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McCain Foods Driving Digital Manufacturing
2
Data and
Analytics
Improving
Manufacturing
Christine Wentworth
Vice President of Agriculture,
McCain Foods
Supply Chain Insights Global Summit 2019
4
Christine Wentworth
Vice President, NA Agriculture
Data Analytics:
First step in a Journey
5
McCain Foods Driving Digital Manufacturing
7
8
9
10
Agronomic
Forecasting
Predictive Quality
& Scheduling
Grading for Payment
& Processing
Right raw
for Right SKU
PROCESSES & DATA ENTRY ARE MANUAL AND SEPARATE
11
….. When our information is managed manually, stored in siloed databases
with limited visibility to make the right decisions for our customers?
How can we BE EVEN BETTER ……?
CONSUMERS WANT TO KNOW:
• Is it safe, local and sustainable
• Ingredients, sources, processes
• Is this product trustworthy?
MANUFACTURERS WANT:
• Brand integrity and consumer loyalty
• Profitability, insights for improvement
• Trusted and consistent supply chain
Farm Fork
$$$$$$$$$$
12
Data Analytics to connect data and provide information for decisions
13
First Question: What raw onion attributes
contribute most to plant performance and
customer quality?
14
First step….
Digitize manual entry and collect data via IOT sensors
Link data with production systems
15
Average Standard Deviation
DIAMETER
SAP Yield 0.70 -0.18
WEIGHT
SAP Yield 0.67 0.38
TEMPERATURE
SAP Yield -0.01 0.38
HEARTSIZE
SAP Yield 0.37 0.90
WALL THICKNESS
SAP Yield -0.98 -0.93
Globe Flat Elongated
SHAPE
SAP Yield 0.89 0.42 -0.95
Brittle Medium Soft
BRITTLENESS
SAP Yield -0.86 -0.16 1.00
RATIO
EXDECAY UNUSABLE
SAP Yield #DIV/0! 0
MECHANICAL DAMAGE
SAP Yield 0.65 0
DRY SUNSCALE
SAP Yield #DIV/0! 0
SPLITS DOUBLES
SAP Yield #DIV/0! 0
INTDECAY
SAP Yield -0.54 0
WATERYORTSCALE
SAP Yield -0.21
WOODYSTEM
SAP Yield #DIV/0!
From To Color
0.75 High Positive Correlation
0.5 .75
0.2 0.5
-0.2 0.2 No Correlation
-0.2 -0.5
-0.5 -.75
-0.75 High Negative Correlation
Digitized raw onion attribute data now can now be compared with
production data to create correlations, context & insights
16
2nd Most important question… Why???
The Most important outcome: More Employee Engagement
•Information hungry
•Desire for root cause
analysis
•Desire for objective vs.
subjective measures
•Builds curiosity and
engagement
•Self directed, agile teams
18
19
• Significantly reduced manual effort
• Visual dashboards and decision making tools
• A more engaged workforce with higher
retention and ability to recruit top talent
• Higher quality products
• Lower costs across the supply chain
• Higher sales
• Delighted customers
Our Future is EVEN BETTER
Autonomous
Manual
Differentiation
Value
CapabilityEnabled By
Integration
Visibility Big Data
Predictive
Analytics
Supervised
Learning
Prescriptive
Supply Chain
Deep
Learning
Self Learning
Supply Chain
Digital Maturity Curve
20
21
Infinity & Beyond…

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McCain Foods Driving Digital Manufacturing

  • 2. 2
  • 3. Data and Analytics Improving Manufacturing Christine Wentworth Vice President of Agriculture, McCain Foods Supply Chain Insights Global Summit 2019
  • 4. 4 Christine Wentworth Vice President, NA Agriculture Data Analytics: First step in a Journey
  • 5. 5
  • 7. 7
  • 8. 8
  • 9. 9
  • 10. 10
  • 11. Agronomic Forecasting Predictive Quality & Scheduling Grading for Payment & Processing Right raw for Right SKU PROCESSES & DATA ENTRY ARE MANUAL AND SEPARATE 11 ….. When our information is managed manually, stored in siloed databases with limited visibility to make the right decisions for our customers? How can we BE EVEN BETTER ……?
  • 12. CONSUMERS WANT TO KNOW: • Is it safe, local and sustainable • Ingredients, sources, processes • Is this product trustworthy? MANUFACTURERS WANT: • Brand integrity and consumer loyalty • Profitability, insights for improvement • Trusted and consistent supply chain Farm Fork $$$$$$$$$$ 12 Data Analytics to connect data and provide information for decisions
  • 13. 13
  • 14. First Question: What raw onion attributes contribute most to plant performance and customer quality? 14
  • 15. First step…. Digitize manual entry and collect data via IOT sensors Link data with production systems 15
  • 16. Average Standard Deviation DIAMETER SAP Yield 0.70 -0.18 WEIGHT SAP Yield 0.67 0.38 TEMPERATURE SAP Yield -0.01 0.38 HEARTSIZE SAP Yield 0.37 0.90 WALL THICKNESS SAP Yield -0.98 -0.93 Globe Flat Elongated SHAPE SAP Yield 0.89 0.42 -0.95 Brittle Medium Soft BRITTLENESS SAP Yield -0.86 -0.16 1.00 RATIO EXDECAY UNUSABLE SAP Yield #DIV/0! 0 MECHANICAL DAMAGE SAP Yield 0.65 0 DRY SUNSCALE SAP Yield #DIV/0! 0 SPLITS DOUBLES SAP Yield #DIV/0! 0 INTDECAY SAP Yield -0.54 0 WATERYORTSCALE SAP Yield -0.21 WOODYSTEM SAP Yield #DIV/0! From To Color 0.75 High Positive Correlation 0.5 .75 0.2 0.5 -0.2 0.2 No Correlation -0.2 -0.5 -0.5 -.75 -0.75 High Negative Correlation Digitized raw onion attribute data now can now be compared with production data to create correlations, context & insights 16
  • 17. 2nd Most important question… Why???
  • 18. The Most important outcome: More Employee Engagement •Information hungry •Desire for root cause analysis •Desire for objective vs. subjective measures •Builds curiosity and engagement •Self directed, agile teams 18
  • 19. 19 • Significantly reduced manual effort • Visual dashboards and decision making tools • A more engaged workforce with higher retention and ability to recruit top talent • Higher quality products • Lower costs across the supply chain • Higher sales • Delighted customers Our Future is EVEN BETTER
  • 20. Autonomous Manual Differentiation Value CapabilityEnabled By Integration Visibility Big Data Predictive Analytics Supervised Learning Prescriptive Supply Chain Deep Learning Self Learning Supply Chain Digital Maturity Curve 20

Notas del editor

  1. And share with you McCain’s use of data analytics in a first step toward digital maturity
  2. What’s better in life than getting an onion ring in your order of fries?
  3. It’s an entire order of onion rings; delicious & crispy!
  4. So what does it take to bring a field of raw onions ….
  5. From the field to the factory….. With all an onion’s natural variability….
  6. To create such a fun & fabulous food that delights people and provides a consistent, quality product at reasonable cost?
  7. How can we be even better in an environment where our data and was gathered manually and stored separately in siloed data bases that gave employees limited visibility across the supply chain to make the RIGHT decisions for our customers everyday?
  8. So we chose to partner with ThinkIQ to help us link our disparite data sources to provide visbility to our people We chose ThinkIQ because of their deep understanding of manufacturing with a strong analytics platform and a vision for the future that aligned with the McCain strategy.
  9. But every journey begins with a single step…. And McCain’s journey was to ask our first, most important question…..
  10. How can we understand in a more granular fashion, what onion attributes contribute to plant performance and customer quality compliance? .
  11. We thought we knew this but we realized we didn’t have methods to understand this in real time. Our first step was to digitize crop data that was manually gathered and very rarely used by production. We realized in order to gather enough meaningful data , we needed to install sensors in our raw grading area to automatically gather enough objective data about the onions for accuracy & reliability and then connect that information with our production system to make sense of it and figure out what was needed.
  12. By Digitizing raw onion data and connecting it with production controls we began to establish correlations we hadn’t seen before. This led to more questions ….. About equipment set up , scheduling practices, agronomic methods in specific scenarios or sets of conditions that could begin to be answered.
  13. And the second most important question was Why? Why do these correlations exist and how can we manage our converting processes more effectively with the onion attributes mother nature gave us that season…. This led to more IOT sensors and better controls… and more questions of Why? And the correlations, and questions and requests for more sensors in new areas continue. We expect these questions and requests to continue and evolve to drive ever more understanding and focused collaboration within our supply chain.
  14. So one simple question and investment in linking our disparate information systems has led to an employee movement. A movement to know WHY? And a grass roots employee culture change to become data driven, self directed and agile.
  15. So are we there yet? No, we will never be there yet because we continuously strive to be EVEN BETTER. And the future is bright for our business and our employees.
  16. One step: one question of how we can BE EVEN BETTER has taken us on a journey of a thousand miles. That journey is driven by our employee base who requests and requires more information and tools that we believe will PULL us into a more advanced Digital environment organically as we mature in our understanding of our business and our customers. And even more important, our end use consumers will require more of us in the future to understand where their food came from and how it was handled. So… What’s our future look like?
  17. So what’s our future? To Infinity & Beyond!