Skip to main content
Red Tractor Trailer

Effective Technologies for Supply Chain Management

Given the real supply chain issues over the last few years, companies continue to look for better ways to use data, improve decision making, and innovate and automate processes.
banner background

Companies continue to be challenged with needing better ways to manage their supply chain. Keys to better management include finding more effective ways to collect and analyze data, improving visibility to make better decisions, and innovating processes that automate and manage risk. As such, there is increasing investment in several technology solutions to achieve an “ideal” supply chain future state.

Several key technologies that companies are incorporating into their supply chain operations include:

  • Internet of Things
  • Robotics
  • Artificial Intelligence/Machine Learning

With the supply chain being a complex disparate network of suppliers, manufacturers, and processes, each technology plays a crucial role in making the network operate more efficiently and effectively.

Internet of Things (IoT)

The IoT has been a transformational development in the information and communication technologies space. Specifically, IoT has become a baseline technology solution that companies are incorporating to provide an “information ecosystem.” IoT, a network of physical devices connected virtually by using sensors, computer chips, and connectivity devices, has enabled rapid and accurate communication of information between machines (machine-to-machine or M2M) and people (machine-to-people or M2P) for more timely and effective decision-making.

With respect to supply chain management, IoT has in part supported other technology solutions that mitigate the “bullwhip effect,” a term used to describe when deficient information about the supply chain leads a manufacturer to experience variation in their production demand compared to actual market outcomes on the retail side, even when there has been little to no change in final demand. This bullwhip effect can ultimately cause downstream implications for the rest of the supply chain since the source of the product is experiencing volatility in its own operations.

More common uses of the IoT help deliver point-of-sale (POS) data at the retailer level to the wholesaler, distributor, and manufacturer to support real-time inventory control and planning and demand forecasts. Less common (but growing rapidly) and more advanced applications of IoT include the use of radio-frequency identification (RFID) tags on physical inventory to improve the logistical efficiency of the supply chain. RFID tags placed with physical inventory provide an opportunity for more data gathering throughout all points of the supply line. Additionally, IoT and the use of RFID tags support real-time decision making as there is real-time visibility into the inventory of a retailer’s wholesaler, distributor, and/or manufacturer. By providing improved access to pertinent information, a system based on IoT can lower variability in ordered and shipped quantities of products and reduce carrying costs by minimizing work-in-progress and finished goods inventories.

To summarize, IoT has become a baseline solution for companies’ supply chain management function, enabling greater and more rapid access to information that is needed to perform critical activities. IoT is essentially the foundation for building a supply chain management function based on robotics and AI as all three technology solutions are interconnected.


There has been a recent and rapid increase in the adoption of physical robots ignited by a robust recovery in global demand following the lockdowns of the COVID-19 pandemic.  According to the International Federation of Robots’ World Robotics Report 2022, the worldwide installation of industrial use robots increased to 517,000 units in 2021, up 31% from 2020. In the U.S. specifically, the metal and machinery and plastic and chemical products industries experienced the most growth year over year, installing approximately 3,814 and 3,466 industrial robots, respectively.1

There are two different robotics applications for supply chain management: software robotics, known as robotic process automation (RPA), and physical robots. RPA is typically a back-end technology solution that supports the automatic analysis of inventory and freight data, rules-based returns processing, and invoice and order management. Physical robots tend to support supply chain operations from an inventory management and order fulfillment perspective, being heavily involved in the order storage and picking processes.

Robots are commonly referred to as automated guided vehicles (AGV) because of their use and function within the order fulfillment process of the supply chain. AGVs are used as an order fulfillment picking solution performing tasks that typically require humans thereby reducing labor requirements for a given task and freeing up the opportunity cost for decision-making that a robot cannot make [at least with non-machine learning solutions]. Using robotics to support the order fulfillment process can improve productivity and reduce downtime which may ultimately lead to cost savings and a more efficient supply chain. 

An advanced application of robotics is the use of autonomous vehicles for transporting goods across the supply chain. Such applications could reduce delivery costs from a payroll perspective and improve the uptime of last-mile delivery. The use and implementation of robotics in supply chain management rest on the concurrent implementation of solid IoT networks and robust AI solutions.

Artificial Intelligence/Machine Learning (AI/ML)

AI and ML can be key value-added technology solutions given the complex decision-making process in supply chain management. AI generally refers to sophisticated computer processes and algorithms that simulate human intelligence and cognitive function. AI essentially is the brain behind the robotic automation mentioned above and is supported by the free flow of information on an IoT network. AI has different levels of sophistication. Machine learning is a subset of AI whereby the machine is not only able to react based on initial input programming but also learns as the machine operates throughout time using either supervised, unsupervised, or reinforcement learning. AI and ML have historically been incorporated as technology solutions in fragments and this is where the opportunity lies to create a holistic supply chain management function based on AI.

AI can be implemented in inventory control and planning, transportation network design, purchasing and supply management, and demand forecasting. AI is being used in the inventory control and planning process where the standard mathematical assumptions underpinning the economic order quantity model are starting to fall short, especially in a post-COVID-19 era where global macroeconomic dynamics continue to evolve at a very rapid pace. The speed to access real-time data and information inputs can be increased with AI solutions and can drive more accurate real-time inventory amounts. AI has also been used in the design of transportation networks since this part of the supply chain tends to be extremely multi-factor and conditional upon each of several stakeholders fulfilling their responsibility of the chain. The purchasing and supply management aspect is also very multi-factor involving several “what-if” scenarios before make or buy decisions. Systematic decision-aid tools via AI have supported this aspect of the supply chain by making the exchange of information between purchases more readily available. Lastly, AI has made considerable contributions to advanced demand forecasts. With the post-COVID-19 economic and supply chain landscape suggesting a trend towards deglobalization, there is uncertainty that can be mitigated in part by more advanced forecasting techniques that take into account not only historical data and outcomes but also outcomes based on current and future observable behaviors.

As companies look for ways to improve their supply chains through technology, they should rely on a trusted professional. Forvis Mazars can provide resources, innovation, and insight to help you with data management, inventory management, data analytics, and technology implementation related to supply chains. We look forward to learning how we can best help you.

Related FORsights

Like what you see?
Subscribe to receive tailored insights directly to your inbox.