A Brief History of Supply Chain Inventory Software, From the ’90s to Now
Materials management is the process of overseeing the flow of materials through a production system.
To do this effectively, you need the right software.
This software has taken a variety of forms over the years, embraced new technologies, suffered stagnant periods characterized by a lack of innovation, and continues to develop to this day.
Supply-chain-inventory software is a tool used in assisting supply chain business processes, including chain transactions, supplier relations, inventory management, and a myriad of other purposes designed specifically to maximize the efficiency of cradle-to-grave inventory management.
Successful implementation of software used in supply chain management is realized throughout the supply chain lifespan in product sourcing, development, logistics, and retail.
In doing so, businesses see streamlined efficiency in both the physical movement of products as well as the information dissemination among necessary personnel and systems.
When asked, “What is state of the art in supply-chain optimization today? What can systems and companies do now that they couldn’t in the past?” Stefan Althoff, senior product manager at Ortec, responded:
“Today, they’re mostly busy organizing and aligning between the different functions. But it still all happens between the four walls of the company itself. The difficulty and challenge lies in reaching out to supply-chain partners, to optimize across the whole process.”
The history of supply chain management from the ‘90s to present day
To understand how supply chain inventory management software has developed over the last 3 decades, it’ll be helpful to first take a glance back at the origins of the supply chain itself.
Globalization and specialization
The growth of manufacturing in China cemented the term “supply chain” in the mid-1990s.
Increasing globalization required more complex methods of supply-chain-management and, subsequently, more complex technologies in order to keep up with the pace of production.
In 2005, the Council of Logistics Management changed its name to the Council of Supply Chain Management Professionals to reflect the increasing abundance of strategic blockades in regular business mechanisms.
The birth of the internet
Supply-chain-inventory software planted its roots in the 1980s with the adoption of personal computers.
Access to computing capabilities gifted supply chain managers with flexible spreadsheets, graphical environment mapping, intuitive interfaces, and a host of other tools used in supply chain management to enable improved logistical outcomes.
Early innovations included optimization of map interfaces for better distribution of products and control technologies for the automation of tracking and handling products.
Digital transformation, big data, and real-time monitoring
Within the last decade, social media’s predominance has grown wide enough that insufficient supply-chain management is now easily noticeable to the public.
Big data, driven by societal pressures to ethically, sustainably, and quickly transport materials, has made analytics and logistics both more critical to supply-chain success, as well as increasingly central for improved system management.
By 2014, only 17% of supply-chain senior executives had begun to implement big data and its associated insights into the structure of their supply-chain management setup. By 2021, this number had risen steeply to nearly 50%.
Moreover, big data’s growing role within supply chain inventory software has introduced a key resource for supply chain managers: monitoring capabilities.
Companies are now forced to adopt technology to enable real-time tracking of inventory (monitoring), as well as to meet the demand of relevant stakeholders.
Despite the expanded adoption of digital resources, the complete integration of technology into supply-chain management is lacking.
According to a recent study from MIT and Boston Consulting Group, just 10% of organizations are currently realizing real financial benefits from the use of AI.
COVID-19 and supply chain resiliency
COVID-19 is not the first epidemic to derail supply chains, yet it is by far the most disruptive.
Historic structural and logistical facets in supply chain dynamics have now received scrutiny for how easily a chink in the armor can hinder the worldwide transportation of goods.
Delays in China’s exporting of critical materials have emphasized the dangerous over-reliance of their manufacturing that the world currently operates under.
Resilience to supply chain disruptions is now a key mechanism supply chain managers are looking into should another worldwide perturbation occur.
Adding storage capacity, increasing inventory levels, employing more staff, introducing AI integration, and ensuring stability during a surge are all ways in which supply chains can remain resilient to disruption.
Supply chain resilience may present additional costs to your company, however, your level of investment in resilient strategies can spell the success or failure of your company should another interruption arise.
The effect of technology on supply chain management
The internet and its expansion
The Advanced Research Projects Agency Network (ARPANET) devised a novel method of communication located on a single network in which multiple computers could interact in 1969.
That starting point spawned the development of multiple networks, enabling expanded connectivity between increasingly global stakeholders.
With the introduction of the World Wide Web in the 90s, networks were fully realized in a global capacity. Which led to an exponential growth in the footprint of supply chain management companies.
Internet access allowed pioneering organizations to begin supply chain orchestration. These multi-enterprise platforms were able to predict and execute variable product dissemination with newly-acquired accuracy.
Collaborative robotics, Industry 4.0 technology, and ongoing digitalization
Recent trends suggest that supply chain advancement is required to stay relevant in an increasingly globalized world.
Industry 4.0 continues to be the next step in supply chain progress. Modern companies are increasing productivity via the utilization of robotics and automation.
This shift indicates a much larger looming transition from a labor-intensive workforce to one which relies on the piloting of robots. Workers are not likely to be excited about this new role, and it is up to supply-chain managers to make them feel comfortable with where the industry is heading.
MIT Researchers found that robot-human teams were 85% more productive than either alone.
Cooperation between employees and robots will be vital to ensure the success of supply chain processes in the near future.
Looking forward: The future of supply chain management software
Supply chain orchestration
Cooperation between partners and employees will be required to stay prosperous in the new age of supply chain dynamics.
Successful supply chain orchestration takes many forms, however, a few key factors play an important role.
Multi-Business Supply Chain Connections: Supply chains are becoming a larger and more complex endeavor by the day. This is not meant to be overseen alone. Leveraging the right relationships with required businesses to guarantee an efficient network is the key to future profitability.
Centralized and Thorough Supply Chain Management: Visibility among interwoven processes in supply chain operation will grant your business more avenues for savings and growth. Supply chain inventory software is increasingly utilized to expand this visibility.
Order Handling: Supply chain orchestration wouldn’t be complete without the flexibility to automate order flows within your company. AI has been increasingly integrated into supply chain sectors to accomplish this goal and streamline the consumer and business experience.
Digital twin models
The degree of intricacy supply chains now exhibit presents a challenge to modeling and predicting structural deficiencies.
A supply chain digital twin is a virtual copy of the real supply chain used to gauge supply chain dynamics and opportunities for improvement.
These models run in real-time, accounting for informational inputs from work orders, sales, demand, and a variety of equally important variables.
This data is sourced using several methods, including IoT devices, supply chain databases, vendor statistics, user information, and AI.
Artificial intelligence and machine learning
Supply chain inventory software has graduated from its nascent stages and now relies on machine learning to stay ahead of the curve.
Machine learning enables software to ascertain knowledge of your supply chain through data analysis, and then make decisions without the need for human intervention.
While the integration of AI into supply chains is nothing new, developing industries could benefit from its advancement and increasing utilization.
Robotics, self-driving trucks, and supply chain orchestration are all facets of supply chain management that will profit from the learning capabilities of novel software.
How do we know supply chain software is headed in this direction?
Increasingly sophisticated algorithms used for the predictive and responsive analysis of supply chains will soon be too complex for humans. Machine-learning capabilities will be required to stay relevant in an increasingly modernized space.
The Internet of Things (IoT)
The IoT is a system of interconnected objects that are embedded with sensors, software, and other technologies that compare data with other objects without requiring human intervention.
Its deployment is accelerating. A recent survey found that 77% of respondents had fully deployed at least one IoT project – up from just 21% in its 2018 survey. It stands to reason that the pandemic accelerated IoT integration due to remote monitoring and control capabilities.
Its adoption spells victory for AI advocates as the IoT often incorporates machine learning into its data collecting processes.
Control engineering logic
Like a human troubleshoots computer issues to keep it running smoothly, control engineering is employed to process data about the state of supply chain operations and then apply algorithms to reach an optimal state.
For example, manufacturing supervisors consistently receive data regarding the viability of their machines. Control engineering logic uses such data to determine when the machine may shut down, or what state to keep them in for optimal utility, and then employs algorithms to ensure ideal conditions.
Cloud and edge computing
Cloud computing, a method of centralized computing accessed through the internet, is expanding its use by integrating with localized computing, or edge computing.
Edge computing is quickly adapting to tackle issues relating to processing data across billions of sensor-equipped objects connected through the IoT.
What are its advantages?
Firstly, network latency is considerably shortened.
Cloud access is initially filtered through edge computers to prevent the onslaught of millions of digital signals being sent to the cloud at once.
Several machines operate in areas with no internet connectivity. Edge computers can be utilized to process information locally, and then later to the cloud once a connection is regained.
Looking for a supply chain management software solution that’s ahead of the curve?
Verusen is a dynamic and user-friendly materials management software solution that provides AI-powered insights to help users make better business decisions.
It’s designed for organizations of any size, in any industry, and offers unlimited SKUs and integration with other systems.
To learn more about how Verusen can help you streamline your business, request a demo of our platform.