Finding Common Ground Between the CFO and COO–Part 3

[Continued from Part 2]

The concept of market segmentation—segmented down to a single customer, if necessary—has been driven to a large extent by consumers empowered by the Internet. (Here I use the term “consumer” in the broadest sense. In a supply chain, the “consumer” may be a company or even a buyer within a company in the supply chain.)

Consumers no longer need to be satisfied with what is available to them locally, regionally or even nationally. Instead, a buyer has virtually direct access to a whole world of manufacturers, wholesalers, distributors, brokers and retailers offering a huge array of products, services, delivery methods and terms of service.

Many product offerings are configurable via the seller’s Web site to meet specific requirements or tastes. Too, frequently, the various sellers are willing to offer the products via custom-tailored terms, conditions, and delivery methods. We refer to this combination of product plus related delivery terms and options as the “augmented product” of the “offer.”

Employing Business Intelligence (BI) to Segment Your Market

Business intelligence—regardless of whether it is done with specific BI tools, or just by leveraging the native capabilities of Microsoft® Excel™—can help an business better understand who buys what from the firm, and why. Here are some examples:

Hospitality Industry

A hotel franchise uses BI analytical applications to compile statistics on average occupancy and average room rates to determine revenue generated per room. It also gathers statistics on market share and data from customer surveys from each hotel to determine its competitive position in various markets. Such trends can be analyzed year-by-year, month-by-month or day-by-day, thus giving the corporation a clearer picture of how each individual property is faring.

If these data were extended to include related matters such as

• Business versus pleasure occupancies
• Local event calendars by postal codes
• Other potentially influencing factors

Then the hotel chain could begin to discover who uses their services under what circumstances and, perhaps, why their customers chose their hotels over the chain’s competitors. With this information in hand, the chain would be in an increasingly better position to construct “offers”—preferably irrefusable offers—to their clientele (or prospects) based on dates, reasons for travel, and more.

Take for example a hotel where the occupancy rate is typically below 50 percent on Sundays through Wednesdays. How much time would it take to discover businesses in the region that bring in folks regularly for training, small group conferences or other business purposes during the week.

Having identified these business organizations, making them customized offerings would make sense. For some businesses and business purposes, a discount of 35 percent off the nightly rate might be sufficient to garner the business. For others, a steeper discount might be necessary because the folks to attend their events are typically paying out of their own pockets. So, offer them a flat rate of $69 per night and throw in a free shuttle to and from the airport and to and from the conference sessions.

Since the hotels truly variable cost (TVC) for filling an additional room or ten rooms is very small, almost every additional dollar of revenue gained through such offers will fall directly to the bottom line of the business.

Let us assume that (to make the math easy) a hotel typically rents its rooms for $200 per night (annual average). This hotel’s business intelligence analysis shows that Sundays through Wednesdays during the months of January through April, they are going to have an average of 50 empty (in-service) rooms per night. If this hotel can construct a compelling offer that will fill just half of those rooms (25 rooms) at $70 per night, that would be about 1,733 nights at $70, or $121,310 in additional revenues annually.

If we assume that the truly variable cost (TVC) per additional room per night is $10, then we must subtract $17,330 from this figure to get our throughput of $103,980. That is more than $100,000 in increased annual revenues even though the rooms are being let at far below the “going rate” via the irrefusable offer.

[To be continued…]

Why Small Businesses should invest in an integrated Excel-based Business Intelligence solution: Reason No. 4 | The BI Blog - Powered by Alchemex

Why Small Businesses should invest in an integrated Excel-based Business Intelligence solution: Reason No. 4 | The BI Blog - Powered by Alchemex

 Business intelligence investments should always be made with the goal of using the information supplied for one of three purposes (listed in priority order):

1. Increase Throughput
2. Decrease Inventories or other demands for (new or increased) Investment
3. Slash or hold the line on Operating Expenses while sustaining significant growth in Throughput

Why Small Businesses should invest in an integrated Excel-based Business Intelligence solution: Reason No. 3 | The BI Blog - Powered by Alchemex

 Business intelligence investments should always be made with the goal of using the information supplied for one of three purposes (listed in priority order):

1. Increase Throughput
2. Decrease Inventories or other demands for (new or increased) Investment
3. Slash or hold the line on Operating Expenses while sustaining significant growth in Throughput

Why Small Businesses should invest in an integrated Excel-based Business Intelligence solution: Reason No. 2 | The BI Blog - Powered by Alchemex

Why Small Businesses should invest in an integrated Excel-based Business Intelligence solution: Reason No. 2 | The BI Blog - Powered by Alchemex

Another reason Microsoft Excel -- a tool you probably already own -- should return big ROI to your small-to-mid-sized business enterprise if you learn to apply it with gusto!

Take a look!

Decision-making about ROI and your technology spending

Dan Gilmore wrote in “The ‘Probability’ of Supply Chain ROI” propounds properly and rationally the fact that any “forecast,” including forecasts of ROI (return on investment) should not be a single number. Rather, as anyone properly trained in statistical methods will tell you, it should be a range of numbers. The range of numbers would generally be calculated based on a single calculated value plus and minus values that represent the confidence intervals or, simply put, how likely the statistician believes his estimates the calculates will approximate reality. A larger range indicates lower levels of confidence and a smaller range higher confidence levels.

Now, while Gilmore is mathematically correct, the fact remains that most small-to-mid-sized businesses (SMBs) simply do not have anyone trained in statistics on their payroll and they are not likely to go out and hire a statistician to produce ROI forecasts for their IT projects – since this would, by definition, automatically reduce the ROI of the enterprise as a whole in the short term.

Back on a growth trajectory

Gilmore makes another comment in his article with which I wholeheartedly agree: “[T]here is some evidence that companies are in fact looking at investments that can help them to get back on a growth trajectory (read: increasing Throughput) without having to add much in the way of head count (read: Operating Expenses) by achieving productivity gains.” Given the world-wide economic malaise that is showing some signs of lessening (for the moment, at least), Gilmore’s description probably suits the vast majority of SMBs across the U.S. and beyond.

Furthermore, many others besides me have written that a firm stand on return on investment will be the hallmark of technology spending in the 2010 and beyond. So, I can hardly fault Gilmore for suggesting that SMB executives and managers need to become increasingly sensitive to and realistic about ROI for every kind of investment in their firms’ futures.

Too much complexity already

Despite my agreement with Gilmore on theoretical grounds regarding forecasts – including ROI forecasts; and despite my agreement with him regarding the goal of companies to get back on a growth trajectory through wise investment of capital resources, I must disagree with him on the matter of adding useless complexity to the return on investment forecasting process.

Allow me to explain why I use the harsh term “useless” to describe such an effort in the development of a ROI forecast for an IT project.

First  of all, let me say that statistical methods ought to be applied where they make sense. Statisticians generally agree that a valid statistical sample must contain at least 30 members. This works great where you have 30 dogs, 30 cows, 30 houses, 30 automobile, 30 miles of roadway, and so forth for comparison. Then, of course, you need to factor for environmental differences. Thirty or more cows all in the same pasture, eating the same foods, and enjoying the same climate would make a pretty good statistical sample for some studies of cows. On the other hand, three Holstein cows in northern Minnesota, two long-horns in west Texas, 15 black whiteface cows in eastern South Dakota, and ten mixed-breed cows in central Florida are not likely to constitute a good “sample” for cow studies.

Why?

Simply because there are too many environmental dissimilarities surrounding the cattle. By the time these factors were accounted for, (generally speaking) any results would have such a large confidence interval as to make any prediction almost meaningless.

When considered as a whole, a typical SMB has tens of thousand of variable at work within the enterprise. Any number of those variables are likely to dramatically separate it any “sister” enterprises in a sample group used to forecast ROI outcomes.

Of course, the fact that traditional ERP – Everything Replacement Projects – are going to affect the whole enterprise is a big part of the problem of predicting ROI outcomes. With tens of thousands of variables at play, picking the winning number is far more challenging than winning the lottery.

Reducing the scope reduces the complexity

First of all, a good many SMBs today have a “pretty good” ERP system in place – regardless of its brand. Unless there is some pressing reason to undertake a traditional ERP – Everything Replacement Project, it is probably a far better idea to consider a New ERP – Extended Readiness for Profit project instead.

Narrowing the scope of the project reduces the complexity. And, reducing the complexity increases the likelihood that your ROI forecast will be more on-target. Allow me to give you a couple of examples:

• Warehouse automation – Suppose your Current Reality Tree (CRT) shows you that, in order to increase Throughput, you need to enable your warehouse to be able process 80 to 100 shipments per hour, rather than the present 30 to 40 per hour. Furthermore, you need to do this while holding the line on Operating Expenses.
  
• Business intelligence – After reviewing their Current Reality Tree and laying out a Transition Tree (TrT), your marketing and sales team has assured you that, if they are provided tools with which to better understand customers and markets, they will be able to segment the market and create UROs (un-refusable offers) and increase Throughput by 130% in two years. No additional staff or other increases in Operating Expenses would be required.

If your executive management team were to elect to pursue either of these projects – or both – the goals are specific and measurable – as would be the expected outcomes. ROI calculations become simple:

Where T = Throughput (Revenues less Truly Variable Costs), OE = Operating Expenses, and I = Investment.

Simple. Elegant. And ROI calculations are far more likely to be right than any calculation around traditional ERP – Everything Replacement Projects.

©2010 Richard D. Cushing

The New ERP – Part 38

Yet another "Why ERP implementations fail"

Sue Bergamo, former CIO at Aramark's WearGuard & Galls companies, recently posted an article entitled "Is Your Implementation in Trouble?" (Bergamo 2010) In her writing, she listed seven "high level categories…. in the order from the highest to lowest number of responses" from her informal LinkedIn survey. Here is how the list shaped up:

1. A misconception of business expectations
   
2. The lack of top level leadership involvement in the project
   
3. Business processes were not correctly redefined and continued to be inefficient
   
4. The impact of the organizational change was not addressed properly and caused a major upheaval in the company
   
5. The vendor wasn't managed correctly and over-promised, then under delivered [sic]
   
6. Project management was weak and over-customizations lead to increased scope and time
   
7. The integration of diverse applications was harder than anyone expected
   (Bergamo 2010)
   

We are going to drill-down on these and put them into the context of the New ERP – Extended Readiness for Profit, contrasting them with traditional ERP – Everything Replacement Projects to see if using the New ERP approach would have mitigated the failures.

1. Misconceptions of business expectations 

Ms. Bergamo does not explain in her short article precisely who had the misconceptions of business expectations. We do not know whether, in the Bergamo survey, the misconceptions were held by all or part of the management team involved in the ERP acquisition, by the vendors and resellers involved, and/or by third-party consultants that may also have been a part of the ERP project. My experience tells me, however, that if persons were involved in a traditional ERP – Everything Replacement Project the likelihood is very high they had and held "misconceptions of business expectations."

Why do I say this so boldly?

Because, unfortunately, most organizations do not begin their search for traditional ERP with clarity on the three critical factors we have discussed earlier in this series:

Management Factor Number 1: What needs to change

If executives and managers applied rational tools to determine precisely – not vaguely – what needs to change so that the business could make more money tomorrow than it is making today, the "business expectations" would be clear.

Sadly, many organizations today still pour in traditional ERP like some kind of "miracle-working additive" that is supposed to make their whole enterprise run smoother, cleaner, faster and, as a result, produce more profit. It seems that 20 or 30 years of experience with ERP not delivering its supposed miracle-working power in so many implementations is not yet enough to convince some. Such executives continue to drink the proverbial Kool-Aid offered by ERP software vendors and VARs just as if the hundreds of bad experiences being reported are only mirages and the same could not and would not happen to their firm.

I have walked into dozens of traditional ERP projects where, if asked, no one in executive management could tell me what measurable improvements were expected when the implementation was complete. If they were able to reply at all, their answers sounded more like they were drawn from a séance than from a the mouth of an executive. They might sound something like these:

"We expect that this new ERP system will enable us to grow while holding down our operating expenses."

"We are counting on this new system to help us ship more efficiently."

"Our ERP vendor told us that this new system should help us reduce our inventories without sacrificing customer service. Plus, it will help us build 'best practices' into our back-office."

Now, I have no problem with "We expect that this new ERP system will enable us to grow…" provided that statement is followed by specifics like:

• How much is it likely to "help us to grow"?
  
• What specific changes will the new ERP system bring about that will lead to the expected growth?
  

In the absence of specifics, how can those who complained in Ms. Bergamo's study even complain that they had a "misconception of business expectations"? Was their expectation that they would mystically grow, but they did not, in fact, achieve their concept of mystical growth? Did they expect that profits would mystically improve, but the mystical improvement in profits never appeared?

Just what were their "business expectations"?

If "business expectations" were not defined in clear cause-and-effect terms up front: if the executives and managers could not – in advance – link specific anticipated changes in the "system" (i.e., how the organization itself functions) to the anticipated and measurable improvement, then they have no one to blame for "misconceptions of business expectations" other than themselves. If they drank the vendor's or reseller's Kool-Aid about ROI (return on investment) and did not establish for themselves the metrics for specific and measurable change, they cannot blame the vendor or reseller. It is management's job to know these things and not to simply take a salesperson's (or even a consultant's) word on such matters.

Management Factor Number 2: What the change should look like

If executives and managers have proactively worked out rational cause-and-effect relationships, and have determined clearly and specifically what needs to change, the next step become relatively easy. That step is for the management team to establish what the change should look like.

If applying new technology in the organization (i.e., the system) will "increase Throughput by an estimated 12.5% over 12 months by providing improved market segmentation for Product Line C," then what the change should look like will be described in terms of the changes required to "improve market segmentation for Product Line C." That change might take the form of new market data collection techniques, new automated surveys, or some other form. Nevertheless, there should be no confusion in the management team members' minds as to what the change should look like when it has been implemented.

Similarly, the management team should understand the changes necessary to leverage "improved market segmentation" into "an estimated 12.5%" increase in Throughput over 12 months. The executives and managers should have a clear understanding of how the new market segmentation data will lead to new "offers" in the marketplace that will, in turn, lead to the anticipated growth.

Management Factor Number 3: How to effect the change

What we have described above are real and concrete "business expectations." There is no easy way to misconceive "business expectations" that are so clearly articulated. This kind of "expectation" can be the basis of concrete action. These kinds of "expectations" can become a guide for how to effect the change.

The meaningless séance-induced "business expectations" so frequently articulated by executives and managers surrounding traditional ERP hold little hope of functioning as a guiding light for concrete action on the part of anyone in the organization. It is no wonder that "expectations" are only met in so few traditional ERP implementations.

[To be continued]

©2010 Richard D. Cushing


 

Works Cited

Bergamo, Sue. CIO Update: Is Your ERP Implementation in Trouble? Feb 01, 2010. http://www.cioupdate.com/features/article.php/3862056/Is-Your-ERP-Implementation-in-Trouble.htm (accessed Feb 02, 2010).


 

Business Intelligence and “Tribal Knowledge” – Part 3

In Part 2 of this series, we ended by say just how valuable it is to begin unlocking the "tribal knowledge" that is undoubtedly resident in the minds of the people you have working in your business enterprise.

Taking another look a the client's situation I have been referencing in this series, the client has a complex sales cycle that involved multiple individuals and organizations in the processes of funding and purchase decision-making. To refresh our collective memories, here's a list of the participants we have previously identified:

• The school district, including administrators and, sometimes, board members
  
• The school(s) and the school(s)'s administrators
  
• Teacher(s)
  
• The school(s) and/or school district's IT department
  
• Government programs and associated bureaucrats
  
• Not-for-profit or other sponsors
  

Now, it seems clear, each of these participants that may be involved in the process of a single sale to a school or district will likely have somewhat different motivations for buying the products offered by my client. For example, what excites a teacher about using the product in his or her classroom will probably have an influence on the school and school district's administrators. But in order to get the administrators to look upon the purchase favorably will involve other satisfactions and assurances than those required by the teacher alone. The same may be said for all of the potential parties involved.

Asking the right question to unlock "tribal knowledge"

While looking at current sales accompanied by geographic, demographic or even salesperson correlations may be helpful in seeing some patterns that can be leveraged to increase Throughput, if an organization is going to come up with real breakthrough offers – so-called "mafia" offers, because they are offers that can't be refused – will probably require more than that. It requires unlocking tribal knowledge so that the firm's mark can be segmented and the "offers" can be ever more targeted and effective.

When many companies begin this process, they begin by asking the wrong question (in my opinion). They ask their sales and marketing team something along this line: "Why do our customers buy from us?"

Of course, this makes sense, doesn't it? This question correlates to the data the management team looked at in Part 2 of this series. They looked at differences between Category A sales and Category E sales and now they want to know why so many customers in Category A bought from us.

The right question to ask, however, revolves around what is keeping the company from making more money tomorrow than they are making today, and that question would be: "Why did so many potential customers in Category E not buy from us?" After all, it is the lack of sales that is keeping the organization from increasing Throughput; therefore, it is essential to find out what is keeping sales from happening. In theory, all of the prospects have already been exposed to the factors that caused those who already purchased to decide favorably.

Unlocking "tribal knowledge" to identify patterns and constraints

If my client's team were to begin by sitting down with their sales team, they might put forward a challenge something along these lines to them:

"I want each of you to list the 5 top things – from your experience – that keep you from selling more (fill in the blank)." (The blank might be a product, a product line, in specific geographic areas, or in specific demographic categories.)

[Note: Ideally, it would be good to correlate the results of this into a Current Reality Tree to further unlock potential root causes, as there likely are some that should be addressed. However, let us leave that aside right now and just consider the matter with regard to "business intelligence."]

Discussions evolving from the resulting list of sales inhibitors – along with some provocations to think below the surface – might result in some fascinating factors emerging. The results might lead to understandings similar to this hypothetical list:

• It seems like it is easier to sell Product A into school districts where the administrator is younger and, therefore, more likely to be attuned to technology in education.
  
• It seems like it is easier to make a strong and effective ally of a teacher with more than 5 years in service, but fewer than 15 years. (This might be because the less experienced ones don't have the confidence to bring new ideas to their administration and the ones with more than 15 years in-service are "stuck" in their old ways.)
  
• It seems like it is more difficult to sell Product C into inner-city school districts with high populations (fill in the blank with an ethnicity).
  
• We have not yet discovered how to interest upper-class suburban school districts in our Product Line B.
  

Now, from the tribal knowledge the management team has just begun to unlock, there should be a two-pronged approach to moving forward:

1. Statistical verification
   
2. Development of new "mafia" offer concepts
   

Which portion of this two-pronged effort should receive the major emphasis should be guided by another tribal knowledge factor – intuition – which is right far more often than it is wrong. If the team intuitively senses a strong, "YES! We've hit on something that rings true." Then, the emphasis should probably be given to the development of breakthrough thinking for new "mafia" offers to overcome the constraint and in Throughput.

On the other hand, if the team is more reserved about an emerging concept – if they believe it has some validity, but would feel more comfortable if it could be further corroborated, then the team should put the emphasis on statistical verification.

Statistical verification

The process of statistical verification gets us back to "business intelligence" in an information technology sense. However, it is likely that the appropriate demographic data is not presently available to the firm at this moment. For example, school or school district administrators' and teachers' years in service is probably not a data point currently being collected.

In this scenario, if I were on the management team at this client, I would strongly suggest that we take two or three years of sales history and take a survey. If the firm presently has excess capacity, then take some of the excess capacity resources and put them to the task of calling these customers to gather the demographic data in question: "Years in service."

[Note: If the firm is going to have this done, there might be other demographic data that has come to light and may be of value as well, such as the inner-city ethnic composition of the schools and school districts. And, by the way, while this survey is being undertaken, I would add another element: Gather email addresses and permission to correspond with these parties electronically with occasional messages "including helpful news about technology applications in education and other valuable education insights."]

Similarly, even without formal data accessibility or a lot of detailed research (although much would probably readily accessible via the Web), the firm's team could probably add reasonably accurate demographic data regarding "inner-city" versus "upper-class suburban" schools and school districts that could be used for further analysis. These data may be refined and made more accurate as time and data availability allow.

Once the demographic data is collected, it will be a relatively simple matter to see if there is a real statistical correlation matching the team's intuitive sense regarding years in service for administrators and teachers.

Development of new "mafia" offers

As we said above, a "mafia" offer is simply an offer constructed in such a way that it is simply too good to be refused. This means understanding the motivations of the market segment you are approaching and, as the organization grows in its application of this powerful blend of tribal knowledge and business intelligence, its ability to segment its market into smaller and smaller elements will grow. This will tend to increase the firm's ability to offer even more targeted "mafia" offers.

Going back to some of the examples mentioned above, consider the following:

• It seems like it is easier to make a strong and effective ally of a teacher with more than 5 years in service, but fewer than 15 years. (This might be because the less experienced ones don't have the confidence to bring new ideas to their administration and the ones with more than 15 years in-service are "stuck" in their old ways.)
  • Example question to ask: How can we develop a "mafia" offer that will convince teachers with less experience to become a stronger and more effective ally in bringing out products to their superiors?
    
  • "Mafia" offer concepts: This might involve a "hand-holding" offer with more direct involvement with the teacher in this process, or simply providing more effective "ammunition" so the teacher feels better equipped to address questions from his or her superiors in administration.
    
• 
  
  
  It seems like it is more difficult to sell Product C into inner-city school districts with high populations (fill in the blank with an ethnicity).
  • Example questions to ask: What can we learn about the specific culture (ethnicity) so that we can construct "mafia" offers that will overthrow the reticence exhibited by this culture in adopting our products for education?
    
• We have not yet discovered how to interest upper-class suburban school districts in our Product Line B.
  • Example questions to ask: What are the objections raised by those in upper-class suburban school districts when approached regarding Product Line B? How can we develop new "offers" that overthrow these objections?
    

Hopefully, if I have been clear, you are beginning to see how power the blending of tribal knowledge with computer-based, low-cost business intelligence could be to help your firm segment its market and create breakthrough "mafia" offers that should lead to increased Throughput.

Contact me rcushing@geewhiz2roi.com if you have questions or would like assistance in applying these techniques effectively in your organization.

©2010 Richard D. Cushing


 

Business Intelligence and “Tribal Knowledge” – Part 2

In Part 1 of this series, we were talking about a firm that had identified that the thing that was keeping them from increasing Throughput – from making more money tomorrow than they were making today – was understanding their customers and the other participants in the decision-making process better. I also pointed out that this firm already had some data available to them that could be used to begin the process of understanding their customers better. They had, of course, their historical sales data. But this firm also had available to them an independent database that contained some additional demographic data about their customers and prospects that could be correlated with their own sales data.

I closed Part 1 by suggesting that they could employ these available data to begin exploring relationships such as:

• Sales by salesperson
  
• Sales by salesperson by geography (e.g., city, state, region)
  
• Sales by salesperson by demography (e.g., size of school or school district)
  
• Sales by product line by geography
  
• Sales by product line by demography
  
• Sales by salesperson by product line
  
• Sales by salesperson by product line by geography
  
• Sales by salesperson by product line by demography
  

There are other data elements (dimensions) available to virtually every firm that we are not including here. For example, if you introduce the additional "time" dimension, it may be easy to spot trends over time – e.g., salespersons, regions, or demographic groups where sales are growing or decreasing over time.

This is an example of what business intelligence practitioners call "cubing the data." The data is summarized by various "dimension." In the example above, the sales data is being summarized and the "dimensions" are:

1. Salesperson
   
2. Geography
   1. City
      
   2. State
      
   3. Region
      
3. Demography
   1. Size of school (number of students)
      
   2. Size of school district (number of students)
      
   3. Teacher/student ratio
      

As I said, all of this can be done using low-cost tools available to almost every small-to-mid-sized business and already on the desktop of almost every computer. Microsoft Excel, especially Office 2007 and later versions, is capable of digesting a large set of data within its own operating context. However, if you or your firm has a Standard Query Language (SQL) server and these data reside in a relational database (such as Microsoft SQL Server, especially SQL Server 2005 and later), you have even more relatively low-cost tools to manipulate and digest even larger data sets. SQL Server 2005 and later is even capable of calculating and summarizing data cubes on the fly. These pre-digested data may then be presented to Excel as a presentation tool and user-interface.

Introducing "tribal knowledge"

So what is keeping companies from leveraging the data that they already have in order to use the insights discovered through such analyses? Generally, in small-to-mid-sized businesses I find the following factors are holding them back:

1. Uncertainties regarding the value – I have to put this one at the very top of the list for one simple reason: If executives and managers in the firms were convinced that discovering new factors about their marketplace – market segmentation – would help them make more money tomorrow than they are making today, they would find a way to get it done.
   
2. Uncertainties regarding the costs – Sadly, the business intelligence community itself has much to do with making small businesses wary of the costs moving into the realm of business intelligence. Many who make their money by selling and implementing business intelligence tools want you to believe that is not possible to make real gains and reap significant business benefits without investing in expensive business intelligence software and spending lots of time, energy and money to build expensive data warehouses and, perhaps, hundreds or even thousands of "cubes." This is simply not the case, but it is frequently the belief.
   
3. Uncertainties about how to get started – Again, in part to the pseudo-mystique surrounding the world of "business intelligence," many executives and managers do not feel that they "have what it takes" to get started benefiting from understanding their customers and marketplace better by leveraging the data they have been collecting in their ERP systems for years. There are simple ways to get started and one can always make the leap to more sophisticated business intelligence applications when conditions warrant.
   

But, wait!

So far in our discussions I have intentionally left a tacit implication on the table. That implication is the one that drives far too many executives and managers in companies of all sizes, and it is this: What is valuable and can be leveraged in "business intelligence" is found in our data systems and the data stored or collected.

This is very far from true!

Some of the most important contributions to making computer-based "business intelligence" valuable do not come from the data, nor from the software. These valuable contributions come from the people that have worked in your enterprise year after year. Your people know things about your customers, your prospects, your products, your industry and your marketplace. I call this kind of knowledge held within a business enterprise "tribal knowledge."

Now, tribal knowledge in every organization extends well beyond the examples I will suggest in this series, but I think you will begin to see just how adding tribal knowledge into the blend with the data you have available to you extends the power of business intelligence and may lead to truly valuable breakthrough thinking.

Suppose that in analyzing sales data currently available, they looked at the data summarized in a certain way and the graph looked like the following figure:

The questions that ought to be asked when looking at such a data summarization should be along these lines:

• Why are sales in category 'A' five times better than sales in category 'E'?
  
• What can we learn from what we do to get the results in category 'A' in order to apply it to the other categories?
  

Now, let me bring this down to more practical examples:

• Categories are product lines: What factors make Product Line A perform so well? Do we sell it differently than Product Line E? Do we promote it differently? Do we sell it to different kinds of customers? If so, what are the differences between the kinds of customers? How can we apply what we know about how we sell Product Line A to improve results for Product Line E?
  
• Categories are salespersons: What does 'A' do to get results that 'E' does not? Are these results simply differences by sales territory? Are there demographic differences in 'A's customer list from the customer lists of the other salespeople?
  

Naturally, this of questioning can go on and on, limited only by the management team's ability to think of the "right" questions to ask. Some of the questions can be answered using the data and re-summarizing it in a different way. For example, to answer the question, "Are these differences [between salesperson results] simply differences by sales territory?" it may be necessary to re-summarize the data by sales territory. However, if salespersons and sales territories are synchronous and exclusive, then one might need to compare similar but broader territorial results to see if a pattern exists. (For example, if the salesperson assigned to Washington State is Category A, then one might compare results for other West Coast states to see if they are similarly high even though different salespersons are assigned to these territories.)

The basic point, however, is that the people involved in your organization are carrying about with them "tribal knowledge" that can help you and your management team discover new ways to segment your market and increase Throughput.

[To be continued]

©2010 Richard D. Cushing


 

Business Intelligence and “Tribal Knowledge” – Part 1

Recently I was working with a client and, at the opening of the meeting, I asked the six members of their management team who were gathered around the table the following simple question: "What is keeping your from making more money tomorrow than you're making today?"

Their responses were telling: (approximate quotations)

1. "We don't understand our customers: Who is buying, why they buy, or how they go about the process of getting a purchase authorized."
   
2. "The amount of time it takes us to respond to a lead or prospect. We might get 20 to 300 leads from a trade show, but it might takes us three weeks to six months to get back to them after the leads have gone through all the hands and processes in our organization."
   
3. "We don't have a good way to turn the data we possess into information that would be valuable for decision-making."
   
4. "We don't have a good way to classify accounts in our customer relationship management (CRM) software so we know how to best approach them regarding our products and services."
   
5. "We don't understand the secondary participants involved in our sales process with a prospect."
   
6. "Our customers lack the funds to buy our products."
   

What is interesting about this is that, if we take number 2 out of the mix (this is clearly a policy constraint) the other five responses all have to do with "business intelligence." These folks needed to understand their customers better in virtually every aspect.

Now, in their defense, this firm has a fairly complex sales cycle with, potentially, a number of different parties involved. Here's a brief description of the participants and their relationship to the sales process:

• School District – Usually, it is the school district that will end up "owning" the product after purchase. Frequently it is at the district level, as well, that the purchase commitment must be authorized.
  
• School(s) – The individual schools and school administrators may have an impact on the purchase decision. The school(s) must be willing to take on the product before the school district will authorize the purchase, even if the teacher may have convinced the district administrator and board that it is the right way to go.
  
• Teacher(s) – Teachers function mostly as influencers and catalysts to the sale. The teachers often are sold on the product and then become an advocate to aid in getting the product approved at the school and district levels.
  
• School District IT Department – Since the products generally involve technology, it is not uncommon for the schools' or the district's IT departments to have de facto veto power over any pending purchase of such technology.
  
• Government Programs – Since most of the schools in the U.S. are publicly funded, the funding for many of the purchases flows directly or indirectly from some government program. Such programs often set requirements and seem to have a never-ending series of "hoops" that must be jumped through before funds are made accessible for specific purchases.
  
• NFP or Other Sponsor – When the school districts' ability to access funds for a desired product purchase falls short, sometimes not-for-profit (NFP) organizations become a supplemental source of funds. Sometimes, it is even the NFP, seeking a place for its funds in community projects, that becomes the initiator of the whole process. Other times, the interested teacher may know that the school or school district have no money for the purchase, so he or she will seek aid from a NFP organization simultaneous with presenting the matter to the school and district decision-makers.
  

As you can see, with all of these participants, and no single path for each approach, it is understandable that this organization is discovering some challenges in "understanding" their customers. Add to this the fact that their business itself was changing. They were diversifying from the product around which the business had originally been built – beginning to sell a broader range of related products into the same marketplace.

Tools at their disposal

Now, this firm does have some tools at their fingertips. They purchase the use of data made available from a data aggregator that provides a database of schools, school districts and related parties. Some demographic data is included.

Now, I am not privy to exactly what demographic data is available to them – our discussions didn't go to that level. However, for the sake of this discussion, let us say that they have just the following data points for each school and district (in additional to standard data like addresses, phone numbers, and so forth):

• ZIP code
  
• Number of students
  
• Number of teachers
  

Using a tool as rudimentary as Microsoft Excel's OLAP capabilities, it would be relatively easy to spot correlations in the data between product sales (by dollar or by units) and these demographic characteristics:

• Which regions of the country account for the most sales? The least sales?
  
  • Using the first digits in the ZIP Code gives you 10 regions automatically
    
  • Using the first three digits in the ZIP Code gives you a breakdown by what the USPS call SCF (Sectional Center Facility)
    
• Which states account for the most sales? The least sales?
  
• Which cities account for the most sales? The least sales?
  
• Which districts produce the highest ratio of unit sales to students? Which ones have the lowest ratio? What about the unit-to-teacher ratio?
  

My guess is, if they had graphs of these data – especially TOP and BOTTOM data – their sales and marketing personnel would immediately begin to see some patterns emerging.

Likely, however, they have other data already in their possession that would give them additional insights as to sales patterns leading to a better understanding of their customers' behavior. Take the following examples:

• Which salespersons produced the highest sales in terms of dollars and units? Which ones produced the least?
  
• Within each salesperson's sales, are there significant differences by sales by geographical region or SCF?
  
• Are there correlations between salespersons' sales and the discounts offered? (This would be an indicator of price sensitivity and should be correlated by other factors, like geography or average sale size.)
  
• Do correlations exist between salespersons' sales results and the products or product configurations they sell most frequently?
  

Little of this kind of analysis was being done in a formal way at this firm. However, it seemed that they already knew they needed to "understand their customer" better. They just had not yet thought about how to leverage what they already had in their hands in order to begin segmenting their market and understanding the factors leading to less success or more success (read: Throughput).

We will talk more about this in the next post in this series.

©2010 Richard D. Cushing

The New ERP – Part 35

Death by data

Writing for the Aberdeen Group, Matthew Littlefield and Shah Mehul suggest, "The only way for manufacturers to achieve world-class performance [is] by providing greater visibility into what [has] long been the black box of production. And the only way to do that [is] to start collecting a lot more data on work in process (WIP)." (Littlefield and Mehul 2009) This is an all-too-common misconception that originated long before the inception of the computer, but has been dramatically augmented and expanded since computing power was made available to the business at low cost and on an unprecedented scale with the introduction of the personal computer.

A never-articulated, but oft-held, belief amongst business executives and managers is that more data leads to better management. This thought has been sometimes carried to the extreme in the minds of some executives – and fully supported by their all-too-willing IT departments – to the point that the concept may be formulated along the following lines:

1. More data will help me make better decisions
   
2. Better decisions means that, as a manager, I will be more effective and make fewer mistakes
   
3. If I can know "everything" – have all the data – about my operations, I can manage flawlessly
   

Even as I write this, I am certain that there are business owners, executives and managers busily scouring the Web for new "business intelligence" tools as the next real wave in ERP.

Nevertheless, all the data can tell an executive is what has happened. Data, by its very nature, is entirely historical. (Yes, there are "forecasts," but forecasts – if they are known for anything – are best known for being wrong. Not a reputation likely also sought by executives and managers in pursuit of "flawless" management.

What the historical data cannot tell the executive is, "What lever should I push or pull to produce some particular outcome in the future – an outcome that assures improvement and not just added cost, expense or consumed capital?" Only a sound theoretical framework about how the executive's "system" – read: whole organization – works (or fails to work) can aid him or her in finding "the right lever" and applying the correct amount of force in the proper direction.

Employing reams of data will not keep you and your management team from spending precious time, energy and money optimizing the efficiency of departmental silos while reducing the efficiency of the organization as a whole. Investments in business intelligence in the absence of a sound theoretical framework will not prevent you and your managers from building work-arounds to keep work moving instead of solving problems that repeatedly delay revenues or disrupt operations. In fact, data – wrongly understood and improperly applied – may actually move your management team to take actions that sacrifice quality and lead time in a mistaken attempt to increase production or meet standard cost goals.

What's wrong here?

As H. Thomas Johnson, professor of Business Administration at Portland State University, puts it, "Causing [such] destructive practices is the assumption that financial information not only defines the purpose of the business, it also provides the primary means to control the financial outcomes of a business…. A key reason [that] American companies fail to emulate Toyota's long-term financial results is their belief that managers can use financial targets as 'levers' to control those results." (Johnson 2006)

Professor Johnson's argument is precisely the reverse of that stated by Littlefield and Mehul. Johnson argues that U.S. executives and managers tend to believe that they can employ relatively linear and one-dimensional data – the data they use to report on the financial performance of operations – to "understand, explain, and control" the results of those operations, "even though the results emerge from nonlinear and multidimensional operations." Toyota's executives and managers do not make this same mistake.

In fact, while Littlefield and Mehul state that "world-class performance" can only be achieved by companies developing systems to give them "greater visibility into… the black box of production," Toyota has, in fact, achieved "world-class performance" by virtually assuring that accounting has no visibility into "the black box of production." In Toyota's arrangement, corporate finance knows only two things about "the black box of production": 1) what goes in, and 2) what comes out. Everything else is invisible to "accounting." In fact, it may be because "Toyota makes virtually no use of management accounting targets (or 'levers') to control or motivate operations" that they have achieved financial performance levels that are "unsurpassed in its industry." (Johnson 2006)

Understanding your operations

Inside "the black box of production," Toyota's managers are highly visual in their management style. They do not believe that they "know" or "understand" what is happening on the shop floor simply because they have worked in the plant ten years, or 20 years, or more. They believe that to understand how to improve again and again, they must thoroughly understand what is happening today – everyday. Toyota managers employ genchi genbutsu ("going to the place") to see first-hand where and why there is any delay or disruption in production of quality products. These managers understand that the sought-after financial "results ultimately emanate from, and are explained by, complex processes and concrete relationships, not by abstract quantitative relationships…." (Johnson 2006)

Whether you and your management team choose to employ the Toyota method of genchi genbutsu and asking "Why" five times to get to the root of what needs to change, or if you choose to employ the Thinking Processes (as we have discussed elsewhere on this site and in this series on The New ERP – Extended Readiness for Profit), do not fall for the line that "more data will help you manage better." Avid IT staffers aided by value-added resellers (who genuinely believe the mantra to be true) are more than happy to have you spend your money on systems to collect, organization and report on more and more data. However, if you do not yet understand your "system" thoroughly – if you have not yet developed a sound theoretical framework by which to manage your enterprise – most or all of what you spend to obtain "more data" will be wasted.

©2009 Richard D. Cushing

Works Cited

Johnson, H. Thomas. Manage a Living System, Not a Ledger. December 2006. http://www.sme.org/cgi-bin/find-articles.pl?&ME06ART83&ME&20061210&&SME& (accessed November 18, 2009).

Littlefield, Matthew, and Shah Mehul. Operational Excellence in the Process Industries: Staying Profitable Through the Downturn. White paper, Boston, MN: Aberdeen Group, Inc., 2009.

The danger of "We know!" - Part 2

In Part 1 of this series, I discussed how many executives and managers fail to reap benefits from new methods and ideas -- especially if these new methods and ideas arrive in the form of a "consultant" -- simply because these executives and managers believe that the already know what can be known about their organizations and their industries. This prevents many organizations from growing to their full potential.

W. Edwards Deming put it bluntly: "Information is not knowledge. Knowledge comes from theory."

Unfortunately, what far to many executives and managers have is a lot of information about their businesses and their industries. What they desperately lack is "theory" by which to interpret and understand the information at their disposal.

G. K. Chesterton put it this way in Tremendous Trifles (Beaconsfield, Britain: Darwen Finlayson, 1968): "One of the four or five paradoxes which should be taught to every infant prattling on his mother's knee is the following: that the more a man looks at a thing the less he can see it, and the more a man learns a thing the less he knows it. The Fabian argument of the expert, that the man who is trained should be the man who is trusted would be absolutely unanswerable if it were really true that the man who studied a thing and practised it every day went on seeing more and more of its significance. But he does not. He goes on seeing less and less of its significance."

Think of Sir Isaac Newton and the story of his having begun his development of the theory of gravity because he had seen an apple falling from a tree. Surely there had been tens of thousands of individuals that had witnessed objects falling to the ground under the influence of gravity for several millennia prior to Sir Newton's experience. Yet, no one understood "gravity."

It has only been since Isaac Newton put a "theory" around gravity that men could take what they had experienced with gravity and put it into a framework -- a theoretical context -- that made the experience understandable to them. Furthermore, the framework (the "theory") gave men the opportunity to predict outcomes of certain actions relative to the gravitational affects. This meant that men could plan and execute with some real certainty as to the results they would obtain under "gravity."

Precisely the same is true of business.

Executives and managers have all manner of data in their hands relative to the performance of their enterprises. What they lack is a "theory" by which that data may be abstracted and understood for the purposes of effective management. A framework that will help them bring simplicity out of the complexity before them.

In all too many cases, the missing "data" for beginning the process of ongoing improvement is to be found within the organization at all. The missing component for executives and managers is quite often this simple point: there is a simple method available to help organizational leadership logically analyze what they already know internally.

In the absence of a "tool set" that helps management bring forth "knowledge" from their "information," executives and managers tend to continue "tinkering" with their businesses. They make changes here or there to see if the change helps.

Sometimes such change seems to help, other times the change actually makes things go worse than before. Still other times, the change is made and their is no perceptible affect on the organization at all.

This is no way to run a business -- or any other kind of organization!

Executives and managers are yearning -- sometimes without even recognizing what is lacking -- for a simple, effective tool to help them gain control of their enterprises once again.

[Next time: Gaining Control]

Contact me!

...

Extending the Power of Your Information Technologies

In today’s exceedingly challenging business environment, it is becoming increasingly important for executive management to establish corporate strategies that include extending the reach and power of the organization's information technologies beyond the four walls of the firm. If your company is not building "communities" of customers or con-necting with your vendors and customers in real time up and down your supply chain, then it is likely that you are falling behind your competition.

No Technology for Technology's Sake

I am not advocating new "gee-whiz" connections beyond your enterprise just so the CEO can brag about them on the golf course or in the steam room at the club. Before embarking on a spending spree to extend your IT systems beyond the walls of your enterprise, it is important that you determine what you want to accomplish by moving forward with such efforts. Generally speaking, the valid reasons for investing in the extended enter-prise may be reduced to three fundamental categories:

1. Increasing throughput,

2. Reducing inventories or the need for new investment, and

3. Slashing or holding the line on operating expenses.


Let's consider some of the thinking that might go into such an analysis.

Increasing Throughput

When considering increasing throughput, your team should ask questions like these: Could a CRM (customer relationship management) system, a corporate blog or forum, or other enterprise extensions improve our ability to connect with our customers? Could such efforts improve our comprehension of our customers' needs enough that fresh new insights would result from understanding them better? Could the new insights lead to improved products, enhanced market segmentation, and the ability to create superior win-win offers?

If the answers to any or all of these questions are affirmative, then the next step would be to quantify the estimated impact and to set specific goals for any investments in new technologies. Each individual part of the IT investment plan should be directly correlated to expected quantifiable results. How many new customers will be added? How many additional sales to existing customers are to be expected? What additional market share are we likely to gain as a result of these efforts and investments?

Reducing Inventories or the Need for New Investment

The questions that should arise regarding inventories or investments should be along these lines: Will improved supply chain visibility with our customers allow us to better manage and reduce the volume of inventory lying between our manufacturing plants and our products' end users? Will linking our inventory systems with those of our suppliers allow us to reduce lead times and, as a result, reduce the amount of inventory we keep on-hand? Will improved end-to-end supply chain linkages reduce losses due to obsolescence and shrinkage?Again, if asking these questions leads to some "yes" answers, then the organization should take steps to quantify the benefits that are likely to accrue to the organization from reduced carrying costs, managing and handling less inventory, and (if true) the reduction in a potential investment in additional warehouse or production space, for example.

Slashing or Holding the Line on Operating Expenses

Generally, this area faces a two-fold battle: First, most organizations today have already done all the cost-cutting that they really can (or should) do. This is no longer the 1980’s – the heyday of cost-cutting as U.S. industry was struggling against the onslaught of Japanese products. Second, when you are talking about implementing new technologies, it is really difficult to get buy-in from your organization if the move is likely to lead to a significant reduction in the workforce.
However, results stemming from efforts to increase throughput (revenues) and reduce inventories are likely to drive growth, on the one hand, and internal improvements, on the other. Normally, then, a case can be made on the basis of these combined factors (i.e., growth and internal improvements) that your organization can support 30%, 60% or even 100% growth in the near future with little or no growth in operating expenses. The net result is often estimated and stated as savings in FTEs (full-time equivalents, i.e., the average cost of a full-time employee). In this way, the effect of “holding the line on operating expenses” may be properly factored in to the benefits accruing from investments in new technologies.

Conclusion

There is no longer a place for business as usual. In today’s highly competitive markets – driven to a significant degree by the international reach of the Internet and other technologies – every business owner, CEO, and CFO should be considering how extending their information technologies beyond the four walls of their enterprise might lead to in-creasing throughput and reducing inventories, while holding the line on operating expenses. However, every investment in technology should be carefully planned, be geared to achieving measurable goals, and fully aligned with the enterprise’s strategic and tactical objectives.

©2008 Richard D. Cushing

Getting IT right!

Writing in InfoWorld magazine (6 Jan 2003), Ephraim Schwartz said:

"The goal of IT, since its inception, has been the timely (a relative term) delivery of information to those who need it. Behind this goal is an unspoken belief in technology: If IT could deliver to its internal enterprise customers all of the information all of the time, it would be impossible for them to make a mistake."

Understanding the difference between data and information

More likely than not, many of the folks working in your organization's IT department don't actually know the difference between data and information. To be fair, they are not alone: Many people working as supervisors, managers, and executives probably don't recognize the difference between data and information either.

• Data are the bits of information your various systems store. The system may be any kind of system -- not necessarily and IT-related system. Those old metal filing cabinets still found around many offices store data, just like that 160 gigabyte hard-drive on your desktop computer stores data.

• Information is data transformed (e.g., gathered, analyzed, collated, sorted, coded) to allow the user to rapidly digest and comprehend the implications of the underlying data for timely, accurate, and effective decision-making.

For example, a 300-page report printed on green-bar paper, like an old mainframe computer used to spit out for us at a firm I worked at years ago, is data. Make no mistake, the data -- in the 300-page report -- contained everything we needed to know to make an effective decision. However, it its form as a report, it was not readily digested and comprehended for effective decision-making.

At another firm for which I consulted a few years ago, one of the firm's key production managers would take home several reports from their existing system almost every night. Working at home in the evenings, he would comb through these various reports and, using an assortment of colored highlighters, would mark up the reports with various colors to guide his production decisions the following day.

What was he doing? He was transforming data into information.

The data contained in the aforementioned 300-page report could have been more easily digested and decision-making could have been faster and more effective if the data had been presented, perhaps, in a chart, a graph, or even reduced to some form of exception list.

Placing the information in its context

Data content may typically be broken down into three general classes for most organizations:

• Operational data such as orders, purchases, inventory, and so forth;
• Process data such as schedules, routings, bills of material, logistics, and similar; and
• Administrative data including accounting, customer lists, vendor lists, employee lists and more.

The data context, however, must be understood before effective decision-making may be done for any particular organization. The context of the data give the data meaning within the framework where it is to be applied. The context includes such elements as:

• The organization's purpose,
• The organization's strategy,
• The organization's vision and mission,
• The organization's execution model,
• The organization's capabilities and competencies,
• The organization's structure,
• The organization's policies and procedures, and
• The organization's values and culture.

Clearly, depending on an organization's purpose or strategy or production model (for example), essentially the same data may drive two different organizations to make equally effective but totally different decisions.
It should be part of every organization's IT strategy to mandate the transformation of the huge volumes of data being collected into information by their IT systems. This transformation, in itself, should be flexible, timely, and subject to ad hoc transformation, as well.

That's what business intelligence is all about. In today's world, this is all about survival, not just improvement or excellence.

"Business, we know, is now so complex and difficult, the survival of firms so hazardous in an environment increasingly unpredictable, competitive, and fraught with danger, that their continued existence depends on the day-to-day mobilization of every ounce of intelligence."
-- Konosuke Matsushita, founder of Matsushita Electric (Panasonic) as quoted in Managing on the Edge: How Successful Companies Use Conflict for Competitive Advantage by Richard Pascale (New York: Simon and Schuster, 1990), p. 51.