Some of the ML Use Cases in Inventory Management - The Writters

Some of the ML Use Cases in Inventory Management

In a global market that will be creating room for many competitors till the day-end, some of the award-winning companies are transforming their methodologies with Artificial Intelligence and machine learning – for the sake of gaining a competitive edge.

Additionally, the QuickBooks Hosting Providers support the fact that the supply chain and inventory management is such a domain – still missing the media limelight, but the industry leaders are still working hard – for developing the applications supporting the AI and machine learning technologies.

Even many renowned companies will be opting for the machine learning principles – this will help them optimize the existing business-processes in ways, which could have been deemed science fiction twenty-years ago, from handling the customer-service inquiries to planning for the other month’s shelf supply – based on the datasets acquired from the satellites.

Also, one can’t deny the fact that the Supply chains and other inventory management systems will primarily be embodying the smart-automation concepts – over the next four to eight years.

Henceforth, there are listed-below highlighted use-cases – or the applications – we may say – all of them have successfully been able to link inventory management with machine learning technology. The benefit of doing the same will be that they will provide an iceberg view of what may happen. 

Use-Cases of Inventory Management Somewhere Depicting ML Principles

With these use-cases, the new frontier of supply chain and inventory management will now be allowing the companies to leverage data availability in speedier ways— like preventing malfunctions which will be costly, exceeding customer expectations related to the product’s demands and the associated services, and at last, enhancing the ROI ratio for the long-term benefits.

So, without waiting for more minutes, the supply-chain experts will now be able to offer the services desired by their customers – after reviewing the use-cases.

# Use-Case Number One -> Robots – Seeing to Customer Satisfaction

There were two of the largest American retailers which were using robots – as a part – of their inventory management. Over their 2016 summer, Lowe’s – one of the retailers – decided to introduce its LoweBot in its eleven stores – throughout the San Francisco Bay Area.

Merely, such autonomous retail robots not only offered helping hands to the customers in the necessary times but also created real-time data-visualization standards – with the use of machine learning and computer vision – the purpose of scanning the inventories and looking for the necessary patterns in products or pricing tags – was now achieved well -with lesser discrepancies.

Indistinguishably, The five-inches bilingual retail-robot and its features have helped the customers a lot to identify their choices – from the available products and take the ones they have been deliberately looking for. 

All this was possible through advanced voice recognition, searchable computer display, and unavoidably the laser-based sensors (they use a similar technology as the one used by the autonomous vehicles). With all such help, the robot may confidently navigate well around the store. 

If one goes as per the initial feedback from Kyle Nel, disruptive innovation VP and also, the executive director of Lowe’s Innovation Labs, the connected-customers were assertively be appreciating the convenience and the efficiencies of these bots, while on the other hand, the employees loved the fact that LoweBot has allowed them more to spend more time for consulting with the customers thereby working well with the creative projects – you may also include the Cloud QuickBooks hosting projects in the list.

At times Lowe was testing the robots at the front end, Walmart had decided to test the same – with the behind-the-scenes approach. If we discuss the June 2016 happenings, it can be identified that Walmart had announced proprietary-testing of drones in the warehouses massively – with a thought that this will improvise the inventory management plans. 

According to Walmart, the manual-checking of the existing inventories may take more than a month even for the experienced employees, but the same task may be completed within a time-span of twenty-four hours – if the sophisticated drones are optimally used that can feasibly fly through the warehouses, scanned- items, and later, they will be identifying the misplaced ones. 

I know you won’t be believing these miracles. If this is so, it is necessary to take a look at what the Walmart CEO stated.  If the delivery robots, internet of things, drones, 3D-printing, and undoubtedly, the self-driving cars are accepted by the organizations, they will be helping the retailers to automate the supply chains further and then, optimizing the inventories too.

# Use-Case Number Two -> IoT – Prevention First

If we plan to adhere to the accurate findings of the Gartner’s estimates, this was investigated that the 2017 end brought some eight-billion connected things and till 2020, the number crossed the twenty-billion threshold. 

Moreover, the ones primarily inclined towards QuickBooks Cloud need to understand the fact that connectivity has the potential to go beyond the location where the things are conditionally kept. This is because the connected monitors – primarily renowned as the supplied equipment – will be sending alerts about the potential issues before they become a threat(s).  Otherwise, this will disrupt the merchandise’s movement.

Primarily, one can’t ignore the 2016 Accenture report about the industrial IoT.  It concluded the predictive-assets which can potentially save till twelve percent on the scheduled-repairs and feasibly, thirty-percent on their maintenance – here, these assets had reduced breakdowns till seventy-percent. In the 2014 year, Intel had released its first pilot with its fully integrated IoT manufacturing – later the result was that nine-million dollars were saved – in just-a-go.  

# Use-Case Number Three -> Predictive Analytics – Weathering Demand

Weather can majorly determine what consumers may buy or they are willing to, as well as when they may plan to conduct their shopping events. If one digs a deep down to the IBM analysis, this may be spotted that it used Watson for forecasting the predictions-  more accurately – about the weather changes.

Similarly, this technology may be used for determining the supply chain availabilities and the unavoidable demands – these demands may also come from the side of Qb Hosting agencies. 

Precisely, in 2015 year, IBM was successful in acquiring the Weather Company for using its massive database and the relevant collection-systems. With this, Watson not only acquires the weather statistics from those stations but also reviewed news feeds and the connected social media accounts- for the sake of creating the pictures that may likely fit in the existing conditions. 

If one analyzes IBM’s Predictive weather analytics a little deeper, this may be concluded that the forecasts for the months were provided in advance, and later they may be paired with the planned and current stock data for the retail locations- handled by the employees. This will be helping the retail owners and managers as they may now make better decisions – strategically – on things like pricing, supply, and promotions.

Were the Applications or the Use Cases really solving the supply chain problems?

Once again, it is necessary to highlight the fact that the companies need to adopt ML technology at early stages – this will be helping the assets show their potential with an increased-profit rate. 

Moreover, the weather-gleaned datasets of IBM or any other top-notch ML company have successfully been able to shower the interesting correlations between consumer purchasing behaviour and the weather phenomena(s). 

Henceforth, the ML or the QuickBooks Remote Desktop Services company must incline towards the cloudy, warm,  and windy solutions derived from the aforementioned use-cases. With them, the supply chain can’t only manage the weather predictions, but also improvise their inventory-management strategies – for capturing the more than fifteen%  of improvement in their sales.

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