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Warehouse Management Systems: Improving B2B Customer Service
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Predictive analytics is a production and warehousing strategy used to keep inventory above the demand level at all times without over or under producing, and over or under ordering. In order for businesses to accurately predict how much inventory they will need available at any given time, they must have the ability to forecast demands, which means that software must be in place and functioning properly at their facility that can handle inventory tracking.
Can your warehouse predict product demand? Does your inventory software offer demand forecasting? Or are you still using guesswork to stay ahead of incoming orders that leaves your company at risk for failing to meet fluctuating customer demands?
In this article, we’ll take a look at how using historical sales data can help you estimate future purchases so that you’re able to predictively produce goods to meet demands. Once your company is able to accurately forecast product turnover, you will be in a far better position to effectively manage your cash flow, capital expenditures, and profit margins.
Demand forecasting, which is a quantitative method of predicting product demand, is defined as a form of predictive analytics that takes into account historical sales data in order to make informed supply chain decisions. These decisions can immediately impact production planning and inventory management, and in the long-term, they can also impact larger business decisions such as whether or not to expand consumer reach into another industry. By utilizing warehouse software with demand forecasting functions, businesses have a much easier time diversifying their sales channels even if only one warehouse is fulfilling orders for both e-commerce and in-store purchases. Being able to predict purchasing trends far in advance is invaluable, therefore engaging software that handles this kind of forecasting can be a priceless resource.
In retail, forecasting helps owners leverage customer purchasing data regarding specific product sales over specific time frames so that their reordering can be as precise as possible. For example, by reviewing last year’s first-quarter toothpaste sales from e-commerce and in-store data, a retailer can use the total-units-sold figure to restock shelves this year.
All aspects of financial planning ultimately rely on accurate projections, and when it comes to accurately projecting inventory needs, a business owner will need to know how to forecast product demand. Once this is achieved, further improvements will naturally occur in the areas of supplier relations and purchasing terms, warehouse capacity utilization and allocation of resources, optimization of inventory levels, and distribution planning and logistics. These improvements can lead to better product lifecycle management, improved customer service, and streamlined warehouse management, among other benefits. So, let’s break down how to forecast your inventory demand by examining qualitative versus quantitative forecasting, which use different historical data sets. At first blush, the terms are fairly straightforward. Qualitative forecasting refers to quality, while quantitative forecasting more literally tracks product quantity, but each takes into account so much more than their names imply.
Quantitative forecasting analyzes historical product sales statistics and product trends data to gauge future demand. Whereas qualitative forecasting, which we’ll delve deeper into later in this article, is the analysis of how the larger economic climate of the industry as well as how emerging technologies could influence product demand, product prices, product lifecycle, and product upgrades. The result of this analysis has the power to take a company in one direction or another and over time will shape the evolving business itself. Put very simply, when you forecast inventory demand, you will rely on quantitative and qualitative data from your warehouse management software.
We just defined a straightforward and simplistic way to forecast inventory demand, but in reality the objective is so much more involved than that. In the next two sections, we’ll cover the types of forecasting that your software should provide as well as the methods it should use to forecast. Starting with the types of forecasting, there are four models which are macro-level forecasting, micro-level forecasting, short-term forecasting, and long-term forecasting. Each of these models take into account sales data over specific periods of time in order to forecast future demand.
Macro-level: This forecasting model falls on the qualitative side of predictive planning. Macro-level forecasting focuses on the overall economic landscape of your industry, especially scrutinizing external forces that disrupt commerce. This model takes into account market shifts that could impact your business.
Micro-level: Micro-level forecasting takes a highly detailed look at the past sales of targeted customer segments to gain insights about the correlation between demographics and products. This forecasting model is considered quantitative, because it is designed to pull up specific data that shows you the unit quantity of organic cat food that millennials bought over an exact period of time, for instance. By examining the realities of which customer segment bought what items during which sales season, you can improve your product offerings as you meet customer demands.
Short-term: This model of demand forecasting is also quantitative and pulls data relevant to a very short time frame or even a particular holiday sale. The purpose of short-term forecasting is to project the needed production planning on a day-to-day scale. For example, in order to stock enough inventory for the upcoming Valentine’s Day promotion, you can review sales data reports for the past three Valentine’s Days to get a sense of what the demand will be this year. Likewise, if you operate a wholesale warehouse facility and dozens of retailers are your customers, by using short-term demand forecasting, you will be able to produce and hold enough products to satisfy all of your retail customers’ orders.
Long-term: While short-term forecasting pulls data from narrow windows of time, long-term forecasting pulls sales data from over a year ago. This type of demand forecasting helps businesses to plan seasonally by taking annual patterns into account which informs their production capacity. When developing your long-term business strategy, implementing long-term forecasting will keep your sales goals clear and your shelves stocked.
Working in tandem with the types of forecasting, which we just defined, are the forecasting methods. These methods are not mutually exclusive. In fact, using two or more simultaneously can provide you with even clearer snapshots of past sales that will help you make even better decisions about your order fulfillment predictions. While there are more than four forecasting methods, for the sake of brevity we are going to focus on qualitative forecasting, time series analysis forecasting, causal forecasting, and simulation forecasting.
Qualitative: Though we touched upon qualitative forecasting earlier in this article, it warrants a deeper review, because unless your warehouse inventory software includes the latest technologies, you will have to engage expert consultants in order to understand the full scope of qualitative developments in your industry. To refresh your memory, qualitative forecasting looks at the overall economic climate and external forces that are shaping the landscape of your industry. This estimation methodology doesn’t include numerical analysis from your software sales data. For instance, when COVID-19 hit, only qualitative forecasting could help predict the future of a business’s sales.
Time series analysis: The time series analysis forecasting method is quantitative by design, but focuses specifically on comparing and contrasting annual or seasonal periods of time and the sales produced therein. When the data within a given series of time frames is analyzed, the result is a more accurate prediction of future sales. For instance, if we return to our Valentine’s Day example, a time series analysis would show all of the individual sales channels for that holiday during past promotions. With that information, a business owner can see clearly that their ecommerce sales far outweighed their local farmer’s market sales, yet their three retail locations did even better. By understanding the channels where products sold and at what quantities, your distribution and stocking efforts become easier to manage.
Causal: Causal forecasting is all about the cause-and-effect relationship between a specific variable and the sales that result. A simple example is the weather and its impact on sales. If you’re hosting a sidewalk sale on a sunny day, you can predict healthy sales, whereas rainy days could yield virtually no sales. Causal forecasting has the ability to become much more complex, and therefore helpful, than merely expecting bad sales from bad weather. When employed to its greatest capabilities, this method of forecasting will shed light on intricate cause-and-effect relationships between one or more independent variables, and those relationships can be categorized as complementary or cannibalistic. If a product is suddenly sold at a discount (variable #1), then the result will likely become an increase in sales (variable #2), which is categorized as a “complement”. That’s not the only impact, though. The product discount, which triggers an increase in sales of that product, could also trigger a decline in the sales of a similar product, otherwise referred to as “cannibalization”. Being able to foresee all of the cause-and-effect impacts of your promotions will help you make better business decisions.
Simulation: Simulation forecasting includes a mix of both qualitative and quantitative approaches, and incorporates all of the forecasting methods we’ve detailed so far. By including all methods in your inventory predictions, you will have the best chance of meeting future product demands and keeping your customers trust and reliance on your company. But the complexity of this forecasting technique, which takes into account both internal and external factors, is easier said than done, unless your software comes built with simulation forecasting capabilities and reporting.
We’ve mentioned the importance of using warehouse inventory software with advanced technology so that the software itself can handle the complexities of predicting product demand for your company, but let’s really dig into that now.
While forecasting product demand will never be 100% accurate, the right software can come pretty darn close and result in measurable improvements in your production lead times and operational efficiencies, which can empower you to better plan your staffing, production, and marketing efforts.
So, what should you look for in software that’s supposed to handle your inventory and demand projections?
• Prepare your budget–The software should include straightforward reporting that takes historical expenses, sales, and COGS data into account to help you prepare your budgets. Inventory accounting should encompass profit margins, cash flow, operating costs, and staffing expenditures, to name a few critical areas.
• Plan and schedule production–Order fulfillment must be the timely result of your production schedule, which means that the software must be able to use past data to assist you in planning and scheduling your production workflow, taking pre-orders and marketing efforts into account.
• Track inventory levels–Perhaps the most vital feature that the software should come with is accurate, apples-to-apples, real-time inventory tracking. You do not want to be sold out unexpectedly and you also don’t want to end up with a costly excess of inventory.
• Develop a pricing strategy–When software comes with detailed accounting, the reporting will straightforwardly expose net profits or losses per product per time frame. At the end of the day, it won’t matter how timely your order fulfillments are or how accurately you stay on top of inventory demands if you’ve priced your products in such a way that the so-called profit margin cannot cover overhead and other business expenses. FTX WAREHOUSE INVENTORY SOFTWARE WITH BUSINESS INTELLIGENCE Accurately predicting product demand in such a way that improves your business operations and increases your revenue might seem like an overwhelmingly tall order, even after reading all of the information we’ve provided in this article. But in reality, it doesn’t have to be if you choose FTx Warehouse Inventory Management. Our premium warehouse software uses proprietary algorithms that are built right into our system. These advanced algorithm capabilities analyze historical sales using all of the models and methods we’ve discussed throughout this blog post. By employing detailed sales analysis, our software can help you predict inventory needs for Days of Inventory. With FTx Warehouse Inventory Management, you will never be left with inventory that does not sell or becomes stale, because our proprietary algorithms take into account your busier weeks of the months and busier months of the year in order to keep your inventory at a manageable level. FTx can help you avoid engaging interest-bearing lines of credit that drain your finances. Why? Because our software helps you avoid holding high inventory quantities that run the risk of products going out of style before you can sell them.Product trends matter, which is why FTx is rolling out a new inventory system called Business Intelligence, or BI for short. Our Business Intelligence feature goes above and beyond our competitors’ inventory management software, because it offers analytics and predictions based on sales trends and customer buying habits.If you would like to learn more about FTx Warehouse Inventory Management and Business Intelligence, Contact Us today.
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