Which of the following is the demand forecasting method for consumer goods that involves using economic indicators to predict trends?

Under the best conditions (which no one would say of 2020), forecasting demand is tricky. The hot product this week may be in the discount bin by the next. Swift changes in customer demand make demand forecasting one of the most significant challenges supply chain management professionals face.

The wide-ranging effects of COVID-19 on everything from reduced shipping capacity to sudden changes in consumer behavior have forced most supply chain professionals to throw their traditional playbooks out the window. In demand forecasting, the pandemic is accelerating the need for innovations that have been percolating in the industry for years. The sudden emphasis the pandemic has placed on certain goods has upended typical demand (even in areas as unexpected as the glass supply chain, which is facing a shortage of glass vials as scores of pharmaceutical companies rush to prepare potential vaccines).

Demand planning includes inventory management, but it goes far beyond that. Supply chain managers need to factor in the roles played by every vendor, supplier, transportation provider, and the internal supply chain into each demand forecast. No matter what chaotic elements outside factors introduce, forecasting is an essential element of supply chain management. It provides the best basis for rationalizing inventory decisions, setting safety stock levels, and preparing your supply chain for the next quarter or the following year.

A well-prepared demand planner has a host of tools and methodologies at their disposal. While forecast accuracy is rarely 100%, even in the best of circumstances, proven demand forecasting techniques allow supply chain managers to predict future demand with a high degree of accuracy. These predictions save companies money and conserve resources, creating a more sustainable supply chain.

What is demand forecasting?

Demand forecasting is a method for predicting future demand for a product. But the strength of the projection depends in large part on the quality and amount of data, the methods for calculating the projection, and the experience used. To create a more accurate demand forecast, supply chain managers often rely on more than one type of sales forecast, each using different demand forecasting methods. Forecast accuracy depends on the accuracy of the data used for demand planning and how outside factors conform to the assumptions used to craft the demand plan.

The first step in creating a demand forecast is to develop a clear understanding of the most important questions you need answers to. This step will determine the types of demand forecasting that will work best and which methods are best to use.

Do you need to understand seasonal demand to optimize inventory levels at different times of the year? Or do you need supply chain analytics to identify unstable links in your supply chain? Historical demand forecast can be a starting point for future demand and forward-looking supply chain operations, however, planning for future events relies on so many more inputs. It’s far from the only indicator.

Types of demand forecasting

Demand planning will often rely on historical data about sales to project future demand. Other factors include seasonal demand highs and lows, economic patterns, and market trends. Demand forecasting software helps develop sales forecasts using statistical forecasting.

There are several different demand forecast types. Demand planning for your supply chain may involve more than one technique.

You may have multiple sales forecasts to cover different periods. Short-term forecasts deliver a higher level of forecast accuracy. However, depending on your supply chain’s cycle length, a long-term forecast may be required for inventory management. Forecasting up to a year out is considered short-term. Long-term demand planning projects future demand for up to four years into the future.

Demand forecasts may be internal or external. An external forecast considers the effects of economic and market changes on future demand. When a demand planner creates an external sales forecast, they consider consumer demand trends, consumer spending patterns, and the broader economic outlook.

On the other hand, internal demand planning focuses on the capacity of your internal supply chain and personnel. Internal demand forecasting can answer questions about what resources are available to meet seasonal demand. Both internal and external demand planning is essential to creating an accurate demand forecast.

Demand forecasting is also divided into passive and active types. Active demand forecasting is dynamic, using predictions about economic trends and the company’s growth plans to create a demand plan. Passive demand planning relies solely on historical data. In some business sectors with products that have consistent demand and a well-established market, passive forecasting may work well. For startups, active demand planning is a must since historical data won’t be enough to give a clear picture of future demand. Even established businesses often choose to engage in active demand forecasting for effective inventory management and supply chain management to increase their market share.

Common methods for creating demand forecasts

There are several different methods of sales forecasting. As mentioned above, many companies create multiple demand forecasts and use more than one forecasting method.

Demand forecasting methods can be broken down into two basic categories: quantitative and qualitative. Quantitative forecasting relies on historical data about customer demand, supply chain performance, seasonal demand, and other data-driven metrics. Qualitative forecasting methods are less tethered to past trends. Qualitative methodologies allow the demand planner to add expert opinions into the mix, both internal and external. Both quantitative and qualitative methods have advantages and disadvantages. The best solution for the savvy demand planner may be a mix of both.

Quantitative forecasting often leverages big data for supply chain management. Some of the most common quantitative forecasting methods include:

• Barometric forecasting. The barometric forecasting method uses current data to project future demand. Barometric demand planning uses statistical analyses to create a demand forecast.
• Trend projection. Trend projection demand forecasts use historical data, including growth patterns, to create a sales forecast. While this may provide an accurate demand forecast for the near future, it can be risky to rely on sales history without considering other factors, especially if you need to create a long-term demand plan for your supply chain.
• Exponential smoothing. This forecasting method uses historical data as an input and creates a result that also considers seasonal variations in sales. It’s a useful quantitative method for startups because demand planning using exponential smoothing can be based on a small dataset.
• Regression analysis. Regressions can range from simple to complex, allowing you to incorporate both internal and external data into your analysis.
• Econometric forecasting. This forecasting model uses the interplay of data about demand with information on external elements that can move demand to create a demand plan. Econometric forecasting requires more sophisticated statistical forecasting techniques than some other methods but may also create a more accurate demand forecast.

All of the quantitative demand forecasting methods lend themselves to machine learning demand planning solutions. Demand planning software saves staff time and allows companies to update sales forecasts continuously. Software that uses AI to react quickly to market trends can provide vital, up-to-the-minute forecasts that will enable you to avoid supply chain pitfalls and improve your inventory management.

Qualitative forecasting methods are less dependent on data and tend to include more human inputs. A qualitative demand forecasting solution leverages the knowledge-base within your company, as well as that of outside experts. Some of the most common qualitative forecasting methods include:

• Sales force composite. Sales team members have the most interaction with customers. They can often spot sales trends before other sources of market information. Your sales personnel also have access to market information specific to your company’s products and clients. Although it may not be based on data analysis or statistical forecasting, the knowledge-base that’s present in your sales force is a valuable resource. This forecasting method brings together the sales team members with supply chain management and others involved in creating the demand forecast. In structured meetings, the sales team takes the lead on sales forecasting. This method leverages internal experience for demand planning.
• Market research. This forecasting method uses data about market trends and opportunities to create a demand forecast. It is useful for startups that don’t have historical data to use for sales forecasting. Market research forecasting should factor in plans for promotion and expansion and information about supply chain capacity and limitations.
• The Delphi Method. The Delphi Method for demand forecasting is sometimes called the expert method. The demand planner assembles a panel of experts and asks them to answer a set of questions. The questions reveal the experts’ opinions about future demand. The planner takes the responses and creates a summary, then shares that with the panel. The experts then answer the questions again, considering the summary from the initial set of answers. The demand planner repeats this process until the panel of experts reaches substantial agreement.

How demand forecasting impacts supply chain management

Supply chain management isn’t just about sourcing raw materials or coordinating transport. It reaches into every facet of modern business operations. And demand forecasting is key to the value that supply chain managers add to their organizations.

Supply chain managers are always looking ahead to solve the next challenge before it turns into a problem. That could mean identifying a shortage of raw materials and finding a supplemental supplier. Or it could involve arranging for additional warehouse space to accommodate seasonal demand. Accurate demand forecasting allows the supply chain manager to set a reorder point that prevents products or components from being back-ordered while keeping the inventory level as low as possible to free up capital for other uses.

Demand forecasting impacts almost every aspect of supply chain planning. The demand planning process allows supply chain professionals to make decisions based on projected demand rather than relying solely on historical sales data or, worse, guesswork. Because every link of the supply chain depends on every other link, the demand forecast helps the supply chain manager map out the steps needed to maintain appropriate inventory levels, meet customer demand, and bring supply chain partners into the demand planning loop.

Customer demand can shift quickly, but demand forecasting is up to the challenge. For example, recent research illustrated how a hybrid demand forecasting model could deliver accurate demand forecasting when there was a high degree of uncertainty because of market volatility.

Demand planning is an essential element of end-to-end supply chain planning. End-to-end planning considers the supply chain as a whole, beginning with ideation and design and ending with the customer experience. The demand planning process allows supply chain managers to convert market research analytics into on-the-ground actions to improve supply chain operations from start to finish.

Reasons demand forecasting is crucial for effective supply chain management

Demand forecasting is part of a suite of tools and methodologies that have elevated the corporate hierarchy’s supply chain professional’s importance. Forecasting demand also serves an important goal of modern supply chain management: sustainability.

Sustainable supply chain management practices include reducing waste at every step in the supply chain. Supply chain planning ties inventory levels to future demand, so there’s less waste and fewer products end up in the recycling bin. Forecasting gives supply chain managers more capacity to plan logistics and transportation for maximum efficiency, reducing fuel consumption. These savings advance the goal of sustainability while also benefiting the bottom line.

The top supply chain management professionals are the ones who can leverage demand forecasting to help set goals and deliver results. A firm grounding in supply chain analytics is the foundation for forecasting accuracy. Many of the most successful supply chain managers have become more skilled at accurate demand forecasting through an advanced degree program such as the UT Haslam MS in Supply Chain Management online.

Components of a successful demand forecasting strategy for supply chain management

To be successful, demand forecasting for a supply chain should include these components:

• Clean, reliable data, including historical data and trend projections
• Actionable inputs, including from sales team members, outside experts, and market research
• Robust supply chain analytics
• Flexibility to find and correct potential forecast errors and to change course as needed
• Collaboration with supply chain partners, both internal and external

For an example of demand forecasting honed to a fine art, you don’t need to look any further than Amazon. The largest eCommerce company in the world also runs one of the world’s most extensive logistics operations. Amazon’s sophisticated supply chain planning allows it to anticipate demand and move products to the warehouses closest to the customers most likely to order them. That next-level demand forecasting, powered by AI, is what allows Amazon to offer 1-hour delivery. In fact, one day soon, your Amazon order might already be in transit to your house, thanks to Amazon’s demand forecasting combined with its deep understanding of customer demand. Amazon’s tech leverages big data and machine learning to take inventory optimization and operations planning to a new level.

While Amazon sets the bar high for inventory management, demand forecasting technologies and supply chain analytics put a winning supply chain strategy within reach of demand planners at companies of all sizes. Demand forecasting is just one of the dynamic aspects of supply chain management that makes the profession exciting and challenging.

Learn more about how the Haslam College of Business at the University of Tennessee, Knoxville can equip you with the skills to move forward in your supply chain management career.