Demand Forecasting: Why Predictive Analytics Is The Answer
In this article, you will envision how advanced supply chains are applying Predictive Analytics to forecast demand for even greater accuracy.
Demand forecasting is a process that involves using data analytics on historical data in order to predict and estimate future customer demand for a product or service. But, if you are interested in this type of article, you are probably already familiar with it.
This article isn’t more of the same. In the article, we aim to clarify how predictive analytics is used to extract future trends from data, and provide you with information on how to make more informed decisions regarding supply chain and stock management.
We’ll start by going over the standards of demand forecasting (definition & benefits). Read on to get a handle of how future supply chains are applying predictive analytics to demand forecasting to the fullest of their potential.
What is Demand Forecasting?
Demand forecasting is a process that involves using predictive analytics on historical data in order to predict and estimate future customer demand for a product or service.
The Benefits
In today’s digital environment, companies have access to an obscene amount of data, but just having a lot of information lying around isn’t going to provide a competitive advantage. Demand forecasting is used by businesses to make more informed decisions regarding their supply chain and stock management.
Demand forecasting takes that data and transforms it, through predictive analytics, into decision-guiding insights and can provide companies with:
• More predictability, both in future market movements and customer demand.
• Accurate inventory management, to shorten the distance between products and users.
• Avoiding cash-flow risks by predicting and anticipating periods of low demand.
How does demand forecasting work? Quantitative vs. Qualitative
The most accurate forms of demand forecasting involve both quantitative and qualitative forecasting. Both of these forms of forecasting rely on the ability to generate insights from different sources of data all along the supply chain.
Qualitative data can be gathered from external sources like news reports, cultural and social trends, and competitor and market research. Qualitative data can also be gathered internally from customer feedback and reviews.
The idea here is that macro trends, general market movements, and customer opinions won’t provide you with exact numbers or enough general data to form a quantitative analysis. It can, however, be a very rich source of information with regard to how certain organizations, industries, and individuals are thinking and acting.
Quantitative data is almost always gathered internally, from sales numbers and peak shopping periods to inventory and search analytics. For the most part, you’ll be working with Big Data and employing modern technologies in order to extract insights from those massive data sets.
These modern technologies include advanced analytics, artificial intelligence, and machine learning.
The general idea is to use qualitative insights to help predict future macro trends and market movements and to use quantitative insights to understand your current customer base’s near-future demand fluctuations in order to make more informed decisions regarding your supply chain management.
How to forecast demand with Predictive Analytics?
Unfortunately, we don’t have access to a black box that takes in trends and customer data on one end and spits out insights on the other. The method that companies use in order to extract quantitative and qualitative insights for demand forecasting is what is known as predictive analytics.
What is Predictive Analytics?
Predictive analytics is a data analysis technique that uses statistical algorithms and Machine Learning techniques to identify the likelihood of future scenarios from historical data. It is an accurate calculation of probabilities based on processing large volumes of data.
If this is starting to sound very familiar, don’t worry, you’re not having deja vu, predictive analytics is the backbone of any successful demand forecasting strategy.
When we spoke about quantitative and qualitative insight generation, what we were really talking about was the use of predictive analytics.
But in order to properly take advantage of the power of predictive analytics, it’s important to keep in mind the factors that will affect the data you gather on customer demand. Much like fashion, customer demand moves in cycles. Predicting how and when these cycles take place is key to proper demand forecasting.
What makes Predictive Analytics different from other types of demand forecasting?
When we talk about demand forecasting, we often talk about forecasting analytics. This form of analytics is similar to predictive analytics, but it has one fatal drawback.
While both predictive and forecasting analytics answer the question of “what will happen,” the former goes a step further to answer “why it happens.” This might seem like a simple difference that doesn’t really provide any tangible benefits, but the reality is that it offers a big leap forward in accuracy.
Knowing why demand is going to spike allows decision-makers to circumnavigate new information, providing more control over how they interpret demand forecasting. The data that is used in predictive analytics is also taken from more diverse and up-to-date sources, making predictions more timely and less likely to change over time.
Factors that influence the customer demand life cycle
Seasonality
Seasonality is exactly what it sounds like. It refers to changes in order volume throughout the year and coincides with a specific period of time, often encompassing a period of a few months. Brands that rely heavily on seasonality for sales will often see a large spike in demand during their peak season (think of a company that specializes in winter coats that sells more during the colder months of the year and less in the summer).
Companies that rely on seasonal demand will reduce inventory in stock during the slow months in order to save resources and ramp up production and operations in the months leading up to their peak season. The cyclical nature of seasonal businesses makes outsourcing their retail fulfillment to third-party logistics companies more attractive.
It’s important to take seasonality into account when using predictive analytics to ensure that your insights aren’t being skewed by what is, in essence, a normal drop in sales.
Competition
Competition will always affect sales, but the introduction of a new player into your area of operation can pull customers away from your products and services. Keeping an eye out for new competition, whether from a company that produces similar offerings or from a tangent solution, is an important part of predictive analytics.
Demand forecasting can sometimes focus too specifically on how current customers will act in the future, and including competition in your predictive analytics will provide you with the insight needed to respond quickly to someone poaching your demand from under you.
Customer lifetime value
Different types of goods will have inherently different demand forecasts. Perishable items, for example, will have more recurring demand, while items like clothing have longer periods of time between purchases.
Understanding your customer lifetime value (the total purchases that your customers make over a period of time) can greatly improve the accuracy of your predictive analytics when it comes to demand forecasting.
Getting a better grasp on the relationship between customers and your products can help you drive recurring revenue and determine what kind of demand you can expect from individual customers over time.
Geography
This is perhaps the most important factor to take into account when using predictive analytics to design a demand forecasting strategy. The distance between the different points on your supply chain as well as the distance between your customers and offerings, is crucial to understanding where your opportunities for growth are.
Amazon, for instance, has famously optimized its demand forecasting in order to optimize the stock within its fulfillment centers, bringing items closer to the customers that are most likely to order them and saving time, fuel costs, and money.
This is also the factor that has the most room for improvement, as it can have a huge impact on your bottom line.
5 Steps to Get Started
1. Setting goals
The goal of demand forecasting is to determine what, how much, and when customers will purchase within a certain period of time. Simply implementing predictive analytics isn’t enough to truly reap the benefits of demand forecasting.
Determine what areas have the most room for improvement and prioritize viable goals for those areas.
Here, your parameters should focus on:
• Time and period
• Types of products
• Type of customers
2. Gathering & recording data
In order to collect the amount and type of data that you need to implement demand forecasting, you’ll need to record information from every available channel. Using an omnichannel strategy is an excellent way to ensure that you’re collecting enough data for your predictive analytics models.
You should be collecting data from:
• Order time and date
• SKUs (Stock Keeping Units) of each order
• Return rates
• Old and obsolete stocks
• Frequency of stock sell-outs
• Market data
3. Cleaning up datasets
Working with big datasets can be tricky. Your analysis will only ever be as accurate as the information you have on hand. Before setting the digital dogs loose on your data, it’s important to clean up large data sets in order to ensure demand forecasting accuracy.
Make sure to check:
• How reliable your measurements are
• Congruence of data across multiple different channels
• How recent the data you’re imputing is
• Unseen biases in specific data
4. Analysis
Consistently repeating patterns are a great indicator of trends that can be used to predict future outcomes. While new information, technology, or competition can alter the course of predictions, these trends are what demand forecasting is all about.
Look for patterns across:
• Seasons
• Holiday
• Geography
• Demographics
• Product lines
5. Budgeting & tracking
Once you have your demand forecast trends, it’s time to start implementing them into your decision-making. Alter your inventory and production lines to be more in sync with demand trends.
Make sure to track sales in order to review how well your forecasts were able to predict actual customer demand. This is perhaps the most crucial step of the process and should be used to inform future predictions and tweaks to your current predictive analytics models.
The Future of Modern Supply Chains Is Predictive
Using predictive analytics to create a detailed demand forecasting strategy is an excellent way to optimize your inventory and provide customers with what they need when they need it, but it’s only a single piece of the puzzle. Supply chain management is changing, and the future of supply chains is predictive.
We’ve recently written a White Paper that goes into detail about how supply chains are changing and how companies need to adapt to the new reality that coincides with these changes.
If you’re thinking of implementing a demand forecasting strategy into your business, why not reach out to one of our consultants? Predictive analytics is a tricky tool to master, but you don’t have to go it alone.
