Posts Tagged ‘Enterprise resource planning (ERP)’

There was no avoiding the buzz surrounding SAP’s recent announcements and demonstration of its newly available in-memory database technology at the SAPPHIRE 2010 conference. There’s a good description of Hasso Plattner’s vision here, and some deep and relevant commentary from Dennis Moore here.  Having read previously that SAP acquired Sybase in part to gain access to its in-memory database technology, I was somewhat surprised to see a demonstration of what many are now calling “HassoDB,” which I can only assume is distinctly not Sybase…makes me wonder whether there will be an internal competition between these two platforms. Hasso has been working on the HassoDB for a number of years and even had a keynote on in-memory technology at SAPPHIRE in 2009. I can’t see him giving this up without a fight. With multiple applications already in production leveraging HassoDB (BWA, etc.), where would Sybase fit? Oh yes, I remember now…mobile computing…or was it buying revenue and improving SAP’s earnings per share? I’m just musing…forgive me.

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?

Before I start on the body of my blog posting, let me state unequivocally that I believe, no, that I know, that computers and software have a huge role to play in decision making and execution in a wide range of business functions.  After all, I have worked in the software industry for the past 25 years.  I am also not one of those wacky people who think that machines are going to take over the world.  However, I am one of those people who believe that humans have unique skills that no machine is able to match currently, particularly the ability to evaluate nuance, uncertainty, and risk.  Computers and programs, on the other hand, are capable of processing huge amounts of data far more quickly than humans, but they always assume that the data they are fed and the algorithms/heuristics they are using to analyse the data are absolutely correct.  In other words, computers are hopeless at evaluating nuance, uncertainty, and risk.

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.

I can’t believe it’s already been 6 weeks, but it must be true because the last “Suitemates” webisode is out – Speak E.A.S.Y.

Have you been following Bilk-Moore executives (played by notable actors Kevin Pollak and Ray Wise) as they navigate the perilous world of prison life and legal appeals?! So… was their guilty verdict overturned? You have to watch to find out!

We’ve had a lot of fun with this. And the fun will carry on as the videos continue to be passed around and viewed. Not to mention the blooper reel comes out next week! And the ‘Elephant in the Room’ contest is still open.

But while the videos were done in jest, they come with a serious message. The corporate landscape is littered with failed ERP-SCM projects. Companies no longer have the IT budgets to support large implementations of complicated licensed solutions…. nor the consulting engagements required to make all the pieces work…. or the maintenance fees that accompany it. Recently, there have been signs of a push back (and we hope we are a part of that). Those in the industry are no longer being seduced by the suites. As we have professed before on this blog, CIOs and supply chain executives deserve to have more confidence in the success of the solution, more accountability on the part of their vendor, and certainly better ROI.

In fact, just yesterday I saw this blog post by Panorama Consulting. Based on results of a recent survey they found that

the average annual cost for companies implementing on-premise ERP packages is $6.2 million, and average business benefits realization is 37.2%. So what do these numbers mean to your business? …In general, the total cost of an ERP system includes software costs, service costs and annual maintenance fees, which are typically about 20% of the cost of the software licenses. Based on these assumptions, when we consider an average cost of $6.2 million, an average payback period of 2.7 years, and average benefits realization of 37.2%, the model shows that the benefit loss from the ERP implementation is $4.3 million! Additionally, it takes you 2.7 years to recover these costs while it should have been only 10 months!

Enough is enough. The model is flawed. It’s time for a new paradigm right? We think so. We’re doing as much as possible to break the mold and move away from the old software model (the tired and failed supply chain software model in particular). We’ve only begun to shake things up. Don’t believe it?…well, stay tuned for some bold moves ahead.

I recently read a blog post titled “Beware Supply Chain Excel Users—YOU are DOOMED!!!!” by Khudsiya Quadri of TEC.  I completely agree with the author that there is a big risk to SCM Professionals who rely too heavily on Excel.  There are all the reasons listed in the article such as  lack of collaboration, visibility, control and no ability to perform “what-if” scenarios.  I would like to add some additional thoughts to this discussion.

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.

What elephant?  Ah, let me tell you…

Our “Suitemates” video series (the third episode came out today by the way) highlights an untold truth: the ERP SCM suites paradigm is irreparably flawed.  Just look at the millions upon millions of dollars invested in failed deployments. The truth is known by many… but publicly declared by few.

With “Suitemates”, we’re pointing out the elephant in the room — and you can too! 

There are several elephants subtly placed throughout the scenes of the six videos.  Not real ones…maybe it’s a picture….maybe it’s a statue….maybe it’s….well, I don’t want to give it away.

If you find them all, you can win an Apple iPad! 

You have to keep your eyes peeled because these elephants are far more difficult to spot than the obvious one that’s been stomping all over businesses for years.

Find out more about the contest on suitemates.com.   C’mon call out the elephant!

As we announced last week, we launched “Suitemates” – a new comedy video series with accomplished actors Kevin Pollak and Ray Wise, who play two imprisoned executives of the fictitious ERP vendor, BILK-Moore. Episode 2 (“Suites are Sour”) of the 6-part series is out today.

“Suitemates” parodies some of the experiences and frustrations with big ERP vendors. It is a satirical look at what has become a very serious issue: the systemic failure of ERP vendors to solve their customers’ business challenges – supply chain management problems in particular – through a collection of complex and disjointed modules. The industry is littered with too many examples of costly, failed implementations and a backlash against ERP vendors has started to build. And since laughter is often the best medicine, “Suitemates” is our creative way to raise the issue…because only when the problem has been identified and accepted, will people recognize the need and benefits to finding a new way.

So, what’s the problem?
Investing in a patchwork approach for integrated supply chain management is a gamble, at best. The industry as a whole is starting to push back and seriously question the ERP model and practices. “Suitemates” is an exaggerated and embellished story of course, but we believe the fundamental message behind the videos will resonate with the business executives and CIOs that are coping with ERP suites on a daily basis.

After all, the limitations of ERP SCM suites are very real:

• ERP systems, given their history as accounting packages, have a very “inside out” perspective of the world and struggle to incorporate data from external organizations as anything other than reference information. The information flow latency, and therefore decision latency, in this arrangement is enormous.
• In today’s dynamic market, you must be able to evaluate situations in seconds, not hours or days, and the evaluation must include the extended supply chain of contract manufacturers and component suppliers. When a single MRP run takes 8 hours or more, there is little opportunity to investigate and evaluate alternatives, let alone quickly reach consensus and alignment across the entire supply chain.
• Even within your company’s own operations, ERP suites provide multiple, separate modules, almost always with different data models and therefore different user interfaces and requiring integration between the modules. Relying on batch processes to convert data between modules, each of which incorporates outdated assumptions about the market, is time consuming and does not lead to alignment across functions, let alone between trading partners.
• What is required in today’s supply chain is a unique marriage of human judgment coupled with machine processing power. A quick and “better” answer that is arrived at through consensus is immeasurably better than the slow and “optimized”?? answers of your ERP’s advanced planning tool.

Bottom line: The supply chain has transformed, so too must the technology approach to solving the emerging challenges.

So, what do you need?

• One service…not many modules.
• An integrated view of the entire supply chain…not just your internal operations.
• Analysis in seconds…not days.
• Collaboration and human judgment…not black box optimization.

Kinaxis RapidResponse is a single service that delivers on the promise of integrating information, capabilities and people.

We share more here (SCM suites vs. RapidResponse) and here (RapidResponse Enterprise) if you are interested in understanding our alternative view of modern-day supply chain management.

As many of you know, upon the merger last year of BILK ERP and SoftMoore, BILK-Moore became the largest and richest enterprise software company in the world.  The merger assured BILK-Moore customers that the company’s slow system integration, underwhelming performance, lack of reliability and increasing costs remained not only an integral part of their service offering, but would become even more prevalent. The combined company offers an extensive product line of uninspiring supply chain software modules that are sure to confuse and infuriate even the savviest of CIOs and supply chain executives.

But last month’s incarceration of BILK-Moore chief executives, Hans Ahnnansoahnn and Eli Moore, hoisted customers’ hopes that perhaps a revolution was finally in view.  Unfortunately, today’s announcement of a legal appeal has thwarted such dreams. BILK-Moore maintains the innocence of its two executives and unequivocally denies the violation of any business laws. While its practices may seem at times questionable and its products without merit, BILK-Moore is confident that the litany of charges will be dropped…. on account of the small print at the bottom of its contracts.

If you would like to follow BILK-Moore’s executives as they attempt to appeal their guilty verdict, visit www.suitemates.com.

And oh, by the way……April Fools! 

“SuiteMates” is our new 6-part comedy video series that premieres today.   Accomplished actors, Kevin Pollak and Ray Wise, play two imprisoned executives of the fictitious ERP vendor, BILK-Moore.  The series represents the growing industry-wide frustrations with ERP supply chain suites, and parodies the tactics big ERP vendors employ to convince customers their products work.  We hope you enjoy the series, and take it in the spirit in which it was intended.

I came across a great blog post by Atul Chandra Pandey from Infosys titled “Y2010 & Ahead – value chain trends in emerging economy” in which Atul emphasized the following trends in the first part of a 2-part series:

• Customer side equations will take prominence over rest of value chain
• Supply chains will get more integrated with marketing and service chains
• Speed and responsiveness will be key drivers for spend on new initiatives
• Cost will continue to play critical role in decision making
• Asset Management will gain more prominence and will help in accelerating “green” initiatives

I responded to Atul in the following manner:

We too are experiencing that prospects and customers are focusing a lot more attention on customer satisfaction as it pertains to on-time delivery of orders, but also to the enquiry-to quote and quote-to-order processes.

I couldn’t agree more with your third point about speed and responsiveness. Overall the trend we are observing is that consumer behaviour is pervading B2B transactions with ever shorter lead times. Coupled with the adoption of Lean and postponement strategies, companies have to be very responsive to changing demand, blurring the lines between planning and execution. These are the business drivers for your third point about agility and responsiveness.

Cost will always be a driver in supply chain management. If we adopt any of the Lean concepts it should be the elimination of waste. All too often I come across situations where the information and decision lead time exceeds the physical lead time to manufacture and/or deliver the order.

But this got me thinking about several other reports and observations that have come across my desk over the past 12 months. 

First and foremost must be the article by Dan Gilmore at Supply Chain Digest highlighting the work done by Supply Chain Digest’s research arm CSCO (Chief Supply Chain Officer) Insights.  There is an excellent report titled ”Next Generation Supply Chain Management: Integrating Planning and Execution” available from this link. (Subscription required).  In the article, Dan Gilmore observes that “For many years, analysts and others have offered separate models of ‘supply chain planning’ and ‘supply chain execution’ processes, and the technology vendors were generally organized in that sense as well. You can find many diagrams that show hierarchical planning processes with no connection at all to execution, for example. The report argues, and the research supports, that this gap must be closed from a process perspective to meet the challenges of today’s supply chains.” I added the bolding because this is the key to being able to provide the speed and responsiveness to which Atul at Infosys refers.  Not only that, but also managing to contain if not reduce supply chain costs will depend on being able to reduce this gap between planning and execution.   

Traditionally we have used inventory to buffer against what we would like to happen (the plan) and what actually happens (execution).  But this is no longer possible.  As the graphic below illustrates, as long ago as 2004 postpone strategies had pushed much of the inventory up the supply chain to the suppliers.  They too have adopted Lean and postponement strategies, leading to even lower inventories.  And then there is the effect of the recent recession.  Nearly all the OEM’s I speak to are struggling to secure supply of components, clearly indicating reduced inventory levels in the suppliers. I wish I had equivalent inventory figures for 2009.  Anyone willing to provide these figures?

Then there is the excellent blog written by Lora Cecere recently titled “Tackling the Black Hole in the Center of Your Supply Chain” in which she states “We now know that fixed data integration, one-dimensional rules mapping, and traditional master data techniques from ERP to Supply Chain Optimization are insufficient.  As a result, plans are created and consumed in isolation, and transactional systems hum along with little– to no — guided intelligence.”  So as the speed of business has increased – some would describe this as volatility – the supply chain systems have not kept up. 

And most of the information is now external to your organization.  Companies have being trying desperately to get point-of-sale information to get early trend analysis of sales.  At the same time, many brand owners have largely outsourced manufacturing, not only lengthening the physical supply of goods, but also the time and effort it takes to make a decision.  All of these factors are only making the gaps between planning and execution even wider.  But the business need is to close this gap; to respond to demand changes quickly and effectively.  As Lora Cecere, states, the solutions from the 1990’s have not kept pace with the business needs.  Throwing more ERP at the problem isn’t the solution.  At their heart, all ERP systems are essentially accounting packages.  They deal with your data – financial and operational – but provide very little help in dealing with the majority of the information, which now exists outside of your organization.

What are your thoughts?  Do you experience this gap?  Are your systems able to cope.  Will your next breakthrough in performance come from learning to plan better, or learning to respond to plan variance?  In other words, closing this gap between planning and execution.  Robust debate encouraged.

CIO.com has a very funny (in a sad but true way) post by Thomas Wailgum outlining the 5 steps to ERP software success.  From his post;

1. Meet with ERP vendor, get taken to expensive sporting event/dinner by sales rep, and select appropriate software package. (And don’t forget to send the check!)
2. Install ERP system!
3. Integrate with other systems. (Repeat as necessary.)
4. Provide thick “How To” manuals—chock-full of Clipart—to users!
5. Sit back, relax and enjoy your new ERP system!*

Thomas has included all sorts of “fine print” around step number 5.  Check out his post to get the details.

I’d like to add a couple of points that Thomas may have overlooked (I’ve also posted this as a comment to Thomas’s article);

3b.  Integrate with other components of the “Suite”  (You don’t think that just because you bought software from the same vendor that you wouldn’t have to integrate it, do you?)

3c.  Make sure that you use high priced integration consultants recommended by the ERP Vendor…this is WAAAAYYYYY to complicated to do yourself.

4b.  Provide lot’s of “happy pills” to your executive team (they are NOT going to be happy with the loss of productivity as people stumble around trying to figure out the new system)

5b. Don’t forget to send in that annual extortion maintenance check.  It’s important to pay so that your ERP vendor can fix their existing bugs – and add new bugs features.

5c.  Invest in some earplugs…you really don’t want to hear the howls of anguish from your users.

Do you have other advice?  Let’s hear it!

Our expectations as consumers are changing.  When I was a growing up (I won’t comment on how long ago that was!) we expected to have to wait for things (“It’s in the mail!”).   As the technology evolves, the time it takes to do things, and the time we are willing to wait to do things has changed significantly (how do you feel when the company you are dealing with wants you to mail or FAX information to them?).  Let’s look at some examples;

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?