© Barloworld Supply Chain Software 2014 INVENTORY METHODOLOGY.

© Barloworld Supply Chain Software 2014

INVENTORY METHODOLOGY


Inventory optimization essentials

2

Exception metrics for highlighting potential availability and coverage issues

Projections that drive to the inventory cover targets

Dynamic calculation of the optimum inventory unit levels

User interface for capturing inventory cover and availability targets


MAKE / BUY TO ORDER:• Customer demand is firm for the full sourcing lead-time cycle OR• Supply point stocks and can supply to lead-times within customer promise date

period• Any investment in inventory is for a customer.• Model inventory cover = customer orders in supply lead-time.

MAKE / BUY TO STOCK:• Customer demand is not firm for the full sourcing lead-time cycle OR• Supply point does not supply to lead-times within customer promise date period• An investment in inventory is expected, covering the

• Order release cycle strategy on a supplier• Unexpected variations in

• Customer demand and / or• Supply point lead-times

• Model inventory cover = cycle cover strategy + safety stock

Primary inventory categories


Safety Stock

Inventory cover for 1 SKU, or many SKU’s

4

TargetRC

TargetRC

Average / ModelCover

RC = Replenishment Cycle (order cycle strategy)

The model cover for all SKU’s should meet or exceed

financial coverage targets


Two Months Safety Stock

Risk

cov

erag

e

High

Low

Individual SKU’s

Long lead time, unpredictabledemand patterns, unreliable

supply, infrequent review

Short (1 day) lead time, very stabledemand patterns, very reliable

supply, daily review

We still get stock-outs

Balancing safety stock cover and inventory cover targets

Medium lead time, stabledemand patterns, less unreliable

supply, more frequent review


Released Investment

First step:optimize the current risk investment

Risk

cov

erag

e

High

Low

Individual SKU’s


Inve

ntor

y C

over

Time

ForecastAccuracy

Lead TimeReliability

Replenishment Cycles

Cycle Stock

Safety Stock

Review PeriodsReviewPeriods

Target Service Levels

Lead Time

Six Dimensional Problem

Time

Inventory optimization – A 6-dimensional solution


7 key processes for inventory management

Product Classification

Demand Planning

Supplier Management

Strategy Modeling

Shortfall Excess Expedite De-Expedite Redistribution

External Orders Internal Orders Constrained

Orders

Inventory Management

Metrics

Behavior rules for investment

Measure primary risk

factors

Quantify cover for availability

targets

Manage outliers

Replenish to cover targets

Data integrity

Metrics


Next step: Reduce the risk & need for safety stock

Risk

cov

erag

e

High

Low

Individual SKU’s

80:20 rule and TARGETS


Two phases to inventory optimization

Establish integrity of data for quantifying

existing baseline

Establish baseline inventory processes for

forecasting and lead-time management

Expand process to Sales and Operations

integration

Inventory Team processes are improved, releasing time for more advanced

planning processes


Phase 1:Start with a time-phased SKU forecast

Forecast is based on how past data points occurred• Extrapolate that pattern into the future• There will always be “error”• For example

GNPt+1= ƒ(GNPt, GNPt-1, GNPt-2, GNPt-3, GNPt-4, GNPt-5, ...,error)

OBJECTIVES:• Generate a statistically reliable forecast to support the ongoing inventory planning processes• Determine the normalized (stable) forecast error input for safety stock cover on each SKU• Establish a process for operational forecast exception management

END GOAL:• Reduce inventory exception management triggered by • forecast volatility• forecast bias

• Establish baseline operations forecast, and forecast process, for input to a sales and operations planning process


Phase 1:Operations forecast management

Manual forecasts

• Separate protected profiles / profiles with disparate / unavailable history

• Manage sourcing of forecast data (sales / market / other)

• Assess / measure the success of the manual forecasting activity• Forecast accuracy• Safety stock

investment

Statistical forecasts

• Measure forecast error impact on inventory investment

• Review variances based on cost to inventory

• Quarantine and manage timing of forecast changes

Group forecasts

• Use demand planning levels to assess customer / product forecast changes

• Track / identify events in history

• Use sales / market data for adjusting /freezing forecasts for groups of profiles

• Monitor the impact of group forecast adjustments on the accuracy of the overall forecasts


Phase 1: Supply performance management

Lead time accuracy

• Review protected profiles / profiles with manual forecasts input

• Review internal policy driver relationship to the length of lead-times

• Lead-time comparison to supply agreements

Delivery volatility

• Measure lead-time error impact on inventory investment

• Review variances based on cost to inventory

• Quarantine and manage timing of lead-time variance updates

Projections

• Provide projections to suppliers

• Track supply delivery to projections

• Track project accuracy


Phase 2:Sales and Operations Planning

Forecast generation with cross-company data• Build demand plan using multi-level techniques• Apply events/profiles (New Part Introduction, promotions, causal's) by product/family/region/other• Use Data Streaming to create a single version of the final forecast


The S&OP process

Definition:• Fully integrated decision-making and planning process• Connects the business drivers across the supply chain• Allows all functions to contribute to tactical and strategic inventory drivers

Objective• Balance demand and supply• Align volume and mix• Integrate financial and operating forecasts

Vital to success of S&OP process:A Centralized Material Planning Function that has S&OP responsibility & authority


Channel

Customer

Product Group

Product

Location

Hierarchy forecasting functionality

Prorate byForecastHistoryCopyAverageNone

Aggregate

Protected Nodes (below primary forecast)

Product Group

Supplier

Product

location

SKU

profiles

Hierarchy transfers

SKU planning forecast

Primary Forecast

level

Primary forecast

level

Statistical

Edited

Prorated

Final

Illustrative structureTerritoryMarket


Demand Planning Workflow

Forecast Accuracy:Measure, Track, and control

Forecast Quality

Sales Conditioning:Cleanse History

Outlier adjustmentStock-out compensation

New Product Introductions:Launch Profiles

Replacement profiles

Forecast Conditioning:Causal & Event Management

Seasonality & AlgorithmsLifecycle & Proration

Sign-offs:Approval staging Financial Review

Gross requirements planningCommitment to material planning

Generation:Forecasting

Generate historyArchive history


Process sample

Apply conditions

Review protected forecasts

Find outliers (exceptions)

Prorate / aggregate

Generate summaries• Forecast accuracy• Forecast summaries• Fill rates• Budget / margin variances

Before meeting: forecast preparation S&OP meeting After meeting: Planning

forecast updates

Customer

data

Operations data Finance

data

Market indicators

Ship to promise date %

Forecast variances

Accuracy counts

Supply on time in full %

SKU forecast updates

Risk calculations for safety stock coverage

Supply forecast performance metrics

Period to date exception management

Customer

data

Operations data Finance

data

© Barloworld Supply Chain Software 2014 INVENTORY METHODOLOGY.

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