## Why most project estimates suck…and how Monte Carlo simulations can make them better

Have you ever been part of a project that was late and over budget? I’d be surprised if you haven’t. We humans are famously bad at estimating the future, and project planning is heavily dependent on our ability to estimate the future. Most of us are optimists and some of us are pessimists, but very, very few of us are realists by nature. Monte Carlo simulations can be useful in our estimation process to help us become more realistic about our estimates, and that realism can significantly improve our ability to deliver results more in line with expectations.

We generally recognize our inability to accurately estimate large projects in one chunk, so we break them up into smaller milestones that are easier to estimate. While the work breakdown process is good, the confidence it gives us in our estimates can lead to larger problems. We don’t ask ourselves often enough how accurate we think those estimates are before stringing them together to determine project due dates. If we did, the conversation might go like this:

“How accurate do you think these milestone estimates are?”

“Pretty accurate. We certainly spent a lot of time discussing them and comparing them to past projects.”

“OK. But if you had to put a number on it, would you say they are 100% accurate?”

“Well, let’s not get crazy. I can’t be sure they’re 100% accurate.”

“So put a number on it. How confident are you that they’re accurate?”

“I still feel pretty good about them. I’d say conservatively that I’m at least 90% sure.”

At this point, we’re about to discover some pretty major problems with our assumptions. We typically string together a number of these milestones, which are dependent on each other, and call them the critical path. The end of the critical path is the project due date.

But if we’re only 90% confident our estimates for each milestone are correct, the likelihood of missing our date is pretty high. Let’s say we have five major milestones in our critical path, and we’re 90% sure each is accurate. To determine the probability that all five will come in as expected, we have to multiply .90 x .90 x.90 x .90 x .90. Even with these high confidence rates, we’re now looking at about a 59% chance of hitting our due dates and a 41% chance of missing them. And that’s with only five milestones and really high (and probably unwarranted) confidence in our estimates. The numbers only get worse from here.

So we start missing deadlines and inevitably either pump more money into the effort or start cutting scope. Our original business case and ROI justification for the effort are now inaccurate because it’s going to cost more and produce less benefits. Sound familiar?

Monte Carlo simulations can help us get a better handle on the probabilities of actually delivering on our timeline and budget estimates. Just as I previously demonstrated using Monte Carlo simulations for sales forecasting, a simulation focused on project estimates can essentially become a “what if” model and sensitivity analysis on steroids for project planning. Basically, the model allows us to feed in a limited set of variables about which we have some general probability estimates and then, based on those inputs, generate a statistically valid set of data we can use to run probability calculations for the entire project.

Great. So now we know how likely we are to miss our timeline and budget. So what?

Once we have a more realistic view our our project timeline and budget, we can do far more effective planning. We can develop contingency planning with full knowledge of the likelihood of needing any particular contingency. Having a better sense of potential budget increases or scope decreases in advance of the project start date will help us make better decisions about starting the project to begin with.

We’ll also be able to better plan our needs from other groups in the corporation who might be involved with the final project but not directly involved in the project. For example, we might need to fit a new product launch campaign into an already packed marketing schedule. Will new site functionality require training for customer service? We’ll need to plan time to pull agents off the phones for their training. Setting expectations with these external groups will greatly enhance at least the internally perceived success of our effort. And that certainly counts for something.

Why go through all this complication? Let’s just take all the estimates we get from the team and double them. That should help get ensure we stay within the timeline.

The “double the estimates” approach is one I’ve seen used before. While it does help create timelines that won’t be exceeded, overestimation can also cause problems. Any coordination with external teams will still be a problem if we end up needing them before we originally planned. And over-allocating time, resources and budget can drive up opportunity costs and limit our ability to produce meaningful results over time.

Monte Carlo to the rescue

I created a free, sample Monte Carlo simulation you can download for use in project planning. It illustrates on a small scale some of the possibilities that can occur with even a minor project. We see that even a five milestone effort with 85% confidence in the estimate of each milestone is expected to be more that 20% overdue. But we can also get a sense of the probabilities of various timelines and use it to refine overall estimates.

By understanding the probability of various delivery dates and project budgets, we can better plan scope, business models and contingency plans. We can better coordinate with other teams who will play a part in the ultimate success of the project once it’s complete. In short, we can become realists and, as a result, deliver much better business results.

What do you think? Would this sort of tool help in your planning? What other methods have you used to set better expectations and plan more accurately?

Click the link below to download a sample Excel spreadsheet I created that builds probabilities for project timelines and budgets using a Monte Carlo simulation:

(If you haven’t already, see my post Why most project estimates suck…and how Monte Carlo simulations can make them better for some background.)

This model takes inputs for five milestone timeline and budget estimates and generates 5,000 possible outcomes. From those outcomes, a series of outputs metrics including overall timeline and budget probabilities are calculated.

Here’s how the file works:

Inputs

On the left of the sheet are the inputs to the Monte Carlo simulation. Fields highlighted in yellow can be changed to modify the structure of the model. (NOTE: Please don’t change fields not highlighted in yellow as these fields all contain calculations.)

Payroll cost per hour: In this simple model, I’ve assumed a single cost per hour for all employees involved in the project. For actual usage, you would want to build upon this to consider different cost levels.

# of people: Again, this is overly simplified to assume the same number of people are working full time on all milestones. This number is used to convert hours into days in orders to calculate project dates.

Start date: The date the project is scheduled to start. The model uses this date to calculate project end dates.

For Each Milestone

# of hours: Enter up to four possible duration in hours estimated for each milestone. To the left of the number of hours, enter a probability of completing the milestone in that number of hours. The probability percentage of the final duration estimate for each milestone is calculated automatically to ensure the probabilities all four possible durations add up to 100%. In the example, the probabilities and durations are largely the same for each milestone. In reality, these are likely to vary significantly depending on the word involved and the general confidence of the estimate for each.

Non-payroll \$: Payroll costs are automatically calculated in the model based on other entries, so here we’re just looking for non-payroll costs associated with the milestone. These could include computer hardware, software licenses or general material. Enter up to four possible non-payroll costs estimated for each milestone. To the left of the number of hours, enter a probability of completing the milestone with that costs. The probability percentage of the final duration estimate for each milestone is calculated automatically to ensure the probabilities for all four possible costs add up to 100%. In the example, the probabilities and durations are largely the same for each milestone. In reality, these are likely to vary significantly depending on the word involved and the general confidence of the estimate for each.

Outputs

As you enter your input variable probabilities, you’ll be able to see the results in the charts and the columns in the Outputs section to the right.

The line charts show the probabilities for potential timelines and budgets.  The possibilities are broken down in deciles between the minimum and maximum possible values.

At the top right of both the timeline and the budget sections, you’ll see an “expected” value.  This is a single number that is the sum of possible durations costs multiplied by their respective probabilities. It can provide useful in comparing different potential investment options.

Monte Carlo Simulation

The actual Monte Carlo simulation, with its 5,000 iterations, can be seen on the tab called Simulations.

The model relies on the RAND() and VLOOKUP functions of Excel. The RAND() function randomly generates a decimal between 0 and 1. We feed that random number into the VLOOKUP function, which references the appropriate “Engine Bins” (hidden) to determine the variable value it uses.  VLOOKUP references the first column of a range (range: Two or more cells on a sheet. The cells in a range can be adjacent or nonadjacent.) of cells (Engine Bins, in our example), and then returns the value from the value column on that row. If an exact match is not found in the Engine Bin, the next largest value that is less than random number lookup_value is returned.

NOTE: Because of the nature of the RAND function, the simulation and its results will rerun every time you load the spreadsheet or change anything within the spreadsheet. This means the output numbers and charts will change slightly with each load, which also proves the point about the effects of randomness. You can also hit F9 to recalculate at any time.

So, in our example, if the RAND() function returns 0.62467547 for one of the non-payroll cost iteration variables the VLOOKUP function will reference the Engine Bins for Non-payroll \$ for that milestone, see it doesn’t have an exact match and then reference the Engine Bin with the next largest value that is less than 0.62467547. Because the Engine Bin ranges are based on our input probabilities, we will select the 1.0 about 60% of the time, 0.5 about 20% of the time, 1.5 about 10% of the time, and (0.5) about 10% of the time. You can see how this plays out in the simulation.

Now we’re ready to run some simulations. For each potential scenario, we add all the selected hours for each milestone to determine duration for that scenario. For each milestone, we multiple the number of hours by our inputted payroll cost per employee to get the payroll cost for the milestone. We then add each milestone’s payroll cost to its selected non-payroll cost estimate and sum them all together for get the scenario’s cost estimate.

The simulation then runs the calculations 5,000 times in order to give us a very statistically valid sample size. We use this data set to generate our probabilities.

## Sales Forecast Monte Carlo Simulation

Monte Carlo Simulation Worksheet

(If you haven’t already, see my post “Why most sales forecasts suck…and how Monte Carlo simulations can make them better” for some background.)

Here’s a breakdown on how it works:

The first sheet illustrates a simplified version of how retail sales forecasts are traditionally developed. Basically, it looks at a couple of historical data sets (sales for the same week the prior year and previous iterations of the promotion we plan to run this week) and develops a forecast as follows:

1. Start with base sales from the same time period last year
2. Apply the current sales trend (which will be determined in this case by average of the previous 10 week comps). This method may vary from retailer to retailer, but this is the general principle.
3. Add the average of the incremental results of previous iterations of the promotion
4. Voilà! This is the sales forecast.

Of course, this number is impossibly precise and the analysts who generate it usually know that. However, those on the receiving end tend to assume it is absolutely accurate and the probability of hitting the forecast is close to 100%.

Which is pretty much impossible.

It would be far more accurate to forecast a range of sales with accompanying probabilities. And that’s where the Monte Carlo simulation comes in.

The second sheet in the file deals with the Base Business.

Inputs

On left side of the sheet are the inputs to the Monte Carlo simulation. Fields highlighted in yellow can be changed to modify the structure of the model. (NOTE: Please don’t change fields not highlighted in yellow as these fields all contain calculations.)

In this simple model, there are three input variables: Traffic, Conversion and Average Order Size. You’ll want to estimate the values and the probabilities of those values for each variable based on what you know about your business.

For example, we might look at five iterations of a particular promotion and determine that we got about 75,000 new visitors three time, 100,000 visitors once and 50,000 visitors once. Traditionally, we would average those and assume we’ll always get 75,000 new visitors. But those variances in this variable combined with variances in other variables, such as average ticket and conversion rate, can lead to wildly different sales. Instead, we can say we have a 60% probability of getting 75,000 new visitors, a 20% probability of 50,000 new visitors and a 20% probability of 100,000 new visitors. We’ll do something similar for average ticket and conversion rate.

Once you get familiar with how the model works, feel free to add more input variables as suits your business. Email me if you have questions about how to do this.

Outputs

As you enter your input variable probabilities, you’ll be able to see the results in the charts and the columns to the right.

The pie chart is a simple binary chart to quickly show the overall probability that this week’s base business will be positive or negative compared to the same week’s base business the prior year. In this case, it looks like there’s about a 73% chance our base business will comp negatively to last year (which is probably really an indication that we hit on the high end of the scale last year).

While pie chart is useful as a quick look, it’s not really enough to base a decision on. The line charts show the probabilities at various comp percentage increments and dollar increments to give us a better sense of where the base business will fall, with about a 35% chance of being around 2% down and another 13% chance of being down 10%. The columns on the right under the Probabilities heading give all the breakdowns.

You’ll note there are yellow highlighted fields in the output section. Those fields won’t change the model, but changing them allows for showing probabilities in different ranges as might be appropriate for your business.

At the top right, you’ll see a section entitled “Probability Weighted Expected Value.” This is a single number that is the sum of possible sales numbers multiplied by their respective probabilities. It should be used with caution. It can be useful for comparing different options and for trending of multiple periods, but it should not be used as a specific forecast for a specific time period.

The actual Monte Carlo simulation, with its 10,000 iterations, can be seen below the charts. See below for details on how it works, if you’re interested.

Incremental promotion

Now that we’ve determined probabilities for where our base business might fall, we can layer in a promotion we might run to increase our odds of growing sales year over year.

The third sheet in the spreadsheet, entitled “Incremental Promo” uses a second Monte Carlo simulation and set up inputs to determine our complete forecast.

Inputs

This sheet works pretty much like the Base Business sheet, but now we’re feeding a simulation that incorporates the variables for the base business and anything we input here that is generated incrementally from the promotion.

Because we’ve likely only run any given promotion a relatively small number of times previously, we have to be really careful about the estimates we make as a small sample size can yield unpredictable results. We should allow for the fact that the promotion could actually cause at least some of our variables to decrement rather than increment. (I read that one of the problems that occurred in our recent economic meltdown was that many analysts built models that had no allowances for housing prices to go down. Let’s not make the same mistake with our models.) You’ll see in the example data that I’ve allowed for the possibility of both conversion and average order size to be lower than any possibility in the Base Business worksheet.

All of the inputs are now combined with the base business variables to create a new simulation, which can be seen on its own worksheet called “Simulation.”

Outputs

In the pie chart, we can see that the incremental promotion will increase our probability of generating a positive comp for the week, but we’re still slightly more likely to have a negative comp. Basically, though, it’s a coin toss. We can see with the line charts the various probabilities at different increments of where we’re likely to end up based on what we know. And now that we have a better understanding of our risks and potential rewards, we can make a better decision.

How the model works

The model relies on the RAND() and VLOOKUP functions of Excel. The RAND() function randomly generates a decimal between 0 and 1. We feed that random number into the VLOOKUP function, which references the appropriate “Engine Bins” to determine the variable value it uses.  VLOOKUP references the first column of a range (range: Two or more cells on a sheet. The cells in a range can be adjacent or nonadjacent.) of cells (Engine Bins, in our example), and then returns the value from the value column on that row. If an exact match is not found in the Engine Bin, the next largest value that is less than random number lookup_value is returned.

So, in our Base Business example, if the RAND() function returns 0.62467547 for one of the traffic iteration variables the VLOOKUP function will reference the Engine Bins for Traffic, see it doesn’t have an exact match and then reference the Engine Bin with the next largest value that is less than 0.62467547. In this case, it chooses the Engine Bin “0” and returns a traffic variable of 300,000. Because the Engine Bin ranges are based on our input probabilities, we will select the 300,000 about 70% of the time, 275,000 about 15% of the time and 325,000 about 15% of the time. You can see how this plays out in the simulation.

In this particular model, we then determine sales by multiplying traffic by conversion rate by average ticket.

The simulation then runs the calculations 10,000 times in order to give us a very statistically valid sample size. There’s no real magic to 10,000, but it’s a large enough data set to give us a reliable result without being so large that it takes a long time to calculate.

We use this data set to generate our probabilities.

Caveats

• The mode is definitely dependent on the quality of the inputs, but those are generally easier to estimate than the outputs
• Using the RAND() function will cause the entire spreadsheet to recalculate each time you load the spreadsheet, enter a new value or hit F9. This means all the probability outputs will change slightly each time. But they won’t change much. This really illustrates how we can tame the uncertainty of the future with ranges and probabilities, but it also shows how impossible it is to be extremely precise. All that said, I wanted the model to work for you as you enter new values, so I left the RAND() functions in. In actual practice, you might want to cut and paste the values after into another sheet after you’ve run the calculation just so you don’t have to deal with slightly fluid numbers.
• It could take a few iterations of learning to get the inputs right
• Running Monte Carlo forecasts in parallel with the current method can help to refine the inputs

## Why most sales forecasts suck…and how Monte Carlo simulations can make them better

Sales forecasts don’t suck because they’re wrong.  They suck because they try to be too right. They create an impossible illusion of precision that ultimately does a disservice to managers who need accurate forecasts to assist with our planning. Even meteorologists — who are scientists with tons of historical data, incredibly high powered computers and highly sophisticated statistical models — can’t forecast with the precision we retailers attempt to forecast. And we don’t have nearly the data, the tools or the models meteorologists have.

Luckily, there’s a better way. Monte Carlo simulations run in Excel can transform our limited data sets into statistically valid probability models that give us a much more accurate view into the future. And I’ve created a model you can download and use for yourself.

There are literally millions of variables involved in our weekly sales, and we clearly can’t manage them all. We focus on the few significant variables we can affect as if they are 100% responsible for sales, but they’re not and they are also not 100% reliable.

Monte Carlo simulations can help us emulate real world combinations of variables, and they can give us reliable probabilities of the results of combinations.

But first, I think it’s helpful to provide some background on our current processes…

We love our numbers, but we often forget some of the intricacies about numbers and statistics that we learned along the way. Most of us grew up not believing a poll of 3,000 people could predict a presidential election. After all, the pollsters didn’t call us. How could the opinions of 3,000 people predict the opinions of 300 million people?

But then we took our first statistics classes. We learned all the intricacies of statistics. We learned about the importance of properly generated and significantly sized random samples. We learned about standard deviations and margins of errors and confidence intervals. And we believed.

As time passed, we moved on from our statistics classes and got into business. Eventually, we started to forget a lot about properly selected samples, standard deviations and such and we just remembered that you can believe the numbers.

But we can’t just believe any old number.

All those intricacies matter. Sample size matters a lot, for example. Basing forecasts, as we often do, on limited sets of data can lead to inaccurate forecasts.

Here’s a simplified explanation of how most retailers that I know develop sales forecasts:

1. Start with base sales from last year for the the same time period you’re forecasting (separating out promotion driven sales)
2. Apply the current sales trend (which is maybe determined by an average of the previous 10 week comps). This method may vary from retailer to retailer, but this is the general principle.
3. Look at previous iterations of the promotions being planned for this time period. Determine the incremental revenue produced by those promotions (potentially through comparisons to control groups). Average of the incremental results of previous iterations of the promotion, and add that average to the amount determined in steps 1 and 2.
4. Voilà! This is the sales forecast.

Of course, this number is impossibly precise and the analysts who generate it usually know that. However, those on the receiving end tend to assume it is absolutely accurate and the probability of hitting the forecast is close to 100% — a phenomenon I discussed previously when comparing sales forecasts to baby due dates.

As most of us know from experience, actually hitting the specific forecast almost never happens.

We need accuracy in our forecasts so that we can make good decisions, but unjustified precision is not accuracy. It would be far more accurate to forecast a range of sales with accompanying probabilities. And that’s where the Monte Carlo simulation comes in.

Monte Carlo simulations

Several excellent books I read in the past year (The Drunkard’s Walk, Fooled by Randomness, Flaw of Averages, and Why Can’t You Just Give Me a Number?) all promoted the wonders of Monte Carlo simulations (and Sam Savage of Flaw of Averages even has a cool Excel add-in). As I read about them, I couldn’t help but think they could solve some of the problems we retailers face with sales forecasts (and ROI calculations, too, but that’s a future post). So I finally decided to try to build one myself. I found an excellent free tutorial online and got started. The results are a file you can download and try for yourself.

A Monte Carlo simulation might be most easily explained as a “what if” model and sensitivity analysis on steroids. Basically, the model allows us to feed in a limited set of variables about which we have some general probability estimates and then, based on those inputs, generate a statistically valid set of data we can use to run probability calculations for a variety of possible scenarios.

It turns out to be a lot easier than it sounds, and this is all illustrated in the example file.

The results are really what matters. Rather than producing a single number, we get probabilities for different potential sales that we can use to more accurately plan our promotions and our operations. For example, we might see that our base business has about a 75% chance of being negative, so we might want to amp up our promotions for the week in order have a better chance of meeting our growth targets.  Similarly, rather than reflexively “anniversaring” promotions, we can easily model the incremental probabilities of different promotions to maximize both sales and profits over time.

The model allows for easily comparing and contrasting the probabilities of multiple possible options. We can use what are called probability weighted “expected values” to find our best options. Basically, rather than straight averages that can be misleading, expected values are averages that are weighted based on the probability of each potential result.

Of course, probabilities and ranges aren’t as comfortable to us as specific numbers, and using them really requires a shift in mindset. But accepting that the future is uncertain and planning based on the probabilities of potential results puts us in the best possible position to maximize those results. Understanding the range of possible results allows for better and smarter planning. Sometimes, the results will go against the probabilities, but consistently making decisions based on probabilities will ultimately earn the best results over time.

One of management’s biggest roles is to guide our businesses through uncertain futures. As managers and executives, we make the decisions that determine the directions of our companies. Let’s ensure we’re making our decisions based on the best and most accurate information — even if it’s not the simplest information.

What do you think? What issues have you seen with sales forecasts? Have you tried my example? How did it work for you?

## My Favorite Business Books of the Year

“I am learning all the time.  The tombstone will be my diploma.” ~Eartha Kitt

“Books are the bees which carry the quickening pollen from one to another mind.” ~James Russell Lowell

“Outside of a dog, a book is man’s best friend.  Inside of a dog it’s too dark to read.” ~Groucho Marx

I love to read books and absorb new information and ideas. In this final post of the year, I thought I would share some of the books that most inspired me this year. I hope you might also get great value from them. Some of them aren’t exactly business books, but I got business value from them and I thought you might benefit similarly.

Without further ado, and in no particular order, here are my favorite books of the year:

This one was recommended to me by Anna Barcelos after I wrote my post on the 4 Keys to a Customer Centric Culture. Luckily, I think I was largely on the same page as Michael Basch, but I learned so much more about company cultures after reading his tome. Basch was a co-founder of FedEx and their initial SVP of Sales and Customer Service. He relays plenty of his learnings at FedEx, but he also relates the stories of other customer focused businesses small and large. He even covers an incredibly innovative Australian dentist office! Many of the stories sparked plenty of ideas in my mind, and I even excerpted one to highlight in my blog post on the power of naivete. Basch gives some very specific and easy-to-follow advice on creating the types of customer-focused cultures that drive businesses that simply succeed more because of their focus on their customers.

Every manager and executive should read this book. Patrick Leach does an excellent job explaining the concepts of probabilistic thinking and decision making, and he does it in everyday language that is easy to consume for business people who don’t necessarily have advanced degrees in mathematics. He makes a very compelling case for using probabilistic thinking to greatly improve the bottom line. This book, more than any other, was the inspiration for some of my posts on Monte Carlo simulations.

This is a book that I actually didn’t love immediately after reading it. However, the concepts I got from it kept creeping back into my brain, and maybe that’s an even better way to value a book. I excerpted a bit of it in my most recent blog post on the power of our hidden brains to dominate our decision making in ways we don’t consciously realize. Author Shankar Vedantam deftly manages to explain complicated brain inner workings through easy-to-read stories that illustrate the concept and leave lasting memories.

I included Dan Ariely’s first book, Predictably Irrational: The Hidden Forces That Shape Our Decisions, as an all-time favorite in last year’s list of best business books. While the experiments and ideas in that book were probably more useful in the marketing and merchandising functions, I would say this book is much more about human interactions and general management and leadership. Ariely focuses on topics such as the effect of pay on performance, the motivational value of creating things, and the high addiction of our own ideas. As you might guess from the title, Ariely’s conclusions are not often what we’d expect but extremely beneficial.

Anyone who’s attempted to implement major change in an organization knows how difficult it can be. This book is an excellent guide for how to implement and encourage change in business and in life. I loved not only the tips but the explanations about why we humans have such a difficult time with change. The Heath brothers use a metaphor of the Rider and the Elephant to describe the rational and emotional parts of our brains that are so fundamental to accepting and embracing change (and making decisions of any kind). It’s an excellent way to visualize the concept and a concept that I’ll not likely forgot.

I actually dedicated a post to this book. This is a fascinating look at the reasons people click with each other. The authors really break it all down and in the process provide an excellent roadmap for creating better connections between people. Used in a business environment, this knowledge can help up create better functioning, happier and more productive teams.

This is not a new book by any means, but I somehow never read it until this year. I found this book to be extremely eye opening and completely fascinating. Don Norman spends a lot of time talking about the design of objects like doors and faucets, yet the design principles he discusses and the human psychology learnings that go into those design principles are absolutely relevant to usable designs of things that didn’t even exist in the time he wrote this (the ’80s) — like websites. I explored just a couple of these concepts, and how they apply to retail websites,  in a post earlier this year.

And just for kicks, since I’m a music nut, here are my top 10 albums of the year:

1. Trombone ShortyBackatown
A great combination of jazz, funk, hip-hop and rock. Trombone Shorty rips on both the trombone and the trumpet. Standout track: “Hurricane Season.”
2. Florence + the MachineLungs
Technically, this record came out in 2009, but the Grammys have nominated them for Best New Artist this year I think I get to include the record in my list. Great vocals from Florence and the drums in particular are amazing on this record. The sound is powerful, a bit dark and different from anything I’ve ever heard. Standout track: “Dog Days are Over”
3. Grace Potter & the NocturnalsGrace Potter & the Nocturnals
Grace Potter can flat out sing, and the songs on this record are top-notch. This is just good, ol’ rock ‘n’ roll and a rollicking good time. Standout track: “Paris (Ooh la la)”
4. The Gaslight AnthemAmerican Slang
Gaslight Anthem are kind of a Green Day meets with Replacements and (not surprisingly since they are from New Jersey) jams with Bruce Springsteen. Standout track: “The Spirit of Jazz
5. Mumford and SonsSigh No More
The bluegrass tinted pop from British newcomers Mumford and Sons is highly infectious. Very impressive vocal harmonies as well. Standout track: “Winter Winds”
6. Sons of SylviaRevelation
A band of brothers (I assume their mother is named Sylvia), these guys have put together what I guess could be called an alt-country record because it’s basically pop music with country instruments. Lead singer Ashley Clark can flat out wail, and the band certainly holds their own. Standout track: “50 Ways.”
7. Sharon Jones and the Dap KingsI Learned the Hard Way
Sharon Jones and the Dap Kings are the best of the new wave of R&B retro, and this record does not disappoint. Standout track: “Money”
8. OzomatliFire Away
I’ve been an Ozomatli fan for a long time, but I think this is their best record since 2001’s Embrace the Chaos. Great combination of Latin, hip-hop and rock, and the songs are lots of fun. Standout track: “Yeah, Yeah, Yeah, Yeah”
9. Aloe BlaccGood Things
I suppose Aloe Blacc is a bit of an R&B retro artist, but he’s got a sound that feels both contemporary and throwback at the same time — and he’s very smooth. Standout track: “I Need a Dollar”
10. SantanaGuitar Gods: The Greatest Guitar Classics of All-Time
Carlos Santana teaming up with a bunch of guest vocalists to record some of rock’s all-time great guitar songs (sort of — I’m not sure Dance the Night Away would be my choice for a Van Halen song). Artistically, this is not particularly impressive. However, it’s a lot of fun to just listen to Carlos Santana wail away — even when he’s stepping all over the melodies. Standout track: “Back in Black”

What were your favorite business books of the year (and music, too)?