Big Data Meets Forklifts!

Transcription

1 Big Data Meets Forklifts! Sponsored by: Presented by: AK Schultz Bill Leber 2015 MHI Copyright claimed for audiovisual works and sound recordings of seminar sessions. All rights reserved.

2 WHAT IS BIG DATA? It is <data/> And it is BIG

3 BUT WHY IS IT CALLED BIG DATA? Data sets so large and complex That it becomes difficult to process Using traditional data processing apps

4 90% OF ALL THE DATA IN THE WORLD HAS BEEN GENERATED OVER THE LAST TWO YEARS! *Source: Science Daily, May 2013

5 *Source: Hewlett Packard, November k TB was created while you have been viewing this slide!

6 *Source: Intel LET S GIVE YOU A SENSE OF SCALE

7 *Source: Intel LET S GIVE YOU A SENSE OF SCALE

8 LET S GIVE YOU A SENSE OF SCALE 1140 GB for every man, woman and child on earth! *Source: Intel

9 *Source: Intel WHY SHOULD YOU CARE?

10 DATA-WISDOM CONTINUUM DATA INFORMATION KNOWLEDGE WISDOM DECISIONS Facts Gathering Research Presentation Organization Gives Meaning Application Synthesized Learning Understanding Interpretation Actionable Change Movement Optimization WHY? Reveals Patterns THE PAST WHAT? Reveals Relationships WHAT IS BEST? Reveals principles THE FUTURE WHAT ACTION? Reveals direction

11 IN SUPPLY CHAIN, WHAT DO WE CARE ABOUT? 1 Enterprise Application Data 2 Automation Sensor Data aka, the Industrial Internet *Source: IBM

12 VALUABLE DATA IS THROWN AWAY! Open Data Personal Data Social Networks User generated Data Internet of Things Commerce Transactions VALUABLE DATA IS BEING THROWN AWAY!!

13 EXAMPLES FROM WAREHOUSING A typical warehouse storing pallets will generate 1 to 2 GB per mos in its WMS. A typical retail distribution ASRS dealing primarily in case handling will generate 25 to 50 GB per mos in its WMS. Approx. 25 times the data for the same case volume 5 to 10 GB per mos of industrial internet data. 3 to 5 GB of data in a data warehouse. This means we are likely not leveraging 300 GB to 1 TB annually per site!

14 COST OF STORING BIG DATA Log first. Ask questions later.

15 WHY TODAY IS A NEW DAY

16 ENTER THE DATA LAKE Traditional Structured Data Raw Unstructured Data DATA LAKE SQL NoSQL!!!

17 BIG DATA = BIG IMPACT Most of it doesn t matter Some of it is crazy important!

18 DATA SOURCES IN DCs

19 DATA SOURCES IN DCs Focus on the biggest piece of the pie: FORKLIFT PRODUCTIVITY!

20 BIG DATA FROM FORKLIFTS! Monitor and log your entire fleet s (x,y,z) position and much more!

21 BIG DATA FROM FORKLIFTS!

22 BENEFITS

23 BREAD-CRUMB VIEW

24 BENEFITS

25 DRIVER INTERFACE

26 BENEFITS

27 DASHBOARD FOR MANAGEMENT

28 CUSTOMER REFERENCE

29 GAME CHANGER

30 Data Aggregation GENERAL GUIDELINES 1 Actionable information Right info, right person, right time. Historical Executive Mgr 2 Predict Failure Resolve before Impact Real-time Line Mgr 3 Forecast throughput volume Predictive Age of Data

31 Pallets USE CASE #1: Drop in Driver Productivity Data Source WMS LMS FMS BUT WHY?

32 % Move Time USE CASE #1: Drop in Driver Productivity % Travel Time per day Data Source WMS LMS FMS %Travel Time = %Time forklift is moving

33 % Move Time USE CASE #1: Drop in Driver Productivity % Travel Time per day Data Source WMS LMS FMS Shift 3 likely the cause

34 Speed (mph) USE CASE #1: Drop in Driver Productivity Avg Fleet Speed (mph) Data Source TRADITIONAL DATA SYSTEMS ONLY PROVIDE HALF THE STORY! WMS LMS FMS Shift 3 slowest moving shift WHY?

35 Breadcrumb View Warehouse Layout Data Source WMS LMS FMS XYZ

36 Velocity Zones Data Source WMS XYZ DATA GIVES YOU THE EXTRA EDGE! LMS FMS XYZ

37 Pallets USE CASE #2: Individual Driver Performance Ave Hourly Moves per Driver (30 day) Data Source WMS LMS FMS BUT WHY? XYZ

38 Performance USE CASE #2: Individual Driver Performance Performance against labor standard (30 day) Data Source WMS LMS Labor Standard FMS LMS provides a good snapshot of driver performance but is it accurate?? XYZ

39 USE CASE #2: Individual Driver Performance Driver 8 Performance Driver 9 Performance Total Moves Avg Moves / hr Total Moves Avg Moves / hr Avg %Travel Time 86% Avg Speed (mph) Avg %Travel Time ARE YOU REWARDING 52% 78% THE RIGHT DRIVERS? Avg %Loaded Time Avg Distance (ft) Avg Speed (mph) Avg %Loaded Time 63% Avg Distance (ft) 1.2 mph 345 ft 5.6 mph 235 ft Safety Violations No. of Exceptions Safety Violations No. of Exceptions

40 Other cases we are studying Effect of speed limit on collisions (impact sensor) Effect of congestion on productivity Effect of SKU type on productivity

41 DATA-WISDOM CONTINUUM DATA INFORMATION KNOWLEDGE WISDOM

42 WE ARE SEARCHING FOR CAUSALITY TIME SPACE RLTNSHP CAUSE EFFECT BIATHALON EFFECT

43 DO SOMETHING WITH THE DATA DOMAIN KNOWLEDGE DATA SCIENCE TECHNOLOGY

44 DO SOMETHING WITH THE DATA DOMAIN KNOWLEDGE DATA SCIENCE TECHNOLOGY

45 HOW TO MOVE FORWARD *Source: Intel

46 HOW TO MOVE FORWARD Everything starts from the data source! Garbage-in is garbage-out. Start collecting accurate data from your biggest cost areas. Start storing more of your data it s not that expensive! Log first, ask questions later! You need x,y,z telemetry not just impact, fleet or labor management to truly understand your operations.

47 HOW TO MOVE FORWARD Combine multiple data sources into a Data Lake i.e. WMS, ERP, LMS and get access to insightful metrics! Build a Big Data Team (Domain, Data Science, Technology). Likely you will need to outsource some of it. Master Information and Knowledge before trying to get into Wisdom. Start small and grow.

48 For More Information: Speaker Website: Speaker #2 website: Or visit ProMat 2015 Booth #2912