LEAN ACCOUNTING: A Strategic Approach to Cost Management

Conventional accounting process – recording, classifying, summarizing business transactions – despite advances in technology – is a complex process, let alone the execution, even the understanding. The entire process is non-value adding and ends up to a wasteful expense.

Lean Accounting refers to the process of managerial accounting and control systems of an enterprise by applying lean methods. Lean methods are drawn from technological innovation called – “lean manufacturing” by Toyota and other Japanese companies in the 1980s. The objective of lean method is to eliminate waste, free up capacity, speed up processes, defect-free, clear and understandable processes. It was in the mid of 2000s that a Lean Accounting Summit was organised in Detroit to propose Principles, Practices and Tools of Lean Accounting.

The objective of this article is to discuss in brief few selected approaches of lean accounting to highlight the benefits of the same:

Value Stream Costing

Value stream costing suggests the manufacturing company to create cost sheets on a weekly basis by recording all the direct costs associated with the production and sale. For instance, every time a payment towards purchase of materials is made, it is recorded in the value stream. Every time a payment towards wage is made, it enters into value stream, irrespective, whether it’s direct or indirect. Overheads need not be allocated at all, if such process is going to take higher efforts. The weekly summarising ensures a continuous control and also quicker understanding by anyone.

Plain English Financial Statements

Financial Statements must be such that they are understandable by anyone in the company. This ensures minimization of errors in financial statements, misleading window-dressing and allows for meaningful analysis for decision-makers.

Hoshin Policy Deployment

Contrasting the long-term business strategy planning, Hoshin policies are strategic statements which clearly state plan of action for the next year. Be it the resource planning, marketing targets or cost standards at all levels of management. Another differentiator of Hoshin policy is that it develops plans that need to be executed by the plan-makers themselves, rather than the subordinates. (Maskell & Baggaley, 2006)

Target Costing

A cost control mechanism successfully demonstrated by Sony’s Walkman case, target costing suggests to set the target market share that the company wants to acquire, and work in the reverse order to arrive at the maximum cost within which the product needs to be produced and sold. Tata Motors Nano is case for this point.

Box Score

Backflush Costing Box Score acts as a daily/weekly scorecard to display for the information of one and all connected to the production process and adjust the velocity of efforts to match the pace required to march towards the set short-term goals. A typical box score card would contain operational, capacity, and financial goals and easily conveys the distance to cover. (Lean Accounting, n.d.)

Generally used with JIT systems, this approach suggests eliminating the regular cost tracking systems, instead carry out the costing process after the production run. Costs are ‘flushed back’ to cost units using real data. This eliminates the cost of work-in-process and simplifies the costing system.

Above-discussed are only few of the operational strategies that (mostly manufacturing) firms can install into their processes and are bound to yield results. Having said the same, it must also be noted that there are certain challenges that a firm that adopts a lean accounting needs to face – (i) Is this going have its impact on matching compliance requirements – like the accounting standards; (ii) What would be the cost of transforming into the new system and how long does it might take to see the payback? (iii) What would be the potential reactions from different stakeholders?

Despite its limitations and challenges, lean accounting charters continue to be desirable. Organisations across, (specially the advanced economies) have been consistently adopting and at the same time innovating lean accounting practices, in parts, at least.

GETTING YAHOOED: CASE OF STRATEGIC FAILURE

“In 1998, yahoo had the chance to buy Google for $1-2 Mn in its nascent years. They said Google’s PageRank ain’t worth the pennies. In 2002, Yahoo had the might to buy Google for $5 bn. They said Google is overvalued. In 2008, Microsoft proposed to acquire Yahoo for $45 bn. They said they are undervalued. Today Yahoo got sold to Verizon for a mere $4.8 Bn while Google is valued at over $500 bn. Moral: never underestimate others and overvalue yourself. You lose your value in the process” (Source: A forwarded WhatsApp Message). While this is not completely expressive of the loss of wealth of shareholders, one can’t ignore the fact that the brand Yahoo! that had the potential to be a high value adding company to shareholders’ pocket, has failed in achieving precisely that. Yahoo! continues to be operating with its remaining assets, close to $ 50 Bn (including the payment from Verizon) in the form of holdings in China’s Alibaba and Japan’s Yahoo! Japan. Famously described as the demise of Yahoo!, the Verizon’s acquisition of Yahoo for $4.83 billion is presenting an interesting case for evaluating a multibagger and lessons for emerging new-age companies.

The Emergence

Yahoo, started as an in-campus search engine by two college students in 1994, within two years, went public with its IPO. Between 1997 to the mid of 2010s, the dot-com company has operated in almost all hot areas of the business – search engine, e-mailing, audio streaming. Mostly lead by expansion-through-acquisition strategy, Yahoo! acquired a number of companies from different domains ranging from communications, mailing services, messenger services, online games, web hosting and reached its peak performance state just before the IT bubble burst of 2000. Yahoo! was also one of the very few companies who could survive the dot-com bubble burst. Yahoo! had introduced paid search engine listing much before Google had introduced.

Google played an important role in Yahoo!’s slow death. From being a search engine partner for Yahoo! initially, Google went onto become one of the largest used search engine service provider and continued its foray into every other web & IT-based field that Yahoo! had its presence.

The Slow Death

Yahoo!’s downward journey can be traced back to mid-2000s and to various merger options overlooked by Yahoo!’s management. Be it the rejection of Microsoft’s offer to buy-out Yahoo! for nearly $45 Bn, or the failed merger attempt with the then fastest growing Google or another failed attempt to merge with News Corp. There were also discussions in the lines of buying out Facebook that was then still an emerging business. Yahoo! did enter social-networking and blogging space through an all cash deal, popularly perceived to be a costly buy (approx. $ 1 Bn), of Tumblr, but, in 2013, by when Facebook had created enough entry barriers. Throughout these deals, there have been legal battles on patent issues, employee layoff issues and also issues relating to acquisition terms and conditions. Ironically, to highlight how Yahoo! took most wrong decisions when it came to its acquisitions, it let go of options of M&A with companies like Microsoft, Google, Facebook etc., whereas it bought companies like Geocities by paying $4.5 Bn and Broadcast.com by paying $5.7 Bn that too at the peak of dot-com bubble. The only decision that worked in case of Yahoo! can be the entry into China’s e-commerce space through a 40% stake in Alibaba.com, which is still a face savior for the Yahoo!’s top brass.

Officials at Yahoo! claim it not a mega failure as its being projected by analysts. That is only partially true. Because the core business of Yahoo!, that’s in the hands of Verizon will be clubbed with another (recently acquired) fallen star subsidiary of Verizon – AOL and this is expected to create that much required synergy for the ex-brand Yahoo!. When we take a stakeholder perspective, the death is not that of the stake per se, rather the brand Yahoo!, that is more concerning. As Forbes magazine put it – “the transaction ends the independence of one of Silicon Valley’s most iconic pioneering companies”.

What does it mean?

There are certain perspectives we can form taking the curious case of Yahoo!. The corporate was formed at the right time in the right field. Has been there, did all that was required to be the mainstream new-age player (web, mail, mobile, streaming, and all); so much to the envy of traditional product-driven businesses. Yet, it could not optimize its position in the market, as against the giants of the likes of Microsoft, Google or Facebook. How does one explain such failure?

Should we say the lack of innovation? – Yahoo! had access to all the resources across the world. There are other companies, which just imitated a working business model and succeeded. Social Networking was not a fresh idea of Facebook. Search Engine was originally Yahoo’s idea.

Should we say access to capital? – Despite continued wrong calls, Yahoo was always on the watch list of investors. Yahoo! has consistently been considered a strong buy, if not for company fundamentals, but, for the kind of growth phase the industry was sailing through. Yahoo! derived its goodwill more from the goodwill of the industry it operated in. And capital was never dearth.

Or should we say killed by competition? – Google which could be described as one of the major competitor for Yahoo!’s search engine and mailing services, was not the only one on the pie chart. There was also Microsoft’s Bing and Hotmail, and other regional players as well. And it would be inanity to say Yahoo! did not have a competitive strategy in place. Competition was always expected, was always present and was always going to be present.

Or was it due to macro-economic crisis? – As mentioned earlier, Yahoo! was one of the successful survivors of dot-com bubble burst of 2000. Global financial crisis of 2008 did not create the kind of damage to web-based businesses like it damaged the financial services or realty and auto sector.

Yahoo! was operating in an industry that had only one direction – upwards – in the last two decades. Economic conditions were congenial – new economic orders, global integration, opened up market places and embracing customers. There was an abundant supply of capital resources and human talent and also easier access to both. Notwithstanding all this, if a pioneering company of the shining industry fails, after two decades of its operations, to sustain and grow, the fingers can only be pointed towards the MANAGEMENT. Managerial decision-making, (as given by the Value Octagon framework of Dr. Chandra) especially at the top level, starts with devising corporate strategy and business model, percolates into capital allocation decisions, financing decisions, creating organizational architecture, strategically driving the costs, managing the corporate risks, corporate restructuring decisions and extends up until the governance mechanism. Yahoo!’s failure can be assigned to most of the above parameters, specifically, to the corporate strategy, risk management and corporate restructuring.

One of the mind-mapping exercises at an offsite event of Yahoo! employees, asked the delegates from different countries to utter the first word that comes to their mind with different brands. For most brands like Apple, Microsoft, Google, the responses were almost unison as Smartphone, Windows or Search Engine. But, with the brand word Yahoo!, there were multiple responses, some said search engine, some said email, some said messenger and so on. What this proves is the lack of FOCUS in corporate strategy for Yahoo! (as suggested by Michael Porter’s Generic Strategies)

Operating in the business where every moment is so dynamic and the diffusion of innovation is the fastest, it was imperative that Yahoo! had to have identified the risks, measured the risks and had in place a RISK RESPONSE strategy – mitigate, transfer or accept risk. Going by the turn of events in the last few years, it’s anybody’s guess that, Yahoo! chose to accept the risk, while failing to do a cost vs benefit analysis between retaining and transferring the risk.

Yahoo!, when it came to its corporate restructuring decisions failed to view itself as part of the BUSINESS ECOSYSTEM. An ecosystem is a broader and an inclusive concept, which suggests businesses operate in a system where every player/stakeholder is not just related with each other, but also influencing each other. Each element is entangled with each other. Co-evolution is the best strategy to win in such a system. (as suggested by James Moore). Firms must pose competitive yet co-operative challenges to the other elements in the ecosystem. Somewhere, it feels Yahoo! missed out on this aspect. It chose to adopt a combating strategy with the potential big players.

Yahoo! rejected Microsoft’s buy-out offer in 2008 claiming the latter is undervaluing Yahoo!. Truth is that it was always Yahoo! that undervalued itself and every other player’s ability in its ecosystem.

Semi-Efficient Market Hypothesis – Does It Hold Good?

“A blindfolded monkey throwing darts at a newspaper’s financial pages could select a portfolio that would do just as well as one carefully selected by the experts” – quotes Burton Malkeil[i] on market efficiency theory. Efficient Market Hypothesis, term coined by Eugene Fama[ii] way back in 1970, and further studies in the area went onto earn him the Nobel. The theory stated that markets are efficient or intelligent enough to subsume all the information relating to firms in general and the price discovered by market is the right price. When, market has used every bit of information in arriving at the price, no one individual market participant can make an excess or abnormal return by beating the market’s intelligence. Only arrival of new information can lead to a change in the price, and the invisible angel in the market will very soon bring the price to incorporate the new information also and thus leaving no further speculative opportunity.

The theory is further enhanced by dividing the level of efficiency on the basis of the kind of information that could have been subsumed by the market. If we presume that a given market is using all the historical information relating to a scrip, we call the market to be efficient in its weak-form; if it is presumed that along with historical data, the market is also using publicly available information, including market sentiments, rumors and expert opinions, we call the market to be semi-strong efficient; and finally if we presume market has access to information that is privately known to only the insiders, and the price discovery mechanism uses such data also, we call the market to be efficient in its strong form.

EMH being a theory, even though seems logical enough, it needs extensive research across markets, across time lines, and through different statistical tests. Hence, EMH studies are a regular research problems, being attempted by capital market researchers since decades and it continues till date.

Semi-Efficient Market Hypothesis

One of the trigger during such research series was to test whether the entire market is efficient, or it is the individual stocks that are efficiently priced. It was then observed that most significant contributions to these theories have concluded based on their tests conducted on a market as a whole, and not individual scripts. Prasanna Chandra[iii] puts it as below:

‘The efficient market hypothesis (EMH) has a cousin, the semi-efficient market hypothesis (SEMH). SEMH holds that some stocks are priced more efficiently than others. Consider two companies, Infosys and a hypothetical start-up firm called Nuvo Software. Infosys is followed by many investors and actively traded. Thousands of portfolios would contain Infosys shares and innumerable security analysts follow it. Hence it likely to be fairly priced. What about Nuvo Software? Very few people follow it; so according to SEMH there is a greater likelihood that it will be mispriced. Extending this idea, one may argue that the market perhaps has several tiers. Put differently, there is a pecking order of efficiency. Most observers of the market are generally sympathetic of the logic of SEMH”

Two observations from the above explanation by the author. One, it may make more sense to study individual stocks to determine the efficiency of a market. Two, efficiency of a market follows a pecking order and hence, it would be impossible for one to put a dichotomizing line between an efficiently priced and a not-so-efficiently priced scrip, as the pecking order is more discrete than continuous.

Sample study for SEMH

We have conducted a sample study on 12 stocks from Indian markets, 4 each from large cap, midcap and small cap to analyse if the prices follow a pattern among these stocks. In essence, we are checking for the weak-form of efficiency (i.e., whether the market determined prices of these stocks are randomly priced, and if so, we conclude that market has used all the historical information, in the form of historical prices, and efficient in its weak-form) of each scrip. Based on the results we can derive inputs for SEMH theory.

Stocks Selected

Stocks selected using convenience sampling as below:

  Large-Cap (forming first 75% of market cap): Infosys, BPCL, SBIN & Tata Steel (random 4 from CNX NIFTY)

  Mid-Cap (forming market cap between 75th percentile to 90th percentile) : Petronet LNG, Reliance Communications, Marico Ltd, Pidilite Industries Limited (random 4 from CNX Midcap)

  Small-cap (forming market cap between 90th percentile to 95th percentile): Cox & Kings Ltd., Gati Ltd., Raymond Ltd., Zydus Wellness Ltd., (random 4 from CNX Smallcap)

Methodology used

As the objective is to test the random movement of the stock prices, a non-parametric runs test is conducted. Runs test follows the mechanism of designating a plus (+) sign when there is increase in price and a minus (-) sign when a decrease in price occurs, when the stock prices are arranged chronologically. A run occurs when there is no difference between the sign of two changes. When the sign of change differs, the run ends and a new run begins. The total number of runs of a series of stock prices are then tested for statistical significance (i.e., whether the runs of the given sample series statistically differs from the number of runs of a purely random series of the same size). Thus for our study we tested for randomness using runs test of the 12 selected stocks based on their weekly closing prices. The hypothesis tested hence for each script was in the below form:

H0: The weekly closing price series of Infosys is randomly distributed

H1: The weekly closing price series of Infosys is not randomly distributed

Test Conducted: One-Sample Runs Test, around the mean

Decision Criterion: Reject the Null Hypothesis if the sig. value is less than 0.05 (95% con. level)

Results

Test for randomness as above is conducted using SPSS 23.0 v. and the results are provided in the table below:

Interpretation

  Null Hypothesis is rejected in ALL CASES, submitting proof to infer that “all the selected scrips, across market capitalizations, do not follow random walk, hence, none are efficiently priced in their weak-form”.

  Based on the above, we can also interpret that, SEMH also does not hold gold. This is evident in the sample case that we have tested above, where all the stocks have emerged to be inefficiently priced.

Conclusion

Contrary to the arguments of few researchers, on the pecking order of efficiency, above results deduce the absence of such order and also leaves ample scope for further stimulations to question the existence of SEMH.

It may be noted that the above conclusions are based on sample study. As has already been mentioned, either EMH or SEMH is still a ‘Hypothesis’ and demands further research using different sample, sample size, timeframe, testing tools and markets.

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[i] (Malkeil, 1973)

[ii] (Fama, 1970)

[iii] (Chandra, 2012)

Are Indian Banks Heading Towards ‘Financial Embarrassment’?

The manner in which US Federal & other central banks of advanced countries are shaping their monetary policies in order to avoid another economic downturn, is going to precisely result in that – claimed Mr. Raghuram Rajan, RBI Governor at LBS last week (Refer Box-1). The practice of zero-lower-bound interest rates of Fed and ECB in order to fuel industrial growth is unconventional and exposing other markets as well to the macro-economic risks. While saying this, Mr. Rajan also ensures to sound confident on the prospects of India and Indian Banks despite the external influences like these.

 

Box 1:The Economic Times 27-June-2015

While the Central Bank and the Government will dwell upon to ensure the external risks of the above-kind, the banks per se, operating in India need to ensure that they are operationally covered to sustain themselves. Ceteris paribus, especially the external risks, how do the Indian banks cope with distress?

Objective of this Analysis: This analysis aims to test the possibility of Commercial Banks in India going bankrupt in the next 2-3 years, purely based on their operational efficacy. 

Methodology: Various ready-made methods of bankruptcy prediction are available, that take the form of survival approach, option-valuation, neural-network models and other sophisticated approaches. These methods focus on risks emanating from externalities as well as internal affairs of a firm. As the aim of this study is to predict possibility of financial distress caused by operational dimensions of the firm, Altman’s Z-Score Model is applied. Edward I. Altman published his Z-Score formula in 1968, which uses multiple financial ratios of the firm to predict the bankruptcy chances in the next two years. Altman had- applied multiple discriminant analysis to a set of public manufacturing firms and arrived at coefficients for different ratios, those when summed,  give a score, based on which one can derive conclusions on the state of distress. Over a period of time, the formula has been modified to suit different set of firms, viz., public-owned companies, privately-owned firms, non-manufacturing firms, firms from emerging markets etc. This analysis uses the Z-Score formula proposed to be suitable for non-manufacturing firms in emerging markets, which is presented below.

Altman Z-Score For Non-Manufacturers & Emerging Markets: Z = 6.56X1 + 3.26X2 + 6.72X3 + 1.05X4 X1 = Net Working Capital / Total Assets X2 = Retained Earnings / Total Assets X3 = EBIT / Total Assets X4 = Book Value of Equity / Total Liabilities Zones of Discrimination: Z > 2.6 “Safe” 1.1 < Z < 2.6 “Grey” Z < 1.1 “Distress” Analysis & Results:  Constituents of BSE Bankex Index were taken as sample (12 banks), data pertaining to their financials were collected from their respective annual reports for the year ended 31-March-2015 and requisite ratios and Z-Score are computed. The data and their computations are presented in the below table (Box-2)   (Figures In) Total Assets Working Capital Retained Earnings Earnings Equity Total Liabilities TA NWC RE EBIT EQ TL Bank Of India In Lakhs 62528474 502170 3185720 754949 3252285 4009869 Axis Bank In Crores 467243 60163 44475 7358 44950 84394 Bank Of Baroda In Crores 676114 11076 37416 4931 37847 36976 Canara Bank In Crores 553152 -26040 26611 2703 27086 25763 Federal Bank In Crores 82908 -2474 7529 1012 7700 2393 HDFC Bank In Crores 607097 37433 62653 10216 63154 59478 ICICI Bank In Crores 826079 109891 83537 11175 84697 211252 Indusind Bank In Lakhs 10911592 1767533 1010103 309822 1063048 2061806 Kotak Mahindra Bank In Crores 148561 42639 21752 3065 22138 31415 Punjab National Bank In Crores 636011 19757 42217 3341 42588 59205 State Bank Of India In Crores 2700110 51323 160641 17517 161388 244663 Yes Bank In Crores 136170 14279 11262 1997 11680 26220   Box 2: Financial Data of BSE Bankex Constituents Altman’s Z-Scores computed for the respective banks, along with the zone they fall, are presented in the below chart (Box-3)  Box 4: Altman Z-Score for BSE Bankex Constituents Kotak Mahindra Bank and Federal Bank were found to be in safe zone. Canara Bank and State Bank of India are in distress zone. All other banks are in the grey zone. Going by Altman’s model we may predict distress zone banks to go into bankruptcy in the near future; grey zone banks need to exercise caution and relook into their operational strategies; whereas banks in safe zone need to maintain status quo. In the context of a probable economic downturn, as discussed above and when considered as a whole, it can be observed that most of the banks in India fall under grey zone, which is not so immune state to be in, (even though it’s not a distress state).  Conclusion: The above results need to be viewed with caution, as Altman model is developed primarily on manufacturing firms and applicability of the same on non-manufacturing firms, especially banking and financial firms are yet to be proven by researchers. Lack of literature on the same is a big limitation for this analysis. As an academic attempt, this study can remain as a record and unfolding of the future can lead to further research in the area and development of a model meant for banking companies. There is no evidence to suggest computation of a Z-Score is a better means of analyzing long-term solvency. Rather, it can be asserted that use of these ratios as predictors of distress is best in complementing a rigorous analysis of financial statements. Evidence from literature does suggest the Z-Score is a useful screening, monitoring and attention-directing device.   Going by the results, it can be concluded that banks of India, have as an undercurrent, a not-so-satisfactory operational efficacy, and unless these are fixed for better, financial embarrassment - in the event of Mr. Rajan’s prediction coming true - is something that could be a reality. References Altman Z-Score. (2015). Retrieved from Wikipedia - The Free Encyclopedia: https://en.wikipedia.org/wiki/Altman_Z-score Altman, E. (2000). Predicting Financial Distress of Companies: Revisiting the Z-Score and ZETA Models. Altman, E. I. (1968). Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy. Journal of Finance. Economic Times Mumbai Bureau. (2015, June 27). Rajan Rings the Alarm Bell: Chances of Global Depression. The Economic Times, p. 1. Retrieved from http://epaperbeta.timesofindia.com/Article.aspx?eid=31815&articlexml=Rajan-Rings-the-Alarm-Bell-Chances-of-Global-27062015001030 Wild, J. J., Subramanyam, K. R., & Halsey, R. F. (2007). Credit Analysis. In J. J. Wild, K. R. Subramanyam, & R. F. Halsey, Financial Statement Analysis (pp. 540-541). Tata Mc-Graw Hill.