This document outlines the logic behind why demand driven MRP (DDMRP) is the best approach for supply chain management. It argues that supply chains experience queues and buffers due to variability, and these buffers increase costs. DDMRP aims to minimize variability by positioning independent inventory buffers and aligning inventory levels across the supply chain. This allows the supply chain to respond autonomously to demand while minimizing unnecessary time, capacity, and inventory buffers compared to other demand forecast-based approaches. The document uses queueing theory and empirical evidence to deductively prove that DDMRP experiences less variability and is therefore superior to other supply chain replenishment techniques.

1. THE SCIENCE BEHIND PROOF THAT
DEMAND DRIVEN MRP IS
ALWAYS BEST*
Supply Chain and Logistics Consulting
SIMON EAGLE
02/12/2015
(* better than the currently known alternatives for SCM)

2. THE LOGIC BEHIND PROOF THAT
DEMAND DRIVEN MRP IS
ALWAYS BEST*
Supply Chain and Logistics Consulting
SIMON EAGLE
02/12/2015
(* better than the currently known alternatives for SCM)

3. 3 S C A L A
AGENDA
1. The nature of proof
2. Queues, Buffers & Variability – theory & practice
3. Flow, Lean & Pull
4. The impact of DDMRP, Forecast Push & Level
Schedule upon Flow and Bullwhip
5. Summary & Conclusion

4. 4 S C A L A
THE NATURE OF PROOF
• It’s very difficult / impossible to prove anything
other than in maths!
• Induction – used by science, there’s always doubt
• Deduction – used in maths, leaves no room for
doubt (eg. if 1+1 = 2 then 2-1 = 1)
“mathematics progresses through a process of deductive reasoning in which
absolute proof can be achieved through use of ‘already known to be true’
statements, or premises, that mean, if the rules of deductive logic are
followed, the conclusion is also necessarily true”

5. 5 S C A L A
WHAT IS OUR PREMISE?
Supply Chains are:
Flows of materials that go through multiple ‘value add’
conversion processes on their way to customers where
they are consumed as finished goods
or
Queues of materials that go through multiple ‘value add’
conversion processes on their way to customers where
they are consumed as finished goods

6. 6 S C A L A
WHY DO WE GET QUEUES?
• “queues are formed when there is insufficient
capacity”
• But not all queues keep growing indefinitely
• So, if we have enough capacity, on the average, why
do we get a queue?
Variability
of
the rate of arrivals at capacity constraints and the rate of
processing by that constraint

7. 7 S C A L A
QUEUE BEHAVIOUR 1
• When the rate of arrivals is greater than capacity
available in the same period, a backlog inevitably forms
• When the rate of processing declines or stops, or there
are no arrivals to process, capacity is lost
• The interaction of these 2 forms of variability is what
generates queues of a finite average length
• The average length of a queue is directly related to the
level of variability and average processing time
• And these queues grow exponentially the greater the
level of capacity utilization

8. 8 S C A L A
QUEUE BEHAVIOUR 2
Shows that:
>> as Variability increases
(and the curves move from
right to left) the queues get
longer / start at lower levels
of capacity utilization, and
>> as capacity utilization
grows so the queues grow
exponentially at higher
levels of utilization
Little’s Law (1961):
Lead-time = System WIP / Ave. Throughput

9. 9 S C A L A
QUEUE BEHAVIOUR 3
Kingman Equation (1961)
Ave. Time in
Q
Variability Capacity
Utilization
Ave.
Processing
Time

10. 10 S C A L A
QUEUING THEORY IN PRACTICE 1

11. 11 S C A L A
https://www.newscientist.com
/article/dn13402-shockwave-
traffic-jam-recreated-for-first-
time/
QUEUING THEORY IN PRACTICE 2

12. 12 S C A L A
QUEUING THEORY IN PRACTICE 3

13. 13 S C A L A
QUEUING THEORY IN PRACTICE 4

14. 14 S C A L A
QUEUING THEORY IN PRACTICE 5

15. 15 S C A L A
QUEUING THEORY IN PRACTICE 6

16. 16 S C A L A
WHAT IS FLOW? 1
The way water runs in a river: autonomous response to
gravity
As applied to Supply Chains: autonomous response to
demand
Flexible: can adapt to sources of variability (constraints)
Process Cycle Efficiency:
Perfect Flow: Value Add Time / Lead-time = 100%

17. 17 S C A L A
WHAT IS FLOW? 2
Glaciers – autonomous, but slow, response to gravity
because highly impacted by sources of variability
Inflexible

18. 18 S C A L A
HOW CAN WE ACHIEVE SC FLOW?
• Respond to Demand and
• Reduce Variability / increase Flexibility or
increase Capacity
What happens to SCs in the presence of Variability?
• wait Time / queuing
• Stock congestion (Little’s Law)
• Unplanned use of Capacity
All are cost generating buffers

19. S C A L A
CapacityInventory Qs
Q. Time
All are cost generating buffers
ALL VARIABILITY IN A SUPPLY CHAIN WILL BE BUFFERED BY A
COMBINATION OF INVENTORY, CAPACITY AND TIME

20. S C A L A
• Variability causes lead-times to be longer than planned
• Service promises are threatened / missed
• Supply schedules are adjusted to prevent the backorder – unplanned change-overs
• Lead-times on other products are extended as a consequence – more service risk
• Unplanned overtime / capacity is put on to catch up
• Planned lead-times are increased (“because ours are too short”)
• More work is released to the factory floor – more WIP, more FG buffer, increase in
capacity utilization
• Extends lead-times
• etc
• etc
In a Supply Chain, the delays caused by variability accumulate but the gains do not.
Most companies operate with excessive levels of (unbalanced) inventory and overly
long lead-times and achieve their service levels through use of expediting / short
notice schedule adjustments and consequent use of unplanned capacity
QUEUING THEORY / FACTORY PHYSICS IN PRACTICE:

21. 21 S C A L A
LEAN
“………. is fundamentally about minimizing the cost of
buffering variability” (Hopp & Spearman” To Pull or Not to Pull, What is the
Question? 1998)
Value, Value Stream, Flow, Pull, Continuous
Improvement (Jones & Womack “Lean Thinking” 1996)
Lean Tools: TPM, TQM, Poke Yoke, Standard Work, Muda
(waste) created by Mura (uneveness), Muri (over-burden),
DFM, 5S, SMED – small batches

22. 22 S C A L A
HOW DOES DDMRP MINIMIZE VARIABILITY?
FG DC2FG DC1PackMfgMfgMfgRM
RM RM
RM
forecast
Suppl.
Suppl.
Suppl.
Suppl.
Cust.
Forecast error induced variability
FG DC1Pack.MfgMfgMfg
RM
sales orders
Suppl.
Suppl.
Suppl.
Suppl.
Cust.
RM
FG DC2
RM
RM
Demand variability
SFG
Planned but independent stock
buffers
FG DC2

23. 23 S C A L A
FORECAST DRIVEN EXECUTION?
Forecast Push DRP/MRP - production is driven by inaccurate forecasts resulting in expediting and schedule
changes as planners inevitably respond to exception messages to protect service levels
Bullwhip - the inaccurate net forecasts and consequent schedule changes amplify demand variability as they are
cascaded up the supply chain due to batching, dependant demand and response delays (ie. latency)
PRODUCTIONSALES ORDERS FORECAST v ACTUAL STOCKS
Forecast accuracy at 100%

24. 24 S C A L A
DEMAND DRIVEN
FG DC1Pack.MfgMfgMfg
RM
sales orders
Suppl.
Suppl.
Suppl.
Suppl.
Cust.
RM
FG DC2
RM
RM
Demand variability
SFG
Planned but independent stock
buffers
FG DC2

25. 25 S C A L A
CAUSES OF BULLWHIP VARIABILITY
Demand variability & schedule changes amplified
through:
 MRP’s network of demand dependencies
 Latency – delayed communications re downstream
demand level changes that cause upstream to over-
compensate and ‘oscillate’
 Mis-reading of demand signals between trading partners
 Excessive batching

26. 26 S C A L A
HOW DOES DD HELP PREVENT BULLWHIP?
• Eliminates replenishment demand dependency
• If applied between trading partners (SaaS) it
provides transparency of demand and inventory
status………..
• ……….and allows upstream inventory targets to be
maintained in line, and simultaneously, with those
downstream to eliminate Latency
• Batching – ensures they are produced only when
needed (and SMED allows them to be reduced in size in
support of Flow)

27. 27 S C A L A
WHAT ABOUT LEVEL SCHEDULE?
What is it?
Make the same stuff in the same quantities every day, week etc
When is it used?
Best when demand is high and very stable but some companies that
have given up on ‘forecast push’ apply it across most of their portfolio
Is it Flow?
No, it’s Push – no autonomous response to demand
How does it compare ?
Like DDMRP, it avoids use of unplanned capacity through re-
scheduling but the greater the variability of demand, the greater
will be the consequent average inventories. DDMRP delivers level
loaded, high utilisation schedules and minimal inventories

28. 28 S C A L A
SUMMARY
• SC variability creates the cost generating buffers – time, capacity and
inventory
• If you can minimise the variability you’ll minimise the buffers
• Executing against inevitably inaccurate forecasts using MRP logic actually
generates SC variability which can be eliminated using DD
• DD’s multiple carefully positioned and planned, but independent,
inventory locations absorb residual sources of variability and break the
demand dependency that otherwise transmits variability
• Replenishing the independent inventory locations against consumption
and aligning them ‘end to end’ allows the (extended) SC to respond
autonomously in line with demand (and minimises the potential for
latency induced bullwhip) with minimal creation of cost generating &
unplanned time, capacity and inventory buffers.
• Then increase SC reliability and flexibility through Lean
• The smaller your batch size the faster will the ‘end to end’ SC respond to
demand

29. 29 S C A L A
CONCLUSION
Case study and experimental* evidence supports the hypothesis that
DDMRP is a significantly superior SC replenishment technique to that of
others currently known
* Dr. JungSook Lee, 2014
If you accept that supply chains are all about material flows that respond to
variability by forming buffers, as described by Queuing Theory, then the
Kingman Equation & Little’s Law logically & deductively prove that DDMRP
is the superior SCM process because it suffers significantly less variability.
QED
Occam’s Razor:
Among competing hypotheses, the one with the fewest assumptions should be selected.
Is DDMRP always the best? Philosophically that’s an inductive question so we cannot answer “yes” but the onus is on
others to develop, demonstrate and explain the fundamental principles of a (simpler) alternative.