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Inventory Guru Introduction
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Introducing the New Inventory Guru
ADAPTIVE INTELLIGENT INVENTORY OPTIMIZATION (AI+IO) NETWORK OPTIMIZATION DEMAND ANALYSIS DEMAND CLASSIFICATION MULTI-ECHELON INVENTORY OPTIMIZER DISCRETE EVENT INVENTORY SIMULATION
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What is new in AI+IO? Demand analysis and advance demand statistics
Mean and std. dev. of nonzero demand and inter-demand interval mean Propagation of these advance demand statistics Demand classification Automatically analyze and classify up to 10 different types of non-normally distributed demand Demand classification for each facility in a given network Inventory policy recommendations Summary and detailed inventory outputs 3 different definitions of service level Type 1 (cycle service level) Type 2 (fill rate): Improved formulation Type 3 (ready rate) Yasin
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What is new in AI+IO? Better inventory modeling with various distributions Normal, Poisson, Negative Binomial, Gamma, and Mixture Distributions Use of historical demand or forecast data to determine a right distribution based on characteristics Dynamic programming approach for the solution of Guaranteed Service Time Algorithm Dramatic improvements in run time. Ability to handle complicated network/BOM structures. Provide a framework for adding new constraints /features. Set service time for different destinations. Takes into account more than one inbound transportation modes. Yasin
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What’s Different in Inputs?
Products: None Sites: None Demand: None Sourcing Policies MOQ is required since it’s used to calculate both the Q and demand variability Transportation Policies Minimum Shipment Quantity— similar to MOQ but considers Mode Minimum/Maximum Service – similar to service time in IP but considers Mode Inventory Policies Safety Stock Rule has been removed since it is now obsolete Multi-Period Inventory Policies Service Requirement is now supported User Defined Customer/Facility Demand Profile Inputs added for Non-Zero Demand Mean, Non-Zero Demand Std Dev and Inter-Demand Interval Mean
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What’s Different in Outputs
Safety Stock Details Now replaced by Inventory Policy Details and Inventory Policy Summary Added recommended control policies Added additional statistics Added additional approximated operating parameters Customer/Facility Daily Demand Replaced by Aggregated Customer Demand and Aggregated Customer Facing Facility Demand Customer/Facility Demand Profile Added demand classification columns and additional statistics Inventory Specific Tableau Outputs
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What’s Different In Settings
Full Scenario Support Control over thresholds Control over outlier handling Single/Multi-Echelon setting is now at global level
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What happens when I upgrade?
All input tables except for User Defined are intact Both User Defined tables are cleared SSO outputs from IO2 are cleared Demand profiles and daily demand details are preserved As usual, a backup of the IO2 model will be made automatically
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New Demand Propagation
Yasin
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What is demand propagation?
Facility1 Customer1 Demand Series1 External Supplier Warehouse Facility2 Yasin Propagated Demand Customer2 Demand Series2
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How does demand propagation work?
Facility1 Customer1 𝝁,𝝈 Demand Series1 𝝁 𝑵𝒁 , 𝝈 𝑵𝒁 , 𝒑 External Supplier Warehouse 𝝁,𝝈 𝝁 𝑵𝒁 , 𝝈 𝑵𝒁 , 𝒑 Yasin Facility2 Customer2 𝝁,𝝈 𝝁: Demand Mean 𝝈: Demand StdDev 𝝁 𝑵𝒁 , 𝝈 𝑵𝒁 , 𝒑 𝝁 𝑵𝒁 : NZ Demand Mean 𝝈 𝑵𝒁 : NZ Demand StdDev 𝒑: Demand Interval Mean Demand Series2
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Example: Demand Propagation
𝑇 4 , 𝑀 4 4 Customer demand 𝜇,𝜎, 𝝁 𝑵𝒁 , 𝝈 𝑵𝒁 , 𝒑 2 𝑇 2 , 𝑀 2 𝜇,𝜎, 𝝁 𝑵𝒁 , 𝝈 𝑵𝒁 , 𝒑 5 Customer demand 𝑇 5 , 𝑀 5 𝜇,𝜎, 𝝁 𝑵𝒁 , 𝝈 𝑵𝒁 , 𝒑 External Supplier 1 𝜇,𝜎, 𝝁 𝑵𝒁 , 𝝈 𝑵𝒁 , 𝒑 𝑇 6 , 𝑀 6 Yasin 6 Customer demand 3 𝜇,𝜎, 𝝁 𝑵𝒁 , 𝝈 𝑵𝒁 , 𝒑 𝑇 3 , 𝑀 3 𝜇,𝜎, 𝝁 𝑵𝒁 , 𝝈 𝑵𝒁 , 𝒑 7 Stockpoint Demand Demand Statistics Replenishment Order Statistics Customer demand 𝑇 7 , 𝑀 7 𝜇,𝜎, 𝝁 𝑵𝒁 , 𝝈 𝑵𝒁 , 𝒑 𝜇,𝜎, 𝝁 𝑵𝒁 , 𝝈 𝑵𝒁 , 𝒑 𝑇 𝑖 , 𝑀 𝑖
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Why we need advance demand statistics?
To take into account batching effect Batch size (Q) might have a great impact on the variability for the upstream echelon To perform demand classification 𝜇 𝑁𝑍 , 𝜎 𝑁𝑍 , 𝑝 are used for algorithm Yasin
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Example-1: Impact of Batching on Demand Propagation
Yasin
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Demand Propagation: IO2 vs. AI-IO
IO2 Solution: Demand Propagation with Batch Size (Q) = 100 AI+IO Solution: Demand Propagation with Batch Size (Q) = 100 Yasin, Mention that more echelon it is more obvious to see the batching effect Simulation Verification
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Demand Classification
Slow Lumpy Intermittent Demand Cut-off value Non-Intermittent Smooth Erratic Yasin Cut-off value
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Demand Classification
Demand size variability Demand size variability Low High Low High 𝜎 𝑁𝑍 ≅0 𝜎 𝑁𝑍 =4 𝜎 𝑁𝑍 ≅0 𝜎 𝑁𝑍 =4 Low Variable Slow Lumpy High Regular Slow Highly Variable Highly Variable Clumped Slow Low Variable Intermittent 𝑝=1.32 Non-Intermittent Regular Smooth (fast) Erratic Low 𝐶𝑉 𝑁𝑍 2 =0.49 Low High Squared coefficient of variation 𝐶𝑉 𝑁𝑍 2 = 𝜎 𝑁𝑍 2 𝜇 𝑁𝑍 2
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Example-1: Demand Propagation/Classification
Non-Intermittent, Erratic 𝜇 𝑁𝑍 =15.41 𝜎 𝑁𝑍 =12.38 p = 1.15 Demand Process in Central Warehouse Intermittent, Highly Variable, Slow Intermittent, Highly Variable, Lumpy p = 2.93 p= 1.87 𝜇 𝑁𝑍 =11.06 𝜎 𝑁𝑍 =9.08 𝜇 𝑁𝑍 =21.89, 𝜎 𝑁𝑍 =13.92 Facility 1 Facility 2
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Use of Demand Classification for Safety Stock Placement
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Lead Time Demand Modeling
Why is the demand class important? Based on the demand class, we identify a distribution to model lead time demand (a LTD Distribution). This LTD Distribution is then used in the multi-echelon SS optimization to more accurately represent the lead time demand - compared to the typical assumption that lead time demand follows a normal distribution Demand Class LTD Distribution Smooth Normal Erratic Mixture of Distributions Slow-LowVariable Poisson/Mixture of Distributions Slow-HighlyVariable Lumpy Negative Binomial
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Control Policy Mapping
Based on the demand class, we recommend an inventory control policy Demand Class Inventory Policy Smooth (Normal) (r,Q) Erratic (s,S) Slow-Low Variable Base-Stock Slow-Highly Variable Lumpy (T,S) AI+IO reports optimal policy parameters.
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How Does AI+IO Compare?
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Lumpy Demand Data Intermittency Variability Demand Class Mean Variance
Demand Size Mean Demand Size Variance Inter-Demand Interval Mean Intermittent Highly Variable Lumpy 22.84 56.06 2.46
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L = 3 L = 5 Lead Time Demand Densities for Different Lead Times
Observed Density AI-IO Assigned Density* Normal Assigned Density L = 3 L = 5 * Negative Binomial Distribution
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Lead Time Demand Densities for Different Lead Times (Cont’d)
Observed Density L = 10 L = 20 AI-IO Assigned Density* Normal Assigned Density
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Safety Stock Results for 95% Fillrate
IO3: Negative Binomial Distribution Normal Distribution AI+IO IO2 / Current Gen Tools Optimal safety stock = 563 Lead Time = 3 Metric Approximation Simulation Type2 95% 94% Safety Stock 537 Metric Approximation Simulation Type2 95% 98% Safety Stock 683 Lead Time = 5 Optimal safety stock = 581 Metric Approximation Simulation Type2 95% 99% Safety Stock 788 Metric Approximation Simulation Type2 95% Safety Stock 590
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Safety Stock Results for 95% Fillrate
Normal Distribution AI+IO IO2 / Current Gen Tools Optimal safety stock = 699 Lead Time = 10 Metric Approximation Simulation Type2 95% Safety Stock 698 Metric Approximation Simulation Type2 95% 99% Safety Stock 984 Optimal safety stock = 834 Lead Time = 20 Metric Approximation Simulation Type2 95% 100% Safety Stock 1257 Metric Approximation Simulation Type2 95% 96% Safety Stock 862
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