Companies are getting serious about modernizing their production processes. Specifically, they are bringing new approaches and methodologies to their supply chains, as well as the practice of inventory management. This is providing a lot of efficiencies, and driving improvements in how “data-driven” businesses tend to work.
Any product business, as a rule, has to have some sort of plan for inventory management. The more detailed and concrete this plan is, the better. Here are five areas where technology is contributing to better inventory management for companies that want to stay competitive as the technology world evolves around them.
Just-In-Time Inventory
The emergence of a just-in-time inventory model coincides in a general way with the cloud era, where new types of portable data became available to businesses. Prior to that time, data was limited in its capacity to be held, manipulated and used on-site. The cloud launched new frontiers of data use, in which phenomena like JIT could grow.
With these new capabilities, planners realized that it’s possible to refine and optimize inventory holding processes according to market and customer demand, closer to real-time.
The basic idea is that instead of having an established stock of inventory for long-term delivery and fulfillment, companies will only stock what they seek to deliver short-term. They’ll source in a dynamic way, not just replacing a stock amount as in traditional models.
It’s an idea with a lot of potential. Dynamic inventory handling works on the principle that having more detailed insights from valuable data will benefit the company’s bottom line.
However, there are certain challenges to the just-in-time inventory model. The savings and the capabilities are only as good as the company’s predictive ability.
We saw the breakdown of some of these sorts of processes in the coronavirus pandemic era. Supply chains were impacted—and continue to be impacted in unanticipated ways. In those types of cases, the benefit of having less inventory on hand is broken down by the loss of business that happens when needed inventory isn’t available.
With the right approach, though, just-in-time can save companies massive amounts of money on real estate and warehouse infrastructure. It’s a question of applying these predictive technologies in ways that are hardened against the above types of supply chain problems.
Lean Manufacturing (And Inventory) Models
Lean inventory, as a concept, goes along with JIT and can complement it in some cases. The nature of it is a little bit different from what just-in-time inventory processes are used for.
Where just-in-time is often applied to a stock inventory as it sits ready for use, lean manufacturing models look at other segments of the production process and try to apply the same predictive methods.
Both lean and JIT utilize structures like kanban and specific planning tools to provide optimized results for companies. Both benefit from analysis of Ohno’s “seven wastes,” namely: Overproduction, Waiting, Transportation, Inappropriate Processing, Excessive Inventory, Unnecessary Motion, and Defects. Lean manufacturing, though, tends to apply to a broader life cycle process and address inefficiencies in its own ways, where JIT has the “inventory domain.”
Better Inventory Tracking
This is another area of inventory management that is quickly evolving to serve B2B customers in various industries.
Companies offering new and improved kinds of GIS tracking and inventory tracking devices and systems can help their clients benefit in some pretty dramatic ways.
For example, consider the use of inventory tracking products for large vehicles like snowmobiles, ATVs and watercraft.
When applied to vehicles like these on the lot, GIS inventory trackers can do a lot to combat loss or shrinkage from theft, vandalism or disaster threats.
An ATV GPS tracker or similar item can also help the client business to know where inventory items are in order to show them off to prospective customers, shield them from damage, or deliver them to a customer in the fulfillment process.
Companies that deal in large items (produced through discrete manufacturing) understand how valuable these systems are from a practical standpoint. They see how better tracking may:
- Be able to move outdoor inventory when hail or sleet is on the forecast
- Know at a glance whether something has been stolen or just moved to another part of the lot
- Locate a product for a test drive or demonstration
- Figure out quickly how many of a given item are on hand
- In addition, real-time fleet analytics GPS can help with optimizing insurance plan discounts according to floorplan analysis and more.
- Through analyzing this information carefully, companies can reduce their liability and create profits that they can pass on to the customer.
The wealth of benefits that these small tracking devices can provide come in conjunction with new applications and dashboards powered by big data and competitive design. The tech industry as a whole has also made a lot of progress in making large amounts of data digestible or presentable to human decision-makers, and that has enhanced our decision support tools significantly.
Real-time fleet analytics provides an example of this type of synergy for business. A small investment brings large returns.
Digital Twinning
This is another process that is deeply wound into industrial planning and inventory models.
Digital twinning is the process of outfitting a specific physical infrastructure or complex system with its own digital or virtual ‘twin’ – a system that mirrors each component and aspect of the physical system or operation. The digital twin then serves as a comprehensive reference.
The idea is that involved users can explore the virtual twin to figure out more about the physical twin in terms of size, shape, placement, hierarchy, or anything else. The digital twin provides more transparency than the physical twin does, typically, and is updated to reflect its physical twin accurately.
Auditors love the capability of digital twinning to show them more about a company’s operations at a glance.
Across the business world, this type of virtual modeling is becoming more common, and executives are considering it as a way to do the books differently in today’s information age. Where organizational transparency is the goal, digital twinning can be part of the solution.
The Ubiquity Of AI/Ml Implementations
It’s obvious to most business people that many of the enterprise benefits of artificial intelligence and machine learning are going to be important in the coming years.
These are two other aspects of technology that have also evolved a lot in just a short time.
In the general analysis of AI, business people often reference the capability of recommendation engines for marketing as well as predictive AI technologies for inventory handling and order fulfillment. Complex systems attached to sensors or other data sources show human decision-makers more of what goes on in an enterprise context, enabling the kinds of valuable insights that business leaders are looking for.
On the machine learning side, data scientists and other professionals look at the capability of computer systems to learn from test and training data and to apply that data to ongoing operations through a process that is often called convergence. Executives learn about how machine learning programs work—for example, how neural networks mimic the cognitive operations of the human brain or how computer vision and image processing work to evaluate a physical environment. For the purposes of inventory handling, that process might go something like this: Computer scanners can look at inventory and comprehend things like volume and placement, reporting those into a visual data dashboard that humans can look at to understand what’s going on.
What all of these approaches have in common is the use of very new technologies to an age-old process—the stocking of shops for product-based business operations.
It’s sometimes helpful to think about multiple approaches as contributing to a modeling effect that gives viewers a birds-eye view of business operations along with detailed knowledge of where each individual asset is and how it can be used in a pipeline. That process of “finding” components and using them demonstrates the value of these explorative systems.
For another example, these systems are also popular in municipal work, where local government agencies have diverse assets. They have vehicles and pieces of equipment. They have products like signs and pieces of sewer and water infrastructure. Then they have people managing processes and looking for these physical items in inventory. Having robust GIS tracking systems makes sense from that standpoint.
It makes sense in business, too, and many of these best practices rely on that basic idea—that by tracking physical items, companies really evolve their insights in significant ways. Virtual management of physical assets is a practice that’s likely to become more a part of our business and personal lives in the future, and the ROI, the practicality, and the evident benefit are what keep so many companies moving forward in the above areas of inventory management.