The Science behind Demand Driven MRP

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

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.

The Science behind Demand Driven MRP

Simon Eagle

The Science behind Demand Driven MRP