Artificial Intelligence in Supply Chain Management
Introduction
There is a big need to optimize the supply chain today. Supply chain companies such as retail, logistics, industrial companies, etc. need to be able to deliver the right products in the right quantities, in the right condition delivered to a very wide variety of locations, all over the world, at the right time for the right cost. For these goals to be achieved, there has to be an implementation of a global, agile and efficient supply chain. Thus, technological inventions, particularly disruptive innovations play a key role in building a system that totally revolutionizes the supply chain.
Globally, most of today’s developing countries, especially in Africa, were not able to catch up with the different stages of the world’s technological and industrial revolutions. However, these nations can possibly prevent this recurrence in the nearest future via the early adoption of new and innovative technologies. Given the pace of change in today’s globalized world, and the urgency of the challenge posed by the fourth industrial revolution, African governments need to focus on channeling their resources and policy-making towards a more adaptive process. Especially in the implementation of innovative technologies in all aspects of logistics and supply chain management of Health care commodities.
Amid all the hype surrounding the fourth industrial revolution and its disruptive technologies, is Artificial Intelligence — the ability exhibited by a machine to perform complex intellectual tasks that were, until recently devoted to humans.
Though artificial intelligence, AI, has been around for the last half-century and has greatly improved performances in many industries, the technology has not been fully exploited in supply chain management, SCM. The flows in today’s supply chains are usually fast-moving and dynamic but are often handled manually. This can inhibit the organizations’ capability to adapt to the quick changes in demand. To be able to quickly respond to these changes, cooperation between the purchasing company and the supplier is needed.
The use of Artificial Intelligence could provide a dynamic solution to numerous problems encountered in the logistics and supply chain management of health commodities.
In general, Artificial Intelligence can be used in novel ways to rationalize things, automate processes, and improve overall experience and performance in supply chain activities
Rationale
The supply chain of many companies has been stymied by poor decision-making, which is at the forefront of activities involved in logistics and supply chain management. These decisions are often products of the collection and subsequent analysis of essential data plus other logistics and supply chain data, gathered across all levels of the logistics system and supply chain. It has been shown that human errors are inevitable and a major factor indicated for poor logistics decision-making. Therefore, a better refined, well calculated, and systematic intelligence, presented via Artificial Intelligence would help with optimum data management and analysis that would provide an improvement in the overall performance of the logistics system and supply chain.
In the same vein, Automation is a central feature in supply chain management. Replacement of certain manual procedures and processes carried out by humans with automated machines that work with Artificial Intelligence improves the effectiveness and efficiency of supply chain operations. This could be evident in an automated warehouse management system.
There are numerous advantages in the application of Artificial Intelligence to different components of the supply chain such as supply planning, sourcing, demand and inventory, production, information flow, warehouse, transportation and reverse logistics.
The impact of Artificial Intelligence in improving the overall performance of the supply chain cannot be overemphasized.
What is Artificial Intelligence?
Artificial intelligence (AI) is an area of computer science that emphasizes the creation of intelligent machines that work and react like humans. Some of the activities computers with artificial intelligence are designed for include:
- Speech recognition
- Learning
- Reasoning
- Self-correction
- Planning
- Problem-solving
Basic Concepts Relating to Artificial Intelligence
Algorithm and its advancement
In mathematics and computer science is a sequence of instructions, typically to solve a class of problems or perform a computation. Algorithms are unambiguous specifications for performing calculations, data processing automated reasoning, and other tasks. They have improved in recent years as well, allowing the detection of patterns and discovery of correlations that were difficult or impossible to find by humans or conventional technology alone. For example, smart algorithms can offer valuable information such as the number of trucks available for delivery ahead of time so customers can know the price and approximate time frames for future deliveries.
Big data
Is a field that treats ways to analyze, systematically extract information from, or otherwise deal with data sets that are too large or complex to be dealt with by traditional data-processing application software, such as Microsoft Office Excel
Machine Learning
Is the scientific study of algorithms and statistical models that computer systems use to perform a specific task without using explicit instructions, relying on patterns and inference instead.
It is seen as a subset of Artificial Intelligence. Machine Learning builds a mathematical model based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to perform the task.
Supply chain practitioners usually use old-school statistics to predict demand; but with the recent rise of machine learning algorithms, we have new tools at our disposal that can easily achieve very good performance in terms of forecast accuracy for a typical industrial demand dataset. These models will be able to learn many relationships that are beyond the ability of traditional statistical models. For example, how to add external information (such as the weather) to a forecast model.
Robotics
Is an interdisciplinary branch of engineering and science that includes mechanical engineering, electronic engineering, information engineering, computer science, and others. Robotics deal with the design, construction, operation, and use of robots as well as computer systems for their control, sensory feedback, and information processing.
These technologies are used to develop machines that can substitute for humans and replicate human actions. (Wikipedia contributors, 2019)
Artificial Intelligence in form of soft wares and programs are infused into these robotic systems and are used in Warehouse Managing System (WMS) in putting away, picking, packing, identification of products, in avoiding collusion with other robots and objects.
Internet of Things (IoT)
The Internet of Things, (IoT) is a collection of interconnected physical devices that can monitor, report on and send and exchange data. IoT devices are typically connected to computer systems via data or Wi-Fi networks.
IoT devices use sensors to measure specific aspects of the world around them, including location, temperature, humidity, light levels, movement, handling, speed of movement, and other environmental factors. IoT devices come in many forms including RFID chips, smart devices, and mobile sensors.
In the supply chain, Internet of Things devices is an effective way to track and authenticate products and shipments using GPS and other technologies. They can also monitor the storage conditions of products which enhances quality management throughout the supply chain. AI is based on four identified attributes in supply chain models that include:
· optimization
· prediction
· modeling and simulation
· decision support that can be used to supply chain management
Supply Chain Management
Supply Chain
The supply chain is a global network used to deliver products and services from a supplier to customers through an engineered flow of information, physical distribution, and cash. It involves the following flow of materials and activities:
· Flow of physical materials and services
· Flow of cash from customer to supplier of raw materials
· Flow of information
· Reverse flow of product returned
The purpose of Artificial intelligence in supply chain management is to leverage data to gain useful information to make better decisions. In other words, it encompasses using Artificial Intelligence in novel ways to rationalize things, automate things and improve the overall performance of the logistics cycle and supply chain.
Supply Chain Challenges
Since the beginning of commerce and manufacturing, all supply chains have had a similar goal: to move goods or services efficiently through the system without bottlenecks, overstock, or undersupply. One of the most persistent issues in achieving this is rising costs. Another major problem is customer service: the right product is delivered in the right quantity at the right time. “This can get complicated quickly when you have different stakeholders involved. Human error is almost inevitable”.
AI Solutions for Supply Chain Problems
Three main areas where AI may most effectively be implemented are:
· Predictive analytics
· Warehouse management
· Procurement via chatbots
Predictive analytics
Predictive analytics is a type of machine learning or artificial intelligence where large data sets are mined for information that can be used strategically. Predictive analytics could help manufacturers set prices, forecast demands accurately, and even prevent disruptions in the supply chain by scheduling repairs in anticipation of a breakdown.
One of the most advanced examples of AI usage in predictive analytics is IBM’s Watson Supply Chain, which gathers and interprets internal data in addition to data from weather, news, and social media sources.
Warehouse management
Artificial intelligence appears to be a good fit for warehouse management systems (WMS) which are responsible for smooth operations at company facilities. Predictive analytics and WMS is still a very new topic. It’s not something that’s been perfected in any way, shape, or form.
Procurement via chatbots
Chatbots can be installed quickly and rapidly adding transparency and efficiency to the procurement process.
One of the areas that appears the readiest for implementation is the chatbot, which can interact with all players in the supply chain, provide real-time updates, answer frequently asked questions, send compliance or government notifications, and more. Eventually, these chatbots may be able to negotiate prices or handle more complex matters.
Impact of Artificial Intelligence on Logistics
Predictive capabilities will rise — Companies get to become more proactive by having a tool that can help with capacity planning and accurate demand forecasting. When they know what the market expects, they can quickly move the vehicles to the areas with more demand and thereby bring down the operational costs
Robotics — You cannot talk about artificial intelligence without mentioning robotics. Even though robotics is considered a futuristic technology concept, the supply chain already makes use of it. They are used to track, locate and move inventory within the warehouses. Such robots come with deep learning algorithms which help the robots make autonomous decisions regarding the different processes that are performed in the warehouse
Big data — Big Data helps to optimize future performance and forecast accurate outlooks better than ever. When the insights of Big Data are used along with artificial intelligence, it helps to improve different areas of the supply chain like supply chain transparency and route optimization.
Computer vision — When you are moving cargo across the world, it is always good to have a pair of eyes to monitor, and it can be best when it comes with state-of-the-art technology. Now you can see things in a new way by using computer vision which is based on artificial intelligence for logistics.
Autonomous vehicles — They are the next big thing that artificial intelligence offers the supply chain. Having driverless trucks can take a while, but the logistics industry is now making use of high-tech driving to increase efficiency and safety.
Impact of Artificial Intelligence on Supply Chain
Reduction in operating costs, inventory, and response time — Artificial Intelligence allows greater contextual intelligence which provides the knowledge needed to reduce operations costs and inventory, and respond to clients quicker.
Supply chain management productivity — AI can provide an unmatched analysis of supply chain management performance, which, in turn, helps to determine new factors affecting that performance.
Accurate demand forecasting — AI is capable of analyzing enormous volumes of data, thus enhancing demand forecasting accuracy
Improve supplier selection relationship — AI can analyze supplier-related data such as on-time in-full delivery performance, audits, evaluations, and credit scoring and provide information to use for future decisions regarding certain suppliers. As a result, a company can make better supplier decisions and improve its customer service.
AI-Enhanced customer experience — AI changes relationships between logistics providers and customers by personalizing them.
AI improves production planning and Factory scheduling by enabling the analysis of a wide range of constraints and optimization for them.
Better management — AI solutions can help raise red flags to produce warnings and help all stakeholders manage disruptions better.
The Drawbacks of Artificial Intelligence in Supply Chain Management
Since its inception, AI has come under huge scrutiny and Luddites because of the threat it brings along with it. The idea that machines would become more powerful than humans and would leave them behind eventually, is disturbing. But a threat to privacy and jobs is what causes trouble.
Threat to Privacy
Many companies are afraid to share company-specific information with other parts of the supply chain since the information could be used in ill-meaning ways. This mutual distrusts obstructs the cooperation between organizations.
Threat to jobs
A 2013 Oxford University study predicted that 47% of jobs could be automated by 2033. Advanced technologies would eliminate a number of tasks and roles but would create some as well. Moreover, Long N. T (2018) argues that quintessential human traits like communication, empathy, intuition, and the ability to contextualize, interpret, or question data are irreplaceable.
Lack of tech talent and supply chain intelligence
For supply chain management, the reality is AI is still in its earliest stages
Something which may be holding back the widespread adoption of AI is the lack of tech talent to make these things happen. In the same vein, a lack of supply chain intelligence leaves business executives overwhelmed by the speed of decision-making required in today’s supply chain environment. The existence of a gap between the complexities and capabilities leaves them vulnerable to competition from inside their industries and non-traditional competitors who are able to respond more quickly and shrewdly in their markets via supply chain intelligence. In other words, due to very few or lack of supply chain professionals with formal and advanced education in the field, supply chain strategies and operations often hampered the very business they were meant to enhance.
The future of Logistics and Supply Chain Management with AI as a tool of Innovation
Supply Chain Management (SCM) is indispensable to businesses. The utopian scenario is one in which planning, order management, procurement, manufacturing, warehousing, inventory, and delivery all seamlessly work together to deliver unmatched customer service. In the last couple of years, supply chains have been increasingly using AI solutions for forecasting and demand management and we’ve witnessed improved results. According to the SCM World Future of Supply Chain Surveys, 2017, as many as 47% of supply chain leaders expect that AI / Machine Learning is going to impact SCM strategies. Driverless trucks, smart warehouses, and drones doing last-mile delivery are a few of the latest innovations, and there’s much more in store.
the enthusiasm for AI is well-founded and the value, while lacking in some areas, is evident in other areas (like pattern recognition and machine learning). Technology already plays a significant role in some of today’s advanced supply chain and logistics solutions, increasing effectiveness, and efficiency, and automating many tasks for supply chain managers and planners.
Therefore, with the recent technological breakthroughs in big data, algorithm developments, and ever-increasing processing power, we are likely to see an explosion of AI technology driving more sophisticated solutions in the supply chain to speed and improve the delivery of products and services to customers. Companies relying on manual methods and simple software solutions will not be able to keep pace with their more sophisticated competitors. AI could well be a deciding factor in many industries, determining supply chain superiority, driving customer service excellence, and continually improving operational efficiency. Logistics and supply chain managers should pay close attention as more AI-enhanced solutions emerge.
Conclusion
The supply chain is a center of exciting innovation. The most exciting thing about Artificial Intelligence (AI) in logistics and supply chain management is there are many more applications impacting the industry.
While AI systems are able to process a far greater volume of data and therefore information than humans, to date they have suffered from the disadvantage of only being able to choose between known decision options. The quintessential traits like innovation, communication, creative thought, empathy, intuition, and the ability to contextualize, interpret, or question data are irreplaceable and remain the preserve of humans.
Automation and Artificial Intelligence should be embraced as it attracts talent and provides a competitive advantage. Skilled operators and automated efficiencies will improve the bottom line and not necessarily decrease labor. Consequently, companies should expect need to be more proactive in meeting industry expectations.
However, it is best to note that adapting to these industry shifts won’t be easy. Not only will companies have to invest in more tech-forward and data-driven processes; but they will also have to continue learning about new strategies that will help them drive business growth.
