Posts Tagged ‘Demand driven’

I came across the following blog post at The Manufacturing Blog that gives a plain English description of pull-model production methods – how to be lean and demand-driven.

I particularly like Stephen’s analogy of push vs. pull manufacturing:

By trying to guess potential demand, manufacturers often found themselves in the same situation as someone carrying an umbrella on a sunny day because the forecast predicted rain: extra effort for no reason. Modern manufacturers prefer to stick their head out the window and check for rain before grabbing their umbrella, so to speak, limiting waste and maximizing efficiency.

As Stephen points out,

Understanding the many complex strategies behind these new manufacturing methods can be as difficult as predicting the weather, as they have brought along with them a series of three-letter acronyms that dominate jargon-filled conversations about current manufacturing trends, like JIT, TPM, QRM, and JIS. These letters don’t exactly help to explain the basic ideas behind pull-production manufacturing, which actually make a lot of sense when spoken in plain English.

Thus, Stephen gives us a plain English guide to the ‘alphabet soup’ and breaks things down to the key concepts of lean manufacturing, Six Sigma and flexible manufacturing. It’s a good overview.  And the long list of acronyms in the post reminds me of our Suitemates “Suites are Sour” comedy video episode – the acronyms in our industry are not just limited to manufacturing and supply chain terms, but also applies to the associated technology applications!

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

This month’s challenge discusses a maker of wireless and radio frequency components that is starting to see signs of an economic recovery but is very concerned with the risks of overestimating or underestimating the resurgence of business.  They are struggling with what is needed from a process and technology standpoint to get an accurate picture of demand going forward.

We were fortunate to have Dr. Larry Lapide, a research affiliate with MIT’s Center for Transportation & Logistics,  join us in providing his take on a solution to the challenge as described below:

Hinnts Wireless has been fortunate to weather a bleak two years. As CEO, I watched annual revenues fall 20% to approximately $225 million. A maker of wireless and radio frequency components for OEMs and system integrators, Hinnts is highly dependent upon commercial and industrial markets. As they slowed, we slowed. Now we are beginning to see our sales trickle back, and our major customers indicate that within six months they expect their orders to get back to prerecession levels. In addition, we are launching a number of new products over the next six months that promise to hit new markets and bring in new customers.

While the severity of the market slowdown came as a surprise, I do not want us to be surprised when our markets rebound. Our management has always believed — perhaps, erroneously — that Hinnts has fairly level month-to-month sales volumes for all product lines, and so we relied on simple methods for scheduling production and triggering our supply chains. But as our markets get more complex, our scheduling will as well, and we cannot afford to overestimate or underestimate — as we’ve done in the past. I see now that every dollar counts, and the money we’ve lost on obsolete or discounted inventories, overstaffing, or missed sales would have made the last two years more bearable.

I would like to have a more accurate picture of our customers and their plans and forecasts, and use that information more proactively to integrate production with our suppliers and to staff our plants. With a recovery likely, is it time to invest in processes and technologies to help Hinnts and our suppliers sense and respond to demand quickly and cost effectively? Can I afford the investment, and what can Hinnts expect to gain?

What would be your recommendation?  Here are ours….

Lora Cecere, while at AMR Research, published a list of predictions for 2010 that are CPG and retail focused, which is her specialty.  What is interesting about the list,  in my opinion anyway, is that we will see a lot of retail behaviour begin to percolate down the supply chain.  This is my first prediction, but it is really an observation of an acceleration of this phenomenon, rather than a beginning.  At the heart of Lora’s predictions is the shift of power over the last 20-odd years from the brand owner to the retailer to the consumer.  This is associated with a big increase of online shopping, or as Lora states “What’s old is new again, with e-commerce rising from the ashes of the dot.com bubble.”

It is this re-emergence of e-commerce that has given the consumer the buying power.  Of course, the recession has had a role to play by making price a key buying criterion, possibly the key criterion.  But e-commerce allows the consumer to compare several alternatives, in terms of both brand and price, in a fraction of the time and with greatly reduced effort.  While mall shopping isn’t going away any time soon, there is no doubt that e-commerce has shifted the buying power to the consumer through ease of use and ease of choice.  For example, my daughter went to the mall with a friend and came home excited about a skirt she had seen in a store but couldn’t afford it.  My wife called her friend to get a few more details and then spent about 20 minutes on the internet finding the best deal, which was 30% less than the store price, including shipping.  It would have taken her at least 20 minutes to drive to the mall and the 30% reduction does not include the cost of driving to the mall.  And it arrived in time for Christmas.  What’s not to like about this story?

So how does this relate to our market, which is much more in the low volume, high mix and build-to-order category, rather than the high volume, low mix and make-to-stock environment typical of CPG?  As has been commented by many people before me, retail-like behaviour is being adopted in more industrial environments.  I visited a fab-less semiconductor manufacturer in late December that is wrestling with increasing demands for a much wider choice of product capabilities coupled with expectations of greatly reduced order to delivery lead times.  The lead time expectation is a lot less than the manufacturing lead time so the semiconductor manufacturer is looking at postponement strategies including, very importantly, die reservations in the foundry, which of course they do not own.  Because of a greater product portfolio they cannot afford to keep the same levels of inventory because of the associated risks of price reduction and obsolescence.  As I stated in the opening paragraph, the adoption of consumer behaviour in a business-to-business environment has been increasing over the past few years, and will only accelerate.

Perhaps it is Lora’s point about the effect Wal-Mart is having on the supply chain that best captures the impact consumer and retail behaviour is having on the larger manufacturing sector.  Because Wal-Mart is such a dominant player, initiatives enforced by Wal-Mart soon trickle down the supply chain through multiple tiers of supply and affect other industries too.  Lora selects 3 initiatives: “sustainability scorecards, rethinking inventory strategies, and the initiation of the Supplier Alliance Program.” 

Let’s start with inventory.  For centuries, inventory has been used as a buffer between demand and supply, starting with grain silo’s and other food stores.  The fab-less semiconductor manufacturer I mentioned above is, like many other manufacturers, adopting postponement strategies including reducing inventories to preserve cash, while at the same time being faced by the need for shorter order to delivery times.  Obviously there is a lot that can be done to improve manufacturing flexibility and shorten change-overs, but the biggest gains are to be had in reducing the order processing times, especially the time it takes to determine if the order can be delivered on time and in full.  Given the reduction in inventory, the issue has gone from available-to-promise (promising from finished goods inventories) to capable-to-promise (determining if the products can be manufactured in time), blurring the distinction between execution and planning.

The sustainability scorecard will perhaps have the biggest and widest long term effect on the supply chain.  As stated in a New York Times article, “In the future they may also have information about the product’s carbon footprint, the gallons of water used to create it, and the air pollution left in its wake.” With the impact of environmental legislation also trickling through the supply chain, particularly the electronics supply chain, it will only be a short time before a full product sustainability scorecard will be required including “carbon” accounting.  I think it is only some time before we will have a “carbon cost of goods sold.” And, as commented on in the NYT article by Tim Marrin, associate director of external relations for Procter & Gamble, “The last thing a supplier really wants is when you’re doing a separate index for every retailer.”  Wal-Mart has the market “muscle” to see this through and to ensure a standard is adopted across the industry.  For assembled products, such as consumer electronics, this means that the suppliers to the brand owners will also have to conform.  Which is how we will see the “trickle down” effect influence the adoption of a sustainability index on labels permeate other industries.  To be fair to high tech, particularly computing, they have had a start rating in effect for some years.  But the Wal-Mart initiative will take this to a whole new level of detail and accountability.

Am I just still too full of Christmas “cheer”?

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.

In a recent Wall Street Journal article, “September Sales May Foreshadow Holidays,”  it is stated that stores are slashing inventories to avoid price cutting. Inventories are at an all time low. How will retailers and suppliers respond when demand changes?

Per the article “Retailers and analysts will be closely watching September sales reports due Thursday from key store chains for any sign they may need to adjust their already-gloomy holiday forecasts. ”

There is always a fine balance between meeting profitability targets and revenue. In the retail business, competition is fierce and retailers need to have the product ready to ship. With the current economic uncertainty, retailers are choosing to stock less with an expectation that they will be able to respond to the market demand when it improves. However they are faced with global supply chains, long lead times and there will be a capacity constraint on suppliers when the demand increases.

There are a number of approaches that retailers need to consider:

• Get closer to customer demand. Analyze point of sale data
• Analyze demand trends (units and $) by product, by region, by time
• Apply supply risk management strategies (minimize single sourcing, establish VMI hubs, standardize on components or materials across products, identify product configuration alternates)
• Have the ability to make the appropriate decisions regarding allocation (by region, customer segmentation)
• Perform contingency planning scenarios – adding capacity, alternate shipping methods
• Have the tools in place to remove the latency from the change in consumer demand through the supply chain to the individual manufacturer. Ensure that any change is communicated throughout your supply chain in minutes to hours, versus days or weeks.

History will not predict the future in consumer spending. Crystal balls won’t work either. Retailers will need to watch consumer demand patterns very closely and be ready to engage their supply chain at a moment’s notice. Collaboration will be required between the trading partners. In today’s economic climate, speed wins.

I have the privilege of participating in an upcoming IndustryWeek webcast with some notable thought leaders in the industry.  This is a must-attend event for anyone struggling with volatile demand…and really, who isn’t!

DEMAND MANAGEMENT: A GAME CHANGING COMPETITIVE ADVANTAGE
Thursday, Sept.24th | 11:00 am Pacific; 2:00 pm Eastern

with…

Karl Braitberg, Vice President, Customer Value Chain Management, Cisco

Braitberg will discuss how Cisco has adopted a number of demand management programs around customer segmentation and demand/supply balancing to become very responsive to demand volatility.

Dr. Larry Lapide, Research Affiliate, Demand Management, MIT Center for Transportation & Logistics

Lapide will discuss the fundamental aspects of demand management, the benefits that companies’ demand management processes can realize, and the outcome of the research done by the Demand Management Solutions Group (DMSG).

The bad news? Demand volatility is here to stay. The good news? There’s a lot that can be done to increase your supply chain agility, and your confidence in responding quickly and effectively to demand fluctuations.

More details here on the event and speakers.  I recommend registering today!

For many years I have worked in the high tech industry. Recently my experiences have been in the pharmaceutical industry and I have discovered an interesting insight. There is no question that employees in a pharmaceutical company know why they are there. It is all about the customer.  The customers are patients and often the pharmaceutical companies are producing drugs that are saving people’s lives. Employees certainly have a sense of purpose in their jobs and there is no doubt that the customer is #1. It is intrinsic. Everyone from the person making the bulk formula to the shipping clerk to the CEO understand what it means not to deliver on time.

In other organizations the customer is not always #1 or at least certain parts of the organization are detached from the customer. Should that be the case? I don’t think so. Understanding the customer is just as important in high tech or aerospace or consumer packaged goods. It is more difficult to create a culture of ‘customer is always #1’ in some organizations. However everyone gains a greater sense of purpose in their jobs if the company operates this way. How many buyers are expediting or cancelling parts orders and don’t know who they are affecting?

I am not suggesting that other metrics, like revenue or profit are ignored but customer value, customer satisfaction, and customer responsiveness are differentiators for companies. Is this difficult to measure? Certainly. Your CFO cannot objectively measure customer satisfaction as he or she can measure inventory turns or revenue. One has to consider the opportunity for increased market share and separating your company from your competition.

An excellent post by Colleen Crum that spans a number of topics touches on poor customer service inevitably being the result of poor decisions and poorly designed processes that lack integration.
‘Advances in communicating information across a supply chain aid in better decision making and collaboration’

This confirms that there are a number of opportunities to improve your focus on the customer.

a) Continuously communicate the value of the customer to all employees
b) Identify key objectives linked to customer value for all employees
c) Ensure that processes are integrated such that individual contributors at all levels understand their impact on the customer
d) Ensure that the appropriate tools are in place that identify the customer impact of your actions. This may be the buyer expediting a part or a production scheduler adjusting a production plan.

The end result of having the appropriate people, processes and tools is a leaner, more efficient workforce rewarded by providing customer value which results in positive impact to other key metrics such as revenue, margin and shareholder value.

In case you didn’t see this…

AMR Research is planning a December feature to provide insight regarding steps to create a demand driven supply chain organization. Several of the questions they want to answer include:

1. How have leading supply chain organizations evolved and developed?
2. What are the key phases of organizational maturity?
3. What opportunities exist that tie demonstrated performance to effective organizational design?
4. Guidance on best practies in the construction, development and implementation of a demand driven supply chain organization

They have launched a 10 minute survey in order to collect survey information.  You can access the survey here if you’d like to complete it.  If you have questions, you can send an email to David Acquino.