THE SCIENCE BEHIND PROOF THAT DEMAND

DRIVEN MRP IS ALWAYS BEST*

Supply Chain and Logistics Consulting

SIMON EAGLE02/12/2015

(* better than the currently known alternatives for SCM)

THE LOGIC BEHIND PROOF THAT DEMAND

DRIVEN MRP IS ALWAYS BEST*

Supply Chain and Logistics Consulting

SIMON EAGLE02/12/2015

(* better than the currently known alternatives for SCM)

3 S C A L A

AGENDA

1. The nature of proof2. Queues, Buffers & Variability – theory & practice3. Flow, Lean & Pull4. The impact of DDMRP, Forecast Push & Level

Schedule upon Flow and Bullwhip5. Summary & Conclusion

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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”

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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

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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

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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

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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

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QUEUE BEHAVIOUR 3

Kingman Equation (1961)

Ave. Time in Q

Variability Capacity Utilization

Ave. Processing Time

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QUEUING THEORY IN PRACTICE 1

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https://www.newscientist.com/article/dn13402-shockwave-traffic-jam-recreated-for-first-time/

QUEUING THEORY IN PRACTICE 2

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QUEUING THEORY IN PRACTICE 3

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QUEUING THEORY IN PRACTICE 4

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QUEUING THEORY IN PRACTICE 5

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QUEUING THEORY IN PRACTICE 6

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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%

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WHAT IS FLOW? 2

Glaciers – autonomous, but slow, response to gravity because highly impacted by sources of variability

Inflexible

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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

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

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:

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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

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HOW DOES DDMRP MINIMIZE VARIABILITY?

FG DC2FG DC1PackMfgMfgMfgRM

RM RM

RM

forecastSuppl.

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

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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)

PRODUCTION SALES ORDERS FORECAST v ACTUAL STOCKS

Forecast accuracy at 100%

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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

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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

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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)

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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

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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

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CONCLUSIONCase 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.

QEDOccam’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.