In my last post, the topic was categories of performance and how they help create focus on the largest opportunties for improvement. I closed that post with "Categories alone don't create focus, but they are a start."
To create laser like focus, we need another ingredient - meaningful benchmarks. A meaningful benchmark for each category not only creates focus, it creates accountability too. Compared to other tools and topics in the performance improvement arena, there is little discussion about meaningful benchmarks. Yet they are a primary ingredient in Lean Business Intelligence.
Meaningful benchmarks don’t usually come from external sources, they are generated internally. In fact, satisfaction with industry average performance can be a path to complacency. Lean Business Intelligence defines three types of meaningful benchmarks; quantifiable, best-in-class, and goals.
Quantifiable benchmarks are often used in working capital areas of performance, i.e. inventory. A quantifiable value for most actionable categories of inventory can be calculated (i.e. allocated to scheduled jobs, safety stock, etc.) This is further improved by using an actionable unit of measure, like days-on-hand. Such a unit of measure ensures the benchmark remains valid during seasonal fluctuations. (That's not saying the benchmark never has to be recalculated. If business conditions change, then recalculation is required, i.e. because you have a new supplier, customer or capacity constraint that impacts inventory requirements.)
Best-in-class benchmarks are used in an area of performance where multiple shifts or facilities are involved. Performance areas like productivity, scrap levels, etc. Each shift or facility’s performance can be compared to best-in-class. This creates more accountability than an arbitrary goal. And if there are production mix or capital equipment differences, you can factor the differences into the benchmark. Its worth noting that even if best-in-class performance isn’t meeting expectations, usually the best performers will be motivated to maintain their position once others start catching up - automatically moving the benchmark...
Goal level benchmarks are "desired percentage improvements" or other arbitrary goals. They don’t create the accountability that the others benchmarks do, so they are not the most desirable. They are only used when the other benchmark types can’t be used.
Process owners should participate in the creation of benchmarks, to the fullest extent possible. Involving them ensures they understand the reasoning behind the benchmarks. They are more likely to feel motivated by them. The downside of their participation is the desire of some to pad the value. This is especially true of the worst performers in a group. The benchmark process owner must be diligent to prevent padding.
When actual performance is compared to meaningful benchmarks, both focus and accountability is created. The result is meaningful improvement.
Using meaningful benchmarks indicates a business has a solid understanding of its processes. And its taking a fact-based approach to performance improvement. Exactly what Lean Business Intelligence sets out to do.
When teams see problems everywhere, they find it hard to focus. If you’re managing with high level results alone, your data is not helping establish priorities, i.e. its not creating team focus. This leads to whack-a-mole - problems are never permanently resolved because folks don’t stay focused. They have to move to the next issue quickly because - they’re all hot!
To help create focus, every major area of business performance can and should be broken into Categories. Categories; like where an expense or issue occurs, why it occurs, by product family, by department, etc. If it were simple, you probably wouldn’t have a problem. Data in actionable categories simplifies problem solving.
Most often, categories in a given area of performance are obvious, but this isn’t always the case. When the categories aren’t obvious, you can use the 5 whys or 5 wheres to help figure them out, (I made up the 5 wheres, but you get the point.)
Managing with category data indicates an organization has a solid process understanding. Without category data, you can’t be sure the process understanding is deep or comprehensive across the organization. In fact, it's probably safe to say - the understanding varies, some understand it, others, not so much. To keep the entire team pulling the same direction, everyone needs to understand what categories have the largest opportunity for improvement.
It's management's responsibility to ensure an organization manages with category data. Management must make the acquisition or creation of category data a priority.
Categories alone don’t create focus, but they are a start. More on creating focus in a future post.
Spreading financial knowledge throughout an organization is commendable, but financial knowledge alone doesn’t guarantee process understanding. And its process understanding that drives results.
What does Lean Six Sigma have in common with Trump's dominance? Is it messaging, or lack thereof?
Freakonomics Radio Podcast: I Consult, Therefore I Am
Around 19:30 into the podcast is a discussion of a study in India comparing 28 textile company's performance, with and without consulting help. The gist: Is management consulting worth it? You bet it is.
The podcast is from 2012, but its still well worth a listen.
Business Intelligence (BI) software is more accessible than ever to organizations of all sizes. And it's great for taking raw data and drawing conclusions from it. But extracting value from data isn’t as easy as it sounds. If you’re not properly prepared, there are issues that can result in disappointing outcomes. The old adage, (that’s been around since the dawn of the database), “garbage in, garbage out” still applies today. Buyer’s of BI software need to temper their expectations. While it has many benefits, it also has limitations; it's best to be aware of the limitations before jumping in.
BI software is the perfect tool for analysis of large data sets. But, if you’re looking for answers to operational questions, you probably already have tools that can handle that job. The real problem may be that the data you're using won’t provide the answers you need. For example, if you’re trying to understand why you’re experiencing excessive material costs in a manufactured product, but you can’t discern scrap costs from routine material usage, then you need additional data. But just knowing total scrap costs won’t be enough for actionable information. You’ll need to have data about which process steps are generating the scrap. And you may have to expand the data to include scrap by shift or operator - to get to the root causes and eliminate the problem. The same concept can apply to almost any complicated area of performance where data is your only reliable source of actionable information. BI software won't resolve such an issue.
Self-service data analytics, with empowered employees, can be another expectation of BI software. But it's not a given that things will play out like you think. It sounds like an easy task, to get everyone using a “simple tool”, but organizational adoption is highly dependent on the level of training provided and leadership’s embrace of the tool. Company culture can play a big role in the organization wide adoption of BI software. Providing boilerplate training and then turning everyone loose with the tool will probably not lead to the results you expect. The "Empowering Everyone" idea reminds me of a corollary; when everyone's responsible, no one's responsible.
Obtaining organizational alignment can be another touted benefit of BI software. But, even if everyone can see performance results in a particular area of performance, if what they see isn’t actionable, (with clearly defined responsibilities for changing the results) then alignment is unlikely. Alignment only occurs when everyone knows where the largest opportunities for improvement are and who is responsible for addressing them. Without that level of detail in your BI displays, everyone in the organization may continue to work towards their siloed objectives and alignment will remain elusive.
ROI can be difficult to measure with BI software, but if you know what to expect, you’ll get your money’s worth. Maximum benefit is obtained when you go into it with proper expectations, including a good understanding of the support required for your team, to attain maximize business benefits.
I ran across this infographic recently. It covers some useful tools, though I don't use all seven. Pivot tables however, are a minimum skill requirement for a Lean Business Intelligence practitioner. There are hundreds of resources across the internet for learning pivot tables, including good YouTube videos. With Lean Business Intelligence, you have to be nimble with data; and pivot tables go a long way towards that goal. If you're a Lean Six Sigma practitioner and you don't know pivot tables, you're missing opportunities to understand the story your data can tell.
Here's the lead in to the infographic:
Microsoft Excel is packed with useful data management features that don’t see a lot of use, like pivot tables, index and match, and conditional formatting. If you’re just using excel to sum and chart columns, this graphic can show you some other tools to help you become the spreadsheet ninja you always wanted to be.
The BI side of Lean Business Intelligence covers this topic at a high level.
Every organization these days is clear about the need to get its data act together. But that doesn’t mean the path toward data bliss is clear. Data has gravity. It resides in different places at different organizations -- on premise, in the cloud, and flowing from external sources. And the rate of change within organizations is always different. So an approach towards handling data that works for one company may be the exact wrong thing for yours.
There's a relevant discussion in about Microsoft Excel, it occurs around the 19:00 minute mark. It doesn't come to a real conclusion, but you'll hear big data people on both sides of the Excel issue.
It does make the point that the data teams really need, often comes from different sources. That's why Excel is perfectly suited for Lean Business Intelligence, it allows us to manipulate and combine data from various sources - to create a road map to peak performance.
Recently, I've spent a lot of time writing the foundational content for this website, essentially writing the story of Lean Business Intelligence. As an engineer, my strength is in analytics, process improvement and operations, I have no practical work experience in marketing. This changed recently as Jump6Marketing helped me figure out how to tell my story. It was a discovery process and like all discovery processes not every path led to where I wanted to be. But now I can say I've arrived at that starting point. I've learned a lot about marketing from working with Jump6, and it's been fun.
With this foundational material complete, I plan to start posting more frequently. And the material I'll post will provide more Lean Business Intelligence details, examples and in depth discussions on the seven wastes in Lean BI.
I'm looking forward to a fantastic 2016 and I hope you are too.
Steve Job's - 3 ways he made meetings insanely productive. One of the three ways is making sure everyone in a meeting has responsibility for at least one agenda item. In a similar way, Lean Business Intelligence ensures someone has responsible for every category of performance in a Lean BI display...
Both are worth the time they take to read.
The email arrives on schedule with its huge Excel attached. You open it to see the latest numbers. The file containing multiple worksheets and thousands of rows of data, you close it and think "I hope the numbers are better next week/month."
This is a regular experience of managers, supervisors and engineers around the world.
Large amounts of waste are occurring:
- Everyone sorts out what's important to them (if they take the time) and some folks are better at sorting than others
- Trends are difficult to see, it's just numbers. Are there any patterns? A few gifted folks can discern them, but no one else
- Multiple people are spending time manipulating the file, when it could have been done by a single person
- The critical human capital that's managing the operation is being wasted because the information (in the data) is buried
Consider this, what if you looked at the recipients of information like this as customers? Would you spend more time making sure its relevant to them? These are the vital people making the organization run and they should be treated as such.
This is an opportunity to determine what's really important in the data and to present it graphically. With a good graphic everyone understands at a glance what categories of performance are doing well and which aren't. They'll know if a poor number in one area is a one-off event or is part of an on-going adverse trend.
If your process has multiple categories of performance and a lot of data, take time to plan a visual display. Get input from the team. Automate, as much as possible, so very little team member time is spent updating it. The time spent here, promoting team understanding of the data, will pay for itself many times over in improved results.
I'm sharing this now, though I'm only 30 minutes into it. This is a very candid discussion about the inner workings of Google (and Eric Schmidt.) The content is interesting on many, many levels.
Thanks to Six Pixels of Separation for bringing it to my attention.
In the video he discusses the change to AdWords; converting from fixed pricing to auction based. He braced the organization for a major cash flow constraint as a potential result. Instead, revenue tripled. My take away; no one knows the answers, to find the answers be prepared to make mistakes.
Seth Godin puts out another noteworthy post "When in doubt, draw a bell curve." In it, he references this image:
Do you depend on email for routine communications between departments? If so, I can predict the following:
- You have encountered emails that went to someone who's not at work one day and the information wasn't acted on, even though others in the department could have carried out the emailed request.
- Or, in the recipient's overloaded email, the information gets buried in the pile and isn't acted on.
- And, in a meeting when something doesn't happen as expected you hear the statement "I emailed them."
This is as predictable as the sunrise in an operation that is heavily reliant on email communications.
To eliminate these problems and improve the effectiveness of your processes, use online collaboration lists instead. I've used them in multiple situations, like:
- Expedite items between Purchasing and Receiving (instead of emails to the receiver)
- Interdepartmental expedite lists (I'm not big on expedites, but depending on an organization's maturity level they can be essential.)
- Posting of excess inventory items between facilities for potential use by each other (in place of ineffective emails)
- Freight quote comparisons and selection between Sales and Shipping (instead of emailing between department members. This also provided a reference history for repetitive shipments)
- Customer's inventory kitting status (where the MRP system had no such capabilities.) An undesirable manual process, but none-the-less better than its emailed alternative that was resulting in significant customer dissatisfaction.
The above are real examples where emails were replaced with SharePoint lists (or other equivalent online collaboration tools.) All of these lists eliminated multiple issues that had plagued these processes. And, these lists are as easy to set up as a spreadsheet. Start using them now if you want to eliminate a lot of waste-creating "fell-in-the-crack" issues.
Read the NY Times article "The Reign of Recycling". A couple of snippets:
One comments from the article (which has drawn about 500 at post time) "...Years ago my brother, an economist, explained to me that recycling was wasteful, if you considered it from a total resource perspective. Ever since then, he bravely refused to recycle" by mwr.
While not necessarily discounted in most of the article, I have a hard time "throwing away" aluminum cans. They seem pure and ready to be re-made into something else. But, ultimately I have no data.
This can get a bit boring, but forward to the end and you'll see grown men walking on the "bridge". Can you extrapolate into the future?
"Robots Lay Three Times as Many Bricks as Construction Workers" Check out the gifs in this article. The future's coming quick.