RightChain Deployment | Inventory Deployment Optimization

RightChain™ Deployment Inventory Deployment Optimization

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Contents RightChain™ Deployment Principles and Practices

• Postponement • Logistics Network Optimization • Dynamic Deployment • ABC-Item Logistics Strategy • Four-Wall Inventory Management • In-Transit Inventory Management

Supply Chain Speculation vs. Postponement

SPECULATION Commit to inventory as early as possible to reduce unit costs. Examples include forward buys and labeling at manufacturing.

POSTPONEMENT Commit to inventory as late as possible to reduce inventory costs. Examples include make-to-order computers & delayed labeling.

POSTPONEMENT

SPECULATION

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Supply Chain Speculation vs Postponement

Speculate on deployment?

YES (PUSH)

NO (PULL)

Deployment Speculation

Deployment Postponement

FULL SUPPLY CHAIN SPECULATION (PUSH-PUSH) • Make ahead and deploy ahead • aka “make to stock (MTS)”, “build to inventory (BTI)” • Used by most manufacturers • All Manufacturing & Product Configuration Performed Prior to Order Receipt • Product Positioned as Close as Possible to the Customer • Low unit production cost. High inventory levels. MANUFACTURING POSTPONEMENT (PULL-PUSH) • aka “make to order (MTO)” • Final Manufacturing Performed Downstream in the Supply Chain • Final Manufacturing Delayed Until Customer Order Received (Build-to-Order = BTO) • Manufacturing Positioned Close to Customer Locations • Example: Newspapers

DEPLOYMENT POSTPONEMENT (PUSH-PULL) • Make ahead and deploy late • Direct Distribution of Fully Finalized Products from Central Inventory • Shipping Costs may Increase Due to Smaller Shipments & More Expensive Modes • Example: L.L. Bean FULL SUPPLY CHAIN POSTPONEMENT (PULL-PULL) • Make late and deploy late (centrally) • Manufacturing & Logistics are Order Driven • Some Manufacturing may be Executed in Advance • High unit production and transportation costs. Low inventory levels. • High value, high inventory carrying rate, low weight and cube. • Example: Dell Computer

YES (PUSH)

Manufacturing Speculation

Speculate on manufacturing? NO (PULL)

Postponement

Manufacturing

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Contents RightChain™ Deployment Large Food Company

Contents Finished Goods Inventory Allocation in US Manufacturing

Yr1 Yr2

RightChain™ Deployment | Postponement Simulation Major Health and Beauty Aids Manufacturer

Forecast Error Index

Forecast Demand

Actual Demand

Excess or (Shortage)

Inventory Carrying Cost

Lost Sales Cost

Demand Allocation

SKU PARAMETERS Global Forecast Forecast Error Shortage Factor Inventory Value

Country

U.S.

40% 1.20 10% 1.50 10% 1.50 10% 1.50 5% 2.00 5% 2.00 5% 2.00 5% 2.00 5% 2.00 5% 2.00

160,000 40,000 40,000 40,000 20,000 20,000 20,000 20,000 20,000 20,000

177,248 29,569 62,010 19,499 22,099 33,300 11,410 27,700 10,624 399,573 6,113

(17,248)

$ $ $

-

258,721 $

400,000

40% 50%

Japan Korea

10,431

83,452

$

-

(22,010) 20,501 (2,099) 13,887 (13,300)

-

330,157 $

20.00 $ 30.00 $

Hong Kong

164,005 $

$ $ $

-

Selling Price

Taiwan

$

-

31,485

Inventory Carrying Rate

40%

Malyasia Australia Canada

111,096 $

-

$ $ $ $

-

199,507 $

8,590

68,716

$

-

Mexico

(7,700)

-

115,493 $

Netherlands

9,376

75,008

$

-

100%

502,277 $

935,362 $

Global Excess or Shortage

427

Global Error Cost

$

6,407

1,437,639 $ 1,431,232 $

Cost of Immediate Countrification

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Contents Merge in Transit Logistics Model

Contents RightChain™ Nodes & Flows Major Retailer

Contents RightChain™ Nodes & Flows Major Retailer

RightChain™ Nodes | Network and Flow Path Optimization Major Retailer Global Supply Chain Optimization

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Contents Inventory and the Number of Warehouses in a Network

RightChain™ Nodes | Network and Deployment Optimization E-Commerce Fulfillment Example

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Contents RightChain™ Nodes Network Optimization Example

Major High-Tech Spares

RightChain™ Nodes | Network Optimization Major Semiconductor Manufacturer

ERO Service Level Study Five Quarters Ending FY98

Total Transit Cost

Annual Carrying Cost

Total Annual Cost

% local source

Inventory Level White Oak RSL of $1.6M and Boston at $2.5M in '98 (after $500K ramp)

DEPOT

Service Level Statistics

Weighted Avg. Hrs. for Customers within 100 mile radius

Weighted Avg. Hrs. for Customers outside of 100 mile radius

White Oak Max. Total Hours

at 42%, includes 17% cost of capital in K $

Min. transit time in hrs.

Avg. transit time in hrs.

Max. transit time in hrs.

ERO Max. Total Hours

Transit + carrying costs in K $

Admin. time in hrs.

Boston Area Customers

in K $

Assumptions --->

1.18

4.7

1.4

5.75

Boston

15% $1,369 $2.0

$840

$2,209

1

3.56

12.3

1.18

4.7

1.4

5.75

Boston

100% $2,461 $4.1

$1,722 $4,183

1

0 3.56 11.3

$3,647

10.2

4.23

2.5

1.7

4.48

Dulles

100% $1,925 $4.1

$1,722

1

0.2 6.14 9.2

9.1

2.76

2.1

2.1

4.25

Philadelphia 100% $2,405 $4.1

$1,722 $4,127

1

0.2 3.18 8.1

specifically: Airville, PA.

Boston & Dulles

10.2

1.18

2.5 B 1.38 B n/a D 1.66 D 4.54

100% $1,910 $5.0

$2,100 $4,010

1

0.2 2.52 9.2

14.5

13.31 12.1 n/a

11.78

None

0% $1,177 $3.0

$1,260 $2,437

1

7.8 11.78 13.5

CONCLUSIONS: o The best cost and service level combination is in Dulles, Virginia at $3.6M in total costs with weighted average delivery times within one shift. o The cost of supporting ERO customers with "one shift or less" service levels is $1.2M per year (Dulles $3.6 less Milpitas $2.4M). o Maximum delivery time is two hours shorter in Dulles than Boston (10.2 Vs. 12.3). o Having 2 depots in ERO costs $400K per year and buys us 3 hours of cycle time for customers within 100 miles of Boston (1.38 Vs. 4.48 hrs.). Note: this analysis does not include Boston to Non-ERO customer demand which accounts for 33% of L/I demand. Example IBM Burlington.

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RightChain™ Nodes | Network Optimization Major Biotech Company

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RightChain™ Nodes | Network Optimization Major Heavy Industry Company

% $s Days

Key 10%+ 5% to 10% 1% to 5% <1%

Costs

Scenario

Baseline

Delta

Japan

61.5%

Transportation

$ $ $ $ $

2,095 1,321 1,788

$ $ $ $ $

1,670 1,340 1,661

$ $ $ $ $

425

Japan RDC

Inventory Carrying

(19)

1

Warehousing

127

Duties

36

36

-

Total

5,240

4,707

533

Best Transit Days Avg. Transit Days Worst Transit Days

1

1 2 4

0

1.5

-0.5

2

-2

Singapore

14.0% 1

Assumptions & Notes

Central

Australia

8.0% 2

1. Already at cheapest rate to Singapore 2. All response time improvements outside Japan

Korea

8.5% 2

3. International dateline issues

Synopsis Cost Impact: Additional $500,000 per year

China

3.0% 2

Service Impact: 1-2 days closer to non-Japanese Asian distributo

Singapore RDC

Thailand

1.5% 2

Taiwan

2.0% 2

New Zealand

1.0% 2

India

0.5% 2

Philipines

0.3% 2

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RightChain™ Deployment | Deployment Optimization Major Food Company

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Contents RightChain™ Deployment Large Food Company

Contents RightChain™ Deployment

Major Food Company

Contents RightChain™ Deployment Dynamic Redeployment Optimization Major CPG

Reallocate inventory based on the most profitable strategy, either to make for the local stockout or ship from another location.

Customer Response C-Item Rationalization Inventory Management C-Item Forecasting Models Supply Quick Changeovers, Outsourcing Transportation Central Facility with X-Docking DC Operations High Storage Density, Batch/Field Picking

Contents RightChain™ Deployment

C-Item Supply Chain Strategy

Contents RightChain™ Deployment

C-Item Deployment

Segmented Logistics Strategy Model

Channel 1

Channel 2

Channel 3

Channel n

A

B

C

A

B

C

A

B

C

nA

nB

nC

Commodity 1 A B C Commodity 2 A B C Commodity 3 A B C Commodity n

The logistics strategy addressing customer service policy, inventory strategy, supply strategy, transportation strategy, and warehousing strategy should be segmented by channel and customer class within channel and by commodity and item class within a commodity.

nA nB nC

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Contents Cross Docking Flow Paths

Direct to Consumer Products

The term cycle counting refers to the practice of counting a small portion of the inventories on a continuing and repeated basis. For example, if inventory consisted of 10,000 part numbers, and the company wants to count each part once a year, 40 parts would be counted every day. This is distinctly different from the physical inventory in which all the parts are counted in a short time period. Cycle counting is sampling. Sampling is a term that has a specific mathematical meaning. It is a technique, in which certain members of a population are selected -- called a "sample" -- and a feature of that sample is measured. It is then inferred that this measurement is a characteristic of the population. The purpose of cycle counting is to: • Find inventory errors so that their causes, not just the errors, can be fixed; and second, • Measure and improve inventory record accuracy. • Improve counting productivity via interleaving.

Contents Cycle Counting Defined

Product Value and Criticality

Every Transaction Every Hour

Every Shift

Contents Cycle Counting Frequencies

Every Day

Every Week

Every Month

Every Quarter

Every Year

Product Popularity and Volume

When location inventory goes to zero When you are at a location anyway for putaway, restocking, or picking. When location inventory status changes for example to “quarantine” or “damage”. When you “break open” a new case or pallet of inventory.

Contents Opportunistic Cycle Counting

What are the logistics conditions that lend themselves to local, regional, and central deployment? What are the logistics conditions that lend themselves to speculation vs. postponement? What are the costs of an inventory discrepancy (high and low) at the item and location level? Suggest a re-deployment scheme that will re balance the network and minimize transportation costs. Name between five and ten factors that should be considered in ranking items for cycle count frequency and puts weights on them such that they add to 100.

Contents RightChain™ Deployment Exercises

Contents RightChain™ Deployment Exercises

Suggest a recommended inventory management policy (e.g. Push or Pull, Make to Stock (MTS) or Make to Order (MTO) in each cell.