With the quickening pace of business and consumer expectations for immediate gratification, it’s not enough to conduct inventory controls every month, week or even day. While they’re indispensable, they’re not going to help your company reduce costs and increase revenue, but real-time inventory management just might!

What is Real-Time Inventory Management?

Real-time inventory management is a way of keeping a constant up to date record of your inventory. It’s especially useful if you have an online store or e-commerce, and can also be referred to as perpetual inventory.

Why Is it So Important?

First, let’s take a look at what lack of stock does to your company’s sales:

The results may be disturbing, but the root cause is very simple: Lack of real-time inventory tracking and coordination.

If you could see all of your inventory’s movements in real time, and set up the right automation processes so that when a product’s purchase patterns change, new stock is ordered instantly, you wouldn’t be facing these losses.

For a long time, the industry has struggled with this problem. It was impossible to see what was happening until the reports came in for the day (or the week).

And this is where real-time inventory management comes in.

As a method of tracking, automating and analyzing inventory movement, real-time inventory can help with:

  • Smarter replenishment
  • Better vendor & customer communication
  • Organization
  • Understanding trends, patterns and cycles
  • Decision making

To put it simply: real-time inventory management helps you understand how your products are being sold and when they’re close to being out of stock.

The technology monitors all of your sales channels so don’t worry if you’re selling online and in brick & mortar stores. Omnichannel inventory management isn’t a problem.

And what’s even better: you can get insights from the field without manually going through thousands of reports.

Inventory forecasting is made easy as the tools for real-time inventory management keep track of everything.

They can even predict spikes in sales with access to your previous data, helping you reduce spoilage that adds additional $0.43 cost to every $1 you earn.

And since most of the real-time inventory management tools are stored in the cloud, they’re easily accessible organization-wide. You can cooperate with the sales and marketing departments to predict an increased demand for a certain product.

Finally, you can focus on optimizing your process to make sure that no shopper abandons their $100 cart just because they can’t get the $5 product they want.

Mirror Review Magazine included Algo.ai on a list of the 10 Revolutionary Artificial Intelligence Solution Providers in 2019. As always we appreciate the shout out and the time they took to publish a profile on what we are up to including some gems from Amjad himself on the outlook of AI for jobs and the economy.

“Disruption can be positive as well as negative. I am a ‘cup is half full’ kind of guy, so I look at Artificial Intelligence as positive disruption,” says, Amjad HussainCEO of Algo.ai – a three-year-old startup using big data and AI to make optimized decisions about what products to stock on the shelves of retailers worldwide.

Like skepticism of past industrial revolutions – electricity, the automobile, and the internet – fears of a dystopian future caused by AI are unfounded. These technologies have changed human lives for the better, and AI has the potential to do the same – making lives more joyful and meaningful. Amjad believes the next wave of AI is where machines and people will get to work together, and by focusing on our relative strengths, we can make our weaknesses irrelevant. Already on this path to human-machine collaboration, Algo™ gets trained to take over tedious workflows and time-consuming, redundant tasks, enabling people to do more strategic and proactive work multiplying the overall productivity of enterprises.

Read the full article Algo.ai: Disrupting Supply Chain Management With Artificial Intelligence

Or check out the Digital Magazine edition The 10 Revolutionary Artificial Intelligence Solution Providers of 2019

When you have to manage inventory at a large scale in multiple markets it may seem like a daunting task. However, we’ve pulled together some of today’s best practices to bring you 10 tips for effective inventory management for a global business.

Amjad recently contributed an opinion piece to the Chartered Institute of Procurement and Supply’s Supply Management Magazine.

In it, he explores the idea that a supply chain is a complex system of interrelated parts which needs to be regularly monitored for problems and kept healthy through preventative measures.

Read the article in Supply Management

Fully understanding the data behind your inventory management practices is essential to running an efficient and modern supply chain operation. Across every touch point, data provides actionable intelligence that can help any supply chain provider reduce costs, streamline operations, and maximize potential. If you want to set up your supply chain for success, follow these four easy steps to start maximizing your network’s potential.

1. Develop an Implementation Strategy

Getting the most out of your supply chain operations begins with a carefully implemented optimization strategy. Like nodes in a network, each component of your supply chain and inventory management operations needs to be aware of and in communication with the other nodes to keep traffic flowing and to immediately address any concerns. 

2. Choose Key Metrics

Begin by choosing three to five key metrics that effectively capture the essence of your mission, such as producers in the supply chain, your warehouses, or the end consumption points like retail stores. From there, factor in metrics that have a direct impact on your business, like seasonality for warehouse operations or your inventory turnover. 

Ultimately, this data will help you identify demand: who in your network has a specific need, like on-time, in-full delivery? With proper demand planning and forecasting, your supply chain operations can turn data points and metrics into a comprehensive forecast of the future. Many modern software solutions include AI and machine-learning capabilities to improve decision making and weigh any necessary compromises and tradeoffs. 

3. Identify the Low-Hanging Fruit

From there, you can begin to identify the low-hanging fruit of your inventory management operations. Your data will inevitably point to particular products that are either rapidly flying off the shelves or that just haven’t resonated with customers and are taking up valuable warehouse real estate. Starting with these readily apparent items, you can follow the bread crumbs leading to a proper inventory management strategy. 

4. Use Data Visualizations to Take Action

Brought to life through data visualizations, you can quickly and clearly identify areas of waste, opportunity, and improvement in your supply chain. No matter the specific determining factor, properly understanding your data and its full context ensures time, money, and resources are not being squandered. By ignoring or underutilizing your data and the story it’s telling, you risk missing current and future opportunities that are waiting to be taken advantage of. 

With a comprehensive strategy to assess, analyze, and implement decisions drawn directly from your data, your supply chain operations will have improved inventory management capabilities that can ensure the long-term success of your organization. To be sure, your customers, clients, and supply chain partners will appreciate having access to actionable intelligence that ultimately improves speed, lowers costs, and reduces waste.

If you’re tasked with finding new ways of optimizing your supply chain operations, understanding what’s valuable and what’s worth discarding can help you run an efficient and streamlined operation. Here are some Marie Kondo-inspired inventory management tips to help get the most out of your supply chain:

Inventory management is no longer a matter of guesswork. Thanks to machine learning and prescriptive analytics techniques, better decision-making in operations management makes it easier to predict demand for inventory based on a variety of different factors. With help from all kinds of data, inventory management decisions can be made correctly and successfully to create a competitive edge.

For a retail store, that data can be used to predict and prescribe optimal stock levels — even for products that have no prior sales history. Whether it’s a seasonal product, a new release or an item a store has never carried in the first place, relevant historical information related the product’s details can be used to predict and prescribe its sales potential.

Using big data to assist inventory management can help cut down on costs, maximize sales and ensure a store has the correct amount of stock on hand. The advent of omnichannel retailing and e-commerce offerings like “ship-to-store” options makes accurate inventory management crucial to ensure goods are where they need to be at the right time and aren’t being put to waste.

In one case study, we looked at the distribution and manufacturing arm of a global media conglomerate responsible for shipping an average of 1 billion units of CD, DVD and Blu-ray entertainment titles. Their challenge was to lower operational costs due to declining sales, diminished shelf space and the availability of digital alternatives.

For this vendor, their key goal was to maximize network-wide sell-through of their inventory. Because of the uncertainty of new releases that drive many sales as well as the lack of physical space on an endcap display, determining which titles had the best sales potential was essential.

Harvesting data from sales records, search queries from Google Trends and public sources such as Rotten Tomatoes and IMDb, we built inventory prescriptions taking into account a 150-week period. Building upon each previous week’s data, we scored our prediction performance in order to inform the next week’s prescription. With this data, we were able to make future sales predictions using both data-poor decisions and perfect-foresight decisions.

Being able to accurately manage, predict and organize retail inventory with the help of machine learning leads to retail operations that are far more streamlined and efficient. No matter the product or industry, prescriptive analytics using big data can greatly improve inventory management across the entire supply chain. 

To get an in-depth understanding of the data and methods we used you can download our scientific paper Inventory Management in the Era of Big Data.

In a 2016 research reportWhy Artificial Intelligence is the Future of Growth, Accenture found that adoption of artificial intelligence tech across all industries may double economic growth rates by 2035. AI investment is expected to increase labor productivity by 40 percent. In fact, 70 percent of executives say they plan to “significantly increase” AI investment.

In the realm of inventory and supply-chain management, AI adoption, specifically the use of optimization algorithms, is revolutionizing inventory agility – reducing stock depletions and maximizing stock levels.

“The use of AI in supply chains is helping businesses innovate rapidly by reducing the time to market and evolve by establishing an agile supply chain capable of foreseeing and dealing with uncertainties,” says Accenture Managing Director Manish Chandra. “AI armed with predictive analytics can analyze massive amounts of data generated by the supply chains and help organizations move to a more proactive form of supply chain management.”

Supply chain processes generate giga-tons of data, and AI can deploy predictive analytics to make sense of it all. Freshly updated and analyzed data then builds a solid foundation when it comes to real-time vision and information flow. Every key player across the supply chain is empowered with the best data and maximizes it accordingly.

AI is no longer an “ain’t-it-cool” innovation in the industry but rather a necessity. With the erosion of the brick-and-mortar model and rise of real-time consumer expectations, supply chain/inventory management practices must embrace machine learning that far outpaces the speed of human thought and action. Consider these stats from the 2017 MHI Industry report concerning the speed of supply-chain transactions from just one e-tailer on Black Friday:

“A reported 426 orders per second were generated from the website throughout the day. That equates to over 36 million order transactions, an estimated 250 million picking lines at the distribution centers (DC), 40 million DC package loading scans, 40 million inbound sortation hub scans, 40 million outbound sortation hub scans, 40 million inbound regional sortation facility scans and 40 million outbound delivery truck scans.”

How should industry leaders respond? The answer, according to the report, is clear. Supply-chain companies must embed “analysis, data, and reasoning into the decision-making process. Position analytics as a core capability across the entire organization, from strategic planners through line workers, providing insight at the point of action.”

As Accenture economic research director Mark Purdy concludes, companies that survive will fully invest in the potential power of AI going forward: “To fulfill the promise of AI, relevant stakeholders must be thoroughly prepared – intellectually, technologically, politically, ethically and socially – to address the benefits and challenges that can arise as artificial intelligence becomes more integrated in our daily lives.”