Innovative supply chain companies are increasingly focusing on supply chain and logistics automation, aiming to automate the most common and repetitive manual tasks.
Using software that helps automate time-consuming tasks helps companies streamline supply chain and logistics workflows and processes, enabling them to make strategic resource decisions and allocate their workforce to high value-added operational activities.
The leading technologies needed to enhance supply chain automation software are Artificial Intelligence (AI) and Machine Learning: if workers now waste 20% to 30% of their work week handling documents or document-based information, the use of automated intelligent algorithms can streamline business processes and improve accuracy and response times.
Digitalization can help the supply chain industry achieve significant operational efficiency gains and major cost reductions.
For example, shipping companies need to streamline back-office activities for creating draft bills of lading, shipping quotations and manifests on behalf of third-party partners. However, they often experience process slowdowns due to the length of time to handle spot quotations, Packing List information collection and related data entry errors, complications in container tracking. These problems can be overcome by choosing innovative and scalable technology solutions.
Automation in the supply chain
Supply chain and logistics automation is about leveraging modern technologies to automate common manual tasks, operational activities, and streamline workflows and processes. It involves relying on technologies instead of a human being to perform certain operational tasks.
This may initially generate some misgivings regarding the complete elimination of human involvement in supply chain and logistics activities, and may certainly raise doubts about the reliability of these technologies.
But it should be remembered that the supply chain is made up of people, companies and processes.
Therefore, it is far-fetched to think that it is possible today to eliminate any of these components altogether: on the contrary, technology offers the opportunity to connect these components efficiently and simplify their interaction.
For example, if a supply chain manager has to devote resources just to generate quotations and at the same time needs to save personnel costs, it becomes necessary to make strategic resource decisions.
“The more tasks that are performed by process automation, the more humans are freed to devote themselves to higher value activities.”
Traditional supply chain management can no longer offer companies the flexibility needed given the characteristics of today's ecosystem, which is characterized by volatility, disruptions and unpredictability. For this reason, most companies have begun to automate logistics and supply chain processes with the help of technologies such as Artificial Intelligence, Machine Learning and others.
AI and Machine Learning: technologies to optimize supply chain management
In the logistics world, it can happen that employees spend valuable time reading paper documents or emails in order to extract the information they need to perform operational tasks, such as generating trade quotations.
This is clearly a repetitive and low value-added activity: if a C-level manager is tasked with making strategic resource decisions, he or she needs to direct them toward less time-consuming and more profitable tasks.
This is where automation comes in!
Supply chain and logistics automation is a technological response to the disruptions and unpredictability of today's industrial landscape.
Digitalization and automation of processes and data is a driver for saving personnel costs, reducing the risk of errors and disruptions.
According to a study conducted by IBM, more than 50% of C-level executives use process automation and believe that key operational processes can be enhanced or automated using AI capabilities1.
So let's look more specifically at what is meant by Artificial Intelligence and Machine Learning, and how they can be used in the supply chain context.
In a very simplified way, Artificial Intelligence is the science of making machines do things that would require intelligence if done by humans2. Current technology can only do what it was designed to do. This means that for each problem, a specific algorithm must be designed to solve it.
Machine Learning is a subset of AI techniques that use statistical methods and algorithms to enable machines to improve with experience.
Machine Learning develops algorithms that allow computers to evolve behaviors based on empirical data. In this way, algorithms automatically learn to recognize complex patterns and make intelligent decisions based on the data-an algorithm can improve itself over time by absorbing more examples3.
By bringing the definitions down to the material reality of the supply chain, one can take the example of an algorithm to identify which among several documents received by email are packing lists: one must therefore train the algorithm by showing it many examples of documents manually marked as packing list or non-packing list. The algorithm learns to identify certain patterns, such as the occurrence of certain data or combinations of data, that indicate the possibility that a document is a packing list.
In any case, there are many use cases for digitalization and process automation in supply chain and logistics. These use cases can range from the area of logistics corridors and trade hubs, trade facilitation, consumer contact and interaction points, circular services, supplier relations, digital platforms and marketplaces, supply chain monitoring, risk management, the financial side of trade and supply chain to decision-making processes.
Let us therefore see a specific case of the operation of supply chain automations based on AI and Machine Learning.
Operational automations in real life
The value of automation comes primarily from the efficiency it creates. One of the largest global consumer goods companies reported that the use of advanced automation to solve workflow problems increases workforce productivity by 30% and improves employee productivity by 50%4.
Think of business quote requests that are received via e-mail.
If you apply Machine Learning-based algorithms trained with enough examples, these algorithms will be able to identify patterns, and then highlight and extract information such as the sender's name, contact information, product information, etc., and communicate it to the company's ERP system. All this will be done automatically and in seconds, without any human interaction and with significant efficiency gains and time savings.
"Intelligent algorithms automatically learn to recognize complex patterns and make intelligent decisions based on data."
Supply chain automation can therefore help the transportation industry automate and standardize the extraction of information needed to create quotations with Artificial Intelligence technology. This enables significant operational efficiency gains.
Thus, it is clear that digitalization in shipping meets the need to streamline back-office activities for the creation of draft bills of lading, shipping quotations and cargo manifests, and also enables cost cutting, reduced operational time and streamlined processes.
The supply chain automation paradigm can also be productively applied to other cases: for example, intelligent algorithms can be set up to perform automated data entry operations in ERP or WMS from logistics documents.
Finally, it seems worth noting that organizations that have implemented work process automation using "smart" digital solutions can reduce operating costs by 30% by 20245.
Notes
1. Disrupting Logistics, Future of Business and Finance
2. Raphael, B. The thinking computer, San Francisco, CA: W.H. Freeman
3. Deloitte, Part 1: Artificial Intelligence Defined
4. IBM Institute for Business Value (2018): The Evolution of Process Automation
5. See Gartner press release of June 2021