It’s a problem that plagues both e-commerce and traditional retail merchants the world over. You have your products ready for fulfillment, and expect things to run smoothly and without any hiccups. But out of nowhere, your stock completely sells out, leaving you scrambling to source extra product and causing an uncomfortable delay in the delivery of your goods to the customer. Or just as bad, your products languish in the warehouse, taking up valuable space and costing you money to store them. What gives? And more importantly, could you have somehow predicted these events and prevented their negative effects on your business?
The examples above illustrate the importance of demand forecasting and management when it comes to operating an efficient supply chain. But knowing that something is important and knowing how to successfully implement it in your company are two entirely different things. Read on to inform yourself about the basics of e-commerce and inventory demand forecasting: what it is, which types there are, and how you can go about incorporating it into your operations.
Demand planning, or forecasting, is the practice of using predictive analysis of historical sales data to estimate and make educated guesses about future customer demand for a product or service. This enables businesses to make better decisions regarding supply and the amount of inventory to keep on hand. It’s by no means an exact science, and will almost certainly never be 100% accurate. Because of the number of factors that can affect a customer’s willingness and ability to purchase a given product over time, the actual mechanics and techniques involved in demand forecasting can be quite difficult. It’s for this reason that many businesses use some sort of software or artificial intelligence to make the calculations on their behalf.
Indeed, modern best practices in regards to demand planning almost insist on the use of some sort of software, as the thought of doing all those calculations by hand can be overwhelming even for an economist or mathematician, and it’s cheaper besides. Technology enables a business to analyze more than one market variable at a time, vastly cutting down on the amount of time required to gather useful numbers. Sales trends, seasonality, market fluctuations, and external factors can all be synthesized in one go. For this reason, estimations say that by 2024, approximately 60% of Forbes’ Global 2000 manufacturers will be dependent on some sort of artificial intelligence to help manage their supply chain operations and demand planning.
There are multiple different forecasting methods and models of supply chain demand planning that find their way into common use. Before getting down to brass tacks, however, it’s useful to define a few key terms and metrics.
Macro-level vs. micro-level forecasting is an important distinction to make, for instance. Are you looking at general economic conditions, external forces, and other broad variables influencing commerce at large? You’re likely making a macro-level prediction. On the other hand, if you’re looking at a specific area, industry, or shop, you’re more on the micro side of things.
Another distinction that’s helpful to draw is between short-term and long-term predictions. Short-term forecasting is for a period of 12 months or less. These numbers can help inform the day-to-day operations of a store, or help prepare for a major shopping holiday such as Black Friday. Long-term planning, on the other hand, refers to periods of time in excess of one year. This drives long-term strategies, such as what’s required for opening a new retail location, and helps inform metrics about seasonality.
Speaking of seasonality, that’s another useful term. Seasonality refers to changes in order volume throughout a set time. Seasonal products usually spike in order quantity at specific times of year (think about all the purchases made for trees, ornaments, and gift wrap in December!) but remain relatively low for the remainder of the time.
Having explained these useful phrases, we now turn our attention to three main types of process flow when it comes to demand planning: Qualitative, causal, and historical.
Qualitative forecasting is primarily used when there’s not a lot of hard-and-fast data to work with just yet. This is often the case when a new product or business is launched for the very first time, which happens frequently in the tech field. Expert opinions, market research, and comparative analyses are used instead of black-and-white data to make the best predictions possible, as those numbers simply don’t exist yet.
Causal predictions are highly sophisticated and complex. They take specific data about wider forces in the market, such as competitor activity, socioeconomic factors, and wider market forces in order to create a finely-tuned estimate about future purchasing trends. A firewood business, for instance, would just as likely take into account the average temperature as the prices of their competitors, and therefore have a highly nuanced understanding of customer demand.
The historical model, meanwhile, makes good use of time series analyses to draw actionable conclusions. A business that’s been open for even a year has a year’s worth of metrics to draw from, consisting of four distinct quarters and seasons.
That’s not to say that these models are only ever used separately in a vacuum. Historical and causal planning, for example, are very often used in tandem to make use of the valuable information both past performance and external factors have to offer.
To put it simply, by using management and prediction techniques, it avoids the embarrassing and reputation-damaging event of being sold out of inventory for weeks on end. Customers’ hype only lasts for so long. If they’re in the market for something you’re selling, but you’re sold out, they won’t wait around for very long. They’ll either forget all about it or move onto one of your competitors, losing you the sale and some valuable and much-needed revenue.
What’s more, the proper application of predictive techniques and analytics nets you more money over time while saving you overhead. Without this knowledge, bad decisions are easy to make. But with the right numbers and know-how, you can keep operating costs in check by reducing the amount of overstock you have to keep on hand. Money that was once spent on housing an overage of product in a warehouse can now be funneled back into other aspects of the business, such as product development or marketing. And when you do anticipate a surge, being able to purchase in bulk can gain you a better price-per-unit than you might have been able to negotiate otherwise, reducing your costs even more.
Demand forecasting is a highly beneficial tool that reduces up-front and running costs while maximizing revenue. No matter the size or age of your business, it’s a valuable technique that will without a doubt provide valuable insights. With the advent of AI and machine learning, these numbers are quicker and easier than ever to have close at hand. If you think demand management software is a good choice for you, P2Pseller is proud to offer demand and inventory planning tools with real-time updates, 24/7, for all our e-commerce selling partners. Sell more, scale infrastructure, and grow your business to unprecedented levels. We’re excited to help you smash sales records quarter after quarter, with zero stress and zero phone calls.