About the author

Debojyoti Biswas is a seasoned leader in product management, with a proven track record at global giants like Amazon, where he developed new sourcing strategies generating over a billion in revenue. At Maersk, he spearheads technological innovations in shipping and logistics. Debojyoti also successfully launched GoPuff in the EU market, achieving billion-dollar revenue milestones.

Predictive Analytics in Real-Time Shipment Tracking

Data Analytics is a powerful mechanism that helps projects and businesses assess large volumes of data and derive from it solid actionable insights. Naturally, global logistics, an industry featured by insane amounts of generated data, is one of the beneficiaries of this bleeding-edge science. One of the many applications of Data Analytics in logistics is real-time shipment tracking.

In itself, this technology provides precise updates about shipment locations, which is a crucial point for maintaining efficient logistics operations. Predictive Analytics goes a step further – it analyses real-time and historical data to identify risk patterns and predict delays and disruptions in shipments. By leveraging predictive analytics, businesses can deal with these issues before they occur, dramatically decreasing the usual risks associated with logistics operations.

I am Debojyoti Biswas, a leading expert in product supply chain management. For over 12 years, I have been developing complex data analytics solutions for such companies as Amazon, Oracle, and Maersk. Today, I bring to your attention a study of the benefits and challenges of applying predictive analytics in real-time shipment tracking. Below, we will review some practical solutions I have personally implemented in various cases, to help you optimise your logistics operations.

Aggregating and Analysing Data from Diverse Sources

As I have said above, predictive analytics processes transactional data to identify patterns and deviations and predict possible issues. This data is accumulated from various sources – GPS devices, IoT sensors, RFID tags, and specialised shipment databases. However, the variety of sources implies the variety of protocols and formats in which this data is generated, transferred, and accumulated. This makes the task of aggregating and integrating all this data a challenge in itself, with several problems arising from it.

One major issue is data format inconsistencies. When your data arrives in different formats, you need to normalise it to ensure consistency across the board. This normalisation process involves steps from aligning timestamps to standardising data structures to ensuring compatibility with existing systems. This is a resource-intensive task that requires a systemic approach, lest your real-time tracking system provides inaccurate and/or incomplete insights.

Another typical issue is data latency. Real-time shipment tracking – just as the name implies – relies on immediate updates, but network issues or processing lags can cause delays in data transmission. These delays can prevent accurate, up-to-date tracking, disrupting your logistics operations.

And last but not least, another layer of complexity is added on top of this by real-time processing requirements. The system has to be able to handle large volumes of data quickly and continuously. This requires robust infrastructure and optimised algorithms to ensure smooth data flow and instant analysis.

Data Pipeline for Real-Time Shipment Tracking

Let’s see how you can set up a data pipeline that will help you manage data acquired from diverse sources. There are a lot of platforms and tools available for this kind of task, but for the purposes of this article, I will go with the popular combination of Apache Kafka and Apache Flink.

Key Steps and Considerations for Setting Up the Pipeline

Set up Apache Kafka: collect data from all sources. Make sure that you have properly configured Kafka topics to handle the volume of data and maintain order.
Choose data storage to store raw data. HDFS and Amazon S3 are solid distributed storage solutions that integrate well with Kafka.
To process data streams coming from Kafka, use Apache Flink, as its connectors for Kafka allow seamless integration. Define data processing jobs to handle real-time data transformations and analytics.
Implement data normalisation within Flink jobs. This involves aligning timestamps, standardising data formats, and transforming data into a consistent structure.
Implement data validation checks to ensure the integrity of your data. Flink’s processing capabilities can filter out corrupt or incomplete data.
Finally, monitoring tools like Prometheus or Grafana will help you track your data pipeline’s performance. Don’t forget to set up alerts for processing delays and bottlenecks.

To give you an example: let’s say, you receive GPS data with coordinates expressed in degrees, minutes, and seconds and IoT sensor data with temperature in Fahrenheit. Here’s how you can use Apache Flink to standardise these formats to decimal degrees and Celsius respectively and transform nested JSON structures into a flat format with consistent field names to make the data usable for analysis:

DataStream<String> rawStream = …
DataStream<String> transformedStream = rawStream.map(new MapFunction<String, String>() { @Override public String map(String value) throws Exception { JsonObject json = JsonParser.parseString(value).getAsJsonObject(); // Standardising GPS coordinates if (json.has(“gps_lat”) && json.has(“gps_long”)) { String latDMS = json.get(“gps_lat”).getAsString(); String longDMS = json.get(“gps_long”).getAsString(); double latitude = convertDMSToDecimal(latDMS); double longitude = convertDMSToDecimal(longDMS); json.addProperty(“latitude”, String.format(“%.6f”, latitude)); json.addProperty(“longitude”, String.format(“%.6f”, longitude)); json.remove(“gps_lat”); json.remove(“gps_long”); } // Standardising IoT sensor data if (json.has(“temperature”) && json.get(“temperature”).getAsString().endsWith(“F”)) { double tempF = Double.parseDouble(json.get(“temperature”).getAsString().replace(“F”, “”)); double tempC = (tempF – 32) * 5.0/9.0; json.addProperty(“temperature”, String.format(“%.2f”, tempC)); } // Flattening nested data and renaming fields if (json.has(“deviceData”)) { JsonObject deviceData = json.getAsJsonObject(“deviceData”); json.addProperty(“device_id”, deviceData.get(“id”).getAsString()); json.addProperty(“device_status”, deviceData.get(“status”).getAsString()); json.remove(“deviceData”); } return json.toString(); }
private double convertDMSToDecimal(String dms) { String[] parts = dms.split(“[°’\”\\s]”); double degrees = Double.parseDouble(parts[0]); double minutes = Double.parseDouble(parts[1]); double seconds = Double.parseDouble(parts[2]); return degrees + (minutes / 60) + (seconds / 3600); }});

Predicting Potential Delays and Disruptions

Now that our data is collected and prepared, your next step is to actually use it – that is, analyse the data to identify anomalies and forecast potential issues. Regrettably, albeit not surprisingly, global logistics is affected by a lot of negative factors – from bad weather to traffic issues to any number of other unexpected events.

Weather, subject to both seasonal and sudden changes, is the major factor affecting shipment routes and schedules. Storms close ports while heavy snowfalls and tornados can delay road transport. And I won’t waste your time ranting here about delivery delays caused by traffic conditions, especially in large cities!

As for the umbrella term ‘unexpected events’, this can mean literally anything – from trivial road accidents to major political unrest and even things like piracy. A union strike at a major port that you did not anticipate can effectively halt the port’s operations, disrupting your entire supply chain.

This is why nowadays, supply chain efficiency depends on predictive analytics for its accurate and timely predictions. Without reliable forecasts, logistics companies wouldn’t be able to manage risks and adjust plans to prevent delays – which would in turn mean missed deliveries, higher costs, and dissatisfied customers. So how do we make use of the data available to us to surmount these challenges?

Applying ML for Anomaly Detection

To identify patterns and accurately detect anomalies hinting at potential issues, we employ another cutting-edge technology – Machine Learning.

Implementing Machine Learning Models

Data Collection and Preparation: Start with clean, normalised data – historical (e.g., past shipments data, weather conditions, traffic patterns) and real-time (e.g., data feeds from GPS, IoT sensors, and traffic monitoring systems).
Model Selection: Select the ML models that fit your needs. Some of the commonly used models in logistics are LSTM networks for sequence prediction and Random Forests for handling large datasets with multiple variables.
Training and Validation: Train your models using historical data. Validate the models with a subset of the data to ensure they can accurately predict delays and disruptions. Continuous validation with real-time data helps maintain accuracy over time.

Example: Implementing an LSTM Model for Delay Prediction

import numpy as npimport pandas as pdfrom keras.models import Sequentialfrom keras.layers import LSTM, Densefrom sklearn.preprocessing import MinMaxScaler
# Load and preprocess datadata = pd.read_csv(‘shipment_data.csv’)data = data[[‘timestamp’, ‘weather’, ‘traffic’, ‘shipment_status’]]
# Normalize datascaler = MinMaxScaler()data_scaled = scaler.fit_transform(data)
# Prepare data for LSTMdef create_dataset(data, time_step=1): X, Y = [], [] for i in range(len(data) – time_step – 1): a = data[i:(i + time_step), 0] X.append(a) Y.append(data[i + time_step, 0]) return np.array(X), np.array(Y)
time_step = 10X, Y = create_dataset(data_scaled, time_step)X = X.reshape(X.shape[0], X.shape[1], 1)
# Build an LSTM modelmodel = Sequential()model.add(LSTM(50, return_sequences=True, input_shape=(time_step, 1)))model.add(LSTM(50, return_sequences=False))model.add(Dense(1))model.compile(optimizer=’adam’, loss=’mean_squared_error’)
# Train the modelmodel.fit(X, Y, epochs=50, batch_size=64, verbose=1)
# Predict delayspredictions = model.predict(X)predictions = scaler.inverse_transform(predictions)

Here, we feed historical data to an LSTM model to predict shipment delays. Our model uses past patterns to forecast future delays, allowing us to take proactive measures – which, in the end, is our ultimate goal.

With such an AI/ML model in place, not only will the model predict potential delays in the SC/logistics but it will also help in proactive route planning for the shipment to circumvent the anticipated delay. Such predictive actionable suggestions go a long way in building a shockproof, fungible supply chain solution which is essentially the need of the hour for all businesses.