Archive for December, 2009

We’re taking a short break from blogging over the holidays, so this will be our last post until the New Year.  The entire Kinaxis team would like to take the opportunity to wish everyone a happy, healthy holiday season. 

To our valued readers and subscribers, we are humbled that we are a regular resource for you.  And to everyone that has commented to this blog, we sincerely appreciate the contributions – the conversation is always more valuable when people chime in with their own take on the subject.  We are looking forward to 2010 when the discussions will continue with enthusiasm!

I leave you with Brian Sommers’ must-read blog post over at ZDNet.  It’s priceless stuff.  Dear Santa : The CXO ERP Xmas Letter  (my favorite is the comment to the post whereby a poster criticizes the ERP wish list as “ridiculous”….I think something is ridiculous, but it certainly isn’t that list … )

Cheers to the New Year with more views and news…

Take good care.

With the holiday season upon us, many of us are concerned about making the numbers for the year. Most midmarket companies, whether they are public corporations or privately held enterprises, have revenue targets for each reporting period, usually by fiscal month, quarter and year. These revenue targets are obviously an important part of the financial plan and operational performance for the company.

With manufacturing enterprises, a variety of unforeseen events can occur on a daily basis related to customer demand and the supply of the manufactured goods, as well as the supply of required purchased components from vendors. Even with the best of plans going into a reporting period, actions need to be taken to adjust for these unplanned changes in demand and supply in order to meet the revenue target.

I read the recent report titled “Performance Management in the Midmarket“, by David Hatch and Max Gladstone, from the Aberdeen Group, dated November 2009. The thing that struck me is that based on data collected for the report, the top pressure related to performance management for both midmarket and large enterprises is “lag times and the inaccuracy of operational business decisions”. What is stated is that operational performance is negatively impacted when “the lag time for determining operation performance is greater than the decision window (the opportunity to affect performance based on taking or changing an action)”.

The report goes on to identify the major stumbling block is the identification of source data: “During a fiscal period review process, or within an operational performance review meeting, senior executives often ask for metrics and analyses that send their business managers and teams scurrying to find the data that supports the desired answer”. From my point of view, the problem is that data is not readily available to the decision makers. In addition, reports and analytics need to be available against the data for review and to enable effective operational responses. The data and reporting cannot just be looking at the past, but the analytics need to be forward looking to provide information to decision makers so that actions can be taken to meet the performance metrics.

The operational requirement to meet revenue goals for reporting periods is a common concern among midmarket enterprises and could also be viewed as a fairly standard process across entities. Also, given that the absence of source data and the required reporting and analytics is also common, there exists a real opportunity for a standard solution. Midmarket companies can increase their effectiveness at meeting revenue goals by implementing an out of the box solution. The best solution will have the following key attributes:

• Standard integrations with various MRP/ERP systems
• “Cloud computing” enabled – midmarket companies can subscribe to the software rather than having to stand up and manage the infrastructure and software
• Simple to use with little or no training required
• Easy to tailor and configure if needed

Of course, many other operational issues could be targeted other than revenue management and with today’s technology, many solutions that only large enterprises have the resources to invest in can be made available to the midmarket. What do you think?

Twas the night before 2010; and all through the supply chain

So many problems were stirring, like a runaway train;
The 2010 initiatives were set in the boardroom with care,
In hopes that inventory reductions and delivery improvements soon would be there.

The executives were nested all snug in their beds,
While visions of corporate success danced in their heads;
And IT with their servers, and you with your supply chain cap,
Had just settled down for a 2009 nap.

When out in the market there arose such a clatter,
You awoke from your nap to see what was the matter;
Away to the computer you made with a dash,
Logged in to the system and saw a big flash.
The light on the screen with the words that appeared,
Gave the lustre of hope to supply chains next year;
When, what to your wondering eyes should you see,
But many new initiatives for businesses.. to your glee!

Software as a service is easy and quick,
Just log on to the web and that does the trick;
Rapid implementations are the name of the game,
Just whistle and shout and get rid of your mainframe.

Now, SIMPLE, now FAST now COST EFFECTIVE and PRACTICAL!
On SaaS, on CLOUD, on DISRUPTIVE and INNOVATIVE!
To the top of the supply chain! To the top of your stock!
Now dash away dash away costs - go plummet like a rock!

But as forecasts increase or your customers don’t buy,
You miss your targets and inventory mounts to the sky;
You’re left with poor systems and the consultants just flew,
With all of their knowledge and your confidence too.

And then, in a blog, or a website you heard,
The news of new technology …but it sounded absurd!
As you sat with bewilderment and was turning around,
Down the IT stack it came and oh, with a bound.

It was dressed in one solution from the Demand to the Supply,
And its interface was kind of Excel like…it really caught your eye;
But it also looked flexible and user friendly for many on your team,
You smiled and laughed and started to dream.

With this service you could change demand and watch inventory replan on the fly,
With this service you could say to the word ‘latency’ GOOD BYE;
When your business changes your processes can too,
And adoption will spread as quick as the flu.

Now wouldn’t it be nice to start 2010 without any troubles?
To have demand and supply so smooth without any bubbles?
Well we know that won’t happen; volatility is here to stay,
The question is what to do since it won’t go away?

Do your homework, ask questions, but don’t look too far and beyond,
There are technologies out there to help with the way that you respond;
Your competition may be doing it right so make sure and listen,
Many companies are using services that make their eyes glisten.

All the best in 2010,
Good luck with your plan;
But remember,
You need sensible sense and responsive response as fast as you can!

Start out your new year by attending SCM World Live – The Free-to-Attend Virtual Supply Chain Conference . Link here: http://www.scmworldlive.com

I think the best way to look at this question is to ask a few other questions. Why is Oracle buying everything under, over, and including the SUN? Why did SAP’s licensing revenues decline 31%? Why are CIO’s getting “Suite Fatigue”?

• Oracle is only able to achieve 0%-5% licensing growth by making dozens of acquisitions each year. This is about financial engineering – not innovation.
• With the tremendous corporate political power possessed and well leveraged by SAP – it is hard to imagine their licensing revenues falling 31%. Something is seriously wrong!
• As I travel the world meeting CIO’s – I have now heard the term “Suite Fatigue” in three different continents. I think the ERP vendors are even confused by their dozens of three letter software modules.

There is no question corporations are running out of patience and frothy IT budgets to implement ERP systems – which are only really useful for processing transactions. My belief is right now they are dying a slow death.

However, in my opinion, when corporations are offered a disruptive option (10X more efficient) for what is essentially “add a record – delete a record” – we will see a fast and massive ERP blood bath like no other in history of the hi-tech industry. One smaller hi-tech analogy which I witnessed as a young man was the overnight disappearance of the multi-billion dollar Mini-Computer market. The workstations and PC annihilated what was once a robust business.

My guess is a new Internet-based paradigm (platform) for processing transactions combined with emerging SaaS-based enterprise offerings will cause the death of traditional ERP systems (and their alphabet soup ecosystems). Large corporations are figuring out that customizing SAP and Oracle ERP systems is killing them in multiple ways (implementation cost, consulting, training, user adoption, etc.).

The ERP vendors of today will likely not be the ones who “eat their own children”. My guess is an Amazon (or Google) coupled with SaaS companies offering configurable enterprise applications will spoil the ERP Boondoggle.

In 1994, before Amazon was “Amazon” – I was on a financial talk radio show with Jeff Bezos. I can tell you he is very smart and not afraid of any sized vendor. In fact, he views them as a larger market opportunity.

Stay tuned and stay clear of the blood!

I was on a call with a prospect a while ago whose company is struggling with demand volatility.  To address the issue the company is implementing a statistical forecasting tool. I must admit that it took me some time to convince the prospect that implementing a forecasting tool isn’t going to reduce the volatility.  While I accept fully that one’s ability to create an accurate forecast is related to demand volatility, I am adamant that an accurate forecast does not reduce demand volatility.  Demand volatility is an expression of how much the demand changes over time, and, to some extent, the predictability of the demand.  Forecast accuracy is an expression of how well one can predict the actual demand, whether volatile or not.

Why do I make this distinction?  Bear with me while I go through some personal history as a way of explaining the importance of the distinction.  This goes back to my days as a graduate student at Penn State, back when they were still winning national championships!!  I had studied chemical engineering as a undergrad and had moved on to industrial engineering and operations research.  Chemical engineering, like most engineering, is a mathematically rigorous course in which complex equations are used to predict the behaviour of complex systems such as a distillation column.  The equations are precise and outcomes can be predicted to the umpteenth decimal point.  No-one questions the validity or accuracy of the equations used (though there is continued research to “improve” the equations).  In other words there is an implicit understanding that the equations are not accurate to the umpteenth decimal point even though people calculate to that level of accuracy.  But the equations are sufficiently accurate to design chemical plants.  

When it comes to actually running a chemical plant, all sorts of control systems are placed around the equipment to make sure that the plant operates in a “stable” manner. There are feedback loops and feed-forward loops, and controllers that control other controllers.  It is a very interesting study for those, such as me, who like those sort of things.  My main point is that these systems are assumed to be highly predictable and that their behaviour can be described very precisely by a set of equations.  These are so-called deterministic systems.  According to Encarta, the definition of deterministic is

de•ter•min•is•tic [ di tùrmi nístik ] (adjective)
Definition:
1. relating to determinism: relating to the doctrine or belief that everything, including every human act, is caused by something and that there is no real free will
2. of knowable outcome: having an outcome that can be predicted because all of its causes are either known or the same as those of a previous event

Clearly, I am referring to the 2nd definition, though the 1st suits my purposes very well too, because I want to bring in the element of free will, which leads to unpredictable behaviour, or volatility.  Yet, nearly all of social and business systems are based upon the notion of predictable behaviour.

So I had studied chemical engineering and moved into industrial engineering and operations research at the graduate level.  A core requirement was queuing theory.  If you don’t know what that is, don’t worry you are one of very many.  One of the first things the lecturer told us was that if there was a person checking ID’s at the entrance to a bar (I was at university at the time!) and it took about 1 minute to check an ID.  If people arrived at the door about 1 person every minute, how long would the queue/line be in front of the person checking ID’s.  Fancying myself as quite clever, I raised my hand immediately and replied that on average there wouldn’t be a queue.  Sounds reasonable right.  After all people are arriving about 1 per minute and it takes about a minute to check ID’s, so the system is balanced.  Wrong.  The queue will grow indefinitely.  After years of the deterministic world of chemical engineering I just could not accept this.  After some “field” research I had to accept it, but I still did not understand it.  And finally the penny dropped.  Once the person checking the ID gets behind, that is a queue forms, there is no way to catch up.  It will still take about a minute to check ID’s and people are still arriving about 1 per minute.  So how is the person going to catch up?   The real clue to understanding this is that any time the person checking the ID’s sits idle, because there is no-one’s ID to check, is lost forever and cannot be put to productive use.  So the available time to check ID’s is actually less than a minute.  For those brave enough, here is a reference.

So what’s this got to do with supply chains?  Let’s be honest, any lead time that is put into an ERP system is an average (at best) or an estimate (at worst).  The same is true of production rates and scrap rates.  Yet we spend enormous amounts of time and energy fine tuning MRP and APS systems to provide better results, to the point that the results are more accurate than the input data. (I know that is a contradiction.)  But how many times have we heard “garbage in, garbage out” when referring to ERP systems, or other planning systems, and the underlying input data.  Well, hello!  We’re trying to fix the wrong problem.

There is so much uncertainty related to so many variables in the supply chain that simply having a more accurate representation of the average value of an input variable doesn’t really solve the problem. I am not questioning the value of accurate information.  What I am questioning is the value of spending lots of time and effort to make the inputs very accurate when they are only ever going to be approximations because of the inherent uncertainty in supply chains.  On top of it all, so much of supply chain processes – order taking, purchase order issuing, … – is carried out by human beings that we haven’t a hope of creating metronomic repeatability.  Humans are far better at dealing with uncertainty than are machines, but they are also a lot less predictable than machines.  Let us embrace their capabilities rather than turning them into machines.  Let us give the humans tools in which they can use their judgment, in the face of uncertainty, to evaluate different courses of action quickly and effectively.

Which brings me back to the prospect I spoke to a few days ago.  Having a more accurate forecast isn’t going to remove the volatility/uncertainty from the demand.  Having a more accurate forecast isn’t going to help the supply side to deal with the volatility on the demand side. The supply side is still going to have to be agile and flexible to adjust to the demand changes.  And I don’t care how much time and effort is put into statistical forecasting, in a dynamic market with lots of volatility, the forecast will always be inaccurate.  So the question is where should one spend time and effort.  In making the plan as accurate as possible, including all the input data,  and forever analysing why the actuals didn’t match the plan?  Or in accepting that there is a lot of uncertainty in the supply chain and devising ways to respond quickly and effectively to the change?  Clearly it is important to be able to get a fairly good understanding of the results of alternative decisions, but a quick approximate answer will always be better than a slow more accurate answer, simply because the uncertainty inherent in the supply chain will drown out the “accuracy” of an optimized result.

I know there are a lot of Lean and SixSigma people out there who must be frothing at the mouth.  So let’s hear your comments and rebuttal of my arguments.

To my eternal shame I only came across George Stalk at the Boston Consulting Group today for the first time after reading Dan Gilmore’s blog at Supply Chain Digest.  Clearly this man has been around for some time and I have had my head buried in a tree somewhere.   Of course I had come across his concept of time based competition, but I had never paid more attention to his wider concepts.  I came across a very interesting article titled “Rules of response” , which is at the heart of the concept of time base competition, while browsing the web for more information about George Stalk.  Even though the article dates from 1987, my gut tells me that the principles behind the rules are still very relevant, even if the core numbers have changed somewhat because of the adoption of Lean and other efficiency concepts.

The rules, with direct quotes from the article, are as follows:

• The .05 to 5 Rule
  Most products and many services are actually receiving value for only 0.05 to 5 percent of the time they are in the value-delivery of their companies
• The 3/3 Rule
  The waiting time has 3 components, which are the time lost while waiting for:
  - Completion of the batch a particular product or service is part of
  - Completion of the batch ahead of the batch a particular product or service is part of
  - Management to get around to making and executing the decision to send the batch on to the next step of the value added process
• The ¼-2-20 Rule
  For every quartering of the time interval required to provide a service or product, the productivity of labour and of working capital can often double, resulting is as much as a 20% reduction in costs.
• The 3 x 2 Rule
  Companies that cut the time consumption of their value-delivery systems  experience growth rates of 3 times the industry average and 2 times the profit margins

It was really the .05 to 5 rule that caught my attention because it is the driver behind the rest of the rules.  In his description of the rule, Stalk states that “… a manufacturer of heavy vehicles takes 45 days to prepare and order for assembly, but only 16 hours to assemble each vehicle.  The vehicle is being worked on for less than 1% of the time it spends in the system.”

Ok, so maybe Lean has improved the time it takes to take orders in the time since the article was written, but Lean has also reduced the time it takes to manufacture the product, so the ratio has likely remained the same.  What I find interesting in this split is that much of the non-productive time is spent working out when the order can ship, what components are required, if the capacity is available, …  And then there are all the changes that need to be accommodated, starting from order delivery date and quantities, perhaps even configuration/design changes, through to component delivery schedules.  Outsourcing and off-shoring have only made this situation worse by increasing the number of people/organizations required to participate in the process and extending the delivery time.

Spreadsheets emailed around the world or EDI transactions aren’t going to make a dent in this ratio.  I think Hau Lee has it right.  As I commented recently on a presentation Hau gave, this can only be achieved by “extreme information exchange”.  As importantly though, is “extreme analysis”, or the ability to consume and evaluate lots of data in a short period of time.  Machines can churn through vast quantities of information in a short period of time, humans cannot.  At the same time nearly all supply chain decisions need to be made in an environment of ever changing and ill-defined rules, constraints, and objectives.  Humans can deal with this uncertainty, machines cannot.  What is required is a unique combination of human intelligence and judgement coupled with the processing power of machines to turn what Hau calls “extreme information exchange” into “extreme decision making”.

Change is the only constant.  Dealing with the constant change is where the competitive edge lies today.  As always, let me know what you think.

Many supply chains have more links in them than is initially evident.  The time it takes for information to flow up and down the supply chain can be directly proportional to the number of these links.  Moreover, some of these links in the supply chain can distort, as well as delay the information that needs to flow.  Most importantly, the right information needs to flow through the supply chain.  The speeding up of incorrect information or the wrong plan will only result in more churn, which can result in decreased customer service as well as increased inventory.

What is needed is the capability to simulate changes, determine the best response to these changes, and then communicate the response rapidly across the supply chain.

Consider the following diagram:

Based on the diagram above, there are at least two dimensions of links in a supply chain:  external and internal.  The external dimension is the links between one entity and another, such as a manufacturer and a supplier.  The internal dimension is reflected in the Bill of Material structure within an entity.

Each of these links can delay or distort the information flow along the supply chain.  Related to delays, a signal for a supply or demand change needs to likely flow via an MRP execution, normally only run nightly at best,  then there are potentially orders in place where the MRP execution creates exception messages that need to be acted upon by a planner or buyer before the change signal will flow.  In most cases, planners and buyers receive many more MRP exception messages that can be acted on within a day.  Obviously, since there can be at least several days of delay at each link, and considering these internal dimension links, the overall delay will sum up to a considerable amount of time.

Related to distortion, order policies to bucket orders into lot sizes or minimum buy quantities can magnify the change signal as it flows up the supply chain resulting in the traditional “bull whip” effect.  Given this, the wrong change signals can be continuously flowing through the supply chain.

Many manufacturers have attempted to reduce the delays and distortions along the supply chain by implementing automated rescheduling of placed orders, EDI, schedule sharing, supplier portals, small lot sizes, pull systems and the like.  All of these efforts help, but a more comprehensive solution is required to optimize the flow of the right information rapidly through the supply chain.

The comprehensive solution needs to provide the following capabilities:

• The ability to simulate demand and supply changes within multiple scenarios that can be evaluated against each other for the optimal response plan
• Provide the option of auto rescheduling orders and removing or changing lot sizes
• Include visibility into key supplier data and include in the simulation
• Evaluate the impact throughout the supply chain and communicate and collaborate the needed changes quickly

How many links are in your supply chain?  Have you considered this internal dimension of links?

The latest edition of IndustryWeek’s Manufacturing Business Challenge has been published.

This month’s challenge discusses a $300-million manufacturer of material-handling equipment that’s questioning the value of their Sales & Operations Planning process.

I took part in providing one of the solutions, as did Tom Wallace, well-known writer and educator specializing in Sales & Operations Planning

Tell us what you think of our responses to the challenge as described below:

I believe our sales and operations plan (S&OP) has lost all effectiveness. I am the CEO of Varskeeta Velocity, a $300-million manufacturer of material-handling equipment. Each month, I sit with my leadership team and deal with an S&OP that is a plan in name only. We quickly argue about past performance (and look for more data to make our case), debate the reasons for current performance (and look for more data), try to determine emerging demand and supply issues (and look for more data), and, then, somehow, revise our course to meet the next monthly horizon. We also have little capability of reconciling financial (dollars) and operational (units) information, and have no capability to evaluate alternative scenarios. We’ve done it this way since I took over three years ago.

Yet, while we plan at the 10,000-foot level, I’ve got Varskeeta plants and outsourcing facilities productively running as their local management reacts to daily (or even hourly) demand signals they receive from customers, using that information to also schedule our complex supply base. Our supplier management group and sales teams look to our manufacturing leaders for their guidance and decision-making—not the S&OP. And other than send up the numbers we request, our manufacturing managers and outsourcing partners could not care less about the S&OP.

It’s as if I run two different companies: reality (in units) and S&OP (in dollars). Over the span of six months or a year, actual Varskeeta results do resemble the S&OP projections. But how we get there and the details look extremely different. The people who actually make work happen — plant managers, contractors, engineers, sales managers, procurement and logistics staff — tell me they rarely get any S&OP information in a timely manner, and they either ignore it or work around it, devising their own-short-term sales plan, production plan, inventory plan, product development plan, etc. based on their continuous feed of “live” information.

How can I reconcile this? Or should I? Why do I need an S&OP and S&OP planning if the company runs productively without it anyway? Can Varskeeta develop an S&OP that allows our workforce to manage daily yet has the teeth to guide us toward more successful longer-term objectives?

Here is what we said, what would you have advised?

I was talking to a Contract Manufacturing company the other day and discovered an interesting  predicament.

A hot-button issue for this Contract Manufacturer (and others I’ve talked to as well)  is the need to get better information from the Brand Owner about how the product is selling to the end customer.  The Contract Manufacturer’s concern is that the forecast changes from the Brand Owner often come too late to prevent procurement of components needed to satisfy the current forecast. If the CM had better visibility to demand, and more importantly to the rate of consumption, they could provide an essential service to the Brand Owner by warning them about unrealistic forecasts BEFORE the purchase orders go out..  Additionally, the CM would like to be able to challenge new forecasts as well…if only they had the data.  This could save the Brand Owner millions of dollars in inventory liability!  Unfortunately, the Brand Owners often don’t want to give this information to the Contract Manufacturers. 

Amazingly, when talking to Brand Owners, I’ve often heard them say that if only they had access to the contract manufacturer’s data,  they could evaluate changes to the supply plan and make sure they are realistic before we send it to the Contract Manufacturer. Not only that , but we could get visibility to shortages to the Contract Manufacturers and we could help apply pressure to component suppliers   Unfortunately, the Contract Manufacturers don’t want to provide detailed manufacturing information to the brand owners as they fear that the Brand Owner would want to interfere with their planning processes.

So both Brand Owners and Contract Manufacturers feel that they need more information from the other party to better manage the relationship, but yet neither are willing (or are able to) provide this information to the other party.

Hmmm.

So what is the answer?  I have my thoughts, but I’d be really interested in what you, the reader, has to say.  I suspect that many of our readers either work for a brand owner of some sort or a contract manufacturer of some sort.  What level of visibility do you provide to your business partners?  What information do you hold back? Why?  Do you have clearly defined roles about what the Brand Owner does and what the Contract Manufacturer does?

Comment back and let me know what you think.

As an integration consultant, I have worked with a variety of manufacturers implementing supply chain planning and response management applications.  What I have noticed is that at many of the manufacturers, not all, there are many past due purchase order (PO) schedules in their ERP system. 

Obviously, when a PO schedule is past due, it cannot be determined when that scheduled supply will be available to support production.  In many cases, revised delivery commitments from suppliers are maintained outside of the formal ERP system, such as Excel worksheets or supplier portals.  A variety of reasons exist for not updating these past due PO schedules in the ERP system:  too much time required for updating; suppliers need to be measured against the original schedules for metrics, to name a few.  The paradox is that the cost and potential lost revenue of not being able to effectively plan or respond to demand and supply changes likely greatly exceeds the cost of managing the late PO schedules in the formal ERP system.  However, given the fact that these past due PO schedules exist leads me to believe that implementing a process, or just the simple discipline, to update these to the actual expected delivery dates is just too difficult. 

It seems that there are at least several options to fix the problem. 

One option is to build an interface from the supplier portal if it exists, or if one does not already exist, implement a supplier portal with an interface to the formal ERP system to get the latest supplier delivery commitments.  This option could be very costly and also take some considerable time to implement.  Another option is to simply start maintaining the late PO schedules in the formal ERP system.  This could be accomplished by instilling the discipline and the process within the organization to do this.  Part of the process would have to include a reporting and tracking mechanism to alert buyers of past due PO schedules and also track the progress at working them down.  Although there may be barriers to updating the formal PO schedules, this option could leverage a response management system that can send automated alerts to buyers or planners and track the progress at reducing past due PO schedules. 

To me, the preferred option is to leverage a response management or supply chain management system and import the supplier commitments from wherever they exist, whether it be Excel worksheets, a supplier portal or some other application outside of the formal ERP system.  This provides a capability to evaluate the current planning with realistic supplier delivery dates and to simulate changes to determine the best response to change.  This last option also minimizes the manual effort of updating PO schedules.

Do you have past due PO schedules in your ERP system?  If so, is there a compelling reason not to fix them?