Enterprises often face challenges when it comes to managing their warehouses and ensuring the smooth transfer of products. With an enterprise growing big, the challenges of managing inventory also rise. This results in enterprises spending a large amount on managing the inventory leading to large expenses.
However, in today’s digital era, enterprises are adopting technology for all critical operations to accelerate their digital transformation journey. As per PwC, AI is gradually transforming the corporate world. It can contribute up to $15.7 trillion to the global economy by 2030. Here are some of the areas where enterprises face challenges in inventory management and implementing proper ML/AI algorithms can have a significant impact to the bottomline.
Challenges of Traditional Inventory Management
Today inventory management is a semi-automatic process. This means the decision-making process remains with the humans as they derive insights from loads of datasheets. Rule-based traditional models are complex to maintain. With enterprises growing in size, the amount of data generated also multiplies. This pressurizes the human staff to put in more effort and time, leading to tedious, time-consuming efforts. This leads to endless process of data management.
- Data explosion – Data is an important resource to understand product performance and buyer behavior. However, it has suddenly become a menace difficult to control. If decades ago, getting accurate data was a problem, today it is its abundance that is creating chaos. The process of controlling and analyzing data has become tedious. This has put a lot of pressure on teams to simplify data management. While automated systems can help track and store data; processing requires a different set of skills and efforts from the human staff to process all of it.
- Data tracking – Tracking items in the inventory is increasingly becoming tedious, let alone deriving insights from it. A slight miss in tracking the incoming and outgoing items can hugely impact the product’s performance in the market. This results in demand fluctuations, customer losses and eventually impacting the turnover negatively.
- Business planning – Inventory management plays an important role in business planning and strategizing. Only accurate data can help plan a product roadmap or promotional activity, leading to successful results. This requires real-time collection and processing of all data, which drives accurate decisions on time.
- Operational costs – Huge inventories mean more products, more markets, more delivery, more space, and more staff. This often increases the operational costs of maintaining the inventory taking the share from other essential expenses.
How can AI help optimize inventory management?
Enterprises have always leveraged technology to gain a competitive edge. From automating repetitive processes to implementing cognitive abilities; enterprises across the world have come a long way in terms of conducting business. While automation has become quite commonplace, it is artificial intelligence and machine learning that are gaining traction. This may be due to their ability to optimize processes and provide actionable insights, which further accelerate businesses. As per a recent 451 Research Report, Accelerating AI with Data Management; 68 percent of respondents reported that they are either already using ML capabilities; or plan to within the next three years.
At Gyrus AI, we believe focused AI implementation and leveraging machine learning capabilities can help optimize inventory management; just like other business operations. Let’s look at what enterprises can achieve in inventory management with AI & ML.
AI provides visibility into inventory
Clear visibility into product performance and markets, helps devise sales and promotional strategies. Along with the uniformity and level of granularity of the data; the accuracy of strategy also depends on the timeliness of product data. Visibility into product data can avoid overstocking and understocking.
Inventory can derive greater value from adopting AI according to McKinsey’s global survey. Almost 76% of the survey respondents working at supply chain companies have reported moderate to significant value from deploying AI. AI enables timely, accurate insights and visibility into sales, order, and inventory data through a robust data pipe. This avoids a lot of cacophony in the market and helps deliver good quality data on time. Not only is AI capable of providing insights that were previously unavailable; but it can also take into account various aspects that in some way influence the demand.
AI empowers data mining
Machine Learning capabilities enable AI systems to learn from the data patterns and predict demand for the product. For example, it can help plan inventory in certain geographical locations, as per the seasonal demands. Analyzing the situation can help suggest to overstock or replace the stock with relevant products on the shelf. This helps reduce the pressure on the sales and marketing research teams.
AI improves stock management
Managing stocks well can result in improved customer satisfaction and a sense of fulfillment. A slight deviation in this can lead to shortages or delays; adversely affecting the demand for the particular product, negatively impacting the revenue. Moreover, AI helps stock right with its ability to analyze customer behavior patterns with the help of big data. This minimizes the risk of mismanagement of stocks resulting in improved customer satisfaction. Insights derived from data mining also helps plan the transportation of perishable goods from factories to retail outlets in time; reducing wastage and impacting sales.
Why are enterprises still skeptical about the use of AI & ML?
Though leveraging AI and ML capabilities have proven beneficial in inventory management; there are possibilities of setbacks while implementing AI tools. However, being more in number, the advantages often shadow the setbacks. Nevertheless, it is important to make the team aware of the complications, that may arise during the implementation of AI. Awareness of potential risks and challenges can help reduce or alleviate the risk to a great extent.
There are mainly two areas, where AI implementation and running can turn complicated. Firstly, data presentation. For an AI system to work successfully, you must present it with high quality data. It is with this data, that AI analyzes and derives actionable insights. However, enterprises often find it a task to gather data and feed the systems.
Secondly, implementing AI into existing systems can be time-consuming. Data and processes often load legacy systems; making it difficult for AI systems to sit atop them and integrate. However, these are just a few measures of caution and should lead you to a successful AI implementation. Just ensure to prepare the staff well beforehand.
There’s no doubt, artificial intelligence and machine learning are making great strides in transforming inventory management. It is empowering enterprises to handle both physical tasks and complex data-related tasks; and providing complete control over their inventory while adding value to their business planning and consumer behavior predictions.
According to research, by 2021, inventory management software in the retail sector will see a CAGR of 8.3%. Another research forecasts that the global enterprise cloud services market would grow at a CAGR of 23.83% by 2020. Well! We can already see the trend of applying AI to inventory management picking up pace. Various enterprises leverage AI-powered inventory management tools to their benefit.
AI is a great boon to larger and medium enterprises. Especially those who deal with large inventory and aren’t able to control and manage their inventory data. By handling enormous data and repetitive processes; AI lets human workers focus on more strategic and important tasks at hand. In a few years from now, we will see enterprises leveraging transformative technologies to optimize inventory management.