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Inventory Management Software: Avoiding the Pitfalls

Inventory Management Software: Avoiding the Pitfalls. Inventory management software is at the heart of every retail business.

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Inventory Management Software Avoiding the Pitfalls

Inventory management software is at the heart of every retail business. Thus, it is prudent to ensure that the company is steadfast in its implementation. And in equal measure, they should be wary about a few pitfalls that they ought to avoid.

1. Lack of Inventory Management Software User Training

Inventory management is inherently a complicated subject that requires experience and honed instincts. Despite its name, inventory management does not necessarily revolve around inventory.

Other aspects that affect lists, such as those that affect the inventory status, such as purchases and sales, are part of the whole inventory management paradigm. This is the reason why inventory management, and incidentally the inventory management software, is too broad in scope.

The common pitfall that businesses fall through when it comes to training is the sheer confidence that they have in their staff’s ability to adjust with a new system put in place.

There should never be an assumption that the operating units will hit the ground running. The main barriers include technological challenges, resistance to changes, and lacking consultation.

Although computers and computer systems have been around for a while, we cannot dispute the fact that there are still some people that are reluctant to work with software.

Especially in the blue-collar jobs in which productivity is often translated from the amount of sweat you produce, it can be quite a challenge to get the inventory staff on board with new software, or software in general. This is why there must be a balance within the inventory staff. It is necessary to have a balance between the tech-savvy and the maturity.

Change is never an easy thing to introduce to a workforce that has known to do one thing the same way for a long time. In which case, they need to be given a long enough time to adjust before transitioning.

Consider having a part of the inventory pipeline migrated into the new system while leaving out a majority of the operation the same way. Then, bit by bit, introduce unique aspects of the new inventory management system, until the entire set up has staff acclimated.

Finally, one of the reasons that new inventory management software fails to launch as it was intended is because there was not enough consultation done with the people on the ground.

Since they are the ones who would be working intently with the software, their say must be paramount in the decision process. This is one of the most commonly left out aspects in most business software decisions.

To ensure that everyone is on the same page, all the stakeholders should be part of the product demo and the training. Software companies usually showcase their product if you ask them to.

During these product demonstrations, everyone must be there to witness. Functions and features should be shown, and the team has to approve that these are indeed essential in their job.

1. Losing the Inventory Management Software Forecasts

Artificial intelligence has evolved from a thought study into a full-fledged scientific branch that has business merit. With the advent of machine learning, decision support systems have never been as smart as they are now.

The inventory management system is no exception to the wonders of AI, and it would be wasteful if the business turns a blind eye to this opportunity.

There are quite a lot of areas aside from the obvious ones when talking about forecasting and decision support in inventory management systems. The most obvious one is probably the digital equivalent of the merchant instinct. To understand this a little bit, we first have to talk about how merchants did their business before.

Similar to the modern traders, olden time merchants would buy supplies from suppliers, sell their products then buy a certain amount before the day ends. The crucial decision here is the time when the merchant decides how much he should buy. Of course, as the able merchant that he is, he would look at the remaining stock, count how much he sold, consider the season and how many he can carry in his cart, then buy the number.

The summary of that decision is what is called the merchant instinct. On many levels, the same is still applied by the modern merchant. However, as has been mentioned, this whole process can be done algebraically using an algorithm ultimately designed by a machine or artificial intelligence. The way it works draws parallel to the same merchant that we talked about.

The amount of stock left and what has been sold can be deduced from the sales record and the current inventory. Here is where artificial intelligence shines. The season could be a derived function from previous sales as you might expect, but that is not all.

Several studies show that trends can be formed from other factors as well. Some of these factors include seemingly unconnected entities, like the weather, days of the week, and others. And finally, using the same merchant example, the final decision on how much product to purchase would depend on the report of the system.

As was hinted at, the merchant instinct is not the only aspect of inventory management where AI can be of help. Supplier profiling is the process of deducing the essential facts from individual suppliers based on their punctuality on their deliveries, quality of their shipments, and the precision of the purchased supply.

This is extremely important, considering that the end goal is to optimize the amount of space that the stockrooms and the shelves can accommodate. Remember that empty spaces in stockrooms cost money as well as stocks that are not able to make it there.

The key here is to balance the supply with the demand, and no tool can do so better than a decision support system based on artificial intelligence. Finally, you may not be able to figure it out as soon as the inventory system is deployed, but AI hinges on the data supplied to it.

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