Managing Complexity in Chemical Industry

Source: AT Kearney - Controlling Complexity in the Chemical Industry

Chemical companies are facing increased complexity. Levers of managing better growth: 1. Complexity; 2. Cost of raw materials; 3. production; 4. sales and delivery; 5. sales; 6. delivery.

I. Optimize operations: shutdown lines and locations, improve utilizations, avoid investments.
II. Reduce material costs: reduce number of suppliers formulations and raw materials, introduce technology platforms.
III. Improve productivity: Adapt organization, processes and resources for product development, technical services and sales.
IV. Make top-line improvements: Improve product mix, increase prices, switch or remove under-performing products.

Ascertain complexity by plotting formulation across these two axis:
1. Average sale per formulation ($ million)
2. EBIT margin per formulation

Average sales per formulation - <= $5 million (be alert on complexity managing), $2 million (reducing complexity is a priority).
Complexity tradeoff is defined as the difference of value and cost of complexity.

• Leading indicators of complexity:

1. Focus on revenue growth (topline) rather than profitability (bottomline).
2. Customer value understanding gap.
3. Inability to understand full product or service cost and profitability.
4. Limited or no understanding of PLC Vs unclear responsibility for complexity management.

• Lagging indicators:

1. Flat bottom-line in-spite of growing top-line.
2. More products but no increase in bottom-line.
3. No enhanced value generation by increasing portfolio.
4. No success in product pruning.
   

Complexity and tie-up meagre or limited resources such as:

1. Product development is forced to manage a broad portfolio of projects resulting in overly long development cycles.
2. Purchasing grapples with high transaction costs - managing a large number of suplliers and raw material vendors.
3. Manufacturing faces long lead times, frequent changeovers, increased cycle times, capital investment, and rising scrap and rework levels.
4. Outbound logistics is pressured by long lead times, excess finished goods inventory, loss of sales due to improper planning and demand estimation.
5. Marketing, Sales and Service deliver inefficient sales and service operations, cannibalization of sales, forecasting errors and increased expenditures on advertising a broad range of products and variants.

Managing Working Capital (Chemicals Industry -examples)

Source: KPMG- -Working capital chemicals industry

The Chemicals industry effectively passes rising prices to their customers. Their working capital levels have actually continued to fall ( a sign of improvement efficiency).

Between 2005 and 2007 the median days of working capital (DWC) in the European Chemical sector has fallen from 84.3% to 73.7% a 12.6% improvement. Looking at best performing companies in the sector the improvement is even greater. The top quartile companies have improved their performance over the same period by 22.7%, illustrating that the best keep getting better.

If companies improved their days of debtors (DSO), stocks (DIO) and creditors (DPO) to the same level as the top quartile, these companies could liberate €22billion of cash from working capital.
This released cash from working capital efficency can enable funding of capital expenditure as well as reduce debt, (shareholders). All of these uses would have a substantial positive impact on valuation.

Levers for releasing cash are: improving the stock days and debtor days.
1. Days of inventory outstanding i.e., the days of sales that the company is holding as inventory and
The improvements in inventory are most likely being driven by continual investment in the manufacturing process to improve yields, remove bottlenecks and accelerate production.
Furthermore shifting greater responsibility for raw material inventory onto suppliers in the form of consignment stock drives down the inventory on the balance sheet.

2. Days of sales outstanding i.e., the number of days of sales the company has tied up in debtors
3. Days payable outstanding i.e., the number of days of sales sitting in trade creditors
4. Nearly 50% more cash can be generated by negotiating longer payment terms with suppliers.

• Thus it is critical to focus on the total cost of ownership during negotiations with suppliers and weigh up the benefits of longer terms against potential discounts or shifting ownership of raw material inventory from your balance sheet.
• But over-pushing the suppliers to a position of dis-advantage (in paymnet and supply terms) would make the suppliers un-viable and thus squeeze the supply chain elements (in an event of higher buyer power).
• A way out is to improve your forecasting efficiency and tell the supplier accurately about the supply schedules. This will enable shortening the working capital cycles / improving efficiency.
• Inventory is the buffer between supply - demand thus accurate forecasting of demand and matching suppply to it will enable optimization of inventory.
• Reducing inventory is the second largest cash generator. A better supplier management will enable to reduce inventory.
• Reducing overdue debtors is an effective way of releasing locked up cash.
• Segmenting the customer base to develop strategies that yield the highest return with limited resource. In an ideal world every customer would be called prior to invoice due date to ensure they received the invoice and identify if any problems exist. In reality companies do not have the resource to accomplish this, requiring careful identification of how to manage different types of customers. This should also then be fed through into payment terms, payment methods and credit limits.
• While introducing a new product the intended impact of 'working capital' must be calculated.
• In the current economic environment those companies with financial strength should fair best. The continuous focus on product innovation and high commodity prices is likely to drive business complexity in the chemicals industry and squeeze margins.

Strategy Curves - Industry Structure - Strategic Context

Source: http://www.mckinseyquarterly.com/Using_power_curves_to_assess_industry_dynamics_2222

The power law, where the extreme and the rare have a disproportionate impact

Winner take - all extreme concentration
The probability of finding value in the extremes is much greater that probability of finding value close to the average.
Power law mathematical notation - scale invariance:

1. f(x) = a (x)^k
2. f(cx) = a (cx)^k
3. c^k f(x) is proportional to f(x)

The curve is characterized by a short “head,” this depicts a small set of companies with extremely large incomes, and drops off quickly to a long “tail” of companies with a significantly smaller incomes.
The relationship is simple: a variable (for example, net income) is a function of another variable (for example, rank by net income) with an exponent (for example, rank raised to a power).

Power curves across Industry
Surprisingly the more labor- or capital-intensive sectors, such as chemicals and machinery, have flatter curves than intangible-rich ones, such as software and biotech.


Thus scaling by a constant simply multiplies the original power-law relation by the constant c^k called the scaling exponent.
Thus, all power laws with a particular scaling exponent are equivalent up to constant factors, since each is simply a scaled version of the others.
This behavior is what produces the linear relationship when both logarithms are taken of both f(x) and x, and the straight-line on the log-log plot is often called the signature of a power law. Notably, however, with real data, such straightness is necessary, but not a sufficient condition for the data following a power-law relation. In fact, there are many ways to generate finite amounts of data that mimic this signature behavior, but, in their asymptotic limit, are not true power laws. Thus, accurately fitting and validating power-law models is an active area of research in statistics.


A plot of distribution of net income of the global top 150 corporations does not yield a bell curve. Thus there isn't an even spread of net income values across the mean value. The representation is a power curve - implying that most values (of Net Income) are below average.
Power-law relations are predominantly found in nature and phenomenon that are naturally occurring.

• Gravitation and the Coulomb force, are power laws, (inverse square)
  
• Area of circle - quadratic law

A wide variety of probability distributions have tails asymptotically that follow power law forms.
This 'asymptotically long tails' observation is connected closely with the study of theory of large deviations (also called extreme value theory), which considers the frequency of extremely rare events like stock market crashes and large natural disasters.

Interesting Observations, Examples & Arguments

• Wealth -- social & natural -- seems to obey the power law.
• Number of earthquakes, behavior of piles of grains, population dynamics - behavior and characteristics - exhibit close similarity to the power law.
• Number and the magnitude of 'Solar Flares' the x-ray signature of black holes - exhibit power law characteristics.
• The observed random distribution of digits from 1 to 9 (Benford's Law),
• Benford Law : American astronomer Simon Newcomb noticed that in logarithm books, the earlier pages (which contained numbers that started with 1) were much more worn than the other pages. Newcomb's published result is the first known instance of this observation and includes a distribution on the second digit, as well. Newcomb proposed a law that the probability of a single number being the first digit of a number (let such a first digit be N) was equal to log(N+1)-log(N). The phenomenon was rediscovered in 1938 by the physicist Frank Benford, who checked it on a wide variety of data sets and was credited for it
• The impact of forest fires
• The 80-20 rule applies to the distribution of wealth - the top 20% of the population holds 80% of the wealth in a society.
• The 80-20 rule also applies to major motion pictures.
• Admissions at Ivy League / to colleges follows the 75-25 rule --> nearly three quarters of the undergraduate students at Harvard and other so-called "most selective" universities and colleges come from the top income quartile.
• Applied to financial markets and as repeatedly pointed out by Benoit Mandelbrot and Nassim Nicholas Taleb, "just 10 trading days represent 63% of the returns of the past 50 years".
• The power law also applies to concentration of market capitalization to the shares of a small number of publicly traded companies (e.g., the 80-20 rule approximately applies to the market value of the FTSE 100 compared to all shares traded in London), the size of hedge funds, and a number of other areas in finance.

Analysis - Business Context
An analysis of the top 30 US banks and savings institutions in June 1994, 2007, and 2008, measured by their domestic deposits.
This inequality has been increasing from 1994 (when the number-ten bank was roughly 30 percent of the size of the largest one) to 2008 (when it was only 10 percent as large as the first-ranked institution). It also shows how in 2008, the financial crisis accelerated the growth of the top five compared with the other banks in the top ten as the largest financial institutions took advantage of their relatively healthy balance sheets and absorbed banks in the next tier.
Regulation could put a damper on this crisis-driven acceleration of inequality, but power curve dynamics suggest that it will not reverse the trend.
There are observed long-term patterns of increasing inequality in size and performance in a variety of industries and markets - using metrics such as market value, revenues, income, and assets to plot the size of companies by rank.
Thus the power curves are becoming steeper - i.e., the inequity is increasing. In analysis -

• An industry’s degree of openness and competitive intensity is an important determinant of its power curve dynamics.
• It would be expected that a bigger number of competitors and consumer choices to flatten the curve, but in fact the larger the system, the larger the gap between the number-one and the median spot. (why?)
• After the liberalization of US interstate banking, in 1994, deposits grew significantly faster in the top-ranking banks than in the lower-ranking ones, creating a steeper power curve.
• Greater openness may create a more level playing field at first, but progressively greater differentiation and consolidation tend to occur over time, as they did when the United States liberalized its telecom market.
  

Power curves are also promoted by intangible assets—talent, networks, brands, and intellectual property—because they can drive increasing returns to scale, generate economies of scope, and help differentiate value propositions.
Surprisingly the more labor- or capital-intensive sectors, such as chemicals and machinery, have flatter curves than intangible-rich ones, such as software and biotech.

The fact that industry structures and outcomes appear to be distributed around “natural” values opens up an intriguing new field of research into the strategic implications. Notably, the extreme outcomes that characterize power curves suggest that strategic thrusts rather than incremental strategies are required to improve a company’s position significantly.
Consider the retail mutual-fund industry, for example. The major players sitting atop this power curve have opportunities to extend their lead over smaller players by exploiting network effects, such as cross-selling individual retirement accounts (IRAs), to a large installed base of 401(k) plan holders as they roll over their assets. The financial crisis of 2008 may well boost this opportunity further as weakened financial institutions consider placing their asset-management units on the block to raise capital.

Application of Power curves in strategy setting
As executives set strategy, power curves can be a useful diagnostic tool for understanding an industry’s structural dynamics.
In particular, there may well be commonalities across sectors in the way these curves evolve, and that might make it possible to gain better insights, based on the experience of other industries, into an industry’s evolution.
As the importance of intangible assets increases across sectors, for example, will power curves in media and insurance resemble the currently much steeper ones found in today’s intangible-rich sectors such as software and biotech? Power curves could also benchmark an industry’s performance. Curves for specific industries evolve over many years, so the appearance of large deviations from a more recent “norm” can indicate 1.) exceptional performance, on one hand, or 2.) instability in the market, on the other.

Unlike the laws of physics, power curves aren’t immutable. But their ubiquity and consistency suggest that companies are generally competing not only against one another but also against an industry structure that becomes progressively more unequal. For most companies, this possibility makes power curves an important piece of the strategic context. Eclectic Antidote to the Power Law predisposition

Power law is a useful paradigm & tool for studying many natural & social phenomenon that are consistent with that model. As a descriptive ('positive' in philosophy of science speak) framework, the power law is often superior to the 'normal' distribution and should be adopted and utilized more widely.

From a normative, prescriptive however, the logic of the power law could easily be abused (just as the 'bell curve' has been). To any person of both intelligence and conscience, it is not a laudable nor a desirable thing to have 20%, 25%, or some other percentage of small concentrated elites or 'hits' crowd out higher quality competition and, even, endanger meritocracy and democracy.
The negative consequences of a predisposition to follow and mould activities as per power law can be mitigated by using a long tail strategy by focusing on asymptotic populations, of encouraging eclecticism and accommodating niches.
The power law can then become a useful and desirable framework to identify and tackle problems in business, finance, policy making, and culture.