Scientific Method and Kata

Kata: What is the challenge? Where are we now? What’s our next target? What do we have to learn next; our next experiment?











Scientific Method is the systematic observation, measurement, experimentation, formulation, testing, and modification of hypotheses.

Improvement Kata is the repeated practicing a pattern to learn a skill and way of thinking, to become second nature, with little conscious effort.

The Scientific Method is the basis for the Deming Cycle (Shewhart) and Plan-Do-Check-Act.

  • Question
  • Hypothesis
  • Prediction
  • Testing
  • Analysis
  • Modify hypothesis, or Conclusion

Kata examples include: swimming, riding a bicycle, driving a car. Mastery through repetition. We can do these things without thinking about them. For lean management, building this automatic habit comes from repetition of …

  1. Understand the Direction (next challenge, goal, target condition)
  2. Grasp the Current Condition, and Obstacles
  3. Establish the Next Step (next target condition)
  4. Go See what we have learned
  5. Rinse and repeat


Measuring Forecast Accuracy


Supply Chain Forecast Accuracy is usually measured with Mean Absolute Percent Error or MAPE, the average of percentage errors. But there are several other metrics to consider. Here’s an example …

Continue reading “Measuring Forecast Accuracy”

10 things to improve forecast accuracy


Forecasts are never right but you can improve your forecast accuracy …

  1. You can’t improve what you don’t measure
  2. Aggregate – individual plus or minus swings cancel each other out
  3. Measure forecast accuracy at the right time fences
  4. Review forecasts by exception only.  If the forecast deviation is within expected limits leave well enough alone
  5. Tolerance – decide what ‘good’ and ‘bad’ forecast means
  6. Triangulate – use multiple forecast methods
  7. Automate and remove the human factor
  8. Forecast less – less often, less granular
  9. Demand Forecast Segmentation – Forecast-ability vs. Benefit
  10. Reduce supply lead time, and you won’t need to forecast (as much)


Not so ‘Good to Great’

An executive asked my opinion about having his staff all read Good to Great by Jim Collins (2001).

It’s been years since I first read the book, one of many business books collecting dust in my library.  When I went back to the list of companies used for research I saw some I knew hadn’t sustained their ‘greatness’.  So I looked up their ticker symbols, and here’s the fate of the Not so ‘Good to Great’:

  • Circuit City bankrupt, Fannie Mae in conservatorship, Wells Fargo bailed out by US Treasury.
  • Only Nucor is really great, up over 400%.
  • All others track with the rest of the pack (DJI)

Not so Good to Great
The stocks:

  1. Abbott Laboratories (ABT)
  2. Circuit City
  3. Fannie Mae (FNMA)
  4. Gillette → Bought by P&G
  5. Kimberly-Clark (KMB)
  6. Kroger (KR)
  7. Nucor (NUE)
  8. Philip Morris  (PM)
  9. Pitney Bowes (PBI)
  10. Walgreens (WAG)
  11. Wells Fargo (WFC)

So given the crummy performance, I have to wonder about the premises made that these companies, that had previously gone from good to great, had some special common characteristics or just random chance.




Kanban and Sustainability

LuizDear Larry, Andrew and Guanair; How are you doing ? I would like to share with you that We Won the Whirlpool award Sustainability-2013 , among 536 suppliers and 55 projects, With our project about plastic returnable box saving industrial waste (avoid 75000 cardboard boxes /2500 wood pallets /50 km of plastic wrap /40 ton of contaminated waste in the last 5 years) and inventory reduction from 20 to 8 days of course. We are doing our part for a sustainable world. Thank you very much for the support.

Merry Christmas and happy new year!

Luiz Antonio Basso
Manufacturing Manager
Associated Spring, Barnes Group
Campinas, Sao Paulo, Brazil


kanban1 kanban2

See kanban simulation

Business Model Canvas

Busines Model GenerationRemember how you felt when you first started drawing your first Value Stream Maps? Well that’s how I felt when I first got my hands on Business Model Generation by Alexander Osterwalder, and Yves Pigneur. This is a new visual tool you can use to help connect your value propositions to your customers and suppliers. The Business Model Generation is an easy to use and very powerful diagramming method, or canvas, with nine blocks:

  • Key partners
  • Key activities
  • Key resources
  • Value proposition
  • Customer relationships
  • Channels
  • Customer segments
  • Cost structure
  • Revenue stream

The canvas help you think about how you plan to make money or deliver value by understanding what customer value and how that value is created.

Business Model Canvas
Business Model Canvas

business model canvas poster

For more on business models and business model innovation take a look at Business Model Generation, for examples of business model canvas’ of companies you maybe be familiar with. Also check out Business Model Alchemist by Alexander Osterwalder.

LeanSigma Tool Kit






  • Customer &
    Process Focus
  • Muda, Muri, Mura
  • Value Stream Map
  • Flow, Takt, Pull
  • Visual Management
  • Process Analysis
  • Problem Solving
  • Quick Changeover
  • Standard Operations
  • One Piece Flow
  • Kanban
  • Heijunka
  • Respect for People
  • Total Productive Maintenance
  • Voice of the Customer
  • Critical to X
  • Project Management, DMAIC
  • Statistics
  • Data Collection
  • Rolled Throughput Yield
  • Cause & Effect
  • y=f(x)
  • Measurement Systems
  • Process Capability
  • Graphical Analysis
  • Hypothesis Testing
  • Correlation & Regression
  • Design of Experiments
  • Failure Modes & Effects Analysis
  • Control Plan
  • Minitab
  • Teams
  • Roles
  • Conflict
  • DiSC
  • Giving & Receiving Feedback
  • Leadership Standard Work
  • Project Management
  • Change Management
  • Metrics
  • Process Modeling & Simulation

LeanSigma Green Belt Training


Flow Consulting clients have often requested us to put together a Green Belt LeanSigma class, and we now have enough demand to guarantee the minimum number of students.  So we are partnering with one of our key clients, Gulf States Toyota, to run the class this fall.

Course Objectives:

  • Provide a fundamental understanding of LeanSigma Principles, DMAIC Project Management, Problem Solving, and the Theory of Constraints
  • Learn LeanSigma techniques to reduce waste and process variation and increase speed and efficiency, expand capacity, and improve profitability
  • Receive mentoring time on LeanSigma projects required for the coursework

Course Benefits:

  • Course is taught by experts with real world experience
  • Students gain access to the latest LeanSigma thinking through real-world case studies and highly interactive simulations
  • Participants gain exposure to diverse industries through lively discussion and student interaction

Course Structure & Registration:

  • Date: 9 Days – October 1-3, November 12-14, December 17-19
  • Location: Gulf States Toyota, 20412 East Hardy Street, Houston TX
  • Cost: $4995

Key Goal – If the proper project is brought to the class, the training will be ‘free’ as the results of the LeanSigma project should more than pay for the class.

Contact Ebony Norflis at Flow Consulting

Demand Segmentation – one size fits none


We know better than to try to force fit strategies yet time and again we find businesses planning and managing their processes with a ‘one size fits all’ approach.  Hospital services, insurance underwriting, pharmaceutical production, consumer electronics assembly, integrated supply chain services all struggle with finding the right balance of specialization and common process.  At the extremes there is the chaos of infinite flexibility where every order, customer, patient is an exception to the other extreme where the ‘cats and dogs’ are blocking traffic on the highway.

Simply sorting process demand (orders, service requests, projects, patients) by volume or frequency isn’t good enough.

Adding the dimension of demand variation is an improvement, but optimizing inventory, process performance, supply chain doesn’t end with a 2×2 graph of Volume vs. Coefficient of Variation.  But before we tackle total cost, margin analysis, supply chain policies let’s go over the fundamentals.

ABC Analysis

Gather up representative data for orders, arrivals, requests – whatever you have that represents demand on your process, business, factory, enterprise.  In HR this might be recruiting requests, in product development an inventory of active projects, in a factory grab customer orders. [ABC Analysis: how to]

ABCNow take the history and summarize product, or service and make a PQ Product vs. Quantity Pareto histogram like this …

Sort the product or services from high to low, left to right.  The highest volume activities want to be streamlined, maybe even have dedicated processes and resources.  Call these A items the ‘highway’.  The lowest volume of orders or frequency of service items, the C items may need to be handled on a case-by-case basis.  If you don’t then you’ll have a ‘mixed model’ process where the A’s and C’s get in each others way.


–A’s  = dedicated cell or line or department
–B’s  = mixed model
–C’s  = “craft” or specialty work cell, or premium service department

Cv ExamplesDemand Variation

ABC PQ Analysis only goes so far and as previously discussed [1] [2] [3] doesn’t consider that while most high volume items are usually stable and predictable some of the high volume items may be unpredictable and have erratic demand, and many of the low volume items will be well-behaved and very repeatable.
If you made a time series graph for every product or service you would be able to ‘eye ball’ the period to period variation in demand and perhaps make some judgements on categorizing similar profiles.
Here are three examples from a recent pharmaceutical demand analysis.
The first product has fairly stable weekly average demand.  A weekly Coefficient of Variation of 0.33 might be considered a little high by some.  The spike at around week 52 is a year-end effect, so overall this product is well-behaved.
The second product is half the volume of the first visually more ‘noisy’.  A weekly Cv indicates that this product would be a good candidate for pack-to-order, otherwise significant inventory would be need to be able to cover the spikes.
Product 3 is very unstable.  Any guess on how much inventory to carry, raw material to buy, capacity to reserve, or lead time to promise is certain to be wrong.  This product would be a good candidate for make-to-order, move to a low volume – high mix business unit (and price and staff accordingly), or possibly discontinue.
Any attempt to try to efficiently run these three products through the same forecasting, planning, scheduling, production, distribution process is bound to be flawed.  The lead times, lot sizes, equipment assignment, and other planning parameters should all consider the item’s demand pattern.  Where you find high Cv ‘cats and dogs’ items coexisting with low Cv  ‘steady eddy’ products you need to seriously consider applying demand segmentation and not trying to make one size fit all.

Product Lifecycle

Products and service have a lifecycle; usually new products volume increases and the initial demand is spotty but stabilizes as the product matures.  Then new products compete and demand declines and becomes irregular for the first generation.






Demand Profile

Customer demand can be described, quantified.  Here’s how …
1. Pick product, product family, or business unit of interest
2. Determine an appropriate time unit – hourly, daily, weekly, monthly
3. Gather the true customer demand as best you can.  Be careful about using promise dates instead of requested dates, and be doubly cautious of schedules which are often smoothed, filtered, or otherwise manipulated.
4. Create the graph or time series plot
5. Then calculate some simple descriptive statistics and interpret
An example…
Descriptive Statistics
Then use Excel Data/Data Analysis/Descriptive Statistics and interpret the results.  In this example mean, median, mode are all close to 17 per day.
The range is 49 (max-min) and +/- 3 standard deviations is 34, or -16 to 50 (17+/-3*11)
(or zero to 50 since we don’t usually have negative customer orders, and we don’t usually worry if demand is lower than the mean, it’s when demand is far greater than the mean that things get interesting.)
The standard deviation is less than the mean, so the demand pattern is somewhat stable.
This demand is a bit skewed to the right, but only slightly;
confidence limit estimate =2*SQRT(6/N) = 0.94
A confidence limit estimate for kurtosis is 2*SQRT(24/N) = 1.89.
Since 1.45 is within +/- 1.89, we can say that the data is marginally normal.
The confidence limit is smaller than the mean, so the mean is reliable.


Process Model Selection

Method by Segment


Other considerations

  1. Margin Contribution & Cost to Serve may be more insightful than just looking at demand quantities or sales dollars.
  2. Products and services may have different demand, inventory, customer replenishment, supplier replenishment, sourcing and total landed cost, and other policies.
  3. Managing these policies in a dynamic business can be a challenge; so look toward optimization automation to stay ahead having the right strategies for the right products instead of one size fits none.



Supply Chain Kaizen

Euclides Coimbra latest book recently made it to the top of my reading list.

Coimbra Kaizen Logistics book

Kaizen in Logistics & Supply Chains is a well considered application of lean and blitz to the fields of logistics and supply chain management.  Here you will find detailed advice on flow, takt, cell design, kanban, internal and external logistics, standard work, supermarket design, water spiders, etc.  This book is a must read for any lean or six sigma practitioner interested in applying lean.  Well done Euclides!

Various supply loops are detailed. Mizusumashi Water Spider system is thoroughly and clearly described from the mechanical to behavioral.  Different types of kaizen workshops are also described:

  • Kobetsu Gemba Kaizen – focused improvement typically on OEE
  • Line Design Workshop – for value stream design and implementation (supermarkets, leveling, milk runs, right sizing containers)

Some of the vocabulary may take a little getting used to – ‘border-of-line’ seems to be what I know of as ‘point-of-use’, or line-side stock.  ‘Total Flow Management’ or TFM is just another way of saying Lean Supply Chain.  Don’t let the Japanese words distract you; Coimbra is being very precise – junjo is sequenced supply, kanban is continuous supply.

If you are working on improving your materials management, then you need to read this book, and apply it!


Lean Forecasting

Why can’t Sales give us a decent forecast?  Paraphrasing George E.P. Box, “All forecasts are wrong, some are useful.”  If forecasts are wrong, then why forecast?  Supply chain lead time is often much longer than the customer is willing to wait, and so the business carries inventory somewhere, and inventory is a waste.  The Lean approach to crush lead time and make-to-order isn’t always possible and so we may need to build on speculation, or at the very least buy raw materials.  Even after making customer consumption visible to all, reducing lead time, creating flow, using pull systems, increasing velocity and reducing variation, and establishing a multi-echelon collaborative and process disciplined environment you may still need to ‘predict’ future demand volume, product mix, and/or seasonality. So whether you want to or not, or have a formal forecasting method or not, somewhere in your purchasing and planning world someone is anticipating what the customers may want and is placing supply orders or adjusting inventory levels or rearranging workloads. Lean Forecasting Principles:

  1. Measure forecast accuracy, seek root cause for inaccuracies, and strive for continuous improvement of the forecasting process
  2. Aggregate (family) forecasts are always more accurate than individual SKU’s – plan families, build SKU’s on pull
  3. The ‘best’ forecast is a customer order, the next best is to replenish what the customer just consumed
  4. Forecast accuracy usually degrades the further out – try to match the forecast period to the supply lead time
  5. Relentlessly seek to crush supply lead time and variation (mix and volume), and eliminate the need for near term forecasts
  6. Drive waste out of the forecasting process

A collection of current thinking on Lean Forecasting:

Lean and Forecasting from Michael Balle’s Gemba Coach Column

What Happened to Materials Management and Forecasting Part II by Bill Waddell at Evolving Excellence

Building a Lean Fulfillment Stream by Martichenko and von Grabe   .

Safety Stock


Over time, inventory goes up and down.  As customer order shipments are made inventory transactions subtract the ship quantity from the quantity on hand.  When production is made or goods received into the warehouse the quantity on hand goes up.  This up/down cycle can be thought of as a ‘saw tooth’.  Except it’s never as neat and tidy as this.

Cycle stock can be calculated as ‘Demand during Lead Time’, or if we make or buy product once a period I should have at least enough to last until the next order arrives.  One way to do this is to first determine the replenishment time (RT) or the time between orders.  For high volume products we may make the product every month, so the RT would be 30 days or 4 weeks or 1 month.

Next we need to know the consumption, sales forecast, or historical shipments and then calculate the average demand during the replenishment period.


AD = average demand, RT = replenishment time, or campaign interval, or period between receipts.

In this example we receive 400 every period. Our stock on hand goes from 400 down to zero just as the next resupply arrives.  The average inventory isn’t 400 or 0, it’s 200.  When there are many products being received and issued every day some are near max, some are near zero, but the average for all products is ½ (max – min).

But this is still too simple.  Consumption (issue from stock, or shipments) is never exactly smooth, sometime higher or lower than planned.  Sometime resupply is earlier or later than planned or in different quantity than planned.  So we have two types of supply & demand variation to think about.  We need safety stock and safety lead time to protect our customers from variation in supply and demand.


In this example we have 100 in safety stock.  If everything were perfect inventory would cycle between 500 and 100.  We keep 100 just in case production is late or customer demand is higher than expected.

Demand can come in faster or in greater quantity than we had planned (red dashed line).  If we can’t get the product resupplied sooner we have a period of stock out or back order.


With safety stock we can try to prevent back orders or stock outs.

We dip in to safety stock when resupply is late.

Putting this all together we get …



Demand Safety Stock can be calculated, but first we need to look at some statistics.  Here is graph of weekly shipments for  CARILATUDE 350MG for the period of 1/1/2012 to 12/31/2012.


Here are some descriptive statistics:

Total shipped = 1,066,772
Weekly Average  = 20,449
Max Week = 61,776
Min Week = 6,360
Weekly Standard Deviation = 8,883
Coefficient of Variation = Std Dev/Average = 0.43

How much inventory should we keep for this product?  Do we want to have enough to cover the two big spikes or is it OK to go on back order for a week or two?  Before we can answer these questions we need to know a few things.

  • How often do we receive this product in to stock?  If we make the product once a month, then we need to have enough in stock to last for a month plus any uptick in demand and some more to cover if production is late on the next run.
  • What service level does management want to achieve?  You would need infinite inventory to cover all possible customer demand and supply issues.

We don’t have enough money or warehouse space to hold all the stock we might want.  So we have to accept some level of stock out or back order.  Where we carry more stock than we need we have consumed raw materials, production capacity, machine time, labor that could have been used to make other products.  When we don’t have enough stock on hand we are often forced to change the schedule and make product earlier than planned, and this means other products get pushed out.  The trick is to set just the right level of target inventory with not too much safety stock.


To set Safety Stock we can consider three types of additional inventory based on the source of variation:

Demand Safety – customer demand can come in greater volume than expected and deplete inventory before the next planned replenishment (raw material or production run).

Supply Leadtime – production can be late due to machine breakdown, labor availability, scheduling changes, lost batch, or get hung up for a variety of reasons

Murphy – unpredictable circumstances that management might want to hedge.

There is also seasonality and planned stock builds, but we aren’t addressing those here.

Demand Safety is based on service level and demand standard deviation.

The safety factor is based on the service level management is trying to achieve.

Service Level % Safety Factor
60 0.26
70 0.52
75 0.77
80 0.84
85 1.04
90 1.28
92.5 1.44
95 1.65
97.5 1.96
98 2.05
99 2.33
99.5 2.58
99.9 3.08


When we calculate, for each product, the weekly shipment historical margin contribution average, standard deviations, and Cv and then graph the average vs Cv for each product we get the following Demand Segmentation graph …

Here we see that there are many product with low volume that are very unpredictable (Cv > 1.0)  unfortunately we may have to keep stock for these products.  Because the history shows that shipments are erratic we may have to carry a lot of inventory to be able to cover the unpredictable demand.  Fortunately most of the high volume products are predictable, but some are on the edge of being noisy and so we may have to carry a large amount of safety stock for these too.

Material Classification Logic

Bruce asked “Do any of you have any standard material classification logic that you have used.  I’m working with a client to classify all of the parts they purchase.  In the past I’ve used some standards from NAPM but I can’t seem to find them now on the ISM site.”

I thought for a moment and went browsing and here’s what I can up with:

Have any better ideas, lets us know!

Lean Math

Lean-Math-croppedWe would like to announce a new entrant into the lean blogosphere, it’s called Lean MathTM (

We know what you’re thinking, “Lean Math?!” Now, that’s a subject that evokes passion in the heart of every lean practitioner…right?

But, the truth is effective lean transformations require some level of math, whether it’s the often deceptively simple takt time calculation, sizing kanban, calculating process capability, or anything in between. It’s hard to get away from math. There is no such thing as math-free lean and certainly not math-free six sigma!

Lean MathTM is not intended to be some purely academic study and it does not pretend to be part of the heart and soul of lean principles. Rather, it’s a tool and a construct for thinking. Here we want to integrate lean math theories and examples with experimentation and application.

Some background. Within the next year, the Society of Manufacturing Engineers will be publishing a book, tentatively entitled, Lean Math. Mark Hamel, author of the Shingo Award-winning book, Kaizen Event Fieldbook and founder of the Gemba TalesTM blog, and Michael O’Connor, Ph.D. (a.k.a. Dr. Mike) are co-authoring this work. They are also getting a ton (!) of help from Larry Loucka, friend, colleague, and fellow-blogger at Lean Sigma Supply Chain.

No surprise, we’re the three folks who are launching this blog. The formal launch date is February 14th – because we LOVE math! Ok, love may be a bit strong. We really LIKE math.

Here are some of our first blog posts:

  • Time
  • Cycle Time
  • Square Root Law
  • Min/Max Cut Theorem
  • Coefficient of Variation

Ultimately, we hope that you will join our fledgling Lean MathTM community and that it lives up to our blog tag line, “Figuring to improve.”

Best regards,


Mark Hamel, Larry Loucka, Michael O’Connor, Ph.D.

New set of eyeglasses

It’s never enough just to tell people about some new insight. Rather, you have to get them to experience it in a way that evokes its power and possibility. Instead of pouring knowledge into people’s heads, you need to help them grind a new set of lenses so they can see the world in a new way.

Kamishibai – Work Observation Card

Kamishibai or Work Observation Card is a management tool for starting and improving Leader Standard Work,. Kamishibai, in Japanese means …. paper drama and has come to mean, in the Lean world, a self audit or observation and reflection.  Kamishibai Cards are checklists carried by leaders as part of their standard work as a way to make their work more visible and open to review and self-learning. Kamishibai can be a large display board, or a small workplace or office posting. Here we have a type of kamishibai for leaders, managers, supervisors who may not have an office or a single workplace. Field service managers, leaders covering many sites, or large facilities may not be able to have a ‘war room’ or community space to display their work. None the less these checklist can be shared with workers and peers as part of leadership coaching and team-based problem solving.

Here’s are examples of Leader Standard Work Observation Cards we’ve used.

Kamishibai - work observation card Kamishibai - work observation card



Here are a few References:



Square Root Law – inventory in multiple locations

Got asked what would happen to inventory when the number of stocking locations change.  I thought for a minute and remembered a quick rule of thumb … The Inventory Square Root Law states that …

Average inventory increases proportionally  to the square root of the number of locations in which inventory is held.

X2 = (X1) * √ (n2/n1)

n1 = number of existing facilities
n2 = number of future facilities
X1 = existing inventory
X2 = future inventory

Here’s an example:

Current inventory is 4000 units, 2 facilities grow to 8.  Using the square root law the future inventory = (4000) * √ (8/2) = 8000 units.


* The Square Root Law was mathematically proven by D.H. Maister in his 1975, International Journal of Physical Distribution article entitled “Centralization of Inventories and The Square Root Law.”


Visual Office

Where do you want to work?  If you’ve ever been to Disney or Epcot you may remember the experience.  You park your car or get off the tram, you don’t need to be able to read a map or the signs, you just follow the walkways and it just seems obvious where to go and how to get from here to there.  A ton of science goes into making the experience at the Magic Kingdom completely different from that of the traveling carnival that shows up at the edge of town in the empty lot next to the volunteer fire house.  At the carnival you can’t get from here to there, if you even can figure out where there is.  Kids may have a bunch of fun at either, but the parental stress level is completely different between the two amusements.

Which of the two do you want your office or shop floor to be like, Disney or the carnival?  If Disney is your destination, how do you get from here to there?

  1. Information is the output of an office.  Information can take many forms: email, databases, presentations,decisions.
  2. Wastes of Defects, Storage, Motion, Overprocessing, Waiting, Overproduction all occur in much the same ways as on a factory floor.  For example motion can be seen in walking, reaching, searching, questioning, interrupting.  Each of these activities cause delays and stress.  But we adapt and accept and live with the abnormal.  We just get used to it.
  3. Time is the inventory of the office.  Time piles up in our in-boxes and databases.  Time happens when work stops.  We run out of information, need a signature, find a mistake and then set that work aside and pick up some other job, file, task.  We keep busy.  But the thing we were working on sits and waits, the clock ticking away.

The “Visual Office” helps make the abnormal viable, finding where the wasted time is piling up isn’t easy, but that’s the mission of the visual office; making the piles of time visible, and then once we can see the inventory of time we just might get uncomfortable enough and creative enough to go and do something to reduce the inventory, and speed up the flow.


Here are a couple references …





Table top simulation – dock operations

Table-top simulationSimulation is the act of imitating or mimicking the behavior of some situation or some process by means of something suitably analogous.  The imitation of a process can be used for debugging, and validating process design changes or use to communicate or train associates.

Simulations can be used for:

  • process design
  • testing new ideas
  • debugging designs
  • testing understanding
  • gaining commitment
  • testing alternatives
  • communicating and training

Sometimes the simulation is role playing theater, other times the ‘game’ has logic and is reproducible, with known inputs and expected outputs.  The photo here is of a recent workshop where we studied how the warehouse dock floor would look after changing the pick waving rules and packaging.  Here outbound goods will be switching from trailer loose stack to returnable shipping containers.

Would we need more floor space?  Do we have enough pickers and loaders?  How do we pick and load multiple deliveries nose-to-tail?

While computer modeling is certainly a consideration the use of table-top simulation has many benefits:

  • Many problems are difficult or expensive to test in real life
  • Many people process information visually
  • A number of alternatives can be quickly tested as the team uncovers issues and finds solutions
  • Simulation costs are very low; you don’t need expensive software or extensive training

Here’s the process we used to build our ‘war game’:

  1. Decide what we wanted to test; i.e. the output – in this case floor loading and labor resources
  2. Gather the input – shipping orders for a typical busy day, number of pickers by zone, number of packing loaders, shift schedules, picking and loading rates, floor space and equipment dimensions (carts, containers, trailers, etc)
  3. Determine the constraints, rules; e.g. number of loaders per trailer, length of breaks
  4. Document assumptions; e.g any trailer can be at any dock door, break and lunches can be staggered, etc.
  5. Be creative and design the game pieces (entities) and determine their quantities; in this exercise carts, containers, bins, trailers
  6. Scale physically (1inch=5feet), scale time (1 day of 10 hours took an hour of game time)
  7. Collect metrics, such as; line per hour, wave start and end time, trailer load duration, number of floor spaces occupied, number of time floor space turned over, number of workers needed

Once the model ‘behaved’ like the current process the team began introducing rule changes which uncovered obstacles.  One of the first changes was reducing the wave batch size from 90 minutes to 30.  Next came changes to packaging and trailer loading.  By the end of the workshop new procedures were debugged and ready for full scale dry runs leading to a live implementation next month.  Stay tuned …

Safety Stock to Bridge the Forecast-Accuracy Gap

(From David McPhetrige, founder of TopDown Lean Systems, LLC, providing correct, comprehensive, multi-attribute safety stock analysis,

Surveys indicate that even world-class companies have average forecast accuracy in only the high 70%’s, especially at the SKU or component level that makes or breaks service levels and financial performance. Contributors to forecast inaccuracy include factors that can be addressed through best practices, and factors that will always be unpredictable:

  • Chronic bias, which can be minimized with effective Sales/Inventory/Operations Planning, or SIOP. (It’s a safe bet that world-class businesses are already doing this, and they are still achieving only high 70%’s.)
  • Unforeseen and unforeseeable special causes, such as natural disasters.

The reality is that forecasting can be only so accurate, in part because it predicts the timing and magnitude of only three types of variation:

  1. Trend
  2. Seasonality
  3. Certain foreseeable special causes, such as promotions

A forecast can predict the timing and magnitude of these variations only so well. Realistically, then, many businesses – and even world-class companies – are left with a significant forecast-accuracy gap that, if not bridged, compromises target fill rates, inventory performance and financial goals.

So first, how do you bridge this gap today? Obviously, you strive continuously to improve your forecasting accuracy, and you always will. That said, though, here are some bridging “techniques” that I’ve observed and even been part of, and likely you can think of some that I’ve missed:

  • Mind-set: Complain about, but begrudgingly resign yourself to, suboptimal inventory and fill-rate performance, and related costs and risks
  • P&L: Increase the budget for airfreight, expediters or other expediting costs and expenses
  • Balance Sheet: Increase safety-stock levels
  • Obsolete/Slow-moving: Increase efforts to return excess materials to suppliers

Some of these techniques can and do help achieve service-level targets. But at a minimum, they rarely help, and may in fact hurt, financial performance. Fortunately, a financially-beneficial service-level bridge from forecast to reality is right there in your data! How so?

Well, there is still variation that’s left over after bias, unforeseeable special causes and the three variations with predictable timing: Common-cause random variation in demand (or usage, for components) and replenishment lead time.

The good news is that it is possible to quantify the magnitude of random variation by analyzing historical demand and/or lead-time data. Of course, the timing of random variation is unpredictable (that’s what makes it random), and this means that common-cause random variation must be addressed with properly-determined safety stock.

At this point, you may be saying, “I’m already doing that. I use a statistical technique to determine my safety stock levels.” Forgive the blunt and clichéd reply, but – how’s that working for you? Honestly, is your technique consistently achieving your fill-rate targets on an item-by-item basis? Or does it often seem to put too much inventory in place? And in many cases, does it put too little inventory in place, and you have to subjectively override or increase the calculated safety-stock level?

The fact that your safety-stock calculation is unreliable does not mean that there is no statistical solution. What it does mean, however, is that you must

  1. Properly identify and isolate the common-cause random variation from all the other variations, and
  2. Use a correct, comprehensive statistical safety-stock approach that includes not just common-cause random variations in demand and lead time, but all six factors that affect safety stock, service levels, inventory performance and expediting.

We aren’t talking about a spreadsheet, or perhaps an unused ERP feature. We’re talking about outside expertise in safety-stock analysis.

A recent CSCO Insights executive brief (from Supply Chain Digest and Cognizant) entitled “Five Strategies for Improving Inventory Management Across Complex Supply Chain Networks” recommends this as the first of five strategies: “Get Much More Granular with Safety Stock Management.” (

This brief advises using “many more attributes associated with each SKU.” The result of this expanded effort is compelling: “The greater the [safety-stock] precision a company will have.” The CSCO Insights authors advise that to do this “obviously requires a lot more work,” and that this increased analysis offers “rich dividends.” The brief concludes that pursuing the strategy of a “higher level of safety stock management” may require a “relatively uncommon skill set” that may best be provided by outside expertise.