Tuesday, July 30, 2013

The Best 5 Minutes in Line I Ever Spent

I am in the grocery store the other day, standing in the checkout line.

This is not news, nor is it very surprising. We need food from time to time, be it for survivial, or because a large stampede of in-laws are on their way over, or because my wife has run across a great new recipe we want to try.

Nothing in and of itself worthy of a blog post. For sure.

It was still early in the day, so the store was almost empty. Most people in town were probably still reading their Sunday papers or catching up on a few, much needed, extra hours of sleep.

So why, on this particular occasion, did my wait in the checkout line become a little longer than expected?

And why am I writing about it?

The Person In Front of the Line

The only person ahead of me in the checkout line was an elderly lady. She made mention to the cashier that she did not find a particular brand of barbecue sauce (brand "X" for the sake of this post). She especially wanted this item because she had a coupon!

"Oh, it's on the right-hand side just down there on aisle 5" the cashier told her (Note: having bought barbecue sauce at this store before, I knew she was right about this).

The lady looked somewhat distraught. She wanted to go get it, but was concerned about causing the additional wait. "Do you mind?" she asked.

"Not at all" I replied. Even if I did mind, or was in a rush, Aisle 5 was right there behind us, I knew that X was right there, I was next in line, and it was an early weekend morning, in no immediate hurry for anything.

The lady customer eventually returned. I fully expected her to have a bottle of X in hand, ready to scan, but there was nothing. "It's not there" she stated.

At this point the cashier took matters into her own hands. She left the register with a pleasant comment and proceeded to make her way down aisle 5.

And 30 seconds later back she came with a big 'ole bottle of X barbecue sauce. "Here it is" she said sweetly.

In my mind I commended her, and admired her skill in handling the situation. She did not blame. She did not accuse. She made it seem like the most normal of things that people cannot locate X in aisle 5.

And our elderly customer's reaction confirmed it.

"Thank you" she said. But the thing was, it was a "thank you" without embarrassment, without shame, without a sense of foolishness for having missed it, or any other such thing. From this particular customer's perspective, it might never have came from aisle 5 at all, and the cashier did nothing to make it seem otherwise.

And then she was on her way.

But That's Not the End of the Story

At this point you might be saying "well that's a great example of customer service, with a nice bit of emotional intelligence rolled in" and be willing to go along your way.

I was.

Our cashier looked at me, a hint of 'I'm sorry about that but I'm sure you understand' in her eyes. She scanned my few items - one bag in total - and I was done and proceeded to head for the exit.

I thought it was great what she did that for the customer. I had no problem with it at all. What's 5 minutes on a lazy Sunday morning? I was happy the lady left happy and got to use her coupon (if you ever experienced my wife's wrath for forgetting to use them you'd understand that feeling!).

And then there...

...right before the exit door...

...posted on the bulletin board...

...for all to see...

...was an 8x11 sheet of paper with a selection of each of the store's cashier's statistics for the past week printed on it: seconds per each item scanned, downtime between customers, and rank within the region.

Is What's Measured Important?

Let's think about the implications of this for a minute.

Our cashier is clearly working in an environment where management has focused in on efficiency. Scan those items fast and once you are done with one customer start on another...right away!

In fact, this is so important, and we are so serious about this, we are going to call you out to the world to let them know where you stand in the world of cashier efficiency!

What were her options for dealing with our nice customer who could not find X?

    ::Option A: invite the customer to go back down Aisle 5 to find Barbecue Sauce X
    ::Option B: mumble something along the lines of "I'm sorry you did not find anything", all the while scanning any remaining items

And then going down the option A path, there are two additional alternatives to consider:

    ::Option C: walk down to Aisle 5 and get X for the customer
    ::Option D: mumble something along the lines of "I'm sorry we were out", all the while scanning any remaining items

Figure A
Cashier Option Diagram and Metric Payoff

These paths are diagrammed in Figure A, with the relative impact on the cashier's metrics noted on the right.

While metrics may have been important to the management, they obviously did not influence our cashier in this situation. At each point on the tree, she chose the option that made the metric worse, ultimately resulting in the worst possible metric achieved!

All The World Is A Stage

At this point in the tale we are happy that our cashier is a rebel. She has deliberately refused to be cowed or influenced by the metrics and has flaunted those measures in the sake of something we would consider noble and good: customer service.

She may not have walked a mile, but she certainly went the extra 50 meters (25 each way) up and back on Aisle 5!

One of the problems with using metrics is that they do not represent the whole of the situation. They are incomplete.

Using metrics to manage is akin to driving a car using nothing but the insturement panel. It's all fine and good to have a target speed of 60 miles per hour (or about 100 kilometers per hour), but if you are in rush hour and the car in front of you is stepping on the brakes you'd better be watching the road rather than your instrument panel!

Figure B
The Empty Stage

Figure B depicts a rough drawing of a stage, much like the one my family and I saw when attending a local community theater production of "Tom Sawyer" a few weeks back.

The problem with the stage in Figure B is that it does not provide any cues to the audience. There's just a big 'ole rectangle.

The dynamism of the stage is captured through the movement of the characters, the props, and the lighting.

Figure C
The Metrics Stage

Metrics work like the stage lighting shown in the 10-second video clip in Figure C. We focus on the point where the lighting occurs. If this is the point of action on the stage it is great. However,the "rest of the picture" has receded into the background and is never emphasized.

We are no longer attentive to it.

Should Figure C's lighting continue for the whole length of the show, (in addition to being very boring) we would miss out on a lot of the action that is occuring on other parts of the stage.

Figure D
The Real Stage

In order to avoid this predicament, the lighting of the show is dynamic, moving around to highlight the action taking place wherever it is, more like the 10-second video in Figure D.

Our grocery store story can be imagined in this way. The spotlight first focuses cashier efficiency. The customer has a problem, and the next spotlight highlights their search down Aisle 5. It returns to our cashier in the form of the problem not being solved, and moves again down Aisle 5 and back again, finally centering on a postive experience for the customer leaving the store.

Very little of this is captured by our metrics, because by their very nature they are static and not dynamic!

Old School Management?

Most of us have heard the phrase "what gets measured gets done". A lot of management disciplines subscribe to this theory. Most of these probably belong in what Daniel Pink would call a Motivation 2.0 mindset.

The simple fact is that a lot of management processes still in use originated in a different time and place.

In order to meet the needs of the industrial revolution, we developed techniques that worked well on assembly lines and in other highly repetitive but focused situations. Worker A turns screw #1, Worker B bolts one piece to another, and so on.

Our cashier does not work in this type of environment.

Yes, they scan items and complete transactions. But they also are problem identifers. The last three gallons of milk have been leaking, or customers in search of a certain brand of bread report that it is out.

They are also customer service providers, answering questions, greeting people politely, and making the occasional run down Aisle 5 to retrieve X.

They are wearing many hats in a dynamic environment, where the duties and objectives can change from second to second or minute to minute.

In our story the cashier shifted from efficient scanner to customer service representative. She had to exercise independent judgement, taking into account the context of the situation. Her performance cannot be scripted and precisely measured.

For instance, had it not been an early weekend morning with one customer in line, but a busy afternoon with six customers waiting and the prospect of more showing up in line real soon, she may have decided the cost of providing the personalized customer service of getting X from Aisle 5 to be too costly, and may have referred the customer to the service desk, or flagged down someone bagging groceries to do it, or called a manager.

Specific metrics in these types of settings are not very effective, because their range of focus does not account for the multiple, highly varying factors that are involved. The work environment is not so simple as it once was, and the methods for managing it that may have worked before no longer do.

The Curse of the Specialist?

Like our theater spotlight, one of the problems with functional areas is they focus on a narrower range of issues than the business as a whole, and are thus likely to miss important "pieces of the action".

Since finance folks like equations, evaluate the organization's performance through accounting measures, are more 'left-brained' than average, reside in offices and cubicles rather than 'the floor', and other reasons, the concept of coming up with metrics that can be consumed on their computer screens in the form of dashboards sounds like a great idea.

All the better if these metrics can be directly translated to financial performance.

From this perspective a focus on efficiency is ideal. With 100 customers and 5 cashiers, if we can be 20% more efficient we can get away with 4 instead, with an increase to net income as a result. Our company's performance is improving!

Yet, outside the spotlight, trouble brews. Poor customer service ultimately translates into fewer customers. Our finance team, plugging a change in customer throughput into their revenue side equations, may very well arrive at the conclusion that while short-term gain has occurred it has come at a long-term cost.

The Curse of the Easy?

Scan per item is a simple measure to calculate.

We have a cash register. It knows who the cashier is via log in. It counts how many items are scanned, and knows when the cashier logged in and logged out. All the data for the equation has been programmed in.

Conversely, there is no equipment to measure a customer's smile, or how many times the cashier went down Aisle 5. Implementing a system to track this, if it were possible, would likely be cumbersome or costly. Movement scanners or face recognition software connected to cameras require a capital investment. A logbook system creates additional demands on employee's time.

Unfortunately, this can create a situation where "we manage what we can measure", rather than what we really should measure.

The result is that our cashier is dinged in their performance reviews for the decline in their metrics, but is not correspondingly praised for their superior customer service performance.

As this continues to occur over time, they are going to be more likely to give up the unsung behavior, even though it is arguably more valuable.

What Can We Do?

Some might construe this post to be a case against metrics, which it is not.

So what am I advocating?

Take a Holistic View - metrics are one component of management, but they are only one component, for a number of good reasons we have just discussed. Factor them in, but remember that there is a lot of activity 'on the stage' that is not being captured but can be vitally important. Each function has its viewpoint, and we can avoid a lot of the specialization problems if we alternatively 'wear the hat' of all participants. When encountering a situation, ask yourself a series of questions rather than just the one your specialization would profess: what would finance do? marketing? production? operations? legal? sales? investors? community members?

Go to Gemba - the Japanese term Gemba loosely translated is "the place where it happens" or "where the activity is". One of the tactics used in Lean is to literally stand in one spot 'on the floor' and simply observe...for hours and hours. While this might seem boring, and perhaps sometimes it is, it is amazing to see the subtleties of the situation play out. A financial analyst engaging in this activity will see the unfolding of our cashier story, and will appreciate the significance of the cashier's customer service activity in addition to their efficiency metrics.

Trust in Your People - our cashier story ultimately is one of a triumph of basic humanity vs. an arbitrary system of rewards and punishment, and it forces us to consider alternative philosophies of management and people. In the behavior modification framework that the 'carrot and stick' system originates from, we are nothing more than pleasure seeking and pain avoiding creatures. Yet this does not explain our cashier's behavior in the least. What predominates in our story is one of human to human compassion, a desire to help, and a good deal of interactional common sense. Most will make the right call when given a chance - we don't need to reward them or beat them to accomplish it.

Key takeaway

Metrics are often a useful management tool but do not take the place of reasoned and balanced leadership judgement. The truly adept leader does not view all problems as a 'nail' should they be holding a hammer.

You May Also Be Interested In:
    ::What situations have you encountered where people performed in direct contrast to what their metrics would have incentivized?
    ::What practices would you recommend to avoid 'organizational myopia'?
    ::In what circumstances is 'what gets measured gets managed' an inappropriate framework to apply?

Add to the discussion with your thoughts, comments, questions and feedback! Please share Treasury Café with others. Thank you!

Thursday, July 18, 2013

Why Your Cash Flow Forecast Will Always Be Wrong

Some folks sensed a "negative tone" on my part in our last Treasury Cafe post, "Answer These Questions For A Better Cash Flow Forecast", believing that I advocated an approach that would not require a lot of time, effort and attention.

In a sense, this perception has its merits - I am somewhat cautious about the forecasting process for a number of reasons, but that is by no means the whole story. However, if that is what appears closest to the surface, let's start from there and work our way forward.

Why is it that our cash flow forecasts will always be wrong?

The main objective of the cash flow forecasting process is to provide us a glimpse into the future...and therein lies the biggest problem.

Nobody can predict the future!...for a number of reasons.

Reasons #1 to #3 - Random Events Occur All the Time

Mother Nature delivers her fair share of unexpected windfalls and dissapointments to a business. Had we been forecasting in January, 2011 the cash flow generation of our Japanese business operations for the year would have been wildly off due to the earthquake and tsunami that occurred two months later. Conversely, the cash flow from sales forecast for our Chicago snowblower division would have been understated 4 years in a row (assuming we used average snowfall) from 2006-2010 .

Social factors are another potentially significant contributor to randomness. Imagine being a member of the hapless cash management staff at Abercrombie & Fitch at the beginning of this year, watching the fallout from our CEO's remarks wreak havoc on our cash inflows from sales estimates! Or, suppose we were Paula Deen's cash manager forecasting licensing and advertising revenue about 3 months ago. Would the remainder of this year be anywhere close to our forecast?

Economic and Market Conditions also contribute to uncertainty. Interest rate forecasts we made in the Summer of 2008 would be off by double or triple amounts come that Fall due to the onset of the "Great Recession". And less than a year before that, many would be stuck with investments in Auction Rate Securities because the auctions were failing and investors could not 'cash out' of their investments as planned. The market had never seen something of this magnitude ever in its history.

Reasons #4 to #5 - We Think Like Human Beings

Daniel Kahneman, the Nobel Prize winning scientist credited with a significant role in the development of Behavioral Economics, reports on numerous studies of human behavior which shows that we are quite likely to either overestimate or underestimate the liklihood of low-probability events (original paper here).

In addition, our human forecasting process gives weight, often at the sub-conscious, outside-of-awareness-level-of-thinking, to some events while entirely excluding others (called the "Availability Hueristic"). A lot of our thought processes exist on a "what you see is all there is" basis, with the result being that if we're able to quickly call something to mind we focus on it, and if we aren't able to call something to mind we ignore it. Thus, we individually and collectively possess a stong bias that makes it extremely difficult for us to be 'comprehensive'.

Reason #6 - Model Estimation Error

Statistical models, including those frequently encountered in forecasting such as regression or time-series analysis, if well-constructed will have an error rate that approximates the normal curve. If this is the case, then we can expect about 5% of our estimates to be greater than two standard deviations from the actual values.

In other words, on average 1 day out of 20 our estimation is going to be significantly over or under, even if we have a great statistical process.

Reason #7 - Low Payoff

If it is possible for some people to predict the future, it is quite unlikely they are toiling away day by day in a Corporate Finance group. They are much more likely to be sipping their Pina Coladas on a beach at your favorite tropical island resort after making their fortunes at the race track or in the financial markets.

Reason #8 - The Costs of Accuracy

In our last post, "Answer These Questions For A Better Cash Flow Forecast", we noted that cash forecasting involves a cost / benefit tradeoff. If we want a more precise forecast we are going to have to pay for it, in terms of money, time and attention.

To see how this works, let's consider a simple example. Let's say that customer payments is a line item in our 30-day forecast, and let's further suppose that these estimates come from our sales area, who are the folks in closest contact with the customer.

Over the past year, which is approximiately 2,000 working hours (250 working days x 8 hours per day), let's say that our 2-person sales team generated $10 Million in revenue. This amounts to a revenue generation rate of about $2,500 per hour.

For the sake of improved accuracy, let's further suppose that we implement a new requirement on the sales staff to provide us with collections information that has been validated with their customer's personnel.

Joe, one of our salespeople, knows that Company A's most recent invoice is due in a week. The invoice is for $10,000. He calls over to his AP contact in order to confirm the payment date only to discover they are out of the office. After many calls to others at Company A - going from a contact in purchasing to a manager in purchasing to the office of the CFO back down to the AP manager, who places him on hold for 10 minutes while they find out who has been assigned responsibility for the invoice in question, etc., we finally arrive at the fact that the invoice has been scheduled to be paid 2 days later than originally anticipated.

By the time the exercise has been completed, Joe has spent 2 hours on this task.

Assuming Joe would have achieved the average revenue generation rate during that time, we have forgone $5,000 in additional revenue in order to be 2 days more precise in our cash forecasting accuracy. For the $10,000, let's say that the knowledge of its timing allows us to invest or avoid additional borrowing at an incremental rate of 1% (note: we're being generous with that number given today's rates!). Our total return for those 2 days is a whopping $0.55 (10000 * 1% * 2 / 360)!

Spending $5,000 to earn $0.55 is not a successful business recipe!

Reason #9 - There is No Way to Know When You're Right or Wrong

Suppose I tell you that there is 50% chance of rain tomorrow, and tomorrow it rains. Was I right in my forecast?

What if it did not rain? Was I right in my forecast then?

Unfortunately, there is no way to really know. When Mother Nature "rolled the dice" to determine today's weather and came up "rain", we do not know if those dice reflected a 1% chance of rain, or a 10% chance, or a 50% chance, or a 90% chance, or a 99.99% chance. We only know that it either rained or did not rain. Since we do not know the "probabilities of Mother Nature's dice throw", we cannot calibrate our model against it.

As Taleb points out in The Black Swan "You see what comes out, not the script that produces events, the generator of history."

What we would like to learn as we develop a track record is "oh, it rained today so I see that it should have been a 60% chance rather than 50%". Unfortunately, we only know that it rained.

The process of separating the outcome from a forecast's validity is difficult for many to grasp - "hey, if it rains the forecast that predicted rain was a 'good forecast'". Statistical methods rely on the 'law of large numbers'. If we roll a die and come up with a 3, we need to roll it many more times to understand that a 3 comes up 1/6 of the time, as does 1,2,4,5 and 6. If we 'forecast' a 3 and a 3 is rolled, it is not a 'good call', it is lucky.

Let's take an extreme example to emphasize the point. If your child picks up a 6-shooter loaded with 5 bullets, makes a deal with your neighbor that if they 'win' they get $1 million, put the gun to their head and pull the trigger, and survive, would you call that a "good decision"? After all, they are now $1 million richer. Taking foolish gambles are not sound forecasts even when they happen to payoff. This example illustrates that you cannot base an assessment of a decision's or prediction's quality based upon the single outcome that resulted. And because tomorrow is another day, all we are ever going to get is a single data point.

What Can We Do?

Given this litany of reasons, should we abandon the cash forecasting process?

Of course not!

As we discussed in "Answer These Questions for a Better Cash Flow Forecast", we need some assessment of our future in order to manage our liquidity, financial strategy, metrics, and potential options.

So how to reconcile the fact that we need to forecast even in the face of knowing that it will be wrong?

Encourage Ownership

I can remember a conference session where the speaker emphasized that we should "hold people accountable" for the forecasting process.

In the corporate world, "holding people accountable" is generally a euphamism for "hit your objective....or else", with the "or else" being something along the lines of no bonus, becoming manager of the firm's Siberian operations, getting fired, or some other drastic form of punishment.

The problem with using this "stick" approach, as Daniel Pink discusses in his book Drive (see here for a synopsis by Checkside HR), is that it actually hinders productive, creative, collaborative problem-solving, which are exactly the forces that will make a cash-flow forecast better!

Instead, generate a sense of ownership utilizing people's intrinsic motivation instincts (what Pink calls "Management 3.0"). This can be done through regular team interaction focused on three things: 1) objective review of prior forecasts, 2) open discussion of upcoming forecasts, and 3) illustration of the organizational consequences of both.

As an example, we sit down with our forecast stakeholders and discuss the most recent prior forecast. Without allocating blame, and avoiding a scolding tone, we neutrally comment on where variances have occurred, explore the processes that led to the original forecast, and brainstorm potential methods that might realistically be deployed. We further note that because of these variances on one day we had to arrange emergency, 'late in the day' funding (which is much more expensive), thereby costing the company x.

Or, as future forecasts are developed, we can identify some of the organizational actions that will occur based on it - financing plans, timing strategies, etc. As the consequences are understood, areas where attention may not have been focussed can become apparent. "Oh, I see that extending the term is x amount more costly, perhaps the timing of this large payment can be accelerated".

Be Open-Minded

Given that there are at least nine reasons why the forecast will always be wrong, approach the process with an open, 'willingness to learn' mind set rather than an 'assignment of blame' exercise. Given the many forces outside of their control, it is unreasonable to expect forecast perfection, and those who appear to do so will be resisted and lose respect.

The more information and insight we can gather, the better able we will be to develop the best forecast process possible (even though it will be wrong). The means to open the information spigot is to make conversations and discussions positive experiences, exemplary of respect for each participant's contributions and input.

People will 'clam up' if they sense that a witch-hunt is going on, and will no longer consider themselves stakeholders in the game.

Focus on the Drivers in Order to Learn

Most forecast numbers, at their root, are generated from a Price-Volume relationship. A cash inflow estimate may relate to revenue (i.e. units sold times price), or accounts receivable collections (number in the 'bucket' times payout percentage), or something similar.

Assessing variance between forecast and actual along the driver lines allows us to develop insights. Is our forecast off because of volume reasons or price reasons?

Future actions can be determined based on this type of analysis. If volume is up, what are the market factors that made it so this month, and are they likely to continue or 'revert to the mean'? If payout percentage has dipped, what additional organizational resources would it take to get that figure back up to where we had originally planned?

Maintain a Number of Scenarios

We have established that our cash forecast will always be wrong for at least nine reasons. Practically speaking, we must be willing to consider a number of alternative environments we may be operating in during the future.

Given the critical nature of cash, we cannot use the 'expectations approach' often described in the textbooks. For example, using this approach, if our forecast has a 95% chance of being "off" by as much as 100,000 and a 5% chance of it being 1,000,000, then expectation theory would tell us to hold 145,000 each day.

Unfortunately, this doesn't help us at all!

For 95% of the time, rather than holding 100,000 in "cushion" we would be holding 145,000, thereby increasing the cost of maintaining adequate liquidity during these times.

However, for the 5% of the time where it is 1,000,000, the fact that we have 145,000 isn't going to mean anything significant, since we still won't have enough to cover the error, so we end up bankrupt all the same even though we had calculated this expectation event. We might as well have just held 100,000.

Instead, we need to have contingency plans in place for a number of different events. Is there an alternate funding source we can develop to help us deal with those 5% days, while on the others need only cushion the 100,000?

Or, can we operate as if the 1,000,000 will always occur while maintaining normal practices? This is sometimes possible.

Rather than catalog a long list of events, the impact is inevitably a time-frame issue such as "what is our 'late in the day' capacity?" or "what is our 'liquidity constrained daily market' capacity?"

For example, assume we are a firm issuing commercial paper (CP) to fund its day to day cash needs. In normal markets we can issue 100 million with no problem, while on 'liquidity event' days we can only issue 10 million. Using our cash-flow forecast, we can issue CP in such a way that our daily issuance requirements are no more than 10 million. By using this type of strategy, we have taken the impact of market shocks (due to whatever the nine reasons we know will eventually occur!) 'off the table'.

Of course, market events are not the only source of randomness, so we may need to add other contingency plans for other types of situations that may occur. The result is that we end up with a "playbook" that contains a number of activities and strategies that allow us to "sleep easy" even when the inevitible forecast errors show up, no matter the reason.

Key takeaway

The world is unpredictable enough such that the best laid plans get laid to waste, and so it goes with our cash flow forecast. However, the process is useful even if it is not going to ever be perfect. In order to maximize this usefulness, we need to encourage "collaborative ownership", establish contingency plans, and go through the evaluation exercises in order to derive actionable insights, identify trends, and remain 'on top' of the situation.

You May Also Be Interested In:
    ::What other reasons have I overlooked that will cause a cash flow forecast to be wrong?
    ::What types of forecast contingency plans do you have in place?
    ::What process steps have you undertaken that make the process more productive?

Add to the discussion with your thoughts, comments, questions and feedback! Please share Treasury Café with others. Thank you!