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
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Slide1
APICS PDMBig Data in Supply ChainsUses & Challenges
cliff allen
Slide2Agenda: 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
Slide3Traditional ERP vs. Big DataERP is not going
away……howeverEmerging are: The Edges:ChannelsSocial mediaRFID
PoS
GPS
Blueprint data
Slide4Big DataLots of data is being collected and warehoused
Web data, e-commercepurchases at department & grocery storesBank/Credit Card transactionsSocial Networks
GPS location
Slide5of 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
Slide6Big 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
Slide7A 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
Slide8Complexity of data
Slide9Internet of Things
Slide10Internet 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.
Slide11Making 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.
Slide12Big 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
Slide13APICS 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%
Slide14Prescriptive 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.
Slide15Big 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?
Slide16Big Data – Game changers
Slide17Big 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.
Slide18Big 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
Slide19Big 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
Slide20Big 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
Slide21Eyesee: The eye recognition cameras
Calo (2009): People can be so fake: Truth in privacy overcomes truth in observed situations
Slide22Big 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
Slide237x24 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
Slide24Omni-Channel changing everything
Slide25Omni 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.
Slide26Near 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
Slide27Big Data - Visualization
Slide28Big 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%
Slide29Utilizing 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
Slide30Utilizing Big data to discover and explain
Slide31Data Visualization
Slide32Once Visualized The sweet spots for Big Data & SCMS & OP
Reverse Logistics and Sustainability
Slide33The 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
Slide34Sales & 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
Slide35From 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
Slide36Sales & 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
Slide37The 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
Slide38Data and S &OP
Slide39Big Data – CLSC & Reverse Logistics
Slide40Utilizing 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.”
Slide41Reverse Logistics: Hi Tech Trash
Two Million tons of e-waste goes to landfills each year163K PCs & TVs become obsolete every year
Slide42Eight 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
Slide43Reuse can cycle quickly but what about the others? With Analytics prescrpitive works for all 3
Slide44A 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
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
Slide46Product 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
Slide47Sorting Purification Compounding
Slide48Analytics in non-traditional supply chain markets
Slide49Advanced 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
Slide50Advanced Technology
Process:
Surgical Supplies Pick & Return
Slide51Advanced Technology
Success: Eliminated 12,000 supply errors
Saved 600 hours of O.R. time
Reduced inventory by 15%
Real-time Performance Reporting
Slide52Usage, 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
Slide53Usage, 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)
Slide54Usage, 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
Slide55Big Data: Is Our Security Keeping Pace?
Slide56Big 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?
Slide57Big 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
Slide58Big 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?
Slide59Main 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
Slide60A 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
Slide61Big Data: SCM Jobs
Slide62Careers in Analytics
Slide63PORTLAND 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?*
Slide64Big Data is easy!Questions?