative fixed cost levels and excess quantity of inventory produced to measure overproduction, (ii) utilizes the dynamic slacks based measure model to estimate operating efficiency, and (iii) examines inventory write-downs. Our findings show that overproducing firms experience improved but unsustainable subsequent operating efficiency, as overproduction is a result of optimistic sales forecasting, which leads to higher subsequent inventory write-downs. Overall, this paper suggests that production managers should be prudent in anticipating future sales demand, as short product life cycle might cause higher inventory obsolescence on excess production.

Detecting overproduction: Evidence from inventory write‐down

“…Table 4 reports the estimated regression coefficient α 1 from Equation (1), which captures the association between various overproduction proxies and inventory write‐down. Panel A presents the results using Larson et al's (2015) model (Equation 1a), while Panels B and C present the results using Cotter et al's (1998) model (Equation 1b) and Chan et al's (2021) model (Equation 1c), respectively. In each panel, we report both the contemporaneous and the lead‐lagged associations between overproduction proxies and inventory write‐down for completeness.…”

“…Finally, Chan et al (2021) model inventory write‐down as a function of size ( MV ), growth opportunities ( MTB ), financial performance ( ROA ), cash‐based performance ( CFO ), change in demand and firm capacity utilisation ( SG and OB ), leverage ( LEVERAGE ), big bath reporting ( BATH ), income smoothing ( SMOOTH ) and the beginning balance of the inventory ( LIV ). Accordingly, we estimate a third write‐down model as follows:$$$$ {\displaystyle \begin{array}{cc}I\_{WD}_t& ={\alpha}_0+{\alpha}_1{OV}_{t‐k}+{\alpha}_2{MV}_t+{\alpha}_3{MTB}_t+{\alpha}_4{ROA}_t\hfill \\ {}& +{\alpha}_5{CFO}_t+{\alpha}_6{SG}_t+{\alpha}_7{OB}_t+{\alpha}_8{LEVERAGE}_t+{\alpha}_9{BATH}_t\hfill \\ {}& +{\alpha}_{10}{SMOOTH}_t+{\alpha}_{11}{LIV}_t+ Industry\ Fixed\ Effects+{\varepsilon}_t.\hfill \end{array}} $$$$…”

“…To provide a deeper understanding of the firms that report inventory write-downs compared to those that do not, we examine multiple characteristics of the write-down firms and non-write-down firms. Table 3 reports the mean and median values of the key determinants of write-downs from prior studies (Chan et al, 2021;Cotter et al, 1998;Larson et al, 2015) separately for write-down firms and control firms. We find that write-down firms | 3363 have significantly lower inventory accruals (INVACC t ) on average (mean = 0.00 vs. 0.01; tstat = −2.55), possibly reflecting their attempts to reduce their production levels in the year of the write-down as they realise that there is obsolete inventory.…”

“…Finally, Chan et al (2021) model inventory write-down as a function of size (MV), growth opportunities (MTB), financial performance (ROA), cash-based performance (CFO), change in demand and firm capacity utilisation (SG and OB), leverage (LEVERAGE), big bath reporting (BATH), income smoothing (SMOOTH) and the beginning balance of the inventory (LIV). Accordingly, we estimate a third write-down model as follows:…”

“…In each panel, column (1) reports the regression results as reported in the original papers (Chan et al, 2021;Cotter et al, 1998;Larson et al, 2015) for comparison purposes; column…”