Understanding supply chain operations is one of the most important ways a merchant or other player in the industry can improve their knowledge of the field. After all, without good understanding, it’s much harder to make the kinds of innovative moves necessary to stay ahead of the rest of the pack. From that place of secure knowledge, a merchant, shipper, or warehouse operator can begin to make small cascading changes that could one day impact how the entire industry conducts business. Want that to be you someday? Then read on for our full explanation of supply chain operations, and how you can keep on top of them.
When broken down, there are four major steps every seller has to go through, you included, before your goods finally land on the customer’s doorstep: distribution center integration, inventory management, the actual order fulfillment itself, and any and all returns processing.
First comes integration. In the digital age, the vast majority of e-commerce retail outlets have their shops directly connected with ecommerce order fulfillment companies that service those orders on the merchant’s behalf. Having good, effective omnichannel technology is key to ensure there’s no interruptions or issues with phantom inventory.
Then comes inventory management and receiving. Pallets of your merchandise are shipped to a dedicated ecommerce fulfillment warehouse, which are then subsequently logged into inventory. If your fulfillment company starts slacking on intake, however, your goods can sit in the loading bay for hours or even days, hanging in an unfortunate state of limbo. Best practices dictate that you ought to make sure your received goods are logged into inventory within only a couple days.
Now comes the most important part— the actual picking, packing, and shipping. When an order is placed and is routed directly to the distribution center, your preferred ecommerce fulfillment solution partner is responsible for picking the right items from the pallets, selecting the right box, cushioning them with a fair share of packing materials, and sending them off to be delivered.
Finally, returns. As much as you wish your customers would be 100% satisfied 100% of the time, the simple fact is that returns can’t be avoided. It’s important to have infrastructure in place to support the exchange or returning of goods for a refund. Customers that aren’t burdened by an overly lengthy or obtuse returns process are far more likely to shop from you again in the future, for merchandise they’ll be satisfied with and maybe even recommend to friends.
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.
Demand forecasting is a highly beneficial tool in modern supply chain optimization 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.