To Sell or Not to Sell: The Answer to Demand-Supply Volatility

At the beginning of 2022, Gartner released a report on the pressures surrounding supply chains. While they still believed that long-term stability and equilibrium were promised, 2022 was expected to be full of chaos owing to geopolitical trade risks, inflation, and an ever-changing economic landscape.

All such predictions came true as the 2022 global supply chain was marked by port congestion and shipping delays. The main factors behind these issues included:

• The Great Resignation and labor shortages
• The growing rate of inflation
• Factory shutdowns due to low levels of hydropower
• The highly virulent SARS-CoV-2 mutant strain
• A failure to implement climate change initiatives
• The infamous Russian invasion of Ukraine

That was a lot to process for a single year. Retail giants like Target struggled with bloated inventory just before the critical holiday season began. The products, particularly in the clothing and home goods sections that were all the rage during the pandemic, were left with a cold market by the time of inventory restocking. As a result, the company had to cancel orders with suppliers and slash prices significantly for clearance.

Businesses, small and large, took a major hit in 2022 due to demand-supply volatility. Amidst glaring uncertainties, how were they to charter the right course of action? Current studies put their faith in the forecasting of demand. In this blog, we discuss the proposed answer to demand-supply volatility.

What Is Demand Forecasting, and How Does It Benefit Businesses?

Demand prediction or forecasting is the process of using AI tools for predictive analysis of historical data to predict customer demand in the future. The analysis helps businesses to make supply-related decisions that impact profit margins, inventory management, product planning, and supply chain operations.

The decisions may involve answering questions like:

• How many units of each item will make a fully stocked inventory for each SKU?
• How often should the inventory be replenished?
• How will inventory restocking decisions need to change over time?
• Where does the business see itself a year from now?

Some of the major benefits of this practice include:

1. Budget Preparation

If a business only had to prepare a master budget, things would ease out. However, other budgetary preparations need attention, including overheads, cash flow, labor costs, manufacturing, etc.

Accurate inventory forecasting (including the timing of the sales) will help maintain supply chain efficiency through correct budget preparation. Businesses can handle peak and dry periods better as operational costs fluctuate. For instance – free and paid marketing efforts can be alternated during slow and busy periods for better budget allocation.

2. Scheduling Production

Suppose a business discovers through historical data analysis that demand for a particular product or service is expected to rise in the next six months. This information can help accelerate the production cycle as the product is churned out in greater quantities.

Manufacturers can easily optimize production planning to ensure the inventory is balanced – neither amassed nor insufficient.

3. Developing a Solid Pricing Strategy

Demand prediction is not just about maintaining a balanced inventory. The tools used for this practice reveal other crucial details, such as current market trends, sales potential, and consumer buying patterns.

Based on a combination of these analyses, companies can develop the right pricing strategy to mitigate losses and risks. For example – If the data reveals that demand for a specific product is higher during certain times of the year, the company can strategize accordingly to leverage the hot market.

4. Competitive Advantage

Accurate demand prediction allows businesses to maintain a better market standing compared to competitors. Since a company can keep sufficient stock in a given period, they do not have to worry about stockouts, and loss of customers to competitors.

As a bonus, customer satisfaction and trust also improve.

5. Discovering Seasonal Trends

This benefit is based on a long-term accurate prediction of demand. Over each forecasting cycle, a company can identify certain patterns in consumer buying behavior and, thereby, demand for a specific product or service.

This helps earmark seasonal demand trends for a year. Businesses can prepare their inventory accordingly to prevent backorder and out-of-stock scenarios.

How is Demand Commonly Forecasted?

The drive for accurate demand prediction began in 2020, but things only escalated after the emerging supply chain challenges of 2022. Global Newswire reported that the market size for global demand-supply solutions was $3.62 billion in 2022, and was projected to grow up to $8.681 billion by 2030. That is a CAGR of 10.31%!

This means demand-supply stability will take a couple more years as more and more companies realize the dire need of the hour. Currently, the most common methods of predicting demand include –

1. Quantitative Forecasting

This method works best for companies with years of sales data. However, the analysis takes into account a relevant period to paint the larger picture.

Mathematical models and statistical historical data are used to make predictions based on seasonality, demographics, prices, sales, and income. For instance – A company can use data from the past few years to notice any significant change in consumer buying patterns. The only thing to remember is that the data must not be outdated.

2. Qualitative Forecasting

In this demand prediction method, companies use external data points as their analysis compass. These include geopolitical risks, market volatility, inflationary pressures, changes in import duties, employment shifts, and more.

Almost every business in the market today must use this method, regardless of their industry vertical. This is because maintaining an inventory without considering external factors will lead to blind-sidedness. The business could be left with an overloaded inventory or fail to meet growing demand.

3. Graphical Forecasting

This method is not essentially a separate forecasting method altogether, but a way to represent temporal quantitative data along with complex collections. The graphical method involves displaying the analysis results using graphs and other visualizations.

Using the graph, companies can develop strategies for potential inventory scenarios that crop up in the future. For instance – if the graph shows an increasing demand over the next year, with a sharp fall after, production can be planned accordingly.

4. Trend Forecasting

This method of demand prediction digs deep into past trends in demand for a particular product. An analysis is made of the way demand has changed over a specific period.

Since trend forecasting uses the past to predict the future, it is complicated and, sometimes, uncertain. For example – Trends are often disrupted by factors like stock market downturns and user access to technologies. If there are dramatic shifts in consumer behavior as a result, this method fails to produce results.

5. Seasonal Forecasting

Under this method, seasonal historical data is used to predict future demand. Seasonal forecasting is impacted by factors such as holidays like Christmas and Thanksgiving, major sales seasons, weather changes, etc.

Most companies run seasonal forecasting every quarter, especially if their sales fluctuate considerably during peak seasons.

Best Demand Forecasting Practices

So, what are some of the best practices through which any company can set up a reliable inventory? They are listed below.

1. Set up a Forecast Period

Carrying out demand prediction at odd times of the year, or at a whim, is not a good strategy. Every business needs to establish a definite forecasting time to get actionable insights.

Standard inventory forecasting periods include a gap of three, two, or one month. A shorter forecasting window is useful for deriving up-to-the-minute reports. A good example would be fast fashion brands that churn out clothing items every month.

Manufacturers of durable goods (with a longer lead time) may conduct forecasting with a wider time window.

2. Aim for Precise Inventory Counts

In most cases, a business will have to use a combination of demand prediction methods to get a clear picture. However, the forecasting method is not as much a concern as the need to derive accurate inventory counts.

For instance – the inventory data could be off due to a sudden shrinkage or poor management practices. This will make it difficult to project accurate answers. Such discrepancies highlight the need for a good fulfillment partner with a track record for accurate demand forecasting.

3. Partner with a Reliable eCommerce Fulfillment Service

Partnering with a good eCommerce fulfillment partner is a best practice because small and mid-sized businesses often face struggles with inventory forecasting. Red Stag Fulfillment has observed the following to be the most common challenges involved – the dynamic nature of retail, a fragmented approach to forecasting, and the use of irrelevant historical data.

A reliable fulfillment partner has methodologies in place to overcome such challenges, and they constantly look for ways to improve services and enhance logistics. As a result, they’re able to help brands maintain a balanced inventory, regardless of seasons and macroeconomic changes.

4. Be Open to Adjusting Your Inventory Model

A best practice in inventory forecasting in the current season may not deliver the desired results in the next, and so forth. Brands need to stay agile and nimble to keep abreast with changing consumer demands.

One way to achieve agility is to conduct post-mortem tests on a particular period’s forecasting report. Did the analysis meet consumer needs or did the inventory fall short? When projected inventory counts are checked against real-time results, a clearer picture appears. Since eCommerce changes rapidly, brands’ inventory models must also evolve.

Final Word

According to a 2023 KPMG Global report on supply chain trends, disruptions of the previous year are expected to remain this year as well. As a result, cross-border trade skepticism, dangers of cybercrime, and material access turmoil will mark supply chain operations.

But this cloud has a silver lining – 2023 is also the year when technology investments will continue to grow, thereby morphing supply chains at a lightning pace. Companies are encouraged to adopt real-time data analytics tools (if they haven’t yet) to overcome supply-demand volatility and create a sales execution framework that meets consumer buying patterns.