Do you remember the time when the COVID-19 pandemic lockdowns were announced across the world, followed by the panic purchase of essentials to the point where supermarkets and grocery stores were left with empty shelves? Every day, the newspapers and websites were filled with the news about shops running out of simple items like toilet paper, milk, or bread.
What led to a situation like this? Were companies completely unprepared for this sudden surge in demand for these consumer items? Could the situation have been handled better?
Businesses around the world are pondering upon these questions around optimization problems and each of them leads to a simple answer –effective supply chain demand planning and forecasting.
Understanding and predicting demand in unpredictable times
Modern businesses have access to huge amounts of data so demand planning and forecasting should be easier. But demand planning still remains a game of assumptions and estimations. With COVID-19 shattering most of the old ways of predicting demand and inventory management challenges and uncertainty persisting across several dimensions along with deep changes in consumer behavior, it has become crucial for businesses to understand these new patterns.
Since 2020, consumer behavior has changed drastically and is still shifting as people get used to the ‘new normal.’ The shift to the online shopping space during the pandemic has raised more questions like whether this forced change in behavior will last even after the physical shops open or consumers will move back to their old ways. For example, a Deloitte e-commerce study has predicted that the global online retail volume is expected to grow at a rate of 15% till 2023. Almost 40-50% of consumers who shopped online during the pandemic stated that the crisis will not have any lasting effect on their online shopping behavior in the next year.
While these changes in consumer behavior have been abrupt, the trend is likely to continue in the years to come. To understand these changes based on historical sales data, businesses need to look for more reliable ways to navigate these rough seas through advanced forecasting techniques. With an unprecedented situation like this, organizations need demand planning and sales forecasting more than ever.
Reshaping the manufacturing world to meet new demands
Most manufacturers prefer to leverage analytics data over traditional forecasting and time series techniques. However, things are a little different in the post-pandemic world. While analytics data can produce accurate forecasts, they still work under the assumption that societal and consumer behavior trends remain constant.
Unprecedented situations like the COVID-19 pandemic and its after-effects need solutions that not only learn from historical data but also predict outlier events like COVID-19 and how they can affect your organization with several demand forecasting methods and forecasting models.
Advanced demand sensing and forecasting solutions use Artificial Intelligence (AI) and Machine Learning (ML) to respond and report on new or unexpected shifts in consumer behavior. Although manufacturers have been leveraging ML for a while, the focus is now on leveraging its abilities for demand planning.
With the amount of erratic consumer behavior witnessed during the pandemic, relying only on historical data to forecast or manage or optimize inventory isn’t enough. By clubbing pandemic sales history with cyclical forecasting and promotional analytics, “what-if” analyses can be conducted to test business decisions and future supply networks against worst-case business scenarios.
It is important to forecast complex situations by leveraging the knowledge, skills, and experience of planning experts efficiently across a wide spectrum of data including case studies. Businesses will need to learn from historical data, take into account the new trends, and access detailed visibility into future demand so planners can take account of existing orders, future requirements, and minimize stock outages.
Manufacturers are also increasingly leveraging predictive analytics for long term demand forecasting and short term demand forecasting and while it produces accurate demand data, it is important to have real-time metrics at hand. ML helps fill the gaps in data, cleanses the information to get a more reliable data set, and makes more accurate predictions.
Manufacturers can choose from an array of methods that can serve as a starting point to predict demand.
5 demand planning and forecasting methods that can make a difference
From traditional historical data methods to leveraging AI and ML to make predictions on demand, manufacturers have a lot of choices to consider and avoid out-of-stock situations.
1. Historical data method
Analyzing past sales data is an excellent starting point for any organization. The historical data method helps you get a rough estimate of demand for your products or services by monitoring high and low periods of demand in the past and help you get a baseline prediction.
What makes this process easier? An AI-powered tool can help you get this baseline in a matter of minutes. The solutions powered by emerging technologies like AI or ML take into account your past sales data to make future predictions, so you don’t have to sit and sift through huge chunks of data.
2. Market research and Delphi method
What better way to understand consumer demand than talking to consumers and collecting the data provided by them? Market research will involve an effort to send out surveys to consumers and get their feedback. But it also gives you valuable insights into the minds of consumers.
Similarly, the Delphi method involves talking to market experts to get their opinion on market demand. Both methods involve human interaction and can help you draw on the knowledge of people with different areas of expertise.
But the success of these methods depends largely on your available resources and time. Collecting, collating, and analyzing data collected through these methods is time-consuming and will require expertise to leverage the information in predicting market demand.
How to make it easier? Deploying a data-driven tool that not only collects data from the end-consumer but also includes local market knowledge combined with expert knowledge gathered from various sources to provide forecast accuracy makes the task more seamless and faster so the relevant data is available for quick decision-making.
3. Demand sensing method
One of the most efficient methods of demand planning is demand sensing that uses machine learning to capture real-time variations in purchase behavior and helps you build a data-driven supply chain.
How is that helpful? Demand sensing helps you leverage real-time demand signals so that your supply chain develops the capability to respond quickly to unplanned demand changes. A powerful, AI-enabled demand sensing tool can help you get real-time visibility into short-term demand, improving service levels and forecast accuracy.
4. Predictive sales analytics method
Predictive supply chain analytics helps you not only estimate demand but also understand what factors drive sales and how consumers will behave under certain conditions. The visibility provided by the combination of ML algorithms and advanced IoT highlights every step of the supply chain to build a demand forecast.
With a proper ML-powered tool, you can aggregate historical and new data from different sources like market surveys, social media, customer feedback, ERP, CRM, and others. You can develop predictive models to spot likely consequences and determine relationships between various factors.
5. External macro forecasting method
By analyzing trends in the broader economy and determining how they will affect your business goals, you can redefine or work on your existing goals. By understanding the larger external market forces, you also monitor the availability of raw materials and other factors that can have a direct or indirect effect on your manufacturing and the overall supply chain.
What makes this method seamless? An AI-enabled tool can help you uncover these patterns easily, which can often get missed with manual tracking. Employing an all-powerful tool can improve forecast quality and isolate relevant trends from the market noise.
Plan, execute, analyze with an all-in-one powerful tool
With the COVID-19 crisis testing the resilience of every business, those adopting data-driven decision-making have a better chance of adapting to the change and responding to the changes in demand. For this, you’ll require an all-powerful tool that can not only capture demand signals and fluctuations in real-time but also adjust the existing predictions to accommodate the changes.
ThroughPut’s ELI, an AI-powered planning, and forecasting tool, can help you accurately analyze and correlate demand insights. It uses historical data, analyzes short-term and external market trends, and identifies and predicts future demand patterns so you can get a 360-degree view of market demand for your products and services and adapt your demand plans to the changes in patterns.
Find out what more ELI can do for your manufacturing business. Talk to the experts at ThroughPut and book your demo today!