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August 16‐18, 2016 Johannesburg, South Africa
Insights into Dynamic Buffer Management
Albert van der Wat, Cargo Solutions, South Africa 17th – 18th August, 2016
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Albert van der Wat Albert was born and raised in Johannesburg, and completed his Industrial and Systems Engineering Degree at the University of Pretoria in 2008. Albert is currently working for CargoSolutions, as TOC consultant and Symphony Implementer. Albert has a vast knowledge of how Symphony can improve a company’s operations using TOC based planning, execution and management tools. In addition, Albert also uses the Symphony Simulator as marketing tool to illustrate how TOC and Dynamic Buffer Management can Contact info improve Inventory carrying costs.
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Insights into Dynamic Buffer Management
Presentation Content • How much can a buffer really absorb? • What about long lead times? • How do you manage variance in consumption? • How can you incorporate all the complexities in a single solution?
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How much can a buffer really absorb?
• Hypothesis #1: – A buffer can absorb 20%, 30% and 40% continuous increase in dynamic consumption.
• Hypothesis parameters: – Fast moving SKU • Random generated daily consumption between 0 ‐100 • For 20%: 0 – 120 • For 30%: 0 – 130 • For 40%: 0 – 140 – MOQ : 10 – Lead time: 10 – No Buffer Adjustments 27th International Conference of the TOC Practitioners Alliance ‐ TOCPA
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This is a happy buffer
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What happens with 20% increase?
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What happens with 30% increase?
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What happens with 40% increase?
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What happens with 50% increase?
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With DBM, buffers would adjust accordingly
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How much can a buffer really absorb?
• Hypothesis #2 – A buffer can absorb 20%, 30% and 40% continuous increase in static consumption.
• Hypothesis parameters: – Static moving SKU • Static consumption of 10 units, with random generated consumption frequency • For 20%: 12 units randomly generated • For 30%: 13 units randomly generated • For 40%: 14 unit randomly generated – MOQ = 10 – No Buffer Adjustments 27th International Conference of the TOC Practitioners Alliance ‐ TOCPA
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This is a happy buffer
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What happens with 20% increase?
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What happens with 30% increase?
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What happens with 40% increase?
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What happens with 50% increase?
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What happens with 100% increase?
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With DBM, buffers would adjust accordingly
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Conclusion • Hypothesis #1: – Yes, a buffer can absorb 20%, 30%and 40%, continuous increase in dynamic consumption.
• Hypothesis #2: – Yes, a buffer can absorb 20%, 30% and 40% continuous increase in static consumption.
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But… This is only applicable for short lead time SKUs…. What happens if the lead time increases? 27th International Conference of the TOC Practitioners Alliance ‐ TOCPA
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How much can a buffer really absorb?
• Hypothesis #3 and #4: – A buffer can absorb 20%, 30% and 40% continuous increase in dynamic and static consumption, with a lead time of 30, 90, 150 and 210 days.
• Hypothesis parameters: – Fast moving SKU • Random generated daily consumption between 0 ‐100 • For 20%: 0 – 120 • For 30%: 0 – 130 • For 40%: 0 – 140 – MOQ : 10 – No Buffer Adjustments 27th International Conference of the TOC Practitioners Alliance ‐ TOCPA
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This is a happy buffer 30 Day lead time, Dynamic Consumption
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20%, 30%, 40% and 50% increases 30 Day lead time, Dynamic Consumption
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This is a happy buffer 30 Day lead time, Static Consumption
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20%, 30%, 40% and 50% increases 30 Day lead time, Static Consumption
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This is a happy buffer 90 Day lead time, Dynamic Consumption
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20%, 30%, 40% and 50% increases 90 Day lead time, Dynamic Consumption
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This is a happy buffer 90 Day lead time, Static Consumption
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20%, 30%, 40% and 50% increases 90 Day lead time, Static Consumption
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This is a happy buffer 150 Day lead time, Dynamic Consumption
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20%, 30%, 40% and 50% increases 150 Day lead time, Dynamic Consumption
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This is a happy buffer 150 Day lead time, Static Consumption
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20%, 30%, 40% and 50% increases 150 Day lead time, Static Consumption
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This is a happy buffer 210 Day lead time, Dynamic Consumption
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20%, 30%, 40% and 50% increases 210 Day lead time, Dynamic Consumption
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This is a happy buffer 210 Day lead time, Static Consumption
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20%, 30%, 40% and 50% increases 210 Day lead time, Static Consumption
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Conclusion • Hypothesis #3: – Yes, a buffer can absorb 20%, 30%and 40%, continuous increase in dynamic consumption with longer lead times.
• Hypothesis #4: – Yes, a buffer can absorb 20%, 30% and 40% continuous increase in static consumption with long lead times. 27th International Conference of the TOC Practitioners Alliance ‐ TOCPA
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Can we reduce inventory for stable consumption SKUs?
• If we have a SKU, that we know will have stable consumption for the signification future, can we reduce the stock holding of the SKU?
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Can we reduce inventory for stable consumption SKUs?
Here are our happy buffers, can we reduce the inventory without stocking out?
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Can we reduce inventory for stable consumption SKUs?
• Hypothesis #5: – We can reduce inventory on stable consumption SKUs by 15%.
• Hypothesis Parameters: – Reduce existing buffer by 15% – Reduce red band to 20% – Increase Green band to 50%
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Reduction in inventory 30 Day LT
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Reduction in inventory 90 Day LT
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Reduction in inventory 150 LT
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Reduction in inventory 210 LT
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Reduction in inventory, BUT protective capacity is compromised, for 30 Day LT
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Reduction in inventory, BUT protective capacity is compromised, for 90 day LT
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Reduction in inventory, BUT protective capacity is compromised, for 150 Day LT
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Reduction in inventory, BUT protective capacity is compromised, for 210 Day LT
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Conclusion • Hypothesis #5: – Yes, we can reduce the inventory holding of a SKU with stable consumption, BUT we will not be able to absorb an increase in consumption of more than 20%.
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How do we manage variance? • How can we manage low, medium and high variance in consumption? • Types of variance – Variance in amplitude of Consumption – Variance in frequency of Consumption
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Variance vs Lead Time Matrix
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Conclusion • Buffers can absorb up to 40% of dynamic growth and up to 50% static growth • Buffers can very easily me manged for long lead time items • Stable consumption SKUs can be managed with less inventory, but protective capacity might be compromised.
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Conclusion • DBM can be used to manage variance in amplitude and frequency of consumption • The Variance vs Lead Time Matrix can easily be used to identify what DBM profile should be allocated to which SKU • All types of SKUs can be managed with buffers
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Questions?
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