In the past two decades, widespread adoption of the internet and technology has taken place. Emailing, internet shopping, booking tickets online, downloading music and videos, and other such things have become the norm. In other words, our lives have been digitally transformed. With everything taking place online, one may have imagined the downfall of the logistics industry. However, the logistics industry has also caught up with the trend, even though slowly. More than 85 million packages and documents getting delivered on any given day are proof of the digital transformation in the logistics industry.
Table of Contents
What is Digital Transformation?
When an industry adopts digitalization across every aspect of business, it has undergone a digital transformation. The way businesses interact with their customers and conduct their operations and deliveries changes drastically. As such, the working of the industry experiences a massive difference.
Speed and time are critical factors in the efficiency of the logistics business. With rapid digitalization across different industries, these factors have become even more critical for the logistics industry. The adoption of digitization has brought about the logistics transformation with speedier, leaner, and efficient operations. With the availability of automation services, the adoption of these changes is now faster than ever.
What Causes Logistics Industries To Engage in Digital Transformation?
By 2027, the global spending in digital transformation in the logistics industry is expected to be $84.6 billion. It is indispensable to the business strategy of multinational corporations that run their supply chain operations across countries.
Back-office operations and shipping processes are becoming digital to improve end-to-end visibility. Methods such as automation, paperless bills of lading, and real-time freight rates increase efficiency significantly.
Big Data, Artificial Intelligence, Machine Learning, Natural Language Processing, Blockchain, and Cloud Computing are driving modern digital transformation, replacing older technologies like AS400 green screens. This has allowed logistics organizations to receive quotes and generate inquiries much faster.
The competitive advantage in logistics is derived from speed and time. Riding on the back of the technological revolution, the patience of both B2B and B2C customers has reduced, and everyone wants their packages and deliveries to be before time.
A hyper-connected business world has created a real-time economy. Everything from product development to customer services happens in real-time.
Supply chains are becoming agile across the globe because of automation and IoT. This effect is more pronounced in cold chains. Cold chains are supply chains that transport sensitive items such as drugs and vaccines, frozen and fresh vegetables, chemicals, etc., in a temperature-controlled environment. Delays can cost cold chains dearly since most of these goods are perishable.
IoT and automation have solved this problem in a big way for the cold chains. Now, real-time management of the entire logistics chain is possible through IoT links. You can track data in real-time through sensor-based technology. Similarly, you can track and maintain the environmental conditions of all links in the cold chain to prevent spoiling of goods and losses thereof.
USCS + Gramener – Revolutionizing the Cold Chain Logistics with Big Data & AI
Download Case Study
Here’s how IoT helps the cold chain:
- Real-time temperature, humidity, and location data collection through sensor-technology
- Identification of delay or damage instances or possibilities and instant remedial action
- Efficiency optimization through deep analytics of real-time data
- Enhanced predictive maintenance and cost-efficiency
- Reduced wastage because of optimal temperature and humidity maintenance
Insights into Digital Transformation of the Logistics Industry Sector Wise
Consumer Goods Industry
The global pandemic and volatile consumer behavior have not made things easy for the consumer goods industry. Companies are finding it hard to remain innovative in the face of adversity.
According to a 2020 study, 40 percent of small businesses fail to survive a disaster. Fortunately, digital transformation allows organizations to become more resilient when faced with a black swan event.
- Behavior Analytics & Customer-Centricity: Businesses can utilize customer dynamics, path analysis, cohort analysis, and data mining to track user behavior across platforms, making the entire customer shopping journey visible to them.
- IoT-Integrated Supply Chain: Physical devices that form a network via the internet are often referred to as the Internet of Things (IoT). They can boost fleet management and last-mile delivery, accelerate demand assessment and predictive maintenance, amplify cold chain monitoring, enhance transparency and visibility and improve real-time inventory tracking and management.
Companies Responsible for Fleet Management
Features such as preventive maintenance, route/fuel optimization, fleet tracking, and geofencing can significantly reduce costs and time and enhance utilization rates. Analytical reports, analyzing metrics, and measuring KPIs can maximize profits and productivity and improve process efficiency.
Good fleet management software utilizes analytics metrics, fleet security, maintenance reports, and driver details to monitor the fleet, ensuring their longevity and safe driving.
Public Sector Companies
To increase profitability, productivity, and efficiency, public sector companies can use analytics, big data management, process automation, AI (Artificial Intelligence), IoT (Internet of Things), and blockchain.
Digital transformation helps government organizations tackle red-tapism, bureaucracy, and antiquated infrastructure challenges.
Transporters & Loaders
Digital transformation helps logistics companies track the transportation of goods in real-time, allowing them to identify areas of improvement and improve efficiency. It also enables them to optimize the travel routes, accounts for foreseeable delays, and flag a delivery in the event of an accident or loss of a vehicle.
Digital Transformation in Transportation & Logistics
The following are the four main types of digital transformation:
Process Automation
Supply Chain and Logistics companies often reinvent business processes using machine learning, APIs, analytics, and data to reduce cycle times, improve quality and lower costs.
Technologies like robotic process automation (RPA) can simplify back-office processes, including accounting, legal, etc. Companies can benefit significantly from process transformation.
Business Model Transformation
In some industries, digital transformation is overhauling the traditional business models. It is changing the foundational building blocks of how enterprises deliver value to their customers.
Domain Transformation
Digital transformation can help companies redefine products and services using new technologies, blurring industry boundaries and allowing new competitors to enter the fray.
Cultural/Organizational Transformation
For long-term digital transformation to be successful, organizations must undergo a paradigm shift in their mindsets and culture, incorporating decentralized decision-making processes and flexible workflows in everyday operations.
Digital transformation helps logistics companies accelerate innovation, make better decisions, engage customers throughout their journey, incorporate flexibility into their organizational structure and increase automation.
Technologies such as eCommerce integration, Blockchain, Internet of Things (IoT), Artificial Intelligence (AI), and Supply Chain Digital Twins help companies drive digital transformation.
Digital twin, a virtual representation of hundreds of logistics positions, assets, inventories, and warehouses, uses advanced analytics and AI to simulate the performance of a supply chain, including all the complexities that drive vulnerabilities and risks.
Blockchain allows different business streams such as logistic providers, shipping lines, and carriers to be integrated into a single platform. Supply chains can use IoT to carry out mechanical and technological maintenance, inventory control, fleet tracking, and improve warehouse management.
AI and analytics help tackle governance challenges and long-standing data silos in supply chains, allowing for more visibility and integration among the disparate and remote network of stakeholders. eCommerce integration creates connected systems that allow logistic service providers to run efficient operations and offer a seamless customer experience.
Role of Big Data in Solving Complex Logistics Challenges
41% of respondents in a 2018 Deloitte report say that automation, robotics, and AI are increasingly changing how work is done. A 2016 McKinsey report shows that Big Data analytics will be instrumental in solving complex current and future supply chain problems.
The use of Big Data analytics can help manage road and traffic congestion. For example, in major cities across the globe, toll-tax receipts are used to analyze peak-time traffic. Most toll revenues are generated during peak hours by increased toll-tax in that period. This reduces the traffic congestion as people don’t need to use or avoid using the highway during this period. Air traffic can also be optimized by similarly tracking GPS data.
Companies can optimize their logistics and transportation costs by tracking and analyzing logistics costs during different year periods. As transportation costs differ during different periods, companies can identify the lean periods and schedule their transportation requirements for these periods. This is especially useful for companies that are not dealing with perishable items.
Empty-truck travel during return journeys is a significant challenge of the logistics industry. Tracking using Big Data can drastically reduce logistics inefficiencies. According to a World Economic Forum assessment, logistics is responsible for 13% of world emissions. Artificial Intelligence and Big Data tracking can help in reducing emissions and cutting down energy consumption. Moreover, technological advancements in IoT are helping in the development of autonomous trucks and drones that are more environmentally friendly.
Some other challenges that Big Data is helping solve are:
Prediction of Delivery Based on Current Truck Status
Truck movements are now tracked through real-time GPS trackers. This helps logistics companies keep tabs on their shipments and also inform their customers about predicted delivery timings.
Truck Driver Appointment Scheduling
Driver availability issues have plagued the logistics industry since time immemorial. The COVID-19 pandemic has only aggravated the problem. Given this, technology has been an immense help. Since all links of the supply chain are trackable, scheduling driver appointments has become much easier. Using an AI-driven supply chain appointment scheduler can help organizations and drivers keep track of their deliveries. Moreover, technology is also helping logistics companies to engage driverless trucks in certain terrains. Truck platooning systems sync several trucks into a convoy through automation and allow driverless driving.
Warehouse Automation
With the help of sensor technology and Big Data, inventory tracking and warehouse maintenance are now easier. Automation technology can help warehouses deal with shipment challenges and have better on-time deliveries. Warehouse robots can enhance the speed and accuracy of warehousing tasks.
Fines on Late Deliveries
Late deliveries call for huge fines in the logistics industry. By integrating Big Data across the supply chain, logistics companies can track the location and timing of shipments efficiently. There’s a recommendation to use sensor technology to track and maintain fuel consumption, refilling, air pressure in tires, and scheduled maintenance visits. Thus, delays in the supply chain can be averted and speed optimized for greater logistics efficiency.
Trends in Logistics Digital Transformation
AI, Machine Learning, and Big Data analytics fuel digital transformation in logistics and supply chains. Here’s what they help achieve:
Artificial Intelligence/ML in Logistics
United States Cold Storage Inc. (USCS) is the third-largest public refrigerated warehousing (PRW) Logistics provider in North America. It operates out of 43 facilities spread across 13 US states, offering over 330 million cubic feet of temperature-controlled distribution space and warehousing.
Every eight days, truck drivers log more than 70 hours of drive-time. The time taken to unload the trailers adversely affects the drivers’ service time. It also affects the safety of the food products that have been refrigerated, making it critical that the products be transferred from the trucks to the warehouses efficiently.
Furthermore, these large trucks burn vast quantities of fossil fuel when sitting idle, harming the environment. Also, big retailers such as Walmart levy severe fines on carriers who fail to deliver orders fully or on time.
The United States Cold Storage was looking for a solution that would help predict accurately the time taken to service a carrier and its arrival time, facilitating appointment scheduling. Unfortunately, traffic, previous pick-up delays, fluctuating weather, and mechanical issues make scheduling difficult.
The United States Cold Storage approached Gramener, a design-led data science organization that builds custom data and AI solutions to improve business processes, to develop an intelligent and predictive solution to their problem.
Gramener developed the Intelligent Appointment Scheduler (IAS), an intuitive data-driven application. It uses historical data to automate carrier appointments, ensuring scheduling precision and sufficient staffing.
Deployed across 26 USCS facilities, the IAS can schedule around 650 appointments every day, reducing turn-times by up to 15 percent.
Computer Vision in Logistics
Manual audits by insurance agents to assess car damage can be a tedious and expensive process. One of Gramener’s top clients, a car insurance firm, wanted to automate this process to speed-track claim settlements.
The company wanted to comply with social distancing norms, detect car damage remotely and analyze from multiple angles.
Gramener eliminated the need for human interference by training a classification model using an advanced edge deployment mechanism. This trained model was converted into TensorFlow lite and incorporated into an Android app that could classify live images from a camera.
With an inference time of just two seconds, the model achieved a staggering accuracy rate of 97%.
The advent of these technologies and Big Data analytics have spurred the following trends in logistics transformation:
Autonomous Vehicles and Drones
Autonomous trucks by Mercedes-Benz and delivery drones by Amazon are already in the testing phase. While they are still to become an everyday reality, the day may not be far. Forklifts are there in airport terminals, ports, and warehouses, and robot arms are used in warehouses. With truck platooning systems already underway, driverless vehicles may soon be an everyday reality.
Natural Language Processing (NLP)
With the help of NLP, companies can track invoicing information. Organizations can wave off many financial errors by keeping a tab on financial processing. This helps in efficient freight management.
Blockchain in Transport
Blockchain technologies offer transparency to the customers as they can see every step and transaction. The customer can thus be aware of the entire shipment journey. Blockchains also prevent frauds or shipment loss, prevent delays, provide smart contracts that offer cost and time efficiency, and provide third-party logistics (3PL) confirmation.
Cloud Computing
With cloud-based systems, logistics providers have access to cheaper real-time data storage and enhanced computational capability. They can store and analyze a larger amount of data and thus, improve the response time to emergencies if they occur.
Smart Vehicles
With AI and Machine Learning, vehicles are now smarter. They can predict complex turns, avoid dangers across the way, and distinguish between people on foot and different kinds of vehicles. This can prevent accidents by as much as 80 percent and help drivers track fuel, brake usage, and speed.
Use Cases of AI in Logistics and Supply Chain
There are several use cases of AI in logistics and supply chains. Let’s have a look at some:
- Automated appointment scheduling: AI systems can help in automated scheduling appointments for trucks, managing cargoes, and scheduling employee appointments at various stations.
- Automated inventory management: Automated sensors across the warehouse and retail stores can help track inventory movement. It can prevent stock-outs by scheduling timely inventory refills and managing inventory pipelines. Moreover, Machine Learning can help in predictive analysis for inventory requirements and to avoid over-stocking. Gramener’s Retail Inventory management tool helps in the same.
- Demand forecasting: AI-based demand forecasting is sharper and more accurate than traditional demand forecasting methods such as exponential smoothing methods and Auto-Regressive Integrated Moving Average (ARIMA). This helps organizations plan their workforce and optimize vehicle dispatch to different locations.
- Delivery time and route optimization: AI tracks and analyzes routes for safety and distance. This can help in freight management by selecting the shortest and safest routes. Gramener’s Airline Cargo Optimization tool, for example, helps in identifying air cargo bottlenecks and diverting shipments to prevent delays.
- Damage detection of items: Damaged goods and deliveries are supply chain nightmares. But AI-based Computer Vision technology can help in identifying damaged goods and preventing further damage to goods.
- Automated customer service with chatbots: Customers experience delivery delays and botched deliveries for which they contact the logistics company. Automated service chatbots can tackle low-to-medium level queries of customers directly without human help. They can also transfer more complicated tasks to human counterparts.
Benefits of Digital Transformation in Logistics
Below are some of the most important benefits of digital transformation in logistics and supply chain:
- Decreased margin of error
- Multichannel sales
- Differentiation and competitive advantage
- Strong customer relationships
- Enhanced customer experience
- Greater market-leading capabilities
Logistics Sectors That Have the Greatest Need for Digital Transformation
We have talked about the need for digital transformation across the supply chain industry. However, the need for logistics technology is the greatest for the cold chain sector. As previously discussed, cold chains are supply chains for sensitive and perishable goods requiring temperature control during transit.
Delays in the shipment of fresh produce or pharmaceuticals can elicit huge penalties for freight providers. Food safety, drug safety, and efficient deliveries are essential. However, it is difficult to predict the arrival and complexity of shipments.
Thus, Gramener created a data-driven system to solve carrier issues and help schedule carrier appointments with precision. The solution developed for USCS, a cold chain solutions major, helped them improve their carrier turn-times by 15 percent with 650 appointments per day.
Challenges To Digital Transformation in Logistics
Complex Processes, Structures & Networks
Digital transformation is a sophisticated process and can be difficult to understand. Unfortunately, organizations sometimes implement digital transformation without fully understanding its scope and results.
The supply chain often involves cross-company as well as cross-functional processes. To generate results that are impactful, the context of these processes when undertaking digital transformation must be considered.
The failure to understand the digital transformation process leads to digitization without improvement. Digitization of an inefficient process does not add value to an organization.
Financial Challenges
Digital transformation does not always deliver immediate returns or benefits. Its success is often in the long run, making it difficult to justify business cases for investment. Additionally, quantifying benefits resulting from automation is not always easy.
Furthermore, the advantages of digital transformation are not evenly distributed along a supply chain. It becomes difficult to convince stakeholders who may not directly gain from digital transformation to participate in the process.
Employee Qualification & Technology Competence
Digital transformation is being augmented by new technologies every day. The rapid advances and the time it takes to become well-versed in new domains make it difficult to stay up-to-date.
Supply chain professionals are not IT experts. So, technical knowledge must be merged with their functional expertise for digital transformation to be successful.
IT Security & Data Privacy Issues
Data privacy and security breach are considered serious challenges to digital transformation. In the event of an attack on the weakest link in the supply chain, the virus can quickly spread across the connected network and affect the whole grid.
Learn More About Our AI-driven Warehouse Management System
With United States Cold Storage (USCS) we collaborated to make their warehouse management and logistics operation data-driven.
Together, we developed Intelligent Appointment Scheduler (IAS), an AI-driven automated appointment scheduling system, that has revolutionized carrier appointment scheduling. Using predictive data, IAS recommends and schedules appointments by evaluating various historical parameters such as order complexity, the effort taken to process an order, warehouse load, and the propensity of delay at the carrier’s end.
Contact us for custom built low code data and AI solutions for your business challenges and check out supply chain AI solutions built for our clients, including Fortune 500 companies. Book a free demo right now.
