Are you reporting what you can? … Or reporting what you should?

Too many organizations are missing opportunities to use their data and analytics as a competitive advantage. Often leaders believe that having an abundance of data, reports, and colorful charts is what it means to be data-driven. But gaining competitive advantage through your data and analytics involves making sure leaders have the information they need when they need it.

But it’s extremely common for people in an organization to become complacent with the information that’s always been available, instead of demanding the data that will serve them better. The idea of “if all you have is a hammer, everything looks like a nail” is often evident in performance reporting … with too many indicators describing the same concept, while ignoring other critical parts of the organization.

To get some “out of the box” thinking it may be helpful to do an assessment every so often to make sure that the data and information is actually generating meaningful value to the organization.

Here are four questions to consider:

1) Does the current analytics help your leaders make decisions?
And, does it support them in taking the right action? If not, why? Take a hard look at the data and information available to the users with respect to:

Timeliness: Is information provided in a timeframe where users are actually able to take action? Or is the information so far out of date by the time it’s reported, that it provides little value for performance management?
Trustworthiness: Are leaders having difficulty trusting the data, because of accuracy and reliability issues?
Relevance: Are leaders having difficulty in seeing the relevance of the existing data? For example, seeing your current performance relative to past levels, or relative to industry standards.
Understanding: Do leaders actually understand the data or metrics that are being reported? Do they know where the data comes from, and what it represents? Are some of your metrics needlessly complicated? Is the data presented and charted in an immediately intuitive and visual way? Is it clear who is responsible for what? As best as you can, figure out what do your leaders really should know before taking actions based on the data and analytics.

If you have any of these issues, you’ll need to resolve them if you want to increase the competitive value of your data and analytics

2) Do you have too many metrics?
What are the few metrics (3 to 5) that cover the majority of what’s important, to the majority of the people in your organization? For example, if you have 20 metrics then think about how you can group them into topics and create aggregate metrics (i.e. like a GPA summarizes a student’s letter-grade performance across courses). Reporting dozens of metrics is easy, but not very actionable, because at most times some metrics will look good and some metrics will look bad. Slimming them down to the few metrics is a tough job, but when done right, it can be an incredibly powerful tool for communicating through the organization “what good performance looks like”.

3) What do your leaders want?
What would help them do their job better? Carry out working sessions with your leaders to figure out what the ideal data and analytics would look like, and more importantly, have them describe the actions they would take with this information. Ask them to think outside the box, and not restrict themselves to the data that they’ve seen before. Then ask your leaders to set priorities using the question: If you had to choose one metric, what would it be? If you’re doing this as a group working session, you will generate a lot of informative discussion, and a decent group of new metrics.

4) What is the ROI on the right metrics?
On your top-ranked metric for new data (i.e. data you currently don’t collect or have available), do a cost-benefit analysis. How much is it worth to your organization to have this information? Taking this approach can reveal opportunities to make a strategic investment in order to gain a competitive advantage. See Tip 3 from How to Get the Data You Need for more details.

If done right, this exercise will generate a burning need to make the investments to help leaders have the information they need, when they need it. And in doing so, empowering them to take meaningful actions based on truly relevant data and analytics. Translating analysis into action will create a competitive advantage for your whole organization.

Two Mega Trends: Big Data and the iPad … Where do they converge?

It’s no secret that Big Data is an emerging mega trend now and into the forseeable future. David Feinleib’s slideshare presentation on Big Data Trends shows a concise and current summary of where things are headed in the Big Data movement

Enter mega trend #2, the iPad. The current market share for iPads is strong and is projected to continue until 2016, according to the recent IDC study. I can say first-hand that most executives in our network are now in the habit of bringing their iPads with them wherever they go.

So if the leaders and decision-makers are about to be consumers of Big Data (they may not know it yet), and if they are all toting their iPads to their meetings, there must be an opportunity or two for forward-looking Big Data thinkers. This post is intended to start a conversation around the question:

If Big Data is growing like mad
And business leaders are using iPads more and more …
What’s our collective best guess as to …

… Where these two mega trends converge?

I’m sure this post will generate a decent discussion thread. To kick things off, I’ll put out my own thoughts.

There will be an increasing need to simplify the “so what” message
Tablet apps can be beautiful to look at, but they are rarely as successful when trying to pack a lot of information into a small space. Designers will increasingly need to give disproportionate attention to the “so what” message when reporting Big Data results.

As Lachlan James outlined in the recent post, on Top Business Intelligence dashboard design best practices intentional, effective and clear communication must be priority number one.

So if we agree with that idea, then instead of filling 90% of the reporting space with different charts and tables of results, perhaps the future way of iPad-friendly reporting would be like headlines in a newspaper, with catchy titles like: “We can accurately predict 80% of our adverse hospital events based on these 5 factors” or “65% of our customer retention in the Pacific Northwest and be explained by these 3 attributes”. Underneath the headline would be the supporting detail and charts.

This presents a challenge in automating the process of taking Big Data results, and explaining what they are saying in plain english. Perhaps there is a whole new area of opportunity here, with some links to artificial intelligence.

People will want to play
By the light-hearted nature of the iPad device, it lends itself to playing. Not that one would expect there to be a Big Data version of Angry Birds, but the concept of playing and interacting with Big Data seems like a likely user expectation. Perhaps as leaders interact with the summarized results of Big Data efforts, they will want to do things like:

Evaluate “what if” scenarios, such as “What if this pattern observed in this one customer segment applied to our whole customer base?”
Take an observed Big Data finding and forecast it into the future (i.e. If this trend continues, what will things look like 1 year from now?)
Play with different ways of visualizing complex Big Data results, using different charting tools, plotting symbols, colors, etc. (i.e. a techie version of “arts and crafts”).

Parts of the dashboard may be ever-changing
The nature of Big Data is that it is ever-growing and ever-evolving. Which means that “what was interesting and useful” today, might be taken as a given tomorrow. In addition, as companies use more or and more external data (as opposed to just using their own internal data) it may introduce another element of variability in terms of where the Big Data stories are. So, unlike previous BI and dashboard reporting efforts (i.e. with KPIs and measures that generally don’t change that much), the reporting canvas for Big Data may be constantly changing.

Translating this to the iPad experience, a core competency in reporting Big Data results through an iPad might be “the ability to educate as you go”. Leaders and executives will constantly be exposed to new findings and new measures, and they will need help getting up to speed regarding on what the findings mean. Conceivably, this may need to take place on the fly during the reporting stage, using popup videos or animations – using a broadcast email to communicate updates won’t likely cut it any more!

There will be an increasing need to simplify and track the “doing” step
As is often the case with reporting great results, nothing really matters if there’s no “doing” step. As leaders view the Big Data results in their iPads, they will inevitably get to a point in the meeting where someone says “We should do something about that”. The process of tracking “who is acting on what” will become more important for a few reasons:

Many people will see the results, but it might not be clear if anyone has started taking action. Nobody wants to duplicate efforts, but at the same time nobody wants to drop the ball.
There will be a lot of results, and a lot of actions to take, so if the full value of the information is to be realized then it’s important for there to be a means for tracking the actions.

The reporting of Big Data in the near future may be more like the Social Media experience and the Customer Relationship Management experience, with lots of communication and interaction.

I’m sure there are many people out there who know much more on this subject, so I encourage you to weigh in, whatever your point of view is.

Tips for Executives – How to Create a Culture of Evidence

We’re often asked how do we create a Culture of Evidence? Most leaders know that they should be more evidence-based in how they work, but don’t know how they can go about doing it.

We’ve all heard the phrase “Culture eats strategy for breakfast” and anyone whos attempted to drive change in a complex organization knows how true that statement can be. And, many seasoned leaders know that culture change doesn’t happen overnight, but here are some tips that you can use to get started.

Tip 1: Paint a picture of “What a Culture of Evidence looks like”
If you want to make meaningful progress towards creating a culture of evidence, there’s no better place to start than envisioning your future state. Things to consider include:

How will life be better? For you, your team and for the company?
What opportunities will you be able to access?
What risks will you be able to avoid?
What decisions will be smarter?
What time will be saved?

If you can create a compelling vision of your organization in the future that thrives in a Culture of Evidence, then you can use this to win supporters.

Tip 2: Set the standard for “What counts as evidence?”
In the spirit of “crawl, walk, run”, getting started with using evidence doesn’t have to begin with hiring a team of scientists, researchers and lawyers. To begin with it may be as simple as using data to support your decision-making, carrying out basic research, or using spreadsheets to do “what if” analysis. Most leaders do this already, but many others still rely on their intuition to make their decisions.

The following is an illustrative example of “what counts as evidence?”:

A declarative statement of your position such as “I believe that we should launch a social media awareness campaign for our red widgets”
Some form of objective proof that shows how you formed your position, such as “According to our market data 85% of our target customers have never heard of our red widgets, and 57% of them use social media. The campaign would be cost effective even if it only generated a 5% increase in our market share.”
A disclosure of what you don’t know, such as “Admittedly our market data is one year old, so we’re assuming that the patterns still hold.”
An action statement, such as “I’d like to update our market data but the delays and costs outweigh the risk of missing an opportunity … I recommend that we launch the campaign and track performance.”

The ultimate goal of evidence is that it holds up to the review process, meaning that another leader could review the evidence and arrive at the same conclusions. Along those lines, “what counts as evidence?” could be just that … an objective analysis that has been peer reviewed.

Tip 3: Put the tools in place
To set your team up for success, you will want to make sure that the basic tools are available for evidence-based thinking. Some questions to consider include:

Are the right investments being made to collect the right data?
Does your team have access to the data they need? Is the data being collected at the source, but it’s not being stored in the data warehouse? Or is the data there, but the privacy levels are too restrictive?
Do they have the skills for working with the data, or alternatively, is the right information available in insightful reports or visual dashboards?
Do they have the right technical and human resources perform deeper analyses, in response to important business questions that arise?

Tip 4: Lead by example
If you want to convince your team and your peers that you are fully behind this idea of a Culture of Evidence, then you’ll need to walk the talk. This will require effort at the beginning, but after a while it will become just “the way things are done around here”. Leading by example can include shifting your own language from “I think this is what we should do …” into “The evidence tells me that this is what we should do …”

It can also include making a concerted effort to not do things the old way because “that’s the way we’ve always done it” but instead doing things in ways that are proven to generate the right outcomes. This relates to everyday decision-making and operations, as well as longer-term strategy and planning.

Tip 5: Reward the adopters
It is often said that “you get what you reward”. This is an easy concept to apply to building a Culture of Evidence. For example you can reward your team for using evidence in situations like:

Decision-making on special projects: Projects that have proposals that have supporting evidence are often approved, whereas other projects often don’t.
Decision-making on budget: Budget increases (or exemptions from budget cuts) are generally provided to those departments that can prove that they need it, whereas departments that can’t prove their value miss out.
Decision-making on promotions: Team members that demonstrate the effective use of evidence are generally promoted to higher positions, whereas other team members don’t.

By taking this approach it won’t take long for people in your organization to learn that the way to win is by embracing an evidence-based approach. Team members will either adopt the new direction or self-select themselves out of your organization. Over time this will increase the momentum of the culture change, and gradually you will find that your organization attracts talent that values a Culture of Evidence.

Tips for Executives – How to Get the Data You Need

One of the most common complaints that we hear from leaders and executives is that they have “too much data” and “not enough information”. Some examples of what they mean by “too much data” include:

Reports that consist of pages and pages of numbers
Tables of figures with no overall summary number
Charts that are cluttered and confusing
Analyses that show a lot of numbers but no “so what” message

It doesn’t have to be that way. Here are a few tips that executives can use to get the data they need:

Tip 1: Ask yourself “What information would help me be more effective?”
It may sound selfish, but you should ask yourself “What information would help me be more effective in my job?” This might be information that helps you save your own time, make better decisions, or seize big opportunities.

Another way to approach this question is to review the data that you already have available and ask yourself “What isn’t this telling me?” or “Why is this not useful to me?”

Based on this thought process, prepare a simple table with two columns. In the first column include a description of what you want, and in the second column identify why you want it. Then choose your top 3 to 5 items on the list. Now you’re ready to start the next step – following up with your Data Team and/or your Business Intelligence people to have a first conversation about your top-ranked items.

Tip 2: When people say data isn’t available, use the “5 Whys”
Many data people have difficultly seeing the world beyond the standard data that they use every day. So, when you meet with them and tell them about the data that you need, chances are that they will reply by saying “that just isn’t available”.

When it comes to data – almost anything is available – it’s just a matter of how much you’re willing to fight to get what you need.

The “5 Whys” is a simple process of getting to the root of an issue. When your data people tell you that getting the data you need is impossible, ask “why”. They will give you a list of reasons such as “it’s not in the data warehouse”, or “we don’t measure that”, or “the system doesn’t allow that type of reporting”. Pick any of the reasons, and then ask “why” again, which will generate a new list of reasons. Continue this until you’ve reached the root of the issue (hopefully in 5 or less “whys”). The root issue is often one or more of the following:

Nobody thought to ask for this before
At some point in the past, somebody decided that it was too hard to collect the data
The people running the analysis and reporting are limiting themselves based on the capabilities of their reporting tools
Nobody has thought of taking a prospective data collection approach, and/or nobody has thought of doing a sampling approach (to reduce data collection costs)

Through a few meetings, you now should have the real reasons why you don’t currently have the information you need. You may even have a sense of how much it would cost.

Tip 3: Estimate the cost of not having the information you need
The last step is where you can make your convincing argument. For each of your top-ranked ideas, you can think about what it’s costing you to not have access to that information.

Does it translate to productivity? Lost time? Missed opportunities? Lost revenue? Customer loyalty? Employee turn-over? If so, then you can translate these consequences into real tangible costs. This isn’t an exercise of doing high-precision activity based costing – instead this is just getting the cost estimates roughly right.

These figures give you an idea of how much your organization could potentially invest into better data and reporting. If you’re business-minded and you could work out the actual investment amounts that would still generate a positive return on investment.

Armed with this analysis, now you’re in a position to convince others what this information is worth. Which brings us to our last step.

Tip 4: Gain the support of the leadership team
Chances are that the information that will help you be more effective in your role, will also be useful to others in the leadership team and throughout your organization. If you can gain the support of the rest of the leadership team then you can increase the chances of getting what you want.

Each team dynamic is different, but a one-on-one approach often works well. These can be quick conversations with each leader with a real focus on “what’s in it for them”. You may be surprised with how many of your peers are equally frustrated by the lack of good information.

With the support of the team, the cost of not having the information and some return on investment estimates, you’ll be able to drive to get the information you need to be successful.

These are just a few tips, but I’m sure there are many of leaders out there who have many more great ideas and experiences. If you have suggestions, or alternate points of view, please weigh in.

Note: What is a Data Team?
When we refer to “Data Teams” it’s a catch all for groups of technical, statistical, and subject-matter domain experts that are involved in providing information to support their organization. These teams are sometimes called “Business Intelligence”, “Decision Support”, or “Information Management”, but they can also be internal consultants such as “Operations Analysts”, “Strategic Information” or “Research”. Many of these concepts equally apply to teams of Data Scientists.

How to Get Started With Simulation

Many business analysts decide that they want to start using simulation not just because it’s flashy and high-brow, but also out of pure necessity. These business analysts have taken their spreadsheets as far as they can and are at a point where the spreadsheets are becoming unwieldy and ineffective at providing reliable answers to their important business questions.

These analysts often ask “How do I get started with simulation?” Is there a course that one can take? Is there a tutorial? Is there a book? Ultimately what’s the best way to get started? Here are 4 questions that I suggest they consider:

Question 1: Are you sure Simulation is for you?

I have a belief that most people can learn most things if they are motivated enough, and I believe the same is true with simulation. However, there are some skills that make the learning curve easier:

Are you logical and process oriented? The guts that drive simulation models are driven by process logic. If you’re able to look at a real-life business process and convert it into a meaningful and clear process flow map then this is a good thing.
Have you done any programming? There is a lot of “If … then” logic in simulation models, and having experience with programming (including VBA and complex spreadsheet logic) will only work in your favor. Simulation models are almost never programmed correctly the first time, so debugging skills are also very important.
Are you good at handling a lot of data? There is a lot of data handling to estimate inputs for simulation models, and most simulation model will generate a mass of data. This is a very important skill in being an effective simulation modeler.
Are you good at experimentation? Simulation is like a sand box, and experimenting with your model is a key part of developing, calibrating, and validating your models, as well as designing and carrying out scenario analysis.
Can you work without perfect information? It’s a routine experience that simulation models will need parameters and factors that have no available data. The simulation modeler often needs to form credible assumptions as a workaround to having to deal with incomplete information.

If you can answer “yes” to the above questions then simulation might be a good tool for you.

Question 2: Are you just dabbling or are you ready for a deep dive?

Simulation is often described as both an art and a science. Simulation is one of those skills that seems to be better developed through “doing” rather than reading books or taking courses. I’d highly recommend taking courses if you’re convinced that simulation is for you (I taught a simulation course for 5 years at the
University of British Columbia). However, what you learn from a course won’t really stick unless you are going to be able to work on a real simulation project shortly afterwards.

Simulation is one of those skills where it’s difficult to be effective until you’ve been working with it for a while (i.e. your second simulation project will be much better and easier than your first,… your third simulation project will be even better, and so on). If this is something that you’re just looking to add to your resume then I wouldn’t bother. People who hire talent for their simulation skills can easily differentiate between a dabbler versus an experienced practitioner.

A “deep dive” simulation project allows a new simulation modeler to really understand what they can and can’t do with a simulation model, the effort involved with various model elements, and the associated value that those elements add to the final conclusions. New simulation modelers sometimes learn through their first intense simulation project that simulation isn’t really for them.

Question 3: Do you have a “Simulation Worthy” problem?

Simulation is an invaluable tool if it’s applied to an important problem that cannot be solved using traditional tools. But, if you could effectively answer the same problem using a spreadsheet, then why wouldn’t you just use a spreadsheet? If the business problem isn’t important enough to justify spending days (sometimes weeks) of effort programming and validating your model, then you could be creating a situation where your organization perceives simulation modeling as high effort for low value.

Ideally, you would be in a situation where there is a business problem that has high value (i.e. the potential to support a million-dollar decision, or a strong potential to reduce risk, or increase efficiency). And, ideally, the problem involves complex inter-relationships between resources, and/or processes – the type of logic that is very hard or impossible to set up in a spreadsheet. And finally, the situation requires a handling of uncertainty and variability in order to fully address the business problem. We would argue that if you don’t have a problem that is “simulation worthy” then it’s best to wait until you do.

Question 4: Do you have a good example to start from?

Simulation is not like other mathematical and theoretical disciplines, in that there is no single “right answer”. There are many different ways of modeling a system, all of which can be valid (provided that the assumptions are disclosed), and it often comes down to a balancing act of model accuracy versus model complexity. Simulation modelers often add more detail and logic into their models in an effort to improve the accuracy of the model, but as they do so, the model typically becomes more complicated, more difficult to debug, more difficult to validate, and more difficult to run scenario analysis on.

When new simulation modelers are getting started it can be difficult to make these decisions. A great way to learn is to partner up with a mentor – ideally someone who has done a few simulation projects where the results actually supported a decision outcome. The INFORMS Simulation Society is a good place to start (), and if you can do it, attend the annual Winter Simulation Conference next November.

If you can’t find a mentor, try learning from example models. Our company AnalysisWorks, made a simple simulation model of an Emergency Department that is 100% free and available for
download. Without any programming, you can interact with this 3D animated model to get a sense of the types of things you can do with a simulation model.

How to Allocate Resources as an Executive Team

Many executive teams feel that they could improve how they make decisions about resource allocation. These are decisions such as “which strategic initiatives should we approve for this year?” or “how much budget should we allocate to marketing versus customer service?” or “how many beds should be allocated to the surgical program, versus the medical program?” And this is at a time when organizations are all trying to do more with less – more sales with less sales staff, more shipments with less cost per shipping, more strategic initiatives with fewer leaders to push them forward, and so on.

The following are some of the common challenges that executive teams face when they are making decisions about allocating resources as a team:

Different people involved in the decision-making process have different goals and objectives. A win for one participant is a loss for another.
Decisions are often influenced by personality and emotion, as opposed to based on evidence.
Decision making processes have no feedback loop. As a result nobody keeps score on the quality of the decisions, and decision-making doesn’t improve over time.

Some teams have frameworks that they use to support decision-making, but it’s not working for them because the process, framework, and technology (i.e. spreadsheet, decision-support system) is too complicated to use, or it’s too cumbersome to maintain.

Even worse, is when the team is attempting to use an “off the shelf” solution that doesn’t do a good job of capturing what’s important to them as a team.

At AnalysisWorks we’ve successfully developed and implemented solutions that help teams make resource allocation decisions. These solutions have been developed over years, and we’ve learned a lot of painful lessons along the way. Here are 5 tips for success for allocating resources as an executive team:

Tip 1: See resource allocation as a decision-making process …

… as opposed to a one-time event. For example, it may very well be that you only decide which IT projects will be approved once a year, however, a year goes by quickly, and you’ll be right back at the decision making point soon. See the design of an effective process as an investment for your future, and a means to make the most out of your scarce resources.

Tip 2: Define the common goals that your allocation should be based upon.

This should be something that the entire team can get behind. The financial side is often over-represented in these types of decisions, so it’s important to round out the goals to include non-financial aspects as well. Ideally you should see a connection here to your mission statement, organizational values, this year’s strategies, and your overall strategic plan.

Tip 3: Decide on the rules of the game.

Again, the entire executive team should agree on the rules of the game at the very beginning. The rules should be fair and transparent. The executive team should avoid reverse-engineering the process to justify one-time decisions, and instead the process should be based on agreed-upon principles. Ultimately, if the rules of the game are set up right, they will serve to communicate to the entire team what behaviours will be rewarded. It’s at this stage where the team will need to agree on what objective inputs will be input into the decision making process.

Tip 4: Have an objective party “keep score”.

Building on the previous tip, it’s important that the rules of the game have accurate measurement, and that the score keeping is fair. This is a situation where an objective group or individual should be at the center of the process making sure that inputs to the process are accurate and consistent. Even more important, this person or group will have the difficult job of overseeing that the outcomes of the resource allocation are measured. Specifically, they will be instrumental in closing the loop with respect to the question “Did we get the outcomes we were expecting from our resource allocation decisions?”

Tip 5: Improve now, keep it simple, and learn as you go.

Sometimes executive teams try to make the “perfect” decision-making process before they are willing to use it. At the end of the day, making a minor improvement over your current state is still an improvement in the right direction. For example, if your executive team makes resource allocation decisions based on purely financial information supplemented with qualitative information, then an improvement may be to quantify a non-financial consideration (i.e. Scoring “alignment with our three year strategy” on a High, Medium, Low scale is better than nothing). These processes can grow out of control, so it’s important to keep it as simple as you can get away with. And finally, chances are that along the way the executive team will identify how the resource allocation process can be improved next time. These improvements can and should be incorporated into the next round of decision-making to make the best, simplest process possible. You will know that it’s working if your executive team feels comfortable with the process, and is able to support the resource allocation decisions that are made.

What Every Executive Should Know About Simulation

Simulation modeling is an emerging management tool to support big decisions involving complex operations. The technology has been around for decades and in the last while it’s become increasingly easy to apply to almost any operation, such as patient flow in an Emergency Department, bits of data through a telecommunications network, or a global supply chain.

Just to be clear, the kinds of simulations we’re talking about here are of operations and/or system flows, not flight simulations or video games.

Simulation is not for every organization, but it can be a valuable tool to have in your arsenal. Unfortunately, though, most executives aren’t even aware that it exists. The following tips meant to introduce the topic of simulation are based on 20 years collective experience using simulation in both consulting and academic settings.

Tip 1: Simulation can be a powerful tool to look before you leap

Having a simulation of your operations can be a valuable asset for providing insight into the potential impact of crucial decisions. They can provide powerful supporting information to aid decision-making and investigating the associated costs and benefits of multiple decision options.

Simulation models can be very useful for big decisions: Situations involving the buying, selling, or reconfiguring or key system resources are ideal for simulation analysis. They can allow organizations to analyze multiple potential scenarios and provide insight into nearly any situation involving uncertainty.

Big risk/reward industries such as finance, oil and gas, aerospace have used it for years, but now simulation has become very accessible. Think of a simulation model as the next step up from your most complex spreadsheet.

Tip 2: Don’t use simulation if you’re not ready

Simulation can be expensive between the costs of staff time and software licensing so it is important assess whether or not your organization has the pieces in place to successfully develop a compelling model. It may be premature to jump into a building a simulation if you don’t have a good profile of your activity, work flows, and financials.

Typically, organizations that use simulation effectively have already answered as many questions as they can with spreadsheet analysis. It’s better to use simulation to address business questions that can’t be addressed effectively with spreadsheets (i.e. situations involving complex logic, multiple flows of activity, uncertainty), rather than attempting to use simulation on a problem that doesn’t need it.

Tip 3: It’s not what software you use that’s important

Many tools are available on the market ranging from simple spreadsheet model to complex custom software, but more important than the tool you are using, is the team running the tool.

You need good talent to make a good simulation: There is a big difference between a good programmer and an effective simulation analyst. An effective simulation analyst understands:

How to keep the model manageable, flexible and scalable
The importance of validating the model so you know the results can be trusted
How to come up with good ideas for different scenarios

It is an important first step to decide if your organization is interested in getting into simulation or not. If you’re unsure, you could try working with a university or a consulting firm to try it out. If you are committed to getting into simulation, a critical first step should be to focus on recruiting and developing appropriate talent. It takes time to get really good so don’t train someone if they won’t have an opportunity to use it intensely at least a few times a year.

Tip 4: Think of it as a “sand box” rather than an optimizer

It is important to set expectations right from the start. Simulation is not a crystal ball that shows you the solution to all your future problems. It will not tell you what to do in a given situation.

It is, however, a tool for creatively testing different potential ways of running you system. It will tell you that if you run your system in a particular way, then this is what you may expect as a result.

The ability to generate and interpret meaningful operational scenarios is why having an effective simulation analyst can be the make or break of a successful simulation modeling project.

Tip 5: You should consider using simulation if …

So how can you tell if simulation is the right tool for you? The following are some guidelines to let you know if your situation could benefit from using simulation modeling:

You have a big decision to make with high potential for risk or reward.
You cannot afford to make mistakes and it’s worth investing the time and effort to make sure new processes work as good as they can before being implemented.
You have a good understanding of your operations and system data.
You are able to do your first project with an analyst or team that has a track record of successfully using simulation
You are innovative and ready to utilize a new management tool.
You are committed to use the findings and recommendations, even if they tell you what you don’t want to hear. After all, there is no point in wasting your investment.

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