The modern customer is now only ever a few clicks (and hopefully a few days) away from a product landing on their doorstep, and only a few keystrokes from venting their frustration if they feel underserved. Thus, Ecommerce businesses have never felt a stronger need to meet customer expectations continue if they hope to continue to grow. In the quest to meet these demands, Demand Planning is an essential step. When integrated into your sales and operation planning (S&OP) strategy, Demand Planning enables your business to adequately prepare for different kinds of demand and optimise your supply chain management. And, luckily, there are several Demand Planning tools that Ecommerce businesses can implement to find the balance between revenue and inventory.
What is Demand Planning?
For the uninitiated, we have a great intro article here. In summary, Demand Planning is a sub-process within sales and operations planning (S&OP) and involves predicting future sales. While that might sound like Demand Planning and Demand Forecasting are one and the same, there are a few crucial differences. Mainly, Demand Planning differs in that forecasting demand makes up only one component, alongside managing product portfolios and trade promotions. These components are used to achieve different things in the Demand Planning process, which we’ll jump into further in. Conversely, Demand Forecasting is solely focused on predicting future demand (for a more in-depth explainer, head here).
If you’re already using forecasting in some way - whether for financial, sales, or for product (You might find this helpful) - you’ll already know this technique helps find the balance between having enough stock on hand (and ready to deliver) and making money (and not being left with dead stock). Demand Planning can further assist with this balancing act by enabling you to make more informed decisions that affect demand accuracy, while also improving the efficiency of your operations.
Demand Planning Tools
In order to successfully implement Demand Planning, you’ll need to equip yourself with some tools (as mentioned above):
- Statistical forecasting
- AI Forecasting
We’ll break down what each of these tools does in a second, but first we need to start with the input that will drive all three: data. More specifically, your database.
This should contain all of the information that you’ll use for forecasting demand, as well as additional information that improves the accuracy of your results. Importantly, the data you put into this database should be clean, accurate and useful and cover three main dimensions:
- Product - think product group, family, or line
- Geographical - whether that’s sales regions, or customer geographic information
- Time - i.e. the buckets that your forecasts will fall into (from days and weeks to years) and the horizon (how far into the future your forecast is applicable to)
Statistical forecasting, unsurprisingly, describes a suite of statistical models which are used to produce forecasts for the demand for your products, with different types available depending on your goals and needs. These models primarily rely on historical data and are useful for identifying seasonality, trends, and rates of growth of demand for products. Although these tend to be the cheapest and simplest to implement and use, their usefulness can be limited when it comes to predicting random spikes in demand or market saturation of a product.
AI Demand Forecasting
Forecasting powered by AI and machine learning works in a similar way to traditional statistical methods. But, these models are multivariate, meaning that they can ingest multiple variables that affect demand. As a result, machine learning methods tend to outperform statistical models, especially when it comes to products with patterns and trends that would go unseen by statistics or human analysts.
These forecasts can then be adjusted manually to include information not covered by your forecasting model. Depending on the type of forecasting method you use, this could involve inputting information relating to promotions and marketing campaigns, or changes to the number of stores in a particular region (among many other examples). Your choice of forecast length, and the purpose of your forecasts will also be contributing factors in your choice of forecasting method (head here to find out more). For instance, you might create short-term forecasts - anywhere from several days to several months - for every product to assist you in making replenishment decisions for finished
And, to avoid the headache that is managing anywhere from dozens to thousands of forecast results, picking a forecasting platform (like ours, for instance) can automate this process and make interpreting and utilization of your forecasts that much easier.
Finally, we come to simulations (or ‘what-if’ analyses). These are useful for seeing the consequences of different scenarios, and can help you plan when and where you should apply promotions (as well as which products), when you should launch a new product, and the shape of a product’s life cycle curve.
Uniqueness of Ecommerce
Now that you understand the tools at your disposal, let’s get into how Demand Planning can assist Ecommerce retailers specifically.
When it comes to data, Ecommerce businesses have even more data and data streams available than brick-and-mortar retailers. This can include:
- On-site searches
- Product page views
- Featured products
- SEO rankings
And, these data streams can be integrated into AI forecasting models to help you make even more well-informed decisions. But, these unique benefits come with some unique challenges too. For example, if your store begins to climb higher in SEO rankings, this can lead to an increase in web traffic and an uptick in sales, in much the same way as if you moved your physical storefront from a backstreet in a low-traffic area to a new spot in the heart of the CBD. It’s a good challenge to have, but a challenge nonetheless. In order to mitigate the increased risk of stockouts from this jump in traffic and (hopefully) sales, integrating SEO rankings into your Demand Forecasting and Planning process can help you identify when and which products to order, especially when you face long lead-times.
In most (if not all) industries, Demand Planning is crucial for managing and optimizing your business and supply chain in order to meet demand and satisfy your customers. And, although having accurate forecasting is an important aspect of successfully implementing Demand Planning, the real value comes from the collaborative process that surrounds your forecast. For Ecommerce businesses in particular, the ability to pull unique data streams into your forecast and planning enables you to increase the accuracy of your forecasts, leading you to make more informed decisions regarding everything from inventory to marketing and sales. And, to make this process simpler, platforms like Remi’s can integrate Shopify and Google Adwords, Analytics, and Search Console directly into your model.
Want to find out more? Have a gander at our product page or, if you’re ready to chat, drop us a linehere. For those wanting to know more about Artificial Intelligence - everything from Dynamic Pricing to how algorithms can become poker champions - ourblog has all of the info you’ll need. Find out more about Demand Forecasting as aconcept, how it canbenefit start-ups andE-commerce businesses in particular. Or, pick up acase study to see our platform in action. From helping a Fortune 500 company improve their demand planning (in both their retail and manufacturing arms) to increasing stock availability for an Australian homewares retailer, you’re bound to find something to pique your interest. If, after all of that, you’re ready to get started on improving your own Demand Forecasting strategy,sign up here.