Supply Chain Inventory Software: From the 90s to Now

Supply chain inventory software, then and now

What supply chain inventory software does

Materials management is the process of overseeing the flow of materials through a production system, and doing it well takes the right software. See how materials management transformation can improve your supply chain.

This software has taken many forms over the years, embraced new technologies, gone through stagnant periods with little innovation, and continues to develop today. Implemented well, it is realized across the supply chain lifespan in product sourcing, development, logistics and retail, streamlining both the physical movement of products and the flow of information among the people and systems that need it.

Asked what is state of the art in supply chain optimization today, Stefan Althoff, senior product manager at Ortec, said: "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." (SupplyChainBrain)

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 helps to look 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, with them, more complex technologies 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, reflecting how central supply chain strategy had become to business.

The birth of the internet and the personal computer

Supply chain inventory software planted its roots in the 1980s with the adoption of personal computers. Computing gave supply chain managers flexible spreadsheets, graphical environment mapping, intuitive interfaces and other tools that improved logistical outcomes. Early innovations included optimized map interfaces for better product distribution and control technologies that automated tracking and handling.

Digital transformation, big data, and real-time monitoring

Within the last decade, social media has grown wide enough that poor supply chain management is now easily noticeable to the public. Big data, driven by pressure to transport materials ethically, sustainably and quickly, has made analytics and logistics more critical to supply chain success.

By 2014, only 17% of supply chain senior executives had begun to implement big data and its associated insights into their supply chain management. By 2021, this number had risen steeply to nearly 50%. (Big Data Made Simple)

Big data also brought a key resource to supply chain inventory software: monitoring. Companies now need real-time tracking of inventory to meet stakeholder demands. Yet despite wider adoption, the full integration of technology into supply chain management is still lacking. According to a 2020 study from MIT and Boston Consulting Group, just 10% of organizations were realizing significant financial benefits from AI. (BCG)

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. Delays in China's exports of critical materials exposed the world's over-reliance on a few manufacturing centers. (Harvard Business Review)

Resilience to disruption is now a priority for supply chain managers. Adding storage capacity, increasing inventory levels, employing more staff, introducing AI and ensuring stability during a surge all help supply chains withstand disruption. Resilience can add cost, but your level of investment in it can decide how your company fares in the next interruption. See building back a better supply chain.

The effect of technology on supply chain management

The internet and its expansion

In 1969, the Advanced Research Projects Agency Network (ARPANET) introduced a way for multiple computers to communicate on a single network. That starting point spawned many networks and connected increasingly global stakeholders. With the World Wide Web in the 1990s, networks became truly global, and the footprint of supply chain management companies grew rapidly. Internet access let pioneering organizations begin supply chain orchestration, with multi-enterprise platforms that could predict and execute product distribution with new accuracy.

Collaborative robotics, Industry 4.0, and ongoing digitalization

Supply chain advancement is required to stay relevant in a globalized world. Industry 4.0 continues to be the next step, with companies raising productivity through robotics and automation. See applying 21st-century technology to today's supply chain challenges.

This shift points to a larger transition from a labor-intensive workforce to one that works alongside robots, and it is up to supply chain leaders to help workers feel comfortable with where the industry is heading. MIT researchers found that robot-human teams were 85% more productive than either alone. (Fronetics) Cooperation between employees and robots will be vital to supply chain success. (SCCG)

Looking forward: the future of supply chain management software

Supply chain orchestration

Cooperation between partners and employees will be required to prosper in the new age of supply chain dynamics. A few factors matter most:

• Multi-business supply chain connections. Supply chains grow larger and more complex by the day. The right relationships with partner businesses are key to an efficient network and future profitability.
• Centralized, thorough supply chain management. Visibility across interwoven processes opens more avenues for savings and growth, and supply chain inventory software is increasingly used to expand it.
• Order handling. Orchestration isn't complete without the flexibility to automate order flows, and AI is increasingly used to streamline them.

Digital twin models

The complexity of modern supply chains makes structural weaknesses hard to model and predict. A supply chain digital twin is a virtual copy of the real supply chain used to gauge its dynamics and opportunities for improvement. (AIMultiple) These models run in real time on inputs from work orders, sales, demand and other variables, sourced from IoT devices, supply chain databases, vendor statistics, user information and AI.

Artificial intelligence and machine learning

Supply chain inventory software has moved past its early stages and now relies on machine learning to stay ahead. Machine learning lets software learn your supply chain from data analysis and support decisions with less manual effort. While AI in supply chains is nothing new, developing industries can benefit from its advancement and wider use. See AI and machine learning for MRO.

Robotics, self-driving trucks and supply chain orchestration will all profit from these learning capabilities. As algorithms for predictive and responsive supply chain analysis grow too complex to run by hand, machine learning will be required to stay relevant.

The Internet of Things (IoT)

The IoT is a system of interconnected objects embedded with sensors, software and other technologies that exchange data 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. The pandemic likely accelerated IoT adoption because of its remote monitoring and control capabilities. (ZDNet) IoT data collection often incorporates machine learning, which extends what AI can do across the supply chain.

Control engineering logic

Much as a person troubleshoots a computer to keep it running, control engineering processes data about the state of supply chain operations and applies algorithms to reach an optimal state. For example, manufacturing supervisors constantly receive data on their machines. Control engineering logic uses that data to predict when a machine may shut down, or what state keeps it at optimal utility, and applies algorithms to maintain ideal conditions.

Cloud and edge computing

Cloud computing, centralized computing accessed through the internet, is expanding by integrating with localized edge computing. Edge computing is adapting quickly to process data across billions of sensor-equipped IoT objects. Its advantages:

• Network latency is considerably shorter.
• Edge computers filter traffic before it reaches the cloud, preventing millions of signals from arriving at once.
• Machines in areas with no internet connectivity can process information locally, then sync to the cloud once a connection returns.

What to look for in supply chain inventory software today

• Multi-site, multi-system visibility. One view of materials across every ERP, EAM and site, not plant by plant.
• Works on the data you have. No months-long cleanse before you see value. See the death of the data cleanse.
• Recommendations, not just reports. Stocking, transfer and purchasing suggestions your team approves.
• Built for MRO, not only production. Spare parts behave differently from finished goods. See MRO inventory optimization.

Where to start

Just getting started? Map where your materials data lives today: which ERPs, EAMs and spreadsheets, and which sites. Talk to an MRO expert.

Comparing platforms? See how the options stack up in MRO inventory optimization platforms compared.

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. Explore the MRO software platform, book a demo, or estimate your opportunity in under 3 minutes.

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