Large enterprises face big challenges in managing their warehouses and establishing a smooth flow of the incoming and outgoing items. If left to the mercy of conventional methods (human staff moving items and keeping track of all entries), inventory management can grow to an insanely big size, where operational budgets get consumed faster than light by the black hole.
Artificial Intelligence can offer massive support to companies in several ways. The bigger the size of an inventory - the better use can be found for a trained AI.
To better understand the possible applications of an AI for logistical purposes, let's focus our sights at the most common problems that companies face while managing the inventory and establishing efficient supply chains.
An overabundance of data. Data management becomes a tedious process due to how much of it is usually accumulated by any company. While carefully built and supported inventory management software can track and store data, it would still require tremendous efforts from the human staff to process all of it.
Tracking issues. It is getting harder to track every item in the inventory and to get relevant analysis out of it. Failure to follow every incoming and outgoing item can have devastating effects on turnover, an adequate response to increasing or decreasing demand, timely delivery decisions, etc.
Difficulties with business planning. Inventory management is a big part of successful planning for the companies in any industry, especially the ones that deal with CPGs. Successful growth methodologies require real-time collection and processing of all data, and according decisions and responses to situations.
High operational budgets. As the size of inventory grows, the company struggle more with maintaining the cost-effectiveness across the branch. Whether it be the delivery service, analytical and data mining teams or staff that keeps track of stored items, the funding increases alongside the size of the company. And often these numbers are far from being acceptable.
Let’s delve into the actual discussion and see how an AI can aid the inventory management at the moment. For your convenience, here’s a short recap of what you’re about to get:
Dealing with planning, predictions, and modeling in inventory management
Stocking management and fulfillment
AI can prove to be invaluable in most of the situations described above. Careful implementation can result in a company-wide boost that brings cost-effectiveness, increased turnover, customer satisfaction, and retention. Here's what the companies can do today:
Inventory management is far beyond storing and delivering items from one place to another. It both generates and relies on substantial amounts of data to be effective in terms of time, money and workforce.
Overstocking and understocking are usually caused by failure to see and respond to the change in demand for a specific product. The ability to predict that, as a company, requires extremely competent analysts and experts in business modeling. There are other complicating factors, as more than one warehouse, location-specific demand and more. Each product niche has its specifics that managers and analysts have to adapt to.
Artificial Intelligence is capable of providing insights that were previously unavailable. It can take into account various aspects that in some way influence the demand. AI can analyze over than 50 elements of that, which is vital for the successful planning, stocking, and scheduling deliveries.
An AI tech is showing serious competence in transforming the data into timely actions that can help the business to evolve or respond well to a particular situation. Say, a local hockey team progressed in the Stanley Cup, and there will be a much bigger demand for beverages and snacks in the next matchday in that specific region. Artificial Intelligence can analyze that and come up with a suggestion to overstock for that particular region.
Trained intelligence acts as a supervisor and a watchdog: weather conditions, events, economic situation, how trendy is this or that product, and much more. It can take the decisions made by the management from error-prone assumptions (even though backed up by data) to almost a guaranteed result. While, obviously, significantly reducing the workload of the said staff.
Inventory management has a significant impact on customer satisfaction and sense of fulfillment. Planning errors or inadequate stock monitoring in any warehouse can lead to shortages and delays, negatively impacting the revenue.
As explained in previous sections, AI is already competent in analyzing the customer behavior patterns and a big number of other factors, that help to plan the stocking right. Furthermore, a well-trained intelligence can automate the process of stocking and increase the efficiency in delivery, suggesting the best routes.
Use of AI can minimize the risks of mismanaging the stocking process and help respond to the customer demand accordingly. With the insights provided by data mining, an AI can also help to establish efficient factory-to-warehouse transportation, which is extremely important for more volatile products that have shorter expiry time.
Robots are not a new thing on the market. Companies like Amazon are already using them in their day-to-day logistical tasks, and there is a number of benefits that put robots over human staff, especially in routine operations.
Robots can tirelessly move items around the warehouses, locate wares and scan their conditions. The machines can work around the clock and with more optimal time per action. This alone can save a big chunk of operational budget and allows to allocate more employees to more urgent and vital tasks that require human cognition.
Artificial Intelligence can enhance the process further. United with an intelligence that is capable of analyzing the data, predicting the demand patterns and suggesting optimal delivery routes, it becomes a potent tool that can completely automate internal processes in any warehouse.
The managers must be aware of the complications that may follow while using the AI supply chain and inventory management. While the advantages shadow the potential risks and difficulties, it is still vital to learn them in advance.
One of the strongest tricks of an AI may be its biggest complication. To build and train a custom and efficient artificial intelligence, the software developers require tons of prior data. The development process will get easier the more data you present to the team. It varies depending on what your requirements are: lists of items, under/overstocking statistics, consumer demands, errors and victories in planning, etc.
Everything you can spare will make the AI work more efficiently and with lower risks. And if the data is lacking the required information, the process gets more tedious.
An AI must help your business, not disrupt it. One must be aware that it is a hefty and time-consuming process to embed the AI into your systems without breaking them. Naturally, the bigger and heavier your systems and software are - the more effort it will require to install an AI.
Regardless of the software that you’re using, separate dashboards for the team will most likely be necessary to keep everything as smooth as possible.
Artificial intelligence helps to automate processes connected to the inventory, warehouses, stocking and more. It can offer aid both in physical tasks, such as relocating and tracking items or more complex situations where an advanced insight is required to complete an error-free planning or consumer demand forecast.
If the enterprise is rather small in size, utilizing an AI may seem like an unnecessary move. But as the amount of operations and data to track grows, it becomes harder to control everything manually and protect the company from errors.
An AI is also good and providing cost-effectiveness in the operational department, leaving the routine work for the machinery and enabling human workers to focus their attention elsewhere. Furthermore, an automated warehouse can be expanded without the urgent need to increase the staff accordingly.
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