Can`t walk on water? Yes, you can!

SeatoSky_AliKabas

Accenture Drops Performance Reviews !

And people burst into celebrations!  I am hoping Accenture will implement and recommend to its clients that  BUDGETING and QUOTA setting practice follows the way of PERFORMANCE REVIEWS.

Remember “Corporate Budgeting is Broken” – Lets Fix It – article by Michael Jensen?  First written in the 90s, published in HBR in April 2001. Twenty years later – same shoddy quota management practices are not replaced.

Remember Balanced Scorecard and the attempt to use it in performance management?

And the bonus / commission systems that rely on TARGET achievement?

Laminating Management Processes

Budgeting – Performance Management and Incentive Compensation. These three archaic corporate practices prop each other up like a three legged stool.  Imagine that they are executed simultaneously and by individuals at will.

eBay of performance

How? We let the executors – of any process – bid for resources they need to execute against some share of corporate outcomes (quantity, revenue, fewer defects, bad loans, profits etc.). We would also  facilitate instant feedback, in the form of a score;  allowing executors to reconfigure their “bid”, in the cloud, as frequently as they need to keep their own score(s) high.

Does it not sound like eBay? Imagine bidding with multiple goods to put that desirable thing in your cart. e.g. Exchange two books, a tennis racquet and a bowler hat for a used bike. Funny? That is exactly what happens in business processes – daily.

Soon, the solution will be on a cloud near you.  The foundation for such a powerful change starts with recognizing that ratio based KPIs are trouble! Ask us why! 

 

http://altabering.com/our-product/

 

Is all this tension around performance necessary?

Have you ever wondered why “they” think you need to be under pressure, either against a stretch goal or in competition with colleagues? Who are “they”?

Is it not possible to do better without pressure and tension in large organizations?  

Here is one admittedly simplistic perspective on pressure and tension in any organization.

Individuals with any level of accountability are to some degree afraid that they will not be favorably judged at the end of a period or a deadline, for not delivering against targets and goals, set by fiat or negotiation. Agreed?

There is much talk about how data, knowledge and insight and advanced decision systems will be showing workers and managers how to win, meet targets or come up with a superior return over others.

Yet, do these questions prevail?

  • Do they know how your performance quota was arrived at?
  • Do they know with certainty why you are ranked 37 and not 33?
  • Do they tell you how you could be ranked 29 instead?
  • Why are they hesitant to voluntarily reallocate/share excess resources?
  • Why would they postpone and opportunity to the next quarter?
  • How do they balance revenues today with profits tomorrow?
  • Are they allowed to change their minds?
  • Can they safely decide on an alternate path?

For outstanding results:

Any combination of your answers to these questions is powerful enough to converge performance toward run-of-the-mill outcomes, regardless of the effort that goes into creating an excellent strategy, great goal sharing and teamwork

Could the linch-pin of outstanding performance in large organizations be a system that facilitates transparent and “fair” calibration of resources and expectations, across peers, and spares people from the grinding pressure and non-constructive tension. It is possible and within reach.  

Is that a wish you would entertain for 2015? Ask us about it!

“KPI” for Whale Watching!

                                       

A few days ago, I came across a post that suggested “need-to-know 25 KPIs”. A reader was suggesting a sprinkling of KAIs on the KPIs. (If you can count five KPI and ten KAI for your business, off-the-bat, you may move on.) If you are still reading, however, let us remove the K and think of “performance indicators” and “activity indicators”.

 

Value Based Whale Watching!

Let us say PRINCE OF WHALES is our consulting client. PoW is in the business of providing tourists with close encounters with whales off the coast of Vancouver Island.

Manager Michael Orca knows that more tickets sold to tourists is generally good, more revenues can be good and bad, longer wait lines is good and bad, more passenger capacity (boats) is good and bad, higher ticket price is good and bad, a longer dock is good and bad. Unpredictability of weather is bad, unpredictability of the number of cruise ship passengers is bad, more whale sightings is good, more labor cost is good and bad, more fuel consumption is good and bad. More debt is good and bad. The challenge and excitement of running PoW appears to be in making the right choices for a shot at “maximum value” at the end of the season.

To get a grasp on the business, we set out to normalize all the factors that PoW keeps track of. Revenue/Number of Tickets, Labor Cost/Revenue both quickly make sense. Fuel Consumption/Number of Cruise Ship Passengers, Labor Cost/Whale Sighting or Debt/Whale Sighting do not. Too many factors are involved in the balance and not all are manageable. Some of the normalized measures are just not as relevant to management for value as others. It seems like we need a framework to overcome the KXI confusion and make good decisions.

The success of any value oriented strategy is measured by how well it can generate and attract the funds needed for growth.

Prince of Whales must make sure that it can pay the RENT to financial institutions and the equity investors whose funds were used to buy boats, extend the dock, pay the labor to remain open on rainy days. Even if PoW is profitable, if it cannot attract fresh equity from existing or new investors, in balance with financial debt, the strategy can fall victim to a Catch-22 scenario. As financial credit limits are reached, first source to turn to for operating capital is more vendor credit. The cost of vendor credit is buried in the price of the goods purchased, hence, higher the debt to vendors, higher the costs of production and lower the profit margin.

Next and most expensive source of funds is equity. To the investor, every new project is added to the accumulated investment from earlier projects (invest in boats, invest in new dock, invest in signs, etc.) and the net profit after taxes, from ALL projects, needs to meet the investors expectation.

In CFO jargon, RETURN on CAPITAL EMPLOYED needs to MEET OR EXCEED the INVESTORS` OPPORTUNITY COST.

Translated: Costs can go down, revenues can go up, vendor debt can go up, boats can be sold off, investments can be postponed. OR: debt can go up, ticket prices can go down, investments continue.

Soon, it becomes clear that there are numerous value sets on the “efficient frontier”, where risk, returns and growth find a temporary balance.

Until a balancing model and algorithmic tools are also in place, how many KPI candidates are identified seems to matter very little.

For a better solution visit; http://altabering.com/alta-bering-epo/epo-demo

 

Bewitched, Baffled and Bewildered

http://insideanalysis.com/2014/04/trends-in-predictive-analytics/

quote

“Stagnant deployment. Ironically, despite the hype around predictive analytics, the percentage of companies that have implemented the technology has remained flat.This is hard to explain.”

Here is my explanation –

A. It takes time for people to trust 18 months of machine learning over 15 years of experience.

B. Coin toss will be 50/50 right – if machine learning is 53/47 right, What kind of leverage does it take to justify 3% improvement? Chances that 10,000 clients will default on 500,000 $ mortgages is frightening. Save 2 percent on default it pays the bills for months. 50$ shirts sold to 2,000 more customers is insignificant. The odds are the same, whether it is machine learning or not.

C. Someone may claim predictive analytics is 80/20 right. In that case, they have a rule to hold on to AND turn off the maintenance on predictive analytics. That is probably why prediction is not being adopted all that fast.

Prediction is really no more than bucketing items on a single score. It certainly will not tell us what-to-do to avoid falling in an undesirable bucket.

Is it time to look at Prescribing Action.

Bartering for performance

What if we are is exchanging one resource for another so that two or more  parties can produce to a higher goal than the sum of what each could reach by themselves.  The  “resources” could be “one`s time”, “time expired”, “money spent” or “materials expended” to generate a product, service or even enhanced well being.

The more penned in the resources, the lower will be overall efficiency.  My waking time is 16 hours, assuming it is not my down-time, some days 4 hours in the morning pass without productive work.  Could I have been able to store time, there would be no loss of utility for me. What if someone else could utilize my time in some other activity and pay back down the line? Is there a need to establish some exchange rate? Not really because the rate changes from infinitely small to infinitely large, from freely exchangeable to impossible to exchange, every day, every minute, depending on my circumstances, not the circumstances of anyone else “in the market” for time.

In this scenario, do we need an exchange rate? No we do not! Do we need a currency to collaborate for higher results? No we do not.

If we are thinking enterprise performance, do we need to assign an exchange rate between performance indicators. No, we do not. But how will we know who did better overall?

My work is around re-balancing factors, re-configuring teams so everyone involved is comfortably confident that their leap forward will not sink the boat or drop the oars.

Collaboration in a Field of Sales Superheroes

After searching Sales Performance Management offers listed in the Gartner Magic Quadrant for Sales Performance Management, I concluded that salespeople collaborated so well that there was no need to offer a collaboration solution. There was “incentive pay and commission management”, “people management”, “quota allocation”, “pipeline management” and other useful subjects but nothing to write home about when it came to “sales collaboration”.

To me, sales collaboration is about working together and helping each other to meet planned goals, not just sharing documents. Sales teams need resources and the ability to effectively influence the customer to buy more. Lets say our teams have the best resources instantly available to convince a client. They can still fail to meet their targets because there are not enough clients to serve or win over.

Think of two ice cream-trucks on a summer`s day. It is scorching hot in Venice Beach, tourist buses have just arrived and it is raining at Malibu Beach. One truck gets wiped clean of 1,000 cones in hours, and goes home,  the other waits for its first cone all day.  One exceeds target,   the other comes home empty handed.

End of day, total sales amount to 1,000. The potential was clearly more than 1,000, but just did not happen. Venice met target, Malibu did not, ice-cream man in Venice got a commission, the other not. You get the picture!

So why did they not collaborate? They did not know, and did not care, too busy selling to ask for ice-cream? The message in this example is over-simplified but hardly an exaggeration. Can we agree that all stakeholders stand to gain from collaboration to sell 1,400 instead of 1,000.

Correct goal setting can be hard – it is not just the external factors that matter, but allocation of internally available resources. First question: Why did the company load equal quantities of product into the truck? Is the foot traffic (ice-cream demand) the same in each area? Is consumption per person the same? Is the frequency of consumption the same? Do some people slurp 2 cones at a time? Probably not. Then, there is the salesperson – is one more effective in selling to older folk and the other to children?

I suppose we can say targeting is complex. The path to sales success (meeting a quota) has many factors and striking the right balance between all these factors to achieve higher revenues and higher efficiency differs from Venice Beach to Malibu Beach to Santa Monica! The most important question is “what is the quota?” and “why is that number correct?”

I would suggest that  sharing factual information and decision advice matters more than predicting the weather correctly.

Flatland

On Defining Good Performance

Budgeting and planning are dreaded chores for most managers. Nevertheless, a company cannot thrive without a plan. Planning related processes are intended to answer two critical questions in managing the enterprise: Are internal resources being efficiently allocated? Do targets represent strong, efficient, but achievable goals?

For example, a bank branch can target, for a number of relationship managers, the average quarterly volume of deposits expected from each relationship manager, and average service transaction times at the branches. All of these are interrelated at the branch level. Furthermore, one can’t strike a value-maximizing balance among these items unless their impact on overall enterprise efficiency is taken into account. A balanced scorecard can’t approximate efficient targets because its targets include subjective determinations of what is achievable, and that determination is captured in a static relationship. Most businesses either end up with targets that are too low, and money is left on the table, or the targets are too high, which means demoralized managers with dysfunctional incentives, and the risk of losing them to a competitor. Let’s look at this in more detail.

In most companies, targets are set by business unit managers and are generally understood to get translated into bonus plan targets. So, managers will go through an apparently objective review of historical growth for each line of business, and then extrapolate those results with a modest upward bias. But as no one wants to fall short of their targets, they will be inclined to understate the true potential of their business, at least to some extent. Senior management can impose a global constraint by insuring that all the business unit targets add up to an earnings projection that can be sold to the investment community, but a lot of intercompany negotiations may need to take place to arrive at such a goal.

The second problem is that if there are a dozen or more key performance indicators per business unit, then these KPIs need to be weighted according to some criteria. Considering that a large number of decision making units (DMUs), which may be a branch, a sales manager, a store, etc., are all allocated target values, it is clearly unfair to apply the same standard weight for all as specific conditions may vary widely from one DMU to the other. Furthermore, if these weights are fossilized in a scorecard, then the business can lose its responsiveness to changing conditions in an attempt to look good against the embedded metrics, especially if they are tied to variable pay.

Conventional planning systems have no unified analysis around the inherent trade-offs faced by a manager trying to respond to a shifting market. Even if they agree on the trade-offs for this particular manager, they risk local optimization that may undermine overall enterprise efficiency.

Executives should aim for global optimization – accounting for the constraints of all business units at once. Such optimization can’t be done with a spreadsheet – it requires a much more advanced modeling tool.

The first problem can be solved by a mechanism that separates target setting for the budget from target setting for bonuses, but this can only be reliably done for the company as a whole. The problem remains for how to allocate an objectively set global target to the business units and decision making units below them.

This latter problem, as well as the issue of establishing local targets that are globally optimized, is solved with the Alta Bering EPO™ management technology. Alta Bering EPO™ generates targets for resource allocation and target setting in dynamic manner, enabling managers to do their local best and serve the best interest of the enterprise as a whole.

The 5 Rules of Maximizing Performance

Over the last 30 years I`ve been and garage entrepreneur, a deal-making CEO, and a highly paid performance management consultant to some of the largest companies in the world, pursuing the answer to one fundamental question: “How do you really maximize the performance of a business?”

About seven years ago I began a close collaboration with Mahmut Karayel, a Berkeley Ph.D. in Operations Research. Working together, we’ve developed and refined the answer to this question and demonstrated it in business after business.

We have turned our approach into a management technology platform that helps management teams deliver consistent performance improvement, quarter on quarter, in every aspect of the business where sufficient data exists. But in the process we learned that this journey requires a different appreciation of the business management problem that is able to question and reject even deeply-ingrained ideas, like KPIs and activity-based costing. We don’t presume to have all the answers but we have proven over and over again that these five rules put companies on the path of maximum enterprise performance. Clients who have taken our advice seem to think that we are right.

Rule 1

Treat Enterprise Performance as a Production Problem

Align Resources with Goals
If you survived in business for some time you must be a goal oriented person. You are burning resources (capital, energy, time, or brain cells) to achieve a goal. Pick the right goal; make the resources last.

In an enterprise, you invest inputs (cash, personnel, trucks, vendors, distribution resources, square feet, customer time, etc.) and seek to maximize outputs (revenue, profit, market share, customer count, etc.) If you sense you are faced with a production optimization problem, you’re absolutely correct. You are searching for the best mix of products for maximum profit; you strive to meet market demand with most efficient use of man and machine capacity, you are trying to squeeze a dozen products into the same assembly line; you are optimizing stock to simultaneously minimize working capital and lost customers. Or maybe you are juggling project and service priorities to keep customer satisfaction at the highest level. Regardless, as a manager you are challenged to make the most out of what you’ve got.

In automotive manufacturing, you can confidently stock one steering wheel per car assembly. By contrast, it’s not possible to confidently allocate four minutes to serve a bank customer or fifty dollars per day for sales expenses or ten cents of advertising per ticket sold. So what is different? In today’s world the challenge is to plan and optimize considering that both on the producing and selling and buying end, you are dealing with decision makers with different tastes and preferences and problems. In the modern enterprise, the bill of materials recipes may have to be flexible when applied across the wide range of business units and geographies. But there’s a much more important problem: how do you arrive at the right numbers – the optimal numbers – in the first place? With a fixed recipe production system like a car, you can refer to a blueprint to understand the number of screws, washers, door panels, etc. required to build a car. Efficiency and quality are defined as matching a pre-existing specification as closely as possible. With most service processes, there is no “perfect specification.” Rather, you derive service policies from adaptive pattern recognition and continuous optimization. You are chasing continuous improvement, not a fixed ideal.

How do we recognize improvement possibilities and continuously strive for them? The answer to optimal performance does not live in a manager’s head or in a clever spreadsheet model. It lives in the actual performance data of your organization itself. Revealing current performance levels across all business units, geographies and economic conditions is the key benefit of a good Business Intelligence knowledgebase. With this data, you can start to compare your average performers and laggards to the best performers in the organization, and understand what optimal resource allocation looks like. Then you can work to continually improve, to do more with less, and drive the performance of each unit toward its optimal potential, rather than towards the average performance embedded in most KPI-based plans.

This is the power of enterprise performance optimization.

Rule 2

Eliminate KPIs

KPI is not a Religion
Avoid making formula based KPIs the company religion. Formula KPI’s are prone to manipulation and often used beyond their expiration date. Real KPI’s are very simple or behavioral. “Insanely great!” is not a KPI, yet it fires people into doing the right thing.

In an enterprise, you invest inputs (cash, personnel, trucks, vendors, distribution resources, square feet, customer time, etc.) and seek to maximize outputs (revenue, profit, market share, customer count, etc.) If you sense you are faced with a production optimization problem, you’re absolutely correct. You are searching for the best mix of products for maximum profit; you strive to meet market demand with most efficient use of man and machine capacity, you are trying to squeeze a dozen products into the same assembly line; you are optimizing stock to simultaneously minimize working capital and lost customers. Or maybe you are juggling project and service priorities to keep customer satisfaction at the highest level. Regardless, as a manager you are challenged to make the most out of what you’ve got.In automotive manufacturing, you can confidently stock one steering wheel per car assembly. By contrast, it’s not possible to confidently allocate four minutes to serve a bank customer or fifty dollars per day for sales expenses or ten cents of advertising per ticket sold. So what is different? In today’s world the challenge is to plan and optimize considering that both on the producing and selling and buying end, you are dealing with decision makers with different tastes and preferences and problems. In the modern enterprise, the bill of materials recipes may have to be flexible when applied across the wide range of business units and geographies. But there’s a much more important problem: How do you arrive at the right numbers – the optimal numbers – in the first place? With a fixed recipe production system like a car, you can refer to a blueprint to understand the number of screws, washers, door panels, etc. required to build a car. Efficiency and quality are defined as matching a pre-existing specification as closely as possible. With most service processes, there is no “perfect specification.” Rather, you derive service policies from adaptive pattern recognition and continuous optimization. You are chasing continuous improvement, not a fixed ideal.How do we recognize improvement possibilities and continuously strive for them? The answer to optimal performance does not live in a manager’s head or in a clever spreadsheet model. It lives in the actual performance data of your organization itself. Revealing current performance levels across all business units, geographies and economic conditions is the key benefit of a good Business Intelligence knowledgebase. With this data, you can start to compare your average performers and laggards to the best performers in the organization, and understand what optimal resource allocation looks like. Then you can work to continually improve, to do more with less, and drive the performance of each unit toward its optimal potential, rather than towards the average performance embedded in most KPI-based plans.This is the power of enterprise performance optimization.

 

Rule 3

Create an Internal Market for Resources

No Silos
Eliminating silos means more collaboration and less energy spent on turf wars. Discourage empire building. Encourage sharing of ideas, resources and contacts. Allow managers to sell their vision to the resource in order to get more of his/her time.

Using common resources efficiently requires competition. Otherwise you get all of the problems associated with improper pricing: hoarding, spendthrift behavior, and shortages.Consider that a large automotive company will buy transportation services that exceed the revenues of a medium sized US company. The marketing spend of a US beverage company is larger than revenues of most manufacturing companies in Europe. Yet all of these resources are actually distributed, allocated, spent, and invested throughout the enterprise in a quite haphazard way.It is common practice for allocation to happen either with a few people behind a desk pushing the buttons on a spreadsheet, or through a process of competitive rain dances in front of the executive board. Clearly, neither approach is a substitute for a pricing mechanism.This type of resource allocation is the birthplace of mediocre enterprise performance.Now I can hear voices asking:

“So what do you want to do? Have all our stores bid for fancy packaging supplies before the shopping season?”

“You want our sales reps bidding for transport services every week, to deliver their orders in time?”

“Should individual regions bid for air time or marketing inserts?”

In a word, yes.

But I am not suggesting that we conduct call ins, Sotheby’s style. Rather, we should leverage our massive investments in information technology to develop a system that is more akin to eBay or Google Ads than it is to the Resource Rain Dance. We have been up to our ears in Business Intelligence since 1995. It’s time to use it to plan intelligently.

The Alta Bering EPO platform provides an easy methodology for optimal resource allocation, whatever the changing resources and whatever the changing needs of the organization may be.

 

Rule 4

Cure the Flaw of Averages

Averages are flawed
Do not drive performance toward an average. Investigate the maximum potential instead. Leverage outliers and use them to discover new possibilities. Our modern world is volatile, involving hazards, options, and discontinuities. These phenomena render averages as partial truths, or worse yet, misleading.

Measuring performance with the purpose of improvement and target setting is a time-consuming activity of the modern firm. Estimating performance requires selection of critical factors that are ingredients of important metrics representing resources consumed and values added. Non-parametric approaches such as EPO™ use these factors directly whereas parametric approaches assume a priori functional form. Parametric approaches such as regression may be appropriate when observing the behavior and characteristics of a population without the possibility of intervention. Outcomes are not classified as desirable or undesirable, nor do we contemplate changing the outcomes. In a regression model, the errors (deviations from average) are assumed to be random. However, as our objective is to measure and improve performance, we need to be searching deviations that are due to inefficiency. Typically these deviations are not due to random exogenous factors and good outcomes can be replicated or imitated, improving the performance of the overall population.A parametric regression model requires re-estimation of parameters when new factors are introduced in the environment. This is time consuming re-work. The EPO™ platform handles changing environments non-parametrically and incrementally.In regression, parameters are estimated by the average. If all members of the population tend to the average, estimation is said to be more accurate. Yet, in our competitive economy, “Average resources per average output with as little variance as possible” is the axiom of mediocrity and no degree of parametric sophistication can change the domination of the average. On the other hand, EPO™ tries to seek out the desirable outliers and draws a path from non-performance to these “outliers”. In short, regression estimates what is while EPO™ discovers what could be.It is in fact fortunate that the members of a branch network, in a bank or store chain, are diverse. Each branch is unique in its production function given the internal and external resources as well as management resources. However, regression based allocation of targets stifles overall performance, simply because it is too eager to enforce narrow variance bands around some average, when opportunity to learn and innovate actually lies with the “outliers”. When a branch discovers a new business process and starts to perform better, regression estimate of average performance moves only a little. But the business objective should be to guide other branches to adopt this improved business practice. But how? EPO™ shows which factors are below target and by how much. Regression does not give guidance as to how to move towards the better performers.When targets are set with limited recognition of the unique opportunities, performance defined as compliance with these targets becomes counterproductive. There is no incentive or guidance for each manageable unit to do better than its peers. With EPO™ we are able to discover these opportunities and set guidelines. Alta Bering EPO™ accelerates evolution whereas regression simply observes it.

 

Rule 5

Abandon the Idea of Allocating Fixed Costs

There are three dangers to fixating on Fixed Costs:

1) Since they are “fixed”, managers treat them as a given. It discourages them from challenging the assumptions and innovating.

2) Managers can hide behind the Fixed Costs to justify performance shortfalls.

3) When fixed costs are sunk costs, managers sometimes fall into the trap of feeling obligated to use them.

If you don’t like the cost of something, re-calculate it for the shortest possible term.

In business planning and management accounting, usage of the term fixed costs depends on the intended use. This can be confusing and controversial. Some cost accounting practices such as activity-based costing will allocate fixed costs to business activities, in effect treating them as variable costs.

In accounting terminology, fixed costs will broadly include almost all costs (expenses) which are not included in the cost of goods sold, and variable costs are those captured in costs of goods sold. In practice, depending on whether the management is accounting for past performance or planning future performance, definition of “overheads” tends to shift between “short-term” and “long-term”, (last term and next term). It is unreasonable to expect agreement on the definition of “overhead” outside the accounting department.

So what is the sales manager or the regional manager who feels he is over-burdened with his slice of overhead to do? How can this “hit” to profits be explained to the sales force that just made their revenue targets but missed the overall profitability target (not to mention their bonuses)?

The solution to this controversial management issue appears when economic and accounting treatment of fixed cost meet, in other words, in “the long run”.

The more effective alternative is to focus on ways of understanding operational efficiency first, not just costs, regardless of how much has already been invested in ABC and its various versions.

This is yet another issue directly addressed by the Alta Bering EPO platform.

Think Shift

On Alignment

Time-honored approaches to strategy formulation, corporate performance management, planning, budgeting, monitoring methods and technologies serve management to varying degrees of satisfaction. Shortcomings, where they exist are mostly related to misalignment of targets and conflicting goals. Methods such as the balanced scorecard attempt to structure strategy and name goals provide a framework for agreeing on performance due to stakeholders. The role of executive leadership is to intervene to make sure that limited resources are employed in the right place at the right time and competitive performance is incentivized.

On Defining Good Performance

We believe that the shortest description of good performance is quite simply “More with Less”. But correctly defining “More of what?” in each unique business context is the key to good management and achievement of enterprise targets. The question “How much of what?” follows. No level of experience is adequate in preparing performance recipes for the organization; instead it requires thorough analysis and the right management technology to evaluate available data.

Simply assuming that we understand the question What drives what? is usually a cardinal mistake as there is no definitive direction in business dynamics. Making assumptions about simple, two-dimensional relations on What drives what? will lead to statements such as:

“The higher the number of cheques written, the higher the deposits and the credit line volume.”

“The higher the number of payroll service accounts, the higher the car loans”.

“The shorter the coffee breaks, the higher the number of calls handled”.

Clearly, these statements will not always be valid, regardless of how well they may be documented as business fact.

Carefully reviewing all the performance factors in a multidimensional approach using appropriate management analysis and technology to define what exactly drives good performance is a necessary prerequisite to defining what will constitute performance improvement and, furthermore, what needs to be done to achieve this target.

On Enterprise Information Transparency

We believe that limited access to information is a performance malady. The impact of limiting access to performance information across departmental or transactional “silos” is well understood yet often little is done to remedy the problem. Narrowly defined accountability and the notion of performance privacy around accountability are often to blame. Sharing information only on a need-to-know basis is more often than not a bad idea.

Information transparency is a necessary prerequisite if an organization wants to achieve internal alignment of divisional or functional goals for total value, and be able to rapidly adapt to changes in the market. And this is, of course, where Alta Bering EPO™ makes a real difference.

Information transparency for value alignment
In today’s world – let alone tomorrow’s – “Everybody doing their bit” is just not good enough any more. The whole of the organization needs to work together for the whole of the organization to achieve outstanding results. For instance, if the customer relationship officer or teller is expected to refer daily banking customers to the insurance officer in her branch, should he/she not know how many of them actually purchased insurance policies? Clearly, the teller would benefit from this information and refer more suitable customers.

Information transparency for fact-based adaptation
In the service industries, the complexity of product and service offers, along with the fleeting nature of the client base presents both a challenge and an opportunity to compete better. And organizations striving to improve their competitive edge make great efforts to track customer behavior. Large volumes of data are collected and leveraged to gain insight into customer behavior. Findings are fed into systems that sometimes reach the service agents in near real time. In contrast, the performance contracts, including targets and resource assignments change much less frequently, in some cases quarterly, most often annually. Should these targets be adjusted more frequently to better address prevailing conditions? The answer is obviously “Yes!” The fundamental challenge is in the onerous nature of the enterprise planning process. Reliable estimation of what the organization can achieve, given scarce resources, is the technical challenge that needs to be addressed by management technology.

On Measuring What Matters

A solution to both good resource and target allocation should be able to seek balance and avoid single directional goal definitions. It is when these make up the bulk of balanced scorecard content that problems start to occur.

Measuring what matters is relatively easy. However, recognizing that there is more than one formula for success has its practical difficulties. Given, for example, that the teller is expected to help cross-sell, should his/her performance be measured either in “Number of transactions” or in “Number of customers served” alone? Clearly, they both need to be taken into account. And there may yet be a host of other seemingly peripheral measures that should be included in evaluating existing performance and setting good targets for desired performance improvement.

On Budgeting Performance Planning and Scorecarding

Budgeting and planning are dreaded chores for most managers. Nevertheless, a company cannot thrive without a plan. Planning related processes are intended to answer two critical questions in managing the enterprise: Are internal resources being efficiently allocated? Do targets represent strong, efficient, but achievable goals?

For example, a bank branch can target, for a number of relationship managers, the average quarterly volume of deposits expected from each relationship manager, and average service transaction times at the branches. All of these are interrelated at the branch level. Furthermore, one can’t strike a value-maximizing balance among these items unless their impact on overall enterprise efficiency is taken into account. A balanced scorecard can’t approximate efficient targets because its targets include subjective determinations of what is achievable, and that determination is captured in a static relationship. Most businesses either end up with targets that are too low, and money is left on the table, or the targets are too high, which means demoralized managers with dysfunctional incentives, and the risk of losing them to a competitor. Let’s look at this in more detail.

In most companies, targets are set by business unit managers and are generally understood to get translated into bonus plan targets. So, managers will go through an apparently objective review of historical growth for each line of business, and then extrapolate those results with a modest upward bias. But as no one wants to fall short of their targets, they will be inclined to understate the true potential of their business, at least to some extent. Senior management can impose a global constraint by insuring that all the business unit targets add up to an earnings projection that can be sold to the investment community, but a lot of intercompany negotiations may need to take place to arrive at such a goal.

The second problem is that if there are a dozen or more key performance indicators per business unit, then these KPIs need to be weighted according to some criteria. Considering that a large number of decision making units (DMUs), which may be a branch, a sales manager, a store, etc., are all allocated target values, it is clearly unfair to apply the same standard weight for all as specific conditions may vary widely from one DMU to the other. Furthermore, if these weights are fossilized in a scorecard, then the business can lose its responsiveness to changing conditions in an attempt to look good against the embedded metrics, especially if they are tied to variable pay.

Conventional planning systems have no unified analysis around the inherent trade-offs faced by a manager trying to respond to a shifting market. Even if they agree on the trade-offs for this particular manager, they risk local optimization that may undermine overall enterprise efficiency.

Executives should aim for global optimization – accounting for the constraints of all business units at once. Such optimization can’t be done with a spreadsheet – it requires a much more advanced modeling tool.

The first problem can be solved by a mechanism that separates target setting for the budget from target setting for bonuses, but this can only be reliably done for the company as a whole. The problem remains for how to allocate an objectively set global target to the business units and decision making units below them.

This latter problem, as well as the issue of establishing local targets that are globally optimized, is solved with the Alta Bering EPO™ management technology. Alta Bering EPO™ generates targets for resource allocation and target setting in dynamic manner, enabling managers to do their local best and serve the best interest of the enterprise as a whole.

On the Limits of Predictive Analysis – What to do?

by Mahmut Karayel, Chief Scientist, Alta Bering

The ability to predict the future is a holy grail that mathematicians, scientists, and yes, businesspeople have been chasing for ages. Success has been sporadic at best.

Although coming up with better predictions is an honorable pursuit, we should realize that there are fundamental limits to the usefulness of these predictions. First, timing, size and impact of important events are very difficult to predict. The timing of big events is almost impossible to predict with any consistency. Yet the most useful predictions are those that predict the timing of game changing events. Second, almost all predictive analytics is based on inductive reasoning. Inductive reasoning does not apply when it most counts: when we need to predict paradigm shifts or game changing events. Deductive reasoning offers not much help either. Whereas deductive reasoning applies largely to natural laws, the more interesting and useful predictions all apply to human behavior. Inconsistency of human behavior eludes deductive reasoning.

How have we fared in our historic pursuit to foretell? In short not so well:

We have been more successful in predicting short-term outcomes than predicting longer-term outcomes. Production volume of a factory next month is more predictable than production in 13 months. It takes time to change, and this has been helpful to prediction. It takes time for the ocean to cool off, trees to blossom, people to change their behavior. But as societies become a faster, more informative, and more reactive –specifically, less confident, and less willful –, prediction becomes even harder.

We have been more successful in predicting the behavior of systems obeying natural laws, compared to systems which are influenced by humans. Landing a module at a specific spot on the Moon, and calculating the exact amount of fuel that it will take to do this, has proven to be easier to predict than estimating the number of new bank clients opening a savings account in response to a promotional offer.

Predicting behavior of large number of uniform and independent “agents” is easier than predicting behavior of correlated “agents”. Independence assumption allows scientists to rely on the law of large numbers and the central limit theorem to make prediction somewhat routine. Predicting the outcome of an election from exit polls is a good example of this type of prediction. However what if there was dependence, or rather influence? What if unknown to us one interviewee had influenced the votes of tens of thousands of people and another just voted in isolation? The recent debt crisis in Greece, the housing bubble and subprime mortgage crisis, and the popularity of a particular social networking site are all examples of interconnected behavior, the course of which is much harder to chart.

Lastly, we have relied too heavily on predicting status quo, and in the process eluded predicting the game changing events. The Japan Tsunami and its long term effects, 9/11 and the new world order it created, and failure of Lehman Brothers are all examples of events we would have liked to have foreseen.

Those who have the means and the courage to compare predicted paths to actual outcomes (as a rather banal example consider the price of Oil) will observe that the forecasted paths are always much smoother than actuals observations. Our world is a more volatile place than our predictions would imply. This is undesirable regardless of the quality of the point estimate, since it falsely implies a more stable future than what is in store.

Unfortunately, the prediction profession has inadvertently misled the general population while attempting to remedy these shortcomings.

We have relied too heavily on induction. Reliance on induction (e.g., what will happen tomorrow is what is happening today) ignores scale, sustainability, and paradigm shift. Historical experience and recognition that history repeats itself is important. Yet, this repetition has a bad habit of manifesting itself when least expected and with a new dialectic twist. Further, models typically do not take into account scale and sustainability. If one person becomes rich opening a nail-care salon, this does not predict that the next 1,000 people who open a nail salon will strike it rich. On the sustainability front, induction by itself would be a dangerous approach to deal with slow but important shifts, such as Global Warming. Small changes can be ignored, and interpreted as routine noise. But if they are all ignored, a catastrophe will ensue. The classic example of this is the frog that does not jump out of slowly heating water until it is too late.

Another approach commonly employed, but has proven to be fallacious is to extend natural laws to human behavior. Stating that individuals make independent and rational decisions has been the fundamental pillar of modern economic theory. There are numerous observations and studies that challenge consistency, rationality, and independence of human behavior. Economists insist on this assumption regardless. The truth is inconvenient. A Physicist who does not accept gravitational pull after seeing that everything falls when dropped would surely be mocked. Fortunately, at the time of this writing there is great effort to modify these economic assumptions and place it on a more solid footing.

So what is a practical manager to do? I will humbly suggest the cardinal rule: Be prepared to better deal with uncertainty, rather than assuming that predictions are correct. More specifically:

Assess the present before predicting the future. Accurate data is very important. Assessing the situation today using accurate and up to date data supported by unbiased comparative analytics is more important than doing the next step of prediction. Experienced people can (and surely will) draw their own conclusions from clearly presented data.

Visualize outcomes. Enumerating probable outcomes is often more fruitful than trying to predict what will happen. My high school buddy tossed a coin to “predict” which of the two schools will accept him. The coin rolled downhill and leaned against a wall on its side. (As a bonus we got a lesson on interpretation. As it turns out, this meant “both”, not “neither”.) First know what can happen, then try to estimate what will. This takes experience, insight, imagination and patience. Seek and reward analysts endowed with these traits.

Have a plan. Being prepared for a large portion of the possible outcomes is important. Your plan should detail how outcomes will be dealt with: prevention, mitigation, or transfer of risk. Setting the preparation level at 95% or 90% or 85% is a policy decision that you must be ready to make. This is the human part. Nothing ventured, nothing gained.

Execute quickly. It helps to have a plan to begin with. The majority of managerial errors happen not because prediction was poor, but because when the unexpectedly bad happens, managers tend to get stuck in one of many common dysfunctional modes:

  • Watching events unravel in shock
  • Afraid to convey bad news
  • Denial
  • Looking for a scapegoat
  • Not seeking the required help, trying to do too much
  • Hopes the problem will go away by itself
  • Lost in confirmation bias. Looking only for good (or bad) evidence rather than gathering all the facts.

Incentivize those who are better at dealing with uncertainty. A common mistake in corporate compensation is to reward firefighting. Firefighting managers look really busy, like generals under fire in the heat of war. Very seldom is this justified. The manager is often fighting a fire that he created by not executing a plan. Other managers (admittedly a minority) look like they are not doing much, but keep hitting their targets. These are the ones who know their facts, can visualize, have a plan, and have already delegated execution. Many companies, especially in the finance industry, don’t reward these managers duly.

Rather than waiting for scientists to develop prediction technology with predictable accuracy, it is better to be prepared for a variety of outcomes beyond the one that is predicted.

A successful manager must take risks. It is the nature of business. It is important before you take those risks to:

  • Demand accurate up to date data
  • Visualize and know what can happen
  • Have a plan to deal with it
  • And execute quickly when it happens
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