Big Data for Supply Chain Optimization · The Matas Case CALCULATION EXAMPLE FROM MATAS Item 100059...

Copyright © 2015, SAS Institute Inc. All right reserved.

By: Anders Richter, SAS Institute, Denmark

Big Data for Supply Chain Optimization


Agenda

• Demand-Driven Planning & Optimization

and Big data

• Inventory Optimization (IO)

• The Matas case

• Results and takeaways from implementations

• Further readings


Demand-Driven

Planning &

Optimization

EXPLOSION OF DEMAND-RELATED DATA

Volume

Velocity

Variety

Bulk of this “BIG Data”

is generated outside

the company


Demand-Driven

Planning &

Optimization

THE PROCESS


Inventory

Optimization

TYPICAL NETWORK

DC

Store

Store

Customer

Store

Store

Supplier

Supplier

Supplier

Supplier

Store/ echelon

lvl 1

DC/ echelon

lvl 2

Customer

Customer

Customer


Inventory

Optimization

GOAL AND INPUT

Goal with IO

To find the most optimal reorder levels as to economy and which level should be ordered up to

– in other words finding minimum and maximum. This is done based on constrains and demand

expectations on SKU level

Model types

SS and BS, which are minimizing the cost given the demand and constrains information

Input variable

Costs

Ordering cost, holding cost and penalty cost

Demand

Expected sales in the total lead time, and the uncertainty of this expected demand

Constrains

Service level, service type (fill rate), batch size and minimum order quantity

Combining min./max. with inventory position gives the suggested

order for the SKU


Inventory

Optimization

INDIVIDUAL REORDER LEVEL AND

ORDER UP TO LEVEL

ERP policies

IO policies

70% 10% 20%


Inventory

Optimization

INDIVIDUAL REORDER LEVEL AND

ORDER UP TO LEVEL


The Matas Case

About Matas

292 stores in Denmark

30,000 items

Own brands + Lancôme,

Clinique, etc.

2,100 employees in

stores and

administration


The Matas Case INCOHERENT FLOWS

Order proposals based on DC sales

Manual process –correcting proposals

No link to store replenishment

DC replenishment

Store replenishment

Store manager controls

replenishment

Based on gut feeling and last 31 days of

saleVery time-costly


The Matas Case THE IT SET-UP

Matas DWStore system

RCM

SpaceManERP system

Axapta

POS sales

Stock levels

Assortment

POS sales

Stock levels

Orders

Assortment

Order proposal

store + DC

Order

proposals

storeOrders

Target BI

Order

Assortment

SAS®

DC Suppliers

Orders


The Matas Case REPLENISHMENT NOW – COHERENT FLOWS

SAS®

forecasting (POS data)

Order proposals

to DC (semi-

automated)

Order proposals for stores (locked for

editing)

Reporting on SAS quality

Adjust & Improve


The Matas Case CALCULATION EXAMPLE FROM MATAS

Item 100059 (Eye makeup remover)

Store 15288 (Greater Copenhagen)

FORECASTS

Forecast 42

Std.dev. 26

Lead time 1

Service degree 0,99

Stock holding costs

(n/a)

Size of colli 12

Opening allowed N

Store inventory 65

Min 113

Max 155

Order suggestions 96

RESULTS

INVENTORY

OPTIMIZATION

SALES RECORDS,

PROMOTIONS

INVENTORY

INFORMATION

IT

EM

STORE


Inventory

Optimization

LIMITATION OF IO

• Limitation of IO

• Cannot aggregate orders on supplier level

• So when to use OR?

• When there are constrains on supplier level (minimum order

amount/order size)

• Container optimization

• Push allocation

• Optimal distribution in case of shortages

• Displays


The Matas Case RESULTS AND TAKEAWAYS

Total stock value reduced by 10%

Out-of-stock situations reduced by 2 percentage points

Able to control the out-of-stock on their ABC classification

Man-hours spent on replenishment reduced by 70%

Facts instead of gut feeling

Coherent replenishment flows

Do not forget change management

Matas’ case study is outlined in Chapter 8


Results and

Takeaways

ARGUMENTS FOR STARTING WITH DDPO

The system is objective

• It uses historical information and master data when calculating min./max.

instead of being dependent on a person – both with regard to gut feeling

and skills

Automating the creation of order proposals ensures:

• Time spent on generating order proposals is reduced

• SKUs are not forgotten, and the risk of out-of-stock

situations is thus reduced

• Min. and max. values are always up-to-date

• Individual reorder level and order up to level, not

“one size fits all”


Results and

Takeaways

MARKET-DRIVEN SUPPLY CHAIN BENEFITS

Sense market changes 5X faster

Align their supply 3X faster to fluctuations in demand

With better customer service with substantially less inventory, waste and working

capital (e.g., profitable supply chains)

Bottom-line: Market-Driven processes are designed from the

market-back -- based on sensing and shaping demand and

optimizing supply


Results and

Takeaways

GETTING THEREVision

Phase one:

Limited scope and creating of

the data process, reap the

benefits

Phase two:

Increase scope and

automation in the process

Phase 3 …


Lean Lean

Forecasting Management

(FVA)

Demand-Driven

Planning &

Optimization

FURTHER READING

Market Supplier

Demand-

Driven

Sales & Operations Planning

Market-

Driven

Selling through the channel (pull) Selling into the channel (push)

Supply-

Driven

Supply Sensing

Supply Shaping

Synchronized

Replenishment

Inside-out

Focused

Reactive

Process

Inventory

Optimization

Demand Sensing

Demand Shaping

Demand Shifting

Outside-in

Focused

Proactive

Process

Collaborative

Planning

DDPO

Solution

overview

https://www.sas.com/content/dam/SAS/en_us/doc/overviewbrochure/sas-for-demand-driven-planning-optimization-107293.pdf


Anders Richter

Business Delivery Manager

Commercial & Life Sciences Division

SAS Institute Denmark

E-mail: [email protected]

Mobile: +45 27 21 28 21

Or follow me on LinkedIn

Big Data for Supply Chain Optimization · The Matas Case CALCULATION EXAMPLE FROM MATAS Item 100059...

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