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“…They’ve been in existence for decades, they can spread like wildfire, and no one has quite figured out how to stop their proliferation – even if they really, really want to.”

I came across this entertaining rant in the Sourcing innovation blog.  The idea is that despite many attempts on behalf of IT to get key corporate data out of spreadsheets and into dedicated systems, spreadsheets keep coming back. 

The doctor’s post was prompted by an article in CIO.com that indicates that BI vendors have resigned themselves to the fact that spreadsheets are here to stay and have devised means to incorporate spreadsheet data into their systems despite the fact that studies have shown that 80-90% of spreadsheets contain serious errors. You know it’s true…How many times haven’t we been in the situation where user A’s spreadsheet showed different results than user B’s spreadsheet for the same thing?   OK, so there are a few errors…not a big deal, right?  Wrong!  To see the impact spreadsheet errors can have, check out this CIO.com article from a few years ago that outline 8 of the worst spreadsheet blunders to date. Yikes!

So why do people rely so much on spreadsheets?  There are many reasons I’m sure, but the top culprits are as follows;

• The business system (BI, CRM, ERP, etc) can’t present the information the way they need to see it
• The business system doesn’t support what-if modelling
• The business system is too darn hard to use
• The business system logic is fixed and can’t be changed (within a human lifetime)

Until we get enterprise tools that can change these factors, spreadsheets will continue to proliferate regardless of how much bug spray we throw around.

Why do you continue to use spreadsheets?  Do you have any great spreadsheet blunders to share?  (We won’t tell… honest!)

As most who closely follow SAP know, SAP has been talking about in-memory databases for several years. To those that don’t follow the exciting world of database technology, you might even think SAP invented in-memory databases! The truth is that in-memory databases have been around for longer than most realize. Check out this Wikipedia page and you’ll get a sense for the dozens of innovators that led the way well before SAP stepped onto the field. While you won’t see Kinaxis technology mentioned in the mix, we’ve been at it for longer than most—perhaps even the longest. Despite our laser-like focus in this area, our senior architects continue to admit there’s still room for improvement and are in tireless pursuit of it. However, to borrow from the barber’s famous line from one of my favorite Leone spaghetti westerns (“My Name is Nobody”) – they would also state — Faster than ‘us’, Nobody!

While I wasn’t present to witness it, our development of in-memory technology began 25 years ago with a hand full of brilliant engineers in a basement who founded their own company, Cadence Computer. Their goal was simple: to invent something meaningful and technologically amazing. One of our Chief Architects, Jim Crozman, had an idea to run ERP in-memory—motivated by improving upon a then 30-plus hour run. As you might imagine, finding a machine to run the software that was in his head at that time proved to be impossible, so Jim, along with a small group of talented engineers, did the only thing they could think to do: They invented and constructed a specialized computer (the size of two refrigerators), which would become a dedicated in-memory database appliance—likely a world first. They would call it SP1018. We all know how technologists love a good acronym with some numbers attached to it! At that time, 4MB of RAM took an 8×10 circuit card—and wasn’t cheap! They were packaged into modules with a custom bit slice MRP processing engine capable of 10M instructions per second that could process data in memory at its peak speed. Program and temporary working memory were in their own storage blocks, so the main memory space and bandwidth was reserved for the database. Up to 16 of those processing/memory modules were clustered with a high speed backbone to form single MIMD processing system that could do an MRP “what-if” simulation for a large dataset in minutes. We would go on to sell this computer to GE, at that time an IBM 3090 showcase center. The IBM 3090 had a whopping 192MB of RAM, and sitting next to it, our appliance with 384MB of RAM. IBM’s ERP analytics ran in over three hours, while our appliance replicated the same analytics in approximately three minutes.

Computer architecture and speed has evolved greatly since those trailblazing days. Inexpensive multi-core systems with big on-chip caches are capable of tens of billions of instructions per second. No need for custom hardware today! Speaking of on-chip caches, understanding and leveraging this resource has become the key to maximizing speed and throughput. Memory architecture remains 10 times slower than processor speed, so understanding how machines retrieve data and the treatment of that data within the core is fundamental to in-memory database design. It takes the same amount of time to retrieve 1 byte of data as it does 1 block of data. This makes locality of reference a very important system design criteria, minimizing memory access cycles to get the data you need for processing. Data organization and keeping data in a compact form (e.g. eliminating duplication) and with optimal direct relationships and clustering makes for optimal processing speed (minimize memory access cycles).

At this year’s SAPPHIRE Conference, SAP explained how it has chosen a hybrid row/column orientation as the construct to store in-memory relational data. Indeed, columnar orientation helps with data locality and compaction of a column of data (obvious), and is most effective in circumstances where the use cases are driven by querying and reporting against a database that does not change or grow rapidly or often. Dennis Moore says it best in his recent blog:

“There are many limitations to a columnar main-memory database when used in update-intensive applications. Many SAP applications are update-intensive. There are techniques that can be used to make a hybrid database combining columnar approach for reading with a row-oriented approach for updates, using a synchronization method to move data from row to column, but that introduces latency between writing and reading, plus it requires a lot of CPU and memory to support the hybrid approach and all the processing between them.”

The challenges associated with columnar orientation will be felt most when attempting to drive performance of complex in-memory analytics. By analytics, I don’t mean complicated SQL statements, compound or otherwise. Rather, I refer to compute-intensive specialized functions, like ATP/CTP, netting, etc. That is calculating consequence to input events based upon a model of the business, particularly the supply chain. Columnar organization solves issues for a small subset of problems but makes most usages of the data much worse. Processing usually involves a significant subset of the fields on a small related set of records at a time. Since a single record’s data is spread across different areas of memory by a columnar organization, it causes a bottleneck between memory->cache->processor. A single processor cache line ends up with a single piece of useful information and multiple cache lines are then needed to get just one record’s data. For example, ATP for a single order needs a subset of demand, supply, order policies, constraints, BOMs, allocations, inventory, etc. Perhaps this is the main reason why the PhD students at the Hasso Plattner Institute of Design at Stanford reported only achieving a 10x improvement for their ATP analytic prototype using HassoDB, significantly slower than their raw query performance ratios.

Millisecond query results are at most half of the equation—and definitely the easiest half. Don’t get me wrong, faster BI reports are great. If you’ve been waiting a few minutes for a report, and you can now get it in seconds, that’s real value. The trick is to go beyond “what is” and “what was” analysis, and add “what will be if” analysis. If done correctly, in-memory analytics can achieve astounding speeds as well. For example, the Kinaxis in-memory engine processes analytics (e.g. ATP), from a standing start (worst-case scenario) with datasets consisting of 1 million part records, generating 2 million planned order recommendations following the creation and processing of 27 million dependent demand records in 37 seconds, while handicapping the processor to a single core. Further, eight different users can simultaneously request the same complete calculations on eight what-if scenarios of the data and still get their independent answers in less than 60 seconds. No need for ”version copy commands.” My personal favorite performance test done in our labs involves proving that the more users logged into to the system, the less time it takes for them to receive their results (i.e. average time per request goes down). As impressive as these benchmarking numbers are, these tests do not represent typical user interaction (i.e. batching full spectrum analytic runs). If done correctly, massive in-memory databases with intensely complex analytics can scale to thousands of users on a single instance (think TCO here), each capable of running their own simulations—change anything, anytime, and simultaneously compare the results of multiple scenarios in seconds.    

RapidResponse simultaneously measuring eight scenarios for a user using weighted scorecard

All this speed and scale becomes valuable when businesses can bring about new and improved processes capable of delivering breakthrough performance improvements. With collaboration gaining traction as the new supply chain optimizer, companies are driving innovation toward this area and testing in-memory databases in new ways. For example, not only is it important to monitor changes in the supply chain and the potential risk/opportunity they create, companies now want to know “who” is impacted, “who” needs to know, and “who” needs to collaborate. While this seems like an obvious value proposition, the science involved in delivering this on a real-time basis is staggering.

I’m happy to see SAP draw such attention to the merits of in-memory databases. It serves to validate 25 years of our heritage, our focused research and development, and surely validates the investments made by some of SAP’s largest customers (Honeywell, Jabil, Raytheon, Lockheed Martin, RIM, Nikon, Flextronics, Deere, and many more) to leverage RapidResponse. Whether related to Sales and Operations Planning, Demand Management, Constrained Supply Allocation, Multi-Enterprise Supply Chain Modeling, Clear-to-Build, Inventory Liability Reduction, What-if Simulation, Engineering Change Management, etc., these great companies are experiencing and benefiting from the speed of in-memory technology today.

Why wait?

All too often we don’t put processes in place which couple the human ability to evaluate nuance “intelligently” with the machine ability to evaluate vast amounts of data “dumbly”.  All too often we confuse efficiency with effectiveness, and pursue efficiency over effectiveness, exemplified by the use of the term “machine intelligence”.

Nothing brings this out more clearly than the recent stock market behaviour.  All the “quants” were quick to identify “human error” initially.  Not only did they say it was human error, but it was female error.  I’m surprised they didn’t suggest she was a blond too.  After all, we know how they confuse their B’s with their M’s.  How ridiculous!  Now that calmer analysis has taken place, it would seem that nothing of the sort happened, and not by a female either.  There is a very interesting article – I am sure there must be many more out there – in the Wall Street Journal (WSJ) by Aaron Lucchetti titled “Exchanges Point Fingers Over Human Hands” that analyzes what really went on last week Thursday. Lucchetti makes no bones about the fact that this is a man vs machine tussle:

“In the man-vs.-machine argument for financial markets, proponents of technology say machines do it faster and cheaper. Those in support of human involvement say people can use their experience and pull the emergency brake when the computers, or their programmers, make mistakes.

But when that happened Thursday, it appeared that some humans couldn’t react quickly enough, while those using computers just kept pushing the market lower.”

I would argue that human involvement should have been used to prevent the situation from occurring, not just as an “emergency brake”.

Let’s start by understanding the role of the “quants” in financial organizations.  A “quant” is short for a quantitative analyst.  These are math and physics whizzes that have been brought into financial institutions to create mathematical models to evaluate market behaviour, particularly algorithmic trading.  Algorithmic trading is a trading system that utilizes very advanced mathematical models for making transaction decisions in the financial markets. The strict rules built into the model attempt to determine the optimal time for an order to be placed that will cause the least amount of impact on a stock’s price. Large blocks of shares are usually purchased by dividing the large share block into smaller lots and allowing the complex algorithms to decide when the smaller blocks are to be purchased.  The use of algorithmic trading is most commonly used by large institutional investors due to the large amount of shares they purchase every day. Complex algorithms allow these investors to obtain the best possible price without significantly affecting the stock’s price and increasing purchasing costs.

Let me come clean;  I am an engineer, so I am a “quant” by nature and by training.  But I had the good fortune to study “decision under uncertainty” at the PhD level.  During this time I also came across “fuzzy logic”.  Forget the math and theory.  Fundamentally what it comes down to is that some people (quants) believe that any and all systems can be modelled exactly – given enough time and insight – and that the models can then be used to predict behaviour under any other circumstances.  I think this is a load of hogwash.  No mathematical model is ever complete and data is never 100% accurate.  However, when computers are used by humans to understand “directionally correct” decisions, they are of huge benefit.  In other words the model of the supply chain may indicate a 5.21% improvement in gross margin from 23.42% to 28.63% if supplier A is used rather than supplier B.  I would interpret the result to mean that it is highly likely that we could increase gross margin by more than 2.5% by using supplier A.  It would have probably taken a human months to gather, collate, and analyse the data by hand, and probably with a great deal of “human error”.  The same analysis could be achieved in a few hours using a computer, provided some of the primary data was already available.

There is an interesting little snippet in the Wikipedia description of quants which I think is of particular relevance.

“Because of their backgrounds, quants draw from three forms of mathematics: statistics and probability, calculus centered around partial differential equations (PDE’s), and econometrics. The majority of quants have received little formal education in mainstream economics, and often apply a mindset drawn from the physical sciences. Physicists tend to have significantly less experience of statistical techniques, and thus lean to approaches based upon PDEs, and solutions to these based upon numerical analysis.”

Statistical techniques are based upon uncertainty, or randomness.  Physicists, mathematicians, and engineers, on the other hand, hate uncertainty, and spend enormous amounts of time looking deeper and deeper into atoms trying to prove that everything is predictable, if only we had the knowledge and wisdom to understand the observations.  And they bring this perspective to the analysis of financial market behaviour, as pointed out in the Wikipedia quote.  Einstein once made the statement that “God doesn’t play dice with the universe”, which he came to regret, incidentally.  He was questioning the notion of randomness as opposed to determinism. Determinism is defined as understanding every event in nature as having a particular cause. Randomness defines an aspect in nature that has only a probability such as in quantum uncertainty.  My engineering training was replete with this deterministic attitude which informed Einstein statement, as was the training of my fellow engineers and scientists.  So the quants are in constant pursuit of the ultimate model to describe all situations so that they can predict the movement of the market under any and all conditions.  This is an attitude that is very common in supply chain management too.  I think it is flawed from the start.

In a separate article in the WSJ titled “Did a Big Bet Help Trigger ‘Black Swan’ Stock Swoon?”, it is clear that what happened last week Thursday was not “human error”, but rather “model error” in the sense that there was an over reliance on computer models, which in turn drove market behaviour.

The non-quants have been fighting back for some time since the market crash in 2008 and the whole CDO mess.  A good example of this is Scott Patterson’s book “The Quants: How a New Breed of Math Whizzes Conquered Wall Street and Nearly Destroyed It”.  It is a fascinating read, and very instructive.  But also fairly predictable in the blame game.  What I found most interesting was a comment by a reader of a book review of “The Quants” in the Globe and Mail.  Interestingly the title of the book review is “Quants accept no blame for financial crisis”.  Can’t be more explicit than that.  The reader wrote that

“In finance, you have a lot of people in high positions who are surprisingly innumerate (MBAs and the like) – they didn’t really understand what the quants were doing but didn’t mind as long as they were making money. Let’s not forget who hired the quants in the first place! When you combine this lack of technical oversight with poor regulation, you have a toxic mix.”

I believe we have a very similar situation in manufacturing operations, particularly supply chain management.  Senior management doesn’t really understand the complexities of operations and rely too heavily on the quants.  As long as they see inventories go down and stock prices go up, all is well.

To go back to Lucchetti’s article in the WSJ, the first act in the blame game for the market behaviour last week Thursday was to focus on “human error”.  Clearly a first salvo from the quants.  Later in the article, Lucchetti quotes Jamie of White Cap Trading as stating that

“Markets are a mix of technology and human judgment. Thursday, we saw far too much technology and not enough (human) judgment.”

I could not agree more.  I think I am going to print out that statement in 94 pt font and put it in a frame on my wall.  I would like to see everyone in supply chain management follow my example.

All too often I see this same behaviour in supply chain management where optimization engines are thrown at a problem.  I do not have too much of an issue with the use of optimization engines.  What I struggle with is that there is a slavish belief that the results are accurate to the nth decimal.  There is no understanding of the likelihood of achieving this optimum nor the degree to which the model is inaccurate nor the degree by which the result is affected by inaccurate data.  What happened in the stock markets is a classic example of relying too much on machines in the pursuit of efficiency.  The parallel’s in the supply chain space where we rely too much on optimization, be that Lean or mathematical optimization.  Do you know the first sign of when the quants have taken over your supply chain?  It’s when you hear that your data isn’t clean enough after you have already spent millions implementing an ERP system and countless hours “cleaning” data.

I am not suggesting that we unplug the ERP and APS systems we have deployed over the past 20 years.  I think there is a huge amount of value that has been received from the use of these tools.  But they are tools.  Let us treat them in that manner.

As always, I look forward to a robust debate, perhaps including some of my erstwhile colleagues.

A big limitation of Excel in my view is that it cannot mimic the analytics in the company’s source ERP system.  Why is this important?  If someone is using Excel to make business decisions without all the capabilities the ERP source system has, then they may not be making the right decisions.  How can you make planning decisions if your spreadsheet doesn’t take into consideration functionality like sourcing rules, constraints and order priorities?

A company’s supply chain map is very complex, typically there are internal manufacturing data sources, external manufacturing data sources, inventory site data, etc.  It is possible to get data from multiple sources into Excel, but the big challenge is that the data is not always the same from each source system, so many organizations may have multiple spreadsheets to perform the same function/analysis.  But can any of those spreadsheets be truly accurate if they don’t show a true picture of the whole supply chain?

It is almost impossible to control the integrity of spreadsheet data and access to the spreadsheet.  With multiple people accessing the spreadsheet and no security, how can anyone have any confidence in the data?   In addition, most spreadsheets need to be reviewed by many people which typically requires pushing the spreadsheet around.  Without system standard security, data integrity could be an issue and auditing who made changes could be an issue.   How can there be a high level of confidence in the data and subsequent business decisions made?

I have known many supply chain companies who do make critical business decisions based off of spreadsheets.  For example, one company would use spreadsheets to analyze big order drop ins.  If they had a big order drop in they would use their spreadsheet(s) to determine the effect on their business and when they could commit to the customer to deliver the order.  This would typically require multiple spreadsheets getting data from multiple sources, tons of manipulation, trying to tie data together, and many different users from the organization looking at their piece, which would take several days and by then the data had changed and the end user would only have a 50% confidence level in the answer back to their customer.  This can be crippling if your products are very expensive like in the aerospace industry where the products are multi-million dollar and the customer is the government who may impose penalties if orders aren’t delivered when promised.

You need to:

• get all the supply chain data in one place for visibility (with frequent data refreshes),
• mimic the source system analytics,
• have all the system standard security functionality and
• output data in a familiar “Excel-like” format.

True nirvana is: one source of the truth, multiple users having access at the same time, data integrity, “what-if” capability with the power and flexibility of “Excel-like” outputs.

Financial services and telecommunication companies have pioneered the use of business intelligence (BI) solutions to enable them to analyze massive amounts of data they have accumulated over the years. As a result, considerable insight was gained from data mining and data analysis and thus, the need for BI capabilities grew in the ’80s and ’90s in other industries as well. But despite being a topic explored and written about extensively, there has been only a moderate uptake and mediocre results. Why?

Pure business intelligence (BI) tools suffer from two major drawbacks that prevent them from providing greater value and therefore obtaining greater adoption: They cannot identify causality and, as a consequence, they cannot provide a prediction of future performance.

In the past 5 years, the interest, and indeed the need, for real-time access to operational data has increased dramatically. The promise of real-time operational BI that goes beyond the capturing of static data snapshots and enables users to identify and analyze risks and events, is of major interest to supply chain management (SCM) managers. Driven to improve operations performance, supply chain managers know that better information about their operations and processes lead to better decisions and better supply chain performance.

What do you say when the CEO is asking whether the company will hit its revenue targets for the current reporting period? Can you tell the CEO instantly which customers may be facing late delivery, and which orders may not ship and why? Can you tell the CEO what is causing the late deliveries and how the company could get back on track? You should, because it is in these answers where the business value lies.

We just posted a paper that highlights what’s at the heart of evolving business intelligence into business value. Download it today.

While trying to formulate my ideas about the causes of lack of alignment, I came across a set of postings by Dustin Mattison on his Logipi blog and on one of the LinkedIn discussions, which postulates that the problems at Toyota can be boiled down to organizational structure and culture.  This has been manifested by power “fiefdoms”, lack of transparency, and therefore lack of alignment between different functions.

There is a great section in “The Big Switch” in which Nicholas Carr traces the origins of organizational structures and their impact on performance.  (I wish I had a more formal source, and I am sure some of our readers can point me to one.)  Our organizational structures have been inherited from the military and really date from as far back as Roman times when there was no ability to communicate in real time.  Imagine the time it took to get a message from Rome to Cairo?  As a consequence, hierarchical structures were developed to ensure a process of central command and control.  Loyalty was prized above all else and disloyalty was dealt with very harshly.  The 20th century phenomenon of the corporation used the same organizational structures and same command and control attitudes, largely because the means of communications had not progressed since the Roman times, though the penalties for disloyalty (or poor performance) are considerably less harsh.

The business process reengineering efforts led by Michael Hammer, Tom Peters, and Peter Drucker in the 1990’s was the first attempt to correct this by “de-layering” management. But think about it: They were doing this before the wide-spread adoption of the internet, when faxes were still considered state of the art.  While the enthusiasm for BPR has waned because when put into practice it focused too much on efficiency (read headcount reduction), the fundamental idea that business processes can be more effective – not just more efficient – has been carried forward by Lean and Six Sigma concepts.  And the internet specifically, but technology more generally, is the enabler.  This is what can/does provide/enable the transparency Richard Wilding of Cranfield University talks about in an interview with Dustin Mattison, which is so crucial in breaking down the power barriers to more effective sharing of information across functional and organizational boundaries.

And yet we still have senior management (and professors in business schools) to whom IT in general, but the internet specifically, is a learned phenomenon.  Before anyone thinks “Yeah, yeah”, let me point out that I am one of the people who have “learned” how to use the internet and I am still not comfortable with “tweeting” and “blogging”.  In short, I am not comfortable with that level of personal “transparency”.  At the same time, I am staggered at how many mid-tier managers, let alone senior managers, still receive paper-based reports, scribble all over them, and then send the scribbled notes back to an underling who is supposed to act on the scribbled notes.  This is all about power and has little to do with effectiveness.  They could have just as easily made changes to values in a system and annotated these with some comments. This information would be available immediately to anyone who had to take action or make further decisions based upon the inputs from the senior manager.

Exacerbating the fact that much of senior management does not come from the “internet” generation is the difficulty of using existing IT applications and systems.  The fundamental drawback of existing supply chain systems specifically, but operations systems in general, which prevents their wide adoption by senior management is that they lack the ability for people (read senior management) to perform quick and effective what-if analysis.  It takes too long for them, and in truth it is also too complex, to create and analyze scenarios themselves, so they devolve this to more junior people who don’t really understand what it was the senior manager wanted to investigate in the first place.  More correctly, the senior manager is forced to take a structured approach to investigating and solving an issue whereas in reality problem solving is a very unstructured process governed strongly by exploration and discovery.  Even when senior managers have monster spreadsheets available to them, there is:

• little to no connection to the current situation
• insufficient level of detail to get a realistic evaluation of the future consequences of their decisions on financial and operational metrics, and
• very limited ability to explore multiple scenarios.

They have to wait until the month end or quarter end to get a report on what has happened, and by that time it is almost impossible to deconstruct the cause and effect.

While I realize the limitation of my thinking (fundamentally I am an operations person) and recognize the impact – both short term and long term – that Finance, and HR, as examples, can have on the performance of a company, in companies that sell, design, and/or manufacture a physical product, Operations is the core business process that determines the current and future success of an organization.

All of this gets me to a brief discussion of Sales and Operations Planning (S&OP).  There are many definitions of S&OP out there and also a lot of discussion on S&OP “maturity” models.  At its heart and in its more simplistic form, S&OP is all about demand/supply balancing.  In other words alignment between the demand and supply side of the organization.  In a multi-tiered outsourced environment, this is not a simple exercise, so my use of “simplistic” is not meant to denigrate this level of S&OP adoption.

The greatest long term benefit of S&OP, even if this is difficult to quantify, is increased transparency and alignment, as noted by John Westerveld and discussed by Richard Wilding. AMR Research calls this “East-West” alignment.  And yet there are so many more benefits that are achievable by linking Operations to the Executive, by linking financial measures and objectives such as revenue, margin, cash flow to operational metrics such as orders delivered on-time and in-full, inventory turns, and capacity utilization.  AMR Research calls this “North-South” alignment.  A number of the analysts such as Ventana Research, Aberdeen, Gartner, and AMR Research (now part of Gartner) have referred to this North-South alignment as Integrated Business Planning.  Tom Wallace and Oliver Wight have referred to this an Executive S&OP, and now Accenture is referring to this as “Profit, Sales, and Operations Planning”.  Whatever we call it, there are lots of benefits.

The principle barrier to tapping into these phenomenal benefits is the organizational power structures we have inherited from a previous era.  These will not be easy to break down.  But an S&OP process – however sophisticated or rudimentary – will start this process of greater transparency and alignment.  I’ve been participating in 2 discussions on LinkedIn (Has Sales & Operations Planning (S&OP) improved your forecast accuracy? and  What is your biggest S&OP pet peeve?, both of which require membership) and in both discussions there is consensus that the greatest contributor to the successful adoption of an S&OP process is Executive support because this is required to get everyone to “play nicely” with each other.  Clearly this is simply a symptom of the organizational power structures.  S&OP is challenging these power structures, which leads to resistance. There is plenty of technology out there to assist in this process, but ultimately you will need both for a truly successful S&OP process that contributes massively to your company’s future success.  But there is no need to wait until you have organizational buy-in.  As with all organizational change, showing the people how they will benefit for adopting new practices is the best way of getting their buy-in.  So start small and give people information that is useful to them and over time you will be able to ask for information that is useful to you.  If this is too slow for you, make a pitch to your executive team to make sure they back you up to get faster adoption.  Either way, you should not wait.  The benefit to your company is too great to ignore.  Help us create the organizational structures of the future.

Mail – Before the 1980’s the only option for sending written communications was to write a letter and send it via the post office.   Typically, you could send a letter and depending on how far it had to go, you could expect the recipient to get it in days, maybe weeks for long distances.  In the 1980’s, the fax machine became a popular way of sending written correspondence.  A fax could be sent and responded to instantly; however, in most cases, you had to go to wherever the fax machine was to pick up your fax.  In the 90’s, e-mail became the standard and you could receive your message at your desk seconds after it was sent.  Today, with Blackberry’s and iPhones, messages can be sent and received at any time of the day and night anywhere (almost) in the world.

Banking – Before the advent of bank machines, people used to need to stand in line to see a bank teller to access money in their account.   You needed to get to the bank  when they were open and make sure you took out enough money to cover your needs.  Banking took hours and forget about getting cash on the weekend! With the move to bank machines, you could do most transactions using the cash machine and could do those transactions any time you needed to.  Today, you can bank from your smart-phone, pay for items using a smart-card and even apply for loans over the internet.

Music – If I heard a song on the radio and wanted to listen to that song, I’d have to drive to the record store and get the album. If I was really anxious to get a song, I could get it within a few hours. With the advent of iTunes, I could download the song, move it to my iPod and listen to it in minutes.  Today, I can download a song directly to my iPod touch or iPhone and listen to it immediately.

ERP – Before ERP systems we managed things by paper.  Responding to changes took days, sometimes weeks.  In the 70s, ERP systems became popular, but the systems were expensive, large and cumbersome, and very difficult to maintain.  Responding to changes took days, sometimes weeks.  Today…hmmm…let’s see…well, they’re still expensive, they’re still large and cumbersome, they are still very difficult to maintain.  And responding to change takes days, sometimes weeks.   So…where are the advances in ERP???

Well, maybe our expectations of ERP systems haven’t changed.  Maybe my customer is happy to wait for days or weeks for me to respond, right?  Not likely!  Our customers want responses now. They NEED responses now because THIER customers are demanding a response from THEM!

ERP Systems aren’t the answer.  They do what they do…provide a plan and track transactions.  If you look at each of the examples above, the key changes were not evolutions, they were revolutions within their industry.  We don’t have mail carriers capable of instantly delivering mail (even superman couldn’t do that!), we have completely different technology.  ERP systems won’t evolve into something fast enough to meet our needs.  We need a completely revolutionary technology.  We need a response management system. 

So…why wait?

For those of us a little older, we have to change the way we deal with information and be open minded for the rapid transformations we are facing. For example, when email was introduced it turned out to be extremely helpful and time saving. Nowadays, many became so dependent on email that they forget in many situations it’s more productive to pick up the phone for a quick question rather than to spend time writing down the electronic message and then waiting for the response. Not to mention the extra time and effort to deal with misunderstandings that arises from the lack of emotional translation in written communication.

Of course the number of social and professional network websites only increases the amount of information we are to process. A new way of thinking is needed to be successful in this almost infinite abundance of information in the world.

According to an article I read a couple of weeks ago (P. Hemp, “Death by Information Overload,” Harvard Business Review, September 2009), recent research suggests that the surging volume of information and its interruption of people’s work can adversely affect not only personal well-being but also decision making, innovation and productivity – I guess most of the readers here wouldn’t be surprised to learn that one study indicated that it takes in average 25 minutes for people to return to a work task after an e-mail interruption!

The information age is changing the way some professionals carry on their duties. Take College professors, for instance. They cannot dream to prepare enough for a class, given all the information available to students. Rather than ‘teach’, they are becoming facilitators in the classroom: guiding the students on topics to be further explored. The young students are very quick in validating online and on the spot whatever is shared in lectures.

Here are some takeaways from the article and my own observations:

• The article starts with “Can everyone stop whining about information overload?” It’s our most valuable commodity! I personally have stopped answering “so busy” when people ask me how things are going – I’ve accepted that this is just the lifestyle I chose and so I shouldn’t complain about it!
• I have also stopped feeling guilty about not getting back to some people – let’s face it, it’s impossible to even read all emails! Those of us suffering from “continuous partial attention” (multitasking, anyone?) often feel depleted and demoralized by “the stress of not being able to process information as fast as it arrives – combined with the personal and social expectation, we’ll answer every email message”.
• It takes A LOT of work to maintain all the different networks we belong to after the explosion of the internet. We need a new mindset and new tools – and we need to learn how to use them effectively without going crazy.
• Having extensive networks to care for might seem another source of stress, but in fact it is one of your best tools to surf the information wave. Let go of the feeling that you need to know everything; rather, make sure you have contacts you can tap in to when facing questions you can’t quickly answer – “know who” instead of “knowhow”.
• On the practical level, the article mentions some techniques and tools to help us dealing with the challenge. Here are some tools that you may or may not be aware of:
  - TheBrain: visually associates related pieces of information to organize all your knowledge (Right!)
  - Twine: help get up to date on selected topics of interest (twines)
  - Tools to regulate email volume within organizations – that’s right! Some of them work by allotting ‘credits’ to be spent on email activity e.g. Postware and Attent. I am sure that this would scare some buyers and planners that still use emails to communicate orders and production plans to suppliers!

Some of you might have seen the article (or maybe not, I know you have too much to read!). In any case, I thought I’d contribute this other piece of information to your busy day.