Get More out of Training

Training is one of the key deliverables in any ERP implementation, if new users are comfortable with how to perform their daily duties with the new software then success is very likely.

I do a lot of training in my role. There are a few simple things that a customer can do that I have found greatly facilitates a good training session.

1. Make sure you put on a good lunch

Actually this isn’t important, but I do like good lunches, sausage rolls if given the choice.

2. Have an appropriate venue.
A separate room with a door that closes, desks, chairs, projector, whiteboard, have the room preferably air-conditioned. Off site is the ultimate. A place that is free from distractions is critical, there is nothing worse colleagues drifting in and interrupting attendees or attendees working on other things while they attempt to learn.

If you can set up a room without external internet access even better (though the Facebook / Twitter / Outlook junkies will start to shake after an hour).

People should have their own computer (no” looking on”)
I have done training with none of the above and it makes it hard.

3. Quality Training Manuals
These provide structure for the sessions and ensure no topics are missed. Manuals are also useful for following up after the course and for writing notes in.

4. Make sure the trainer only goes as fast as the slowest person in the course.
Once people fall behind they get flustered, make mistakes and this then feeds on itself. The less competent people need more help, while they might get a bit bored the quick people can most probably learn on their own anyway.

5. 5-7 Attendees per Instructor
Any more and it becomes very difficult to manage the different learning paces and styles of the attendees and answer all questions thoroughly.

6. Use it or lose it
Make sure that the attendees immediately (or within a week) go and apply what they have learned in their production or configured systems.

7. Schedule a follow up course in 6 to 12 months time
People forget, and staff turnover.

©Duncan J. Kennedy 2009

Better Quoting (lets sell it for more than we paid for it)

We sold it for less than it cost us to build it?

While not the most glamorous of topics, having a process that builds a quote (and therefore a selling price) that has a profit margin is important.

How does this possibly happen? The purpose of business is to make a profit?

In my travels as a consultant, I typically see a number of causes for inaccurate quoting and pricing leading to low (or negative) margins at sale.

Typical causes are:

• The price used for the materials in the quote is out of date, inaccurate or non-existent (it might be a new part, the estimator is using historical prices, a standard cost or the price may be highly variable)
  
• Pricing, quoting and then the subsequent build is done in disparate and disconnected spreadsheets/systems and/or on the back of beer coasters
  
• They are selling ‘non-standard’ ‘make-to-order’ goods leading to a philosophy that ‘everything is different’ leading to different sales people quoting exactly the same item at different prices to different customers
  

So we have clearly identified some problems, what are some potential solutions?

Use price lists
Ring your suppliers up; ask for a contracted price (with an expiry date). If the item is a high value, build a structured request for quote process (RFQ) that allows your procurement staff formally record agreed prices. Make sure this process is completed before purchasing / quoting.

Use standard builds (including subassemblies, if required)
You are in the custom “make-to-order” business so it is different each time? You may however find that if you drill down some of your items may have identical sub-assemblies. Having a standard assembly or sub-assembly (therefore a standard price) enables you to look at delivering a price book that your sales/estimating staff could drag and drop a quote from, with the confidence that the costs are correct and up to date.

Report on the difference between the quoted, planned and actual costs for the materials you have purchased
Being able to tell at a glance via a dashboard or similar the difference between the cost that was quoted vs. the cost that we planned to build it vs. the actual cost clearly identifies problem areas to focus your efforts on.

Have at your fingertips recent purchasing history for the items you are purchasing

Being able to find quickly and easily the details on the following pieces of purchasing information for your items will lead to more informed purchasing.

Have I purchased it recently?
Do I have any in stock?
Do I have any on order?
Do I have any supplier price lists for this item?

Have the details from your quote in the same system that purchasing, scheduling and planning your production is in

No more multiple spreadsheets/applications. Just the one system. This will lead to a better consistency throughout the quote to cash business process.

Okay, so what is next?

Implement an ERP solution that allows you to achieve the above and you are well on your way to increasing your profit through better quoting and procurement

©Duncan J. Kennedy 2009

Kardina Park and Correlation.

In addition to using historic trends when attempting to forecast the future, we can use another method called ‘regression forecasting’.

This method of forecasting calculates the strength of the dependency between two variables with a result of 0 representing no dependency relationship at all (totally random) and 1 (direct relationship). This calculation is called a ‘correlation co-efficient’ (drop me an email if you want to know the maths behind this).

If the dependency between the two variables is strong (e.g. if one goes up so does the other one roughly in proportion) and we know the value of one of them we can use this to drive the value of our forecasted variable.

Great, but how is this useful?

Some example of some things we might want to calculate

• Size of winning margin vs. if a team is playing at home vs. away (i.e. is there a home ground advantage) used for calculating the odds when betting?

• Interest rates and the general economy (Reserve Bank)?

• The selling price you plan on setting for your product and the forecasted number of items sold (Marketing Departments)?

• This quarters Sales vs. Next quarters consulting revenue (consulting executives)?

• The movement in the current price of gold today vs. the price of a gold mining stock on the stock exchange tomorrow (stock brokers, hedge funds)?

• Smoking and lung disease?

• The current temperature in the pacific and next month weather (El-Nino effect?)

It’s often easier to start with items that you already think have a strong relationship, however if you have a large data set (for example the sales history for all items for the last two years) you can use ‘data mining’ techniques to sift through data to pick up correlations that you may not have thought previously existed.

If the predictor value is known (or can be controlled) and there is a delay between the two variables this method of forecasting is especially useful. In this circumstance the forecasting can be done with no (or little) history, especially useful if the history is irrelevant, inaccurate, there is not trend or if the data is simply not available.

(btw Shell Stadium a.k.a Kardina Park is Geelong Football teams home ground, a strong relationship exists between winning and playing at this ground)

How to avoid the next “GFC” (Lean)

Firstly apologies to those who thought this post would be about the Geelong Football Club.

This post is about the other (less important) GFC otherwise known as the “Great Financial Crisis”.

In the last two posts we investigated forecasting. There are three components that can contribute to an accurate forecast, Trend, Deviation from the Mean, and Seasonality.

Should the item being forecasted display strong attributes in these areas then the forecast is more likely to be accurate, if it shows none of these then the forecast will be less accurate (the more random the history the harder to forecast).

Using petrol prices as an example.

Prices are slowly going up as we creep out of the economic slump (trend), its higher on the weekend than it is during the week (seasonality) and while we don't know what the exact price is going to be next week its highly unlikely to be less than a $1 or more than $1.60 (deviation from the mean).

However what happens when an unexpected (and catastrophic) event occurs?

Let’s use the stock market as a representation of the general economy and examine the affect of the collapse of the financials system (an unexpected and catastrophic event) on forecasts and the flow on effect of getting the forecast wrong.

All ordinaries from April 2003 until October 2007.

If you were to forecast based on this ‘history’ then your forecast for the following years would show strongly trending increase in demand. You would "plan" around this forecast.

And that’s exactly what manufacturers, distributors and retailers around the world were doing, ramping up production to meet this forecasted increase in demand.

However along came the crash (October 2007 to March 2008).

Our forecast based on 'history' 2003 to 2007 is now hopelessly inaccurate.

The effect on the economy is striking.

Manufacturers and distributors will have production schedules and inventory levels (and loans) based on their forecast of strongly trending increased demand. They now have too much stock, too many staff, be too highly geared and subsequently have lots of problems!

They will need to stop/slow production until this excess inventory is sold which leads to layoffs which in turns leads to reduced demand which is now feeding into a now strongly downwardly heading trend.

But how do we avoid this overshoot when the trend changes and avoid (or at least minimise) it’s nasty after affects?

Essentially it’s by only making and stocking items that you have already sold by not forecasting!

The challenge with this approach is that items with long lead times (i.e. the time it takes to get from raw material to finished good to deliver to customer) need to be forecasted.

Your customers want what they ordered now, not in six months time.

A solution to reducing these lead times is to enable a process where you are making and stocking only items you have either already sold or are likely to sell in the near future (i.e. only selling what customers actually want) rather than speculating on the mid to long term future.

This solution is generically called "Lean" Supply and Manufacturing, which we will discuss in detail in future posts.

But is forecasting better than a coin toss...

On the previous post we identified that by simply picking teams based on last years ladder picked (at that point in the season) on average 5.4 winners. The average punter (and there were 160,000 of them) picked on average 4.4.

But is this better than a coin toss (random chance).

I initially thought average number picked by flipping a coin must be 4.5.

My reasoning was 8 games per round (9 possible outcomes including picking no games) divided by 2. However a colleague of mine quite heatedly (and as it turned out quite correctly) said 4.

Without the aid of a handy formula (or the math skills to use it) to prove this I simply plugged the randbetween(0,8) function into excel and filled down 20,000 rows and took the average. A fairly rudimentary version of a "Monte Carlo Generator" http://en.wikipedia.org/wiki/Monte_Carlo_method and lo and behold an average of approx 4 picks.

Still having trouble wrapping my head around it, if anyone can explain it better please leave a comment in the comments section (or drop me an email).

Lessons learned.

1. Probability appears to be counter intuitive (and difficult to describe, explain or prove).

2. Even TR is right every now and then.

3. The average punter is slightly better than flipping a coin.

AFL footy tipping and inventory forecasting - Is past performance a useful indicator for future performance?

Currently working on an inventory forecasting implementation that takes sales history then forecasts it into the future based on a best fit from a range of different statistical algorithms.

Having read Nassim Talebs "The Black Swan" and "Fooled by Randomness" http://www.fooledbyrandomness.com/ i was initially skeptical that that approach would work or be even vaguely accurate. Surely in these uncertain times trend is dead and forecasting is a waste of time? Isn't 'gut feel' more important than some numbers in a spreadsheet?

To my surprise when back tested it forecasted within 10% of actual sales.

This lead me to a significant conclusion and raised two questions.

1. Conclusion - My maths isn't up to scratch, i have subsequently re-enrolled to university mathematics course to fix this.

2. Question - How can I benchmark forecasted results based on past performance to check if the results may be worth using?

3. Question - Can i relate this concept to something more interesting than ERP?

At that point I decided to apply the concept to AFL football tipping.

The NAB bank has a footballing tipping comp http://tipping.afl.com.au/

I signed up.

To calculate past performance i would use the teams position on the ladder at the end of the home and away season. i.e. for each round I would pick the winner of each game based on who finished higher on the ladder in 2008.

Based on this simple method as at Round 7 i am currently ranked 5,810 out of 168,180 tippers in the 2009 AFL home and away season!

Now for the benchmarking.

1. How does this stack against the other punters in the comp?
2. How does this stack against pure chance (i.e. flipping a coin to pick the winner)?

8 Games per Round (max 8 correct picks)
Average Wins per round predicated by simple method outlined above 5.4 wins per round
Average Wins picked by all contestants for entire competition 4.4 wins per round

The simple method significantly outperforms the average footy tipper with all their experience, judgment and freely available information available at their fingertips.

Faith in forecasting restored (and lack of faith in the 'wisdom of the crowds' http://en.wikipedia.org/wiki/The_Wisdom_of_Crowds reinforced).

Utilization - Not as simple as it appears.

One of the key metrics (and sometimes only metric) used to evaluate the performance of billable staff in a professional services organization is utilization expressed as a percentage of available hours.

The simple formula could be utilized hours / available hours.

But what assumptions are used to calculate these two variables? There is no right or wrong answer but the following should be considered (and the ERP system used to enter and bill time should be able to support these).

Available hours

- Part Time, Full Time and Casual Staff have different work hours and this should be reflected in the calculations?
- Should sick, personal and annual leave be included in the available hours?

Utilized hours.
- Are only chargeable hours to be regarded as utilized?
- Should approved internal activities such as development and pre-sales work be considered utilized?
- Should Travel (both billable and non-billed) time be considered utilized?
- How should utilization be calculated on a fixed price job, simple budget / hours placed against job or should it be first in best dressed until the budget is reached?

With these assumptions taken into account, a percentage utilization should be able to be calculated. Be offering this information real time in a clear and transparent manner embedded in an ERP system this simple calculation can provide good feedback and a benchmark for the performance of your billable consultants in a professional services firm.

Exotic EVM

Now that we have our “Earned Value” the next two indicators are most often used in a project portfolio reporting situation where program managers or senior project managers need some key KPI on a range of projects. These KPI are best displayed in a dashboard manner using an online reporting or collaboration tool such as Windows SharePoint Services™ (more on that later)

The values of these KPI's would provide a risk grading so that at a glance you could see if you had a problem (i.e. an SPI of < .8 is Red, .8 to 1.2 is orange and SPI of > 1.2 is Green)

The key benefit to using these measurement rather than raw dollars or hours is that its relative to the size of the initial budget. For example $10000 over budget might be a small or large depending on the size of the project but .5 as a cost performance index gives a relative measure regardless of the size of the budget that can be used to benchmark either against a corporate standard or against like projects.

The Key two Earned Value KPI that we will look at are:

SPI – Schedule Performance Index
Are we are accruing ‘earned’ costs faster or slower than budgeted (are we ahead or behind schedule) and by how much?
The formula for this is
SPI = EV / PV
A value of Less than one is behind schedule and a value of more than one is ahead of schedule.

Cost Performance Index
Are we under or over budget from an ‘earned’ perspective and by how much?
The formula for this is
CPI = EV / AC
A value of less than one is over budget and value of greater than one is under budget

Looking at our Sample Scenario the Dashboard entry on our Microsoft SharePoint Site for our on the second week would look like.





This concludes the series of articles on Earned Value, next series of articles we will look at using Windows SharePoint Services to help manage your projects.

Earned Value for Dummies Part 2 - The ‘Earn’ in Earned Value.

In the previous article we looked at a simple metric called the variation at completion (or the VAC as it is shortened to) which simply compared the budget at completion (BAC) to the forecasted cost at completion (EAC) to get a figure which indicates a forecasted budget overrun/underrun on the project.

Now let’s look at the ‘earned value’ of the work completed thus far on the project to calculate project performance.


The Earned value is how much of the current cost have been “earned” on the project.


The earned value is simply the total budget (sum of PV or BAC) divided by percent complete (how much of the work has been done). By comparing the Earned value with the Planned Value you get a cost variance which is used to calculate how you are tracking to budget. A positive cost variance is good, negative bad. Let’s look at the example from the first article.


After Johns disastrous second week when he lost his laptop and caused a two week overrun (as he had to re-do his work) John completed the task. Note how at the end of the second week his Earned Value was $0 i.e. he hadn’t earned any of the cost yet as he had not done any of the work.



This cost relationship can be illustrated in a graph to show the trends in the earned value against Actual and budget costs over time (see below)




In the Next Article “Exotic Earned Value” will take a look at the more exotic earned value calculations namely the Schedule Variance, the Schedule Performance Index the Cost Performance Index and the Cumulative Cost Performance Index.

Earned Value for Dummies –Part 1.

This article is part one of a multi part series investigating earned value reporting for projects, tackling the simpler of the concepts first.

Scenario.
You submit your weekly project report to your supervisor and everything looks good, plenty of budgeted time left on each tasks and plenty of time left before the deadlines for each of the milestones. But wait! Something is lurking below the surface much like an iceberg in the sea ready to sink your project. What is missing is how much work is left to complete on each of those tasks and without taking this into account you are not reporting accurately the health of your project.

Wouldn’t it be nice to know if your project is going to go over budget before the budget has been reached? Interested in having a rough idea how much it will go under/over at completion? Step in Earned Value Management (EVM).

Earned Value Management acknowledges that the value of revenue earned on projects with fixed budgets must reflect the percentage completion of all work to be done on the project rather than the hours incurred to date.

Before explaining this in more detail, let’s review some fundamental project accounting principles. These are the foundations upon which EVM is built.


Your project has one task - This task is “Developing an Invoice Report.”
1. The budget is 120 hours over three weeks.
2. At the end of the second week he has spent 80 hours on it.
3. At the end of the second week (Friday night drinks at the pub) your report writer john lost his laptop with all his work on it (no backups), he will have to start again.

Standard Budget to Actual reporting on the scenario
- Planned (Budgeted) Value of the Project = 120 hours
- Actual Costs Incurred = 80 hours
Heath of Project as Reported = 40 hours under budget and everything going well. I might get that bonus this quarter after all.

Earned Value Reporting on this scenario
- Planned (Budgeted) Value of the Project = 120 hours
- Actual Costs Incurred = 80 hours
- Estimated Work Left to Complete = 120 hours
- Estimate at Completion = 200 hours
- Variance at Completion = 120 hours – 200 hours = - 80 hours
Health of Project as Reported = Looks like we are going to go 80 hours over budget. Time to reset expectations with the customer, shift the delivery date (and maybe look at some kind of source repository for our development team)

One method reports we are under budget, one method reports we are over, which one is correct?

There are a number of calculations that can be performed once you have measured the estimate to complete on your tasks, we will stick to those that are the easiest ones to grasp to begin with. We will look at more complex calculations including earned value and schedule variances in future posts.

Pro’s and Cons to using EVM
Positives
1. Gives you a snap shot of how much work is left on a particular project.
2. Useful for project portfolio reporting as it enables you see at a glance with one calculation (VAC or variance at completion) which projects are in trouble.
3. Lets you report on projects that are going to go over before they go over.

Negatives
1. Requires more project management admin time than ‘set and forget’ actual to budget reporting does.
2. Confusing acronyms can sometimes be used (EAC, VAC, Schedule Variance….).
3. May require changes in existing project management processes and/or software.
4. Might be too cumbersome for very small projects.


Summary
Without reporting on how much work is left in your project, you are not reporting on the health of your projects at all.