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APICS PDM Big Data in Supply Chains APICS PDM Big Data in Supply Chains

APICS PDM Big Data in Supply Chains - PowerPoint Presentation

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APICS PDM Big Data in Supply Chains - PPT Presentation

Uses amp Challenges cliff allen Agenda Big Data amp SCM What is Big Data and how does it relate to SCM Using Data on the edges NPI amp Fulfillment The role of forecasting is it changing S amp OP ID: 800404

amp data analytics big data amp big analytics supply time chain real business products product planning sales social manager

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Slide1

APICS PDMBig Data in Supply ChainsUses & Challenges

cliff allen

Slide2

Agenda: Big Data & SCMWhat is Big Data and how does it relate to SCM?

Using Data on “the edges” (NPI & Fulfillment)The role of forecasting; is it changing S & OP?The sweet spot: Reverse Logistics

Omni-channel & SCM

Displaying

meaningful results / communication

Encryption / Safety

Moving forward

Q & A

Slide3

Traditional ERP vs. Big DataERP is not going

away……howeverEmerging are: The Edges:ChannelsSocial mediaRFID

PoS

GPS

Blueprint data

Slide4

Big DataLots of data is being collected and warehoused

Web data, e-commercepurchases at department & grocery storesBank/Credit Card transactionsSocial Networks

GPS location

Slide5

of small businesses now get at least one quarter of new customers via social media.

of young people refer to social media to decide where to go when they go out.

of Americans check their social networks several times a day.

of Americans check brand pages regularly as part of their social

media

activity.

of time spent online is for social media.

If Facebook were a country, it would be

the

world

s

2

nd

largest - 1.3BPercent of 18-34 year olds who check Facebook when they wake up - 48 %Social Media has overtaken adult content as the #1 activity on the web1 out of 8 couples married in the US last year met via social media

78%

61%

27%

35%

27%

Digital engagement is the future

Digital Convergence

Slide6

Big Data: How Much

Google processes 20K TB a dayFacebook has 2.5 PB of user data + 15 TB/dayeBay has 6.5 PB of user data + 50 TB/dayNSA touches 29K TB a day

1000 gigabytes = 1 Terabyte

1000 Terabytes = 1

Petabyte

Slide7

A critical mass of new technologies and consumer and client demand is ushering in a new era of computing, and with it the “Post Digital Age”

6

Billion

People worldwide have access to a mobile phone

The number of mobile-connected devices exceeded the word’s population in 2012

1

2

3

4

Projects measured in years

Vast divide between IT and business

Long adoption curves

Projects measured in yearsVast divide between IT and businessLong adoption curvesProjects measured in monthsBridging gaps between IT and businessAccelerated adoptionProjects measured in weeksIT and business collaborationAccelerated adoption

MainframeClient/ServerWebDigital7

Slide8

Complexity of data

Slide9

Internet of Things

Slide10

Internet of Things : Marketing & SCMInternet of things brings real-time data via scanners and sensors to the

channels and suppliers.Creates real time Point of sales data.

Slide11

Making analytics relevant to the heart of clients’ business with Analytics domains

Finance

Analytics

Risk

Analytics

Workforce

Analytics

Supply Chain

Analytics

Customer

Analytics

Companies should have a more complete intimate understanding of their customers to get them, grow them, and keep them.

Many leaders want to take advantage of the benefits of risk analytics to limit risk exposure or to take certain risks to generate returns.

Finance managers have applied analytics to better understand the present and more accurately predict the future.

Workforce reporting and analytics achieves greater visibility and deeper insights into the most complex workforce-related challenges.Apply analytics to achieve forward-looking insights combined with the disciplined execution of the supply-chain function.

Slide12

Big Data – Why Supply Chain?

INCREASE CUSTOMER ENGAGEMENT—lost market share IMPROVE PRODUCT/SERVICE QUALITY—Toyota OPTIMIZE OPERATIONAL EFFICIENCIES—SW AirlinesPROVIDE FASTER TIME –TO-MARKET POTENTIAL FOR GREATER REVENUE RECOGNITION—Apple & Samsung lead in APP marketSENSE SMALL EVENTS TRIGGERS POINTS (BEFORE THEY BECOME BIG IMPACTS PROBLEMS/PROXIES)—Nokia/Blackberry since 2008

IMPROVE RISK MANAGEMENT—Cost of BP oil spill

Slide13

APICS Big Data Survey,2012

Supply Chain Inventory Levels

Competitive Trends

Actual Product Usage

Forecasting/planning/

scheduling

Actual/Real Time Demand

68%

34%

37%79%

60%

Slide14

Prescriptive Analytics

The emerging technology of prescriptive analytics

goes beyond descriptive and predictive models by recommending one or more courses of action -- and showing the likely outcome of each decision.

Slide15

Big Data – The Mystery

Questions for executives:

What happens in a world of data transparency?

If you could test all decisions, would you be more competitive?

How would your business change with real-time data?

Can data replace some management?

Are Amazon,

Alibaba

, &

Zulily Marketing or Supply Chain Companies?

Is Data security is an growing concern when increasing trends for complex gathering and harnessing of data are exploding?

Slide16

Big Data – Game changers

Slide17

Big Data - AmazonAmazon has filed a

patent for a shipping system designed to cut delivery times by predicting what buyers are going to buy before they buy it — and shipping products in their general direction, or even right to their door, before the sales click.

Slide18

Big Data: Supply Chain

Game Changing TechnologiesRFID & PoS Data:Real time consumptionShelves become the inventory manger

Too much inventory can push “deals” out to users while shopping via GPS

Merchandising and product location

Users include Office Max & Best Buy

Slide19

Big Data: Supply Chain

Customer Analytics BlueprintsCustomer Profile: 360-degree view of the customerMicro-segmentation: Create segments of oneNext Action: Predict and Influence customer decisionsLoyalty Programs: Keep customers by using data applied to particular segmentation

Slide20

Big Data: Supply Chain

Eyesee: The eye recognition camerasEyesee: $5,100 Mannequin uses IBM Cognos softwarecollects data from patrons — logging things like age, gender and ethnicity

recognizes words to allow retailers to eavesdrop on what shoppers say about the mannequin’s attire

Slide21

Eyesee: The eye recognition cameras

Calo (2009): People can be so fake: Truth in privacy overcomes truth in observed situations

Slide22

Big Data: Supply Chain

Customer Analytics BlueprintsIn store cameras with consumer behaviorWalmart: ShopperceptionAvg

Visit duration

% of

vistors

thru Transit Zones

Touches per product / Pick-ups

Return to shelf

Conversion: Touches and not returned

Heat maps: color coded

Slide23

7x24 shelf analysis with multiple and simultaneous people tracking:

Traffic Flow analysis based on zones/time

Heat Maps of conversion rates for each SKU.

Hot activity zones in shelf

More Shopper insights:

Multiple events on the shelf.

Entrance / bounce paths

Average times in zones

Product traction analysis

Real comparative shelf layout performance

Slide24

Omni-Channel changing everything

Slide25

Omni channel is here to stay…

- Make Up For Ever – The cosmetics company put iPads in some of its stores to let shoppers browse products and virtually try various make-up combinations by uploading their own photos- Loyalty cards are on their way out and will be replaced by customized rewards that incorporate social information, shopping behavior, and more.

Slide26

Near Real-time Data & Dashboards

Identifies Actual & Predictive OOS & Overstock Issues At SKU/ Store Level

Enables Root Cause Analysis

Actionable Tasks Prioritized By Profitability

Drive Sales & Execution

New Product Introductions

Closing Distribution Voids

Promotion Execution & Effectiveness

Store Merchandising & Replenishment

Order & Shipment Forecasts

Retail Pricing CompliancePepsiCo Believes In The Power Of Data & Analytics To Drive Supply ChainP

Slide27

Big Data - Visualization

Slide28

Big Data - Visualization

Visual Analytics methods allow decision makers to combine their human flexibility, creativity, and background knowledge to

gain insight into complex

problems.

Example:

- To

predict demand

, Amway China applied

SAS time series forecasting to data from 70 million orders placed over the past three

years improving delivery and inventory by +20%

Slide29

Utilizing Big data to discover and explain

Is not as easy as you might think…Poor and sparse samples, surrogates, bias…As number of dimensions increases it becomes increasingly difficult to add in any data point without giving rise to some kind of statistically significant

pattern

or

cluster

And parametric distributions become unreliableIt is very difficult to discover useful things that are unknown by experts

Slide30

Utilizing Big data to discover and explain

Slide31

Data Visualization

Slide32

Once Visualized The sweet spots for Big Data & SCMS & OP

Reverse Logistics and Sustainability

Slide33

The Role of Forecasting

Forecasting is a vital function and impacts every significant management decision…. And is always inaccurateFinance and accounting use forecasts as the basis for budgeting and cost control

Marketing relies on forecasts to make key decisions such as new product planning and personnel compensation

Production uses forecasts to select suppliers, determine capacity requirements, and to drive decisions about purchasing, staffing, and inventory

Slide34

Sales & Operations Planning

Is an executive decision-making process

Balances demand and supply

Deals with volume in both units and $$$ at aggregate level

Ties operational plans to financial plans: one set of numbers

Is the forum for setting relevant strategy and policy

Slide35

From APICs : – Deep Analytics

Analytics-based reporting tells the S&OP planning teams:

The

data

and the

application o

f analytics is at the heart

of S&OP

Where they are (Current state of the business)

What actions need to be taken and driven down into tactical and operations S&OP processes

What results and trends are emerging from their decisionsWhat corrective steps do the S&OP planning teams which to take

Slide36

Sales & Operations

Planning (

Can be real-time

With Analytics

)

Master

Scheduling

Detailed Planning

& Scheduling

C

APACITYPLANNINGFORECASTING & DE

MANDBusinessPlanning

High Level Enterprise Resource Planning ModelAnnuallyBi-MonthlyWeekly

DailyStrategicPlanning2-10 YearsForecast Only

Forecast OnlyForecast PoS real timeForecast & OrdersOrders OnlyRough-cut Capacity PlanningCapacity Requirements PlanningResource Planning

Slide37

The monthly sales and operations planning process & Collaborative Planning, forecasting, & replenishment with Big Data

End of month

STEP 1

Data

Gathering

STEP 5

Exec

S&OP

Meeting

STEP 4Pre-S&OPMeeting

STEP 3SupplyPlanningSTEP 2DemandPlanning

Statistical forecastsField sales worksheetManagement forecast1-st pass spreadsheetsCapacity constraints2-nd pass spreadsheetRecommendationsFor executive S&OPDecisions

Wallace: 2nd edition Sales & Operations PlanningFirst real time data checkSecond real time Data CheckWith Analytics & CPFR this is real time cutting 1 week or more in POS data

Slide38

Data and S &OP

Slide39

Big Data – CLSC & Reverse Logistics

Slide40

Utilizing Big data Improve CLSC Supply Chains

Supply Chains and Marketing converge with improved POS, velocity with RFID, reduction of lead-times with “make->sell” compression of data & inclusion of “sell-> return.”

Slide41

Reverse Logistics: Hi Tech Trash

Two Million tons of e-waste goes to landfills each year163K PCs & TVs become obsolete every year

Slide42

Eight categories of reverse flows

Products that have failed; are unwanted, damaged, or defective; but can be repaired or remanufactured and resold.

Products that are

unsold

from retailers, usually referred to as overstocks that have resale value.

Products being

recalled

due to a safety or quality defect that may be repaired or salvaged.

Products needing “

pull and replace” repair before being put back in service.Products that can be recycled such as pallets, containers, computer inkjet cartridges, etc. Products

that are old, obsolete, or near the end of their shelf life but still have some value for salvage or resale.Products or parts that can be remanufactured and resold.Scrap metal that can be recovered and used as a raw material for further manufacturing.NoV

ALUEADDVALUEADD

Slide43

Reuse can cycle quickly but what about the others? With Analytics prescrpitive works for all 3

Slide44

A business process approach

Product acquisition is a major driver of successCreating effective remarketing channels is another major driverResearch emphasis has largely been on reverse logistics, disassembly and remanufacturing operations; not acquisition

timing;

This is where Prescriptive analytics takes place

Product returns

 represent a value stream, not just a waste stream

 

Slide45

Time-sensitive product return streams 

  Short life-cycles; high obsolescence riskReturned products losing value rapidly “Value of time” a key prescripter

Examples:

PCs

Printers and Computer Peripherals

Mobile Phones

Telecommunications Equipment

Slide46

Product AcquisitionThe collection of used products potentially accounts for a significant part of the total cost, which can be compared with the last mile issue in distribution of products in the forward supply chain.

The collection may occur by door to door, through service center, through sales center and sometimes by customers. Answer: Proximity and ease of access for customers & timely returns based on prescriptive analytics

Slide47

Sorting Purification Compounding

Slide48

Analytics in non-traditional supply chain markets

Slide49

Advanced Technology & Hospitals

Doctor data tracking has helped reduce the average stay for adult inpatients from 4.2 days in 2011 to four days in 2012. Such efforts also have reduced the average cost per admitted patient by $280, which saved the health system a total of $13.8 million from 2011 to 2012.

Lean beyond shop floor

Current State data

Slide50

Advanced Technology

Process:

Surgical Supplies Pick & Return

Slide51

Advanced Technology

Success: Eliminated 12,000 supply errors

Saved 600 hours of O.R. time

Reduced inventory by 15%

Real-time Performance Reporting

Slide52

Usage, Benefits, and Success of BA

The data Scientist and business exec cannot communicate.

Why BI/BA projects fail

Failure to recognize BI projects as cross-organizational business initiatives and to understand that, as such, they differ from typical standalone solutions

Unengaged or weak business sponsors

Unavailable or unwilling business representatives from the functional areas

Slide53

Usage, Benefits, and Success of BA

Why BI/BA projects fail

Lack of skilled (or available) staff, or suboptimal staff utilization

No software release concept (i.e., no iterative development method)

No work breakdown structure (i.e., no methodology)

Slide54

Usage, Benefits, and Success of BA

Why BI/BA projects fail

No business analysis or standardization activities

No appreciation of the negative impact of “dirty data” on business profitability

No understanding of the necessity for and the use of metadata

Too much reliance on disparate methods and tools

Slide55

Big Data: Is Our Security Keeping Pace?

Slide56

Big Data: Is Our Security Keeping Pace?

Are We Headed Towards “Impossible Privacy”?Another Case: GoogleGoogle has every single email you ever sent using Gmail. They have it stored, indexed, and they have built models of your behavior.

Yahoo and

Facebook

have been doing similar things.

How secure do you feel?

Slide57

Big Data: Is Our Security Keeping Pace?

The “Cloud”: The Risks“The internet of things”Internet security breaches happen often.

If the server goes down, your devices can’t access data. (Both Amazon and Gmail have gone dark).

Lack of access if you have no Internet access

If a hacker gets your password, you may be locked out of all your devices.

Your security is only as good as the weakest link in the chain

Slide58

Big Data: Is Our Security Keeping Pace?

In December & January Target reports another hack for 110 million records

Was this done by a global cybercrime group or an individual?

Slide59

Main Big Data Technologies

Hadoop

NoSQL Databases

Analytic Databases

Hadoop

Low cost, reliable scale-out

architecture

Distributed

computing

Proven

success in

Fortune

500 companies

Exploding interest

NoSQL Databases

Huge horizontal

scaling and

high availability

Highly optimized for retrieval and appendingTypesDocument storesKey Value storesGraph databases

Analytic

Relational DBMS

Optimized for bulk-load and fast aggregate query workloads

TypesColumn-orientedMPPIn-memory

Slide60

A new LanguageMajor Hadoop Utilities

Apache Hive

Apache Pig

Apache HBase

Sqoop

Oozie

Hue

Flume

Apache Whirr

Apache Zookeeper

SQL-like language and metadata repository

High-level language for expressing data analysis programsThe Hadoop database. Random, real -time read/write accessHighly reliable distributed coordination service

Library for running Hadoop in the cloudDistributed service for collecting and aggregating log and event dataBrowser-based desktop interface for interacting with HadoopServer-based workflow engine for Hadoop activitiesIntegrating Hadoop with RDBMS

Slide61

Big Data: SCM Jobs

Slide62

Careers in Analytics

Slide63

PORTLAND STATE UNIVERSITY

MS IN GLOBAL SUPPLY CHAIN MANAGEMENT

PREPARE FOR

AN INTEGRATED FUTURE

Supply Chain

& Your Career

Vice President/General

Manager $

175,260

Corporate Division Manager

$142,000Supply Chain Director/Manager $114,275Logistics Director/Manager $109,760Business Analyst / Data Analyst $101,000Operations

Manager $98,235Purchasing/Procurement Director/Manager $85,070Traffic Manager $69,480Warehouse Director/Manager $84,730Coordinator/Analyst $67,000*Data from Logistics

Management 30th Annual Salary Survey, released April 2014. Salary potential may vary depending on location, experience and education.What type of salary can you expect from supply chain positions?*

Slide64

Big Data is easy!Questions?