In an era of greater demand uncertainty, an increasing competitive industry, and supply risks, the success of an organization hinges on their ability to integrate and plan according to the entire end-to-end supply chain spectrum. One way organizations can achieve this viewpoint is through artificial intelligence.
The beauty of AI is that it continues to transform human-only actions into human-impossible actions. A complex calculation that would take a team of professionals 100 hours to calculate can now be solved on a machine in a few seconds. That type of speed and accuracy simply isn’t possible by humans.
We’re not just here to talk about all the things AI can do that humans can’t, though. The purpose of this blog post is to define artificial intelligence it as it relates to various aspects of SCM and show you how those areas that are most benefited by adopting AI. The end goal: You’ll be able to leverage artificial intelligence in the right places in order to increase the effectiveness of your supply chain and business, overall.
How Do We Degine Artificial Intelligence?
When the idea of artificial intelligence comes to mind, everyone conjures up a different image. For example, some think of fully-automated robots that can function in human society. Others think of programs and softwares that can mimic one specific form of human intelligence.
What qualifies as artificial intelligence versus a super smart computer is hard to establish when everyone has a varying definition of AI.
The causation for such diverse definitions is the relativity and subjectivity of the term “intelligence”. AI is a moving target, so establishing a universal definition is difficult and almost impossible. However, we are only concerned with one definition.
From now on — for the sake of simplicity and clarity — when we refer to artificial intelligence, we will be referring to the ability of a machine to perform human intelligence tasks equal to or better than humans. Specifically, we will look at a software’s ability to perform computational, pattern recognition and planning functions better than humans when it comes to supply chain management.
The use of AI in supply chain management seeks to solve long-, mid- and short-term problems. For example, an AI system can examine strategic decisions such as alliances and facility location or make operational decisions such as vehicle routing and order picking.
Now that we’ve defined AI, we can dive into the areas organizations should begin to apply AI for a competitive advantage.
Using Artificial Intelligence to Gain a Competitive Advantage
Inventory Control and Management
Inventory represents locked capital and resources, but it is a necessary evil in order to fill customer orders and keep them satisfied. The job of an inventory manager is finding the balance between freeing idle resources and maintaining resources so they are available when a customer needs them.
Such a balance can be achieved with the collection of accurate, real-time information about inventory levels, customer demands and the fulfillment cycle time.
Mary Kathryn Allen, Major United States Air Force, developed a system in 1986 to aid the Air Force in managing their aircraft parts. Through an expert AI system, she was able to improve the inventory management efficiency by 8 to 18%.
Many organizations implement inventory management software, but AI does a great job at capturing information at all levels of the supply chain to determine an optimal inventory level.
Transportation Network Design
Due to the combinatorial nature and complexity of transportation network designs, AI systems using genetic algorithms to iterate and improve possible solutions are great for finding global optimal solutions.
For example, an organization specializing in pet toys needs to find the proper allocation of parcel shipments at the warehouse in order to appropriate shipping routes with different destinations and distribute those to the appropriate retail store. The objective of the organization is to find a proper allocation that minimizes cost, but still meets customer needs.
A genetic algorithm (a form of AI) can be used to help solve the problem. Genetic algorithms are more flexible and can be applied to a wide-range of problems without having to modify the algorithm (unlike traditional techniques).
Purchasing and Supply Management
Imagine a system that could complete and automate all of the purchasing managers repetitive tasks and aid in the process of decision-making. The make-or-buy decision seems like a simple process but it in fact involves hundreds if not thousands of what-if scenarios.
Two researchers, Nissen and Sengupta, have proposed an intelligent software that could automate the many procurement tasks — like searching, evaluating, and screening suppliers — and ordering from them.
These systems can aid the purchasing manager in strategic and tactical purchasing decisions while more traditional methods can only aid in supplier selection.
Demand Shaping, Planning and Forecasting
An organization uses demand planning to control inventory, decide on promotional programs, align the workforce levels with peaks and troughs, and new product development. Picking a forecasting model that is suitable for an organization’s environment can be difficult.
For example, some forecasting models are meant for short-term projections, where others are meant for long-term projections. Either way, most forecasting and demand planning models rely heavily on past information. So what happens when you have a product (or service) that has not been around very long, as is common in the healthcare and technology industry?
Artificial intelligence techniques have been introduced to solve this problem. An AI system moves beyond the traditional descriptive methods of demand planning and into the domain of prescriptive and predictive analytics.
For example, an AI system using an agent-based framework with a combination of human expertise and data mining techniques could be used to predict the future demand for products (or services) that have not been around very long.
That is great, but the biggest advantage in an AI system is its ability to perform demand shaping beyond demand manipulation. An AI can produce the most profitable and optimized decision by accounting for all supply-and-demand information.
Customer Relationship Management
Customer relationships and a customer’s perception of an organization affects demand. In turn, this will drive supply chain activities. Typically, customer relationship management is defined as the practices and strategies used by an organization to improve relationships and secure customer loyalty.
As a result, customer relationships can have a profound impact on an organization’s profitability, so it stands to reason that an organization may want to compare the benefits and the costs of implementing a customer relationship management system.
For example, an agent-based AI system can evaluate the influence word-of-mouth has on a product or service and determine a return on investment in their customer relationship management and customer acquisition systems.
Conclusion
Supply chains are collecting a tsunami of data that must be processed by complex machines, as more organizations continue to adopt a holistic approach to supply chain planning. In the not-so-far-off-future, supply chain managers will work more on the planning process than in the planning process.
Supply chain analytics software continues to develop predictive and prescriptive analytics capabilities that help organizations prevent fires before they arise, instead of spending their days trying to put out fires.
From a supplier’s perspective, this technology will continue to improve productivity, efficiency, and profitability. From a buyer’s perspective, this technology will ensure quality customer service by making sure good and services are available at the time consumer wants them. The topic of artificial intelligence replacing human word might still be highly debated, but it’s obvious that AI will only become more and more prevalent within supply chains.
