Demand Planning: What It Is and Why It’s Important

“Demand Planning: What It Is and Why It’s Important” “22662” Blog Credit: Abby Jenkins, October 23, 2020 (Demand Planning: What It Is and Why It’s Important | NetSuite)

Demand planning is a cross-functional process that helps businesses meet customer demand for products while minimizing excess inventory and avoiding supply chain disruptions. It can increase profitability and customer satisfaction and lead to efficiency gains.

Demand planning should be a continuous process that’s ingrained in your business. Fortunately, advances in technology have made accomplishing this possible, not to mention easier.

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What Is Demand Planning?

Demand planning requires analyzing sales as well as consumer trends, historical sales and seasonality data to optimize your business’s ability to meet customer demand in the most efficient way possible.

To achieve this goal, demand planning combines sales forecasting, supply chain management and inventory management. First, it uses data from internal and external sources to predict future demand. That forecast can then inform your sales and operations strategy so you can plan how much product to buy or manufacture in order to meet that demand.[vc_video link=”” title=”Video: What Is Demand Planning?”]”12px”]

Why Is Demand Planning Important?

Effective demand planning delivers both profit and customer satisfaction by helping businesses strike the right balance between sufficient inventory levels and customer demand. That’s not an easy goal, especially since it requires coordination across your entire organization. But the business implications are significant. Excess inventory locks up working capital, adds inventory carrying costs and increases the potential that you’ll be stuck with low value or obsolete inventory. Alternatively, poor planning can result in avoidable supply chain disruptions and leave a company short on products, which can result in backorders, stockouts or costly scrambles for raw materials. All of these issues can result in delays, which leads to dissatisfied customers.

Demand Planning vs. Demand Forecasting

Demand forecasting is part of the larger demand planning process and analyzes internal and external data to predict sales. Typically, forecasts cover the upcoming 18 to 24 months, but the forecast period can vary by product and industry. Companies may adjust those predictions frequently as they review the latest data and changes to market conditions. The demand forecast becomes the foundation of the overall demand planning process as the business figures out how it can fulfill expected sales.

Where Does Demand Planning Fit Within a Business?

The demand planning function requires input from and coordination between several departments, including sales and marketing, purchasing, supply chain, operations, production and finance. Additionally, executives responsible for product portfolio management and overall business strategy play important roles by taking into account lead times for components and production times.

Since demand planning touches so many business functions, the location of employees who handle this responsibility can vary: It may be an independent group or it may be integrated into one of the departments listed above, as well as the procurement or operations departments. Some believe that demand planning, especially the demand forecasting component, is most successful when it is closely linked to sales and marketing.

7 Key Steps for Successful Demand Planning

Demand planning is a multi-step process, and it can get complicated as the size and scope of the company or its forecasting efforts grows. Key steps include:

  1. Create a team. Ensure that the members of the cross-functional demand planning team have clear roles and responsibilities. For example, representatives from purchasing and supply chain groups may be responsible for ensuring the business acquires enough inventory, at the right time, to meet the demand forecast. The finance team is often responsible for building the actual forecast.
  2. Define and aggregate relevant internal data. The various employees involved in demand planning should agree on what data should be included to develop an accurate forecast. The relevant data will vary by company, but should include sales data by channel and location, out-of-stock rates, inventory turnover, lead times, production times, obsolete inventory and other key inventory metrics. Check with your sales and marketing teams about the timing of price changes, marketing campaigns and promotions that could affect demand. Gather information from product teams about new launches, retirements and competitive offerings, as all of those could impact forecast accuracy.
  3. Enhance the forecast with external data. External data is another crucial input for effective demand planning. This could be metrics around the recent performance and delivery timelines of suppliers and distributors or the latest purchasing habits of your key customers. Other external information includes overall economic conditions that may impact sales and shifts in your market or for specific products you sell.
  4. Develop a statistical demand forecast. Collaboratively decide on the type of forecasting model (or models) that makes the most sense for your business and then start building it. This is best done with demand planning software, though some businesses still use Excel or other tools that require more manual work and can be time-consuming and error-prone. Beyond company-wide forecasts, you may want to build predictions by product or product line, or for specific customers or regions.
  5. Challenge the demand forecast. Review, reanalyze and refine the demand forecast with all key stakeholders. Add the most recent data to see if that has a substantial impact on predictions. Question any information that might be incorrect and perhaps remove unlikely outliers that could distort the overall forecast to understand the effects of doing so. It’s also a good time to double check that the demand forecast aligns with the company’s broader financial forecasts.
  6. Weigh forecasts against inventory. Determine how much inventory is needed to fulfill the predicted demand (cycle inventory), including a buffer of “safety stock.” Identify the necessary vendors to meet this demand, and check in with them to make sure they can deliver the necessary products or services on your required timeline. Ensure transportation vendors can handle the volume and meet your schedule for moving goods between locations.
  7. Measure results. Identify key performance indicators (KPIs) that enable you to measure the effectiveness of your demand planning and set targets for each. Your business may track sales forecast accuracy, inventory turns, fill rates, order fulfillment lead times or cost of goods sold (COGS), for example. Continually review performance against these targets and make adjustments as necessary.

Skills for Demand Planners

Demand planners need to have excellent analytical skills, with competency in statistical data analysis and modeling. In addition to their numerical skills, the most successful demand planners are also great communicators since they need to interact with many different departments.

They also tend to be innovators that champion progress through automation, since tools such as demand planning software and supply chain management software can help the company improve its demand planning, ultimately saving money. Demand planners should be familiar with Enterprise Resource Planning (ERP) systems, since this will be the source of data, and eventually become expert users of demand planning software.

Demand Planning Methods

At the broadest level, there are two philosophies that have been applied to demand planning: push and pull. The push method, which was popular for most of the 20th century, assumed that “if we build it, they will come.” Businesses took the approach that building innovative products would create demand for them, so they manufactured products, made them available to customers and waited for sales to roll in. In practice, the success of this strategy was hit or miss. Sometimes demand exceeded supply and shelves were bare, meaning a business missed sales opportunities. Other times, inventory lingered unsold on shelves or in warehouses, increasing costs and hurting cash flow. Companies rarely achieve the perfect balance, even with markdowns and sales.

Today, most demand planning processes use a “pull” philosophy. This starts with gauging customer demand and using that information to guide all other operational planning. The primary challenge of the pull approach is coming up with an accurate forecast of customer demand. Inaccurate forecasts result in the same problems as with the “push” method: missed revenue opportunities and higher costs.

Common models for creating a statistical forecast in the demand forecasting component of demand planning are:

  • Moving average demand: This method assumes that future demand will be the rolling average of the last few sales periods.
  • Linear regression: This method takes previous demand levels and puts them through a least-square regression statistical model to predict future sales. This model, sometimes called the “line of best fit,” plots a curve based on previous demand, and extends that curve to predict future demand.
  • Seasonal trends: As its name implies, the seasonal trends method predicts future demand based on historical sales during particular months or seasons. This method is most appropriate for companies whose sales are highly seasonal.
  • Sales forecast: This method estimates future demand based on sales opportunities and probabilities identified by the company for an upcoming period.

Businesses often use demand planning software in conjunction with these methods to automate certain aspects of modeling and forecasting. Software can also make for more robust and accurate demand forecasts.

Demand Planning Best Practices

Because the demand planning process is complex, best practices generally focus on increasing accuracy through collaboration. Some of these best practices include:

  • Get buy-in and demand accountability from all stakeholders by relying on both statistical modelling and collaborative forecasts that pull in data from various departments.
  • Have accurate inventory data. You can’t have successful demand planning without efficient, accurate inventory management.
  • Include information from the supply chain, weather events and natural disasters, market shifts and consumer buying behavior in your forecasts, a process sometimes referred to as “demand sensing.”
  • Actively shape demand with marketing, promotion and pricing tools.

Another key best practice is to do your due diligence when choosing demand planning software. Software should automate tasks such as statistical analysis for forecasting, tracking KPIs and calculating optimal stock levels, allowing your team to focus on interpreting the results, collaborating with other groups and adjusting plans as necessary. Your software should be easy to use, intuitive and integrate with your inventory management and ERP systems.

The Future of Demand Planning

Advances in demand planning software continue to help companies increase the accuracy of their forecasts. For example, software can connect to point-of-sale data and may be able to pull information from suppliers and distributors, enabling businesses to incorporate real-time data into their planning and analysis.

Internet of Things (IoT) devices can improve demand planning, as well, by allowing companies to receive up-to-the-minute updates on the status of raw materials and inventory. IoT technology can also monitor sales as they happen, so a business can quickly replenish stores or warehouses that are selling through items faster than expected. This visibility can help organizations that use a pull strategy optimize supply or inventory levels and limit the expenses and headaches that come with too much or too little stock.

Additionally, demand planning software is increasingly using artificial intelligence (AI) and machine learning to process huge amounts of data and identify trends and patterns that a human might never identify. Demand planners can then use those insights to make adjustments on the fly.

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