For a small business, it’s the difference between thriving for decades or shutting down within a year. For large companies, it’s the difference between outpacing the competition or burning money that could be used on innovation on your operations.
As technology upends businesses across industries, it’s disrupting traditional functions within businesses as well. Supply chain management, and its subset, inventory management, are no exception.
Today, real-time inventory management software helps business owners make decisions about their goods by evaluating a wide range of data including:
- orders
- damage
- spoilage
- customer reviews
New technology, cloud-based services, and the increasing digitization of the supply chain has made this possible and presents exciting implications for all businesses and their warehouse operations.
Contents
- The Current State of Inventory Management
- The Future State of Inventory Management
- Launching Real-Time Inventory Management Takes More Than Hiring a Couple of Data Scientists
- Why Does Real-Time Inventory Management Matter?
- Real-Time Inventory Management Presents Exciting Possibilities for the Future of Supply Chain Management
The Current State of Inventory Management
Inventory is a major pain point, and people are always coming up with ways to relieve it.
When pen and paper became a hassle, businesses bought mobile barcode scanners that fed into databases and produced detailed spreadsheets.
When Excel workbooks and all those sheets, tables, and complex formulas became too much to manage, businesses purchased software that streamlined the process. Now they use historical data to predict future demand in an effort to avoid stockouts and keep customers happy.
What characterizes the current state of inventory management is a reliance on forecasts. As the adage goes, “Past behavior is the best indicator of future behavior,” so businesses used a combination of experience, formulas, software, and a little gut instinct to order stock.
Now, imagine a business landscape where owners possessed a real-time view of their inventory data that evaluated several variables to indicate just how much to order.
This is where inventory is going.
The Future State of Inventory Management
We’re in the midst of an inventory management revolution. Traditionally, supply chain managers relied on past stockouts and sales data to make inventory purchase decisions. As shopping and supply chain management becomes more digitized, it’ll be possible to incorporate all sorts of product interactions into purchase decisions such as:
- Orders
- Examinations and inspections
- Reviews by previous customers
- Supplier transactions
- Competitor transactions
In other words, the ready supply of data will make real-time inventory management possible and effectively revolutionize how businesses manage their stock.
Launching Real-Time Inventory Management Takes More Than Hiring a Couple of Data Scientists
Predictive analytics is a subset of advanced analytics. In most cases, the term is used interchangeably with machine learning. A large quantity of data about a given question is collected and an answer is spit out. Take this example:
- Based on all of the data about this customer, they will unsubscribe within 3 months.
- Based on all of the data about your product and the market, you should focus your marketing efforts on new moms in urban areas who hold advanced degrees.
- Based on all of the data about this account holder, their behavior indicates they will or have already committed fraud.
Naturally, this extends to inventory management. Based on what we know about this product, online interactions, and your historical data, demand will go up by 40 percent over the next quarter.
But as Harvard Business Review warns, introducing data science and predictive analytics to your business isn’t as simple as adding a data scientist and stirring. It requires an enterprise-wide effort to put the correct infrastructure in place and integrate data to derive intelligent insights.
Generally speaking, implementing a successful predictive analytics project requires the following steps:
- Clearly defining your business objective
- Collecting ample data from relevant sources
- Improving your data’s quality through a data cleaning project
- Selecting a predictive analytics tool
Clearly Defining Your Business Objective
People often assume that predictive analytics is a panacea that will solve all business problems and give companies psychic abilities. With machine learning, you’ll be able to predict what customers want, rapidly identify inefficiencies, and beat your competitors.
Unfortunately, it isn’t that easy.
You see, predictive analytics relies on data, and even if you’ve got a lot of data, you’ve got to make sure the data you’re using is relevant. The only way to determine the relevance of your data – and do so in an organized, consistent manner – is by clearly defining what you want to achieve.
So for a retailer, the objective may be to “Improve the accuracy of inventory management to avoid stockouts and reduce carrying costs.” This can be achieved by daily inventory tracking with the help of an warehouse inventory management software.
Collecting Ample Data From Relevant Sources
Your predictive analytics or machine learning is only as good as the data teaching it. For this reason, 80% of the hands-on work required to launch such a project is preparing the “training data.”
So how do you go about collecting the data you need?
Well, as we discussed earlier, a lot of factors determine inventory, and some are more obvious than others. The digitization of all sorts of data makes it possible to incorporate everything from natural disasters to social media chatter about your brand into your inventory control and management predictions.
Some of the factors a real-time warehouse management system built on predictive analytics can take into consideration include incoming orders, digital marketing statistics, and more.
It can even consider the impact of weather and pull out insights that go beyond predicting that demand for shovels increases in winter.
Case in point: several years ago, Walmart discovered that strawberry Pop-Tarts would sell out whenever meteorologists reported a hurricane. The reason was simple: They’re a tasty snack with a long shelf life that doesn’t require refrigeration.
Consider all kinds of sources when collecting data for your inventory control improvement project. Your order management system, various tools in your enterprise resource planning (ERP) technology, your marketing automation tools, your WMS system or your 3PL System, and even the Weather Network, can come together to help you accurately meet demand spurred by all sorts of seemingly ridiculous variables.
Improving Your Data’s Quality Through a Data Cleaning Project
Your employees enter all sorts of data into your system, but if they skip certain fields, don’t use standardized formatting, or enter inaccurate information, they’ll sully the quality of your data analysis efforts and therefore, the accuracy of your insights. And since you’re pulling data from numerous sources, the impact of errors is multiplied.
Once you’ve collected data from your automated systems, cleaning that data is a pain and includes several tasks including:
- Removing bad data
- Applying the right labels and codes
- Ensuring consistency across your data
- Eliminating duplicate data
Your data cleaning efforts can also be proactive, rather than just reactive. Communicate the impact of bad data to your staff and monitor their compliance with data standardization efforts.
In some cases, updating a database is a low-priority task on an employee’s list of duties, so they may not put as much effort into it. Sharing the benefits of clean data helps your employees understand how the data they provide feeds into larger business objectives.
In the case of warehouses, most of this information is submitted through barcode scanners or radio frequency identification scanners (RFID). But information from other parts of the business, such as marketing or sales, also impacts a company’s ability to predict safety stock and reorder points through real-time inventory tools.
You can purchase data cleaning tools to speed up the process.
Once your data is clean, consider a software that can speak to all of your platforms that deal with inventory. An integrated warehouse inventory management software could be the solution you’re looking for to keep your data clean.
Select a Predictive Analytics Tool
So what do you do once you’ve collected all that information? How do you go from a bunch of numbers to insights that reduce excessive inventory and ensure accurate inventory?
According to one management consulting firm, there are a few recommended steps companies should take when selecting a data management and analytics tool:
- Conduct a research and discovery initiative
- Understand the data analytics market landscape
- Compare your company against others with a capability tree
- Create a decision matrix to compare different vendors
- Use a decision tool to make the final call
Conduct a Research and Discovery Initiative
What is the current state of data analytics within your company? This question extends beyond your warehouse to include your entire organization. Hold consultations with key stakeholders who engage with data most often. They’ll possess insights into what data functionality would be most useful and what your organization doesn’t really need.
By talking to stakeholders, you may find that you don’t need additional data analytics tools. Perhaps your current technology possesses the capabilities needed and your employees just need additional training and education.
Understand The Data Analytics Market Landscape
Take a look at the current data analytics market landscape to build a list of the current solutions on the market. Once you’ve compiled this list, break these market solutions into categories. There are different data analytics tools for different objectives. Types of data analytics tools include:
- Semantics layer reporting tools
- Report writers
- MDX query tools
- Visualization tools
- BI and reporting tools
- Modeling tools
Once you’ve broken up available solutions into categories, you can better assess which tools meet the needs specified by your stakeholders.
Compare Your Company Against Others With a Capability Tree
Once you’ve assessed your company’s needs and then categorized the solutions on the market based on those needs, compare your inventory against your competition to assess how urgently you need to upgrade your tech and processes.
Create a Decision Matrix to Compare Different Vendors
Evaluate each of your vendors based on different capabilities. Your scoring should place greater weight on capabilities that meet your business needs. If you have a decent amount of business to award to vendors, you can distribute an RFP that lists your requirements and ask companies to present their capabilities.
Use a Decision Tool to Make The Final Call
Use decision-making software to plug in your variables and requirements and come to an unbiased, fact-based decision. Just like with data analytics, the results provided by your decision-making software is only as good as the information you feed it, so be sure to conduct thorough research on different vendors. As an alternative, you can use a manual decision-making matrix.
Why Does Real-Time Inventory Management Matter?
You Keep Your Customers Happy
Whether you’re a large B2B company or a B2C company selling fast fashion, keeping your customers happy is important. Customers can be fickle and unforgiving.
They’re even more particular in B2C where they have endless options. In fact, 50% of online shoppers will abandon a full cart if one item they want is out of stock. Real-time inventory management helps shaping your ecommerce customer experience by avoiding stockout, satisfing customers, and turning them into repeat customers.
Pull from Accurate Inventory Counts
Inaccurate inventory counts have a domino effect. If you don’t know how much inventory you have, you don’t know how much inventory you need to order, and you can’t properly predict stockouts.
The sheer frequency with which inventory moves, whether it’s due to orders, spoilage, or damage, makes it hard to stay on top of the exact amount of inventory.
On the other hand, a real-time inventory management system accounts for all movements by receiving real-time data from your:
- Brick-and-mortar store point of sale (POS) systems
- Ecommerce point of sale (POS) systems
- Warehouse management system (WMS)(if separate from warehouse management system)
Generate Detailed Reports Instantly
When you use real-time inventory systems, the data’s already captured. So you can quickly pull reports and warehouse KPIs in time for presentations and meetings. It’s also useful if you want to bring yourself up to speed on the state of the business.
Managers who regularly review data can readily identify areas of improvement and user-friendly report generation tools encourage this behavior.
Enhanced Ecommerce Growth Potential
Scaling your business is simpler when you can predict demand and keep your customers happy. Real-time inventory management tools support business growth by providing enhanced visibility over your entire operation.
Increased Cost Savings
Above all, real-time inventory management allows you to save money. Excess inventory leads to increased warehousing costs and a higher risk of spoilage, theft, and damage. On the other hand, stockouts cost you in terms of lost customers.
With real-time inventory systems, these issues are virtually eliminated.
Real-Time Inventory Management Presents Exciting Possibilities for the Future of Supply Chain Management
Inventory management keeps a company’s most valuable assets – its goods – safe. It also ensures valuable company funds aren’t tied up managing products that may go bad or never sell. Real-time inventory management systems optimize this challenging but vital aspect of doing business. It helps a company leverage an important digital asset – its data – and use it to better understand its customers and the market to make effective business decisions.
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