1. Title
Food Traceability and Risk Prediction Platform Using Apache Spark
and Neo4j
2.
Introduction
This project aims to develop a
traceability platform for the Food supply chain using advanced analytics and
machine learning models. By leveraging Apache Spark for data processing and
Neo4j for representing supply chain entities in a graph structure, the platform
will enhance traceability, improve supply chain transparency, and provide
predictive analytics to manage quality and minimize risks, such as spoilage and
logistical issues.
3. Key
Business Terms
●
Traceability: Ability to track the
origin and journey of Food products from farm to consumer.
●
Spoilage Risk: The probability of Food
becoming unsuitable for consumption due to poor storage, transport, or
environmental factors.
●
Predictive Analytics: Use of data and
machine learning models to forecast potential risks and optimize processes.
●
Clustering and Classification: Grouping
and categorizing supply chain entities to better understand patterns, quality
levels, and risk factors.
●
Recommendation System: An algorithm that
suggests optimal practices, routes, and partners in the supply chain based on
past data and performance.
4. Design
& Architecture
4.1 System Architecture
The platform architecture includes
three main layers: Data Processing, Graph Database, and Machine Learning Models.
●
Data Processing Layer: Ingests data from
sensors, transactional records, and historical data. Uses Apache Spark for data
cleaning, preprocessing, and streaming.
●
Graph Database Layer: Neo4j manages the
Food supply chain as a graph structure, with nodes for different entities
(e.g., Farm, Processing Plant, Distributor) and edges representing
relationships (e.g., SUPPLIES, DELIVERS).
●
ML Model Layer: Various ML models for
prediction, classification, clustering, and recommendation are trained and used
to identify potential risks and optimize decision-making.
4.2 Data Flow and Integration
- Data Ingestion: Collect data from various sources (farms, distribution
centers, IoT sensors).
- Data Transformation:
Use Spark to preprocess, clean, and transform the data.
- Storage in Neo4j:
Load structured data into Neo4j as nodes and relationships.
- ML Model Deployment: Train ML models on historical and streaming data. Store model
outputs (e.g., risk predictions) in Neo4j and periodically update based on
incoming data.
5.
Technologies
●
Apache Spark: For batch and real-time
data processing.
●
Neo4j: For managing supply chain data in
a graph structure.
●
Python: Language for developing and
deploying ML models and data processing scripts.
●
TensorFlow or PyTorch: For building deep
learning models, especially for time series forecasting and classification.
●
Scikit-Learn: For simpler ML models
(e.g., clustering, decision trees).
●
Kafka: For handling real-time data
ingestion if streaming is required.
●
Docker: For containerizing the
platform’s various components for easier deployment and scaling.
6.
Implementation
6.1 Data Collection
●
Define data sources for data
collection from farms, IoT devices, distribution points, and retail outlets.
6.2 Data Preprocessing
●
Use Apache Spark to clean and
transform raw data into a structured format suitable for Neo4j and ML models.
●
Create Spark jobs for batch and
real-time processing.
6.3 Graph Database Design
●
Define Neo4j nodes and
relationships based on the supply chain entities (e.g., Farm, Processing Plant,
Distributor, Retailer).
Graph Database
Design
The updated design will focus on the
traceability of onions rather than eggs:
- Define Neo4j Nodes
and Relationships Based on Supply Chain Entities
○
Nodes:
■
Farm
■
Attributes: farm_id, location, type
(e.g., organic, conventional), owner, certification_details
■
Manufacturer
■
Attributes: manufacturer_id, name, facility_location, processing_capacity, certifications
■
Processing Plant
■
Attributes: plant_id, name, location, handling_capacity, processing_methods
■
Distributor
■
Attributes: distributor_id, name, distribution_centers, coverage_area
■
Retailer
■
Attributes: retailer_id, name, location, store_type (e.g., supermarket), storage_conditions
■
Consumer
■
Attributes: consumer_id, location, purchase_date, feedback
■
Logistics Provider
■
Attributes: provider_id, name, fleet_size, transport_type (e.g., refrigerated), track_and_trace_capability
■
Warehouse
■
Attributes: warehouse_id, name, location, storage_capacity
○
Relationships:
■
(Farm) -[SUPPLIES]-> (Manufacturer)
■
Represents the supply of raw
onions from farms to manufacturers.
■
(Manufacturer) -[PROCESSES]-> (Processing Plant)
■
Represents the processing of
onions for packaging and distribution.
■
(Processing Plant) -[PACKAGES_FOR]-> (Distributor)
■
Represents the packaging of
onions specifically for distributors.
■
(Distributor) -[STORES_IN]-> (Warehouse)
■
Represents the storage of
onions by distributors in warehouses for further distribution.
■
(Warehouse) -[SHIPS_VIA]-> (Logistics Provider)
■
Represents the role of
logistics providers in transporting onions from warehouses to retailers.
■
(Logistics Provider) -[DELIVERS_TO]-> (Retailer)
■
Represents the delivery of
onions to retailers.
■
(Retailer) -[SELLS_TO]-> (Consumer)
■
Represents the sale of onions
to end consumers.
■
(Logistics Provider) -[MONITORED_BY]-> (IoT Sensor)
■
Represents the integration of
IoT sensors to monitor transportation conditions (e.g., temperature, humidity)
for onions.
6.4 Machine
Learning Models
Predictive
Analytics Models
These models help predict potential
risks in the supply chain, such as spoilage or transportation delays.
●
Time Series Forecasting Models:
○
ARIMA, SARIMA: Useful for predicting
temperature, humidity, or demand based on historical data. These models can
forecast conditions that may affect the quality of onions, such as fluctuations
in warehouse temperatures or seasonal demand changes.
○
LSTM (Long Short-Term Memory) Networks:
A type of recurrent neural network (RNN) that captures long-term dependencies
in time-series data. It can help predict conditions impacting onion quality,
such as trends in storage temperature or transportation delays.
○
Prophet: Suitable for seasonality and
trend forecasting, which can predict potential disruptions in the availability
or demand for onions.
Anomaly
Detection Models
These models identify abnormal
conditions that could indicate risks to the quality and safety of onions.Using biosensors
we can detect harmful bacteria at different stages in the supply chain (e.g.,
at farms, processing plants, or retail outlets).
●
Isolation Forest: Detects outliers by
isolating each point in the dataset. It is useful for spotting unusual
environmental conditions, such as unexpected temperature spikes during storage
or transportation.
●
One-Class SVM (Support Vector Machine):
Classifies data as either normal or anomalous. This model can identify unusual
sensor readings that may indicate a risk to onion quality, such as excessive
humidity.
●
Autoencoders: Neural networks that
detect anomalies in high-dimensional data, making them suitable for identifying
abnormal patterns across multiple sensors monitoring onion batches.
Risk
Prediction using Classification Models
These models predict the likelihood of
spoilage or other quality-related risks.
Biosensors for Real-Time Monitoring: Use biosensors
dataset to monitor environmental conditions like humidity, temperature, and
microbial contamination in storage facilities. These sensors can send real-time
data to the traceability platform, improving the system's ability to detect
risks.
Bioremediation Solutions: Using microorganisms to clean
up any contaminants in the environment (e.g., soil or water) where onions are
grown, thus reducing the initial risk of contamination.
Development of advanced molecular techniques, such as PCR
(Polymerase Chain Reaction) and CRISPR-based diagnostics, for quickly detecting
pathogens like E. coli in onions. These methods can be integrated with the
traceability platform to flag contaminated batches in real time.
●
Logistic Regression: A simple and
interpretable model suitable for binary risk predictions, such as predicting
whether an onion batch is at risk of spoilage (yes/no).
●
Random Forest, Gradient Boosting (e.g., XGBoost): Tree-based ensemble models that classify risk levels (e.g., low,
medium, high) based on factors such as transportation conditions, storage
durations, and temperature control.
●
CatBoost: Handles categorical data
effectively without extensive preprocessing, ideal for mixed data types like
batch ID, supplier location, and transport method.
●
Use
biological data, such as microbial profiles, that can be integrated with
the machine learning models for predictive analytics. This can improve the
accuracy of models in predicting spoilage, contamination risks, and other
quality issues.
Classification
Models
These models help sort batches,
identify quality grades, or flag defective products.
●
Support Vector Machine (SVM): Effective
for binary or multi-class classification, such as categorizing onions based on
quality grades (e.g., Grade A, B).
●
Decision Trees and Random Forests:
Suitable for hierarchical classification of onion batches based on attributes
like size, weight, and quality. These models provide interpretability for
identifying specific factors affecting quality issues.
●
Convolutional Neural Networks (CNNs):
Useful for image classification during visual inspections, such as detecting
signs of rot, mold, or other defects in onions. A CNN can analyze images
captured during sorting and flag defective onions.
●
Naive Bayes: Can classify onions based
on categorical attributes (e.g., organic, conventional). It's fast and performs
well when features are independent, which is often the case with product
attributes.
Clustering
Models
Clustering helps group similar
batches, identify quality patterns, or segment distributors based on
performance.
●
K-Means Clustering: Groups onions,
farms, or batches based on similar characteristics (e.g., freshness, size,
location). Useful for segmenting suppliers and identifying quality patterns
among batches.
●
DBSCAN (Density-Based Spatial Clustering of Applications with
Noise): Finds clusters of arbitrary shapes, making
it ideal for segmenting onion suppliers or distributors by geographical
regions. It can help detect regional quality issues or high-risk zones.
●
Hierarchical Clustering: Allows a
hierarchical structure in clustering, which can categorize farms based on
multiple factors like region, size, and compliance history.
●
Gaussian Mixture Models (GMM): A
probabilistic clustering model suitable for grouping batches with mixed
distributions, such as varying freshness levels in different regions or under
different storage conditions.
Recommendation
Models
Recommendation models support supply
chain decision-making by suggesting optimal routes, suppliers, or practices
based on historical data and performance.
●
Collaborative Filtering (Matrix Factorization, SVD): Provides recommendations based on patterns in historical data. For
example, it can suggest preferred logistic providers or routes to minimize
spoilage during onion transport.
●
Content-Based Filtering: Recommends
suppliers or distributors based on attributes such as certification, proximity,
and transport reliability. This model helps in selecting suppliers that meet
quality and freshness requirements.
●
Association Rule Learning (e.g., Apriori, FP-Growth): Analyzes patterns among different conditions (e.g., temperature
ranges or transport durations that correlate with better quality retention). It
is useful for optimizing storage conditions or identifying risky combinations
of transport and storage conditions.
●
Deep Neural Networks (DNNs): Can be used
for complex recommendation systems that factor in dynamic elements like
real-time sensor data and historical quality incidents. DNNs provide more
adaptive and granular recommendations for supply chain decisions.
5. Supply
Chain Optimization Models
These models help optimize supply
chain processes by predicting delays, managing inventory, and minimizing waste,
which is crucial for perishable items like onions.
DNA
Fingerprinting and Whole Genome Sequencing (WGS):
genomic tracing to identify the genetic signature of pathogens. WGS can help
trace the contamination back to specific batches or even the original farm
source.
Strain
Typing: Techniques can differentiate between strains of pathogens
to establish whether cases of E. coli are linked to the same source, aiding the
investigation and narrowing down the origin of contamination.
CRISPR-Based
Gene Editing: Gene editing techniques can be used to
create onion varieties that are more resilient to environmental stressors like
drought or temperature changes, ensuring better storage and longer shelf life,
thus reducing spoilage risk
Microbiome
Analysis: By analyzing the microbiome of onion batches at
different stages, biotechnology can help identify microbial patterns associated
with spoilage or contamination, enabling early intervention
Optimization Algorithms
●
Linear Programming (LP):
○
LP can be used to minimize
costs or maximize efficiency in various supply chain decisions. For onions, LP
can optimize the routing of shipments to minimize transportation costs while
considering constraints like delivery deadlines, temperature control, and
storage capacity.
○
An objective function might be
formulated to minimize the total cost of transportation, considering factors
such as distance, delivery time, and refrigeration needs, subject to
constraints like vehicle capacity and warehouse limits.
●
Genetic Algorithms (GA):
○
GA can optimize complex
problems in the supply chain, such as route planning for delivering onions to
multiple retailers while considering varying delivery windows and traffic
patterns.
○
It can also be used to find the
optimal combination of suppliers that balance cost, quality, and reliability,
helping to ensure a consistent supply of high-quality onions while minimizing
costs.
Inventory Management Models
●
Economic Order Quantity (EOQ):
○
EOQ helps determine the optimal
order quantity that minimizes total inventory costs, including ordering and
holding costs. For onions, EOQ can be adjusted based on factors like shelf
life, storage conditions (e.g., temperature and humidity), and demand variability.
○
The EOQ formula is given by:
EOQ=2DSHEOQ = \sqrt{\frac{2DS}{H}}EOQ=H2DS where DDD is the annual demand for
onions, SSS is the ordering cost per order, and HHH is the holding cost per
unit per year.
●
Reorder Point (ROP):
○
ROP models determine the
inventory level at which a new order should be placed to avoid stockouts. For
onions, this model accounts for the lead time, demand during the lead time, and
safety stock to handle unexpected demand fluctuations or delivery delays.
○
The ROP formula can be given
by: ROP=(Lead Time Demand)+Safety StockROP = (Lead \, Time \, Demand) + Safety
\, StockROP=(LeadTimeDemand)+SafetyStock
○
For onions, factors like
spoilage rates and seasonal demand spikes would influence safety stock
calculations.
Vehicle Routing Problem (VRP) Solutions
●
Ant Colony Optimization (ACO):
○
ACO can solve VRP scenarios
where multiple delivery vehicles need to be routed efficiently. This approach
is suitable for distributing onions from warehouses to retail locations,
considering constraints such as vehicle capacity and delivery time windows.
○
The algorithm uses a
probabilistic technique inspired by the behavior of ants searching for food,
which helps identify optimal paths for delivery routes to minimize total travel
time or distance while ensuring timely delivery of perishable goods like onions.
●
Simulated Annealing (SA):
○
SA can be used to optimize
delivery schedules for onions to multiple destinations. It starts with a random
solution and iteratively improves it by exploring neighboring solutions,
mimicking the process of annealing in metallurgy.
○
For onions, SA can help
optimize routes while considering variables like refrigeration availability and
traffic patterns, aiming to reduce spoilage and transportation costs.
Inventory Optimization using Time Series
Forecasting
●
Seasonal Stock Adjustment:
○
Models like ARIMA or Prophet can predict seasonal demand variations for onions, helping
adjust stock levels proactively. These models forecast demand spikes or dips
based on historical sales data, allowing for dynamic stock management.
○
Predictions can be integrated
with EOQ and ROP models to update inventory policies in real-time, reducing the
risk of spoilage during low-demand periods and ensuring sufficient stock during
high-demand seasons.
Waste Minimization Strategies
●
Perishable Goods Allocation Models:
○
Models like First-Expire, First-Out (FEFO) are
implemented to prioritize the distribution of onions nearing the end of their
shelf life. This approach reduces waste by ensuring older inventory is shipped
out before newer stock.
○
Predictive Analytics can identify which
batches of onions are more likely to spoil based on environmental conditions
and historical data. This information can be used to optimize stock rotation
practices.
●
Multi-Echelon Inventory Optimization:
○
This approach manages inventory
across different levels of the supply chain (e.g., warehouses, distribution
centers, retail outlets). Multi-echelon optimization can be used to balance
stock levels, ensuring that onions are available where needed while minimizing
total inventory costs.
○
Advanced techniques like Stochastic Inventory Models can account
for demand uncertainty and lead-time variability in multi-echelon networks.
These models enhance the efficiency of
onion supply chains by optimizing logistics, managing inventory effectively,
and minimizing waste, contributing to a more transparent and resilient food
traceability system.
6.5 Integration and Deployment
●
Integrate Neo4j and Spark to
allow seamless data flow between data processing and the graph database.
●
Containerize the application
components using Docker for scalability.
●
Deploy ML models and
periodically update predictions based on real-time data ingestion.
7. Testing
7.1 Unit Testing
●
Conduct unit tests on
individual components like data preprocessing, ML model functions, and Cypher
queries.
7.2 Integration Testing
●
Test end-to-end data flow from
ingestion, processing, storage in Neo4j, and prediction with ML models to
ensure all components work cohesively.
7.3 Performance Testing
●
Evaluate system performance to
ensure it can handle high data volumes, especially for real-time data ingestion
and querying in Neo4j.
7.4 Model Evaluation
●
Use metrics like accuracy,
F1-score, precision, and recall for classification models.
●
Evaluate clustering models
using silhouette scores and adjust the parameters accordingly.
●
Measure time series model
accuracy using metrics such as RMSE and MAPE.
8. Reports
8.1
Traceability Reports
●
Purpose: Provide a visual representation
of the entire supply chain journey of each batch of onions, from farm to
retailer, including details about every processing, storage, and transportation
step.
●
Content: Show the path taken by recalled
batches of yellow onions, highlighting entities involved (e.g., Taylor Farms,
distributors, restaurants like McDonald’s). The report will include timelines
and locations, enabling investigators to quickly identify where the onions may
have become contaminated.
●
Focus Area: The report will emphasize
the batches supplied to McDonald's and other affected food service customers,
showing all steps before the voluntary recall was issued.
8.2 Quality
Risk Analysis
●
Purpose: Assess the risk levels for
batches of onions based on factors such as spoilage predictions, environmental
conditions, and transport data.
●
Content: Include risk scores for the
batches involved in the recall, evaluating spoilage risk or contamination
likelihood based on conditions such as temperature and humidity during storage
and transportation.
●
Investigation Aid: Use risk scores to
prioritize the analysis of batches that may have higher contamination risks,
aiding in determining whether slivered onions supplied to McDonald’s were the
likely source of the E. coli outbreak.
8.3 Anomaly
Detection Report
●
Purpose: Identify any anomalies in
environmental conditions (e.g., temperature, humidity) throughout the onion
supply chain that could contribute to quality issues or contamination risks.
●
Content: Highlight unusual readings from
IoT sensors during the storage or transport of recalled onion batches. For
example, sudden temperature spikes in warehouses or during transportation could
indicate potential quality degradation.
●
Suggested Actions: Recommend actions to
address detected anomalies, such as increasing inspections or adjusting storage
conditions for other batches from the same supplier to prevent further
contamination.
8.4 Supplier
and Distributor Performance Reports
●
Purpose: Evaluate the performance of
suppliers and distributors based on key metrics like quality consistency,
delivery time, and customer feedback.
●
Content: Analyze performance data for
Taylor Farms and other distributors who handled the recalled onions,
identifying any patterns in quality issues. The report will assess compliance
with quality standards across different distribution centers.
●
Ranking: Rank suppliers and distributors
based on their performance history, allowing McDonald’s and other food service
customers to make informed decisions about sourcing onions in the future.
8.5 Outbreak
Status and Recall Impact Report
●
Purpose: Track the status of the ongoing
E. coli outbreak investigation and measure the impact of the recall.
●
Content: Provide an update on the number
of reported cases, hospitalization data, affected states, and the steps taken
by Taylor Farms and McDonald’s to address the situation. Include details about
the investigation's progress and any new findings related to the potential
source of contamination.
●
Recall Effectiveness: Evaluate the
effectiveness of the recall measures taken, including the removal of slivered
onions from McDonald's menus in affected states and notifications sent to other
food service customers.
8.6
Visualisations
References
https://www.youtube.com/watch?v=K-s4hr87994
https://www.youtube.com/watch?v=NqsGIjWjiCQ
