Blog | Lean Sigma Supply Chain

Demand Segmentation and Building a Lean Fulfillment Stream

Hot off the press from the Lean Enterprise Institute …

Page 12 & 13 have a brief description of Coefficient of Variation and a SKU Scatter Diagram (weekly volume vs. SKU stability). 10 weeks usually isn’t sufficient for meaningful or statistically significant calculation of standard deviation.

The guidelines given need to be tempered with the granularity of the data. While a coefficient of variation of less than 1.0 can be considered stable for weekly data, it would be considered very noisy when using monthly data and quite stable when using daily demand.

This small quibble aside the authors Martichenko and von Grabe do a wonderful job describing lean principles for the supply chain, or as they prefer, the fulfillment stream.

Replenishment Strategies

Determining an appropriate production model starts with Demand Profile and Demand Segmentation. High volume low variability items, and low volume high variability items behave very differently. How to decide if a particular product is a candidate for a one piece flow cell or a craftsmen job bench? Look to the coefficient of variation for a clue.

Type 1 – Rate-base or Just-in-time

forecasting of the flow rate or takt time
RCCP – rough cut capacity planning to monitor impact of mix and volume on pace maker operation
produce to rate (or TAKT) vs discrete order or customer pull
demand flow vs time-phased requirements planning
maintain flow priority and timing
no detailed Capacity Requirements Planning required
no or minimal shop order launch or inventory transactions
highly visual and standardized shop floor control
“one-piece” flow, zero inventory, standard WIP – work-in-process
seamless flow/pull of material
Dynamic cycle time (Little’s Law)

Type 2 – Pull

combination of discrete forecasting and/or demand rate-based forecasting
MRP planning — pull Kanban, Heijunka visual shop floor control
RCCP, but no detailed CRP
flat Bills Of Materials
more cellular manufacturing
point-of-use vs. central stores
inventory is strategic: standard inventory, time-based replenishment, pull based on consumption vs. push based on demand
based on statistically balanced rate, build to level-loaded demand with calculated standard inventory buffers

Type 3 – Push or Job Shop Discrete

discrete requirements planning (firm orders and long range forecast)
Rough Cut Capacity Plan
time phasing of requirements
application of order policies: lead time, safety stock & time
Capacity Requirements Planning
MRP shop order launch & order maintenance (message filters and “noise management”)
ability to aggregate disparate requirements across multiple products by work center, supplier, product
central stores of inventory
multi-level inventory: stores, pick, kit, move, queue
batch processing
demand leveling difficult and uneconomical

Demand Profile

Maslow’s hammer, or a golden hammer is an over-reliance on a familiar tool; as Abraham Maslow said in 1966 in A Psychology of Science, “It is tempting, if the only tool you have is a hammer, to treat everything as if it were a nail.” So, must every product in every business segment be set up in a one piece flow cell? Or put on kanban with an heijunka to smooth demand? Or run on a rate-based assembly line? Certainly not! One size rarely fits all. But how to know which techniques make sense?

One place to start is to look at customer demand. All lean practitioners know about Takt Time, or the customer drum beat, and is used to match the pace of an operation with customer demand. Takt Time is calculated at Available Time/Demand, and is by definition an average. Customer demand is anything but average, and so we need to understand the variation or range of demand placed on our process.

Here’s an example …

To build a demand profile take the following steps:

Pick the product or product family or business unit of interest.
Determine an appropriate time unit – hourly, daily, monthly.
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.
Create the graph or time series plot as above.
Now calculate some simple descriptive statistics. In this example the average is 17 with a range of 49 and a standard deviation of 11.

What can we conclude? Should we design our operations control around a demand rate of 17 a day? Is the variation in demand something we can deal with? How?

21st Century Supply Chains Require Demand Driven Rules and Tools

Here’s an interesting argument for repackaging MRP for pull by Chad Smith & Carol Ptak
21st Century Supply Chains Require Demand Driven Rules and Tools.

Perhaps new technology can address the limitations of classic material requirements planning.

Any thoughts?

Improve Turnaround, Shutdown, and Outage Duration: After Action Review

The practice of AAR comes from the military, as in the US Army’s TC25-20 “A Leader’s Guide to After-Action Reviews” 9/93. The approach is a classic example of Plan/Do/Check/Act and after an activity, while events are still fresh we ask five questions as follows: