Demand planning

What is Demand Planning?

Demand planning is the process of forecasting future demand for products or services as accurately as possible and using that forecast to fine-tune company output to best meet the needs of customers.

Demand planning is a key part of the supply chain planning process, and when it’s done right, it involves delivering exactly what customers want, when, where and in what quantities. Along with increased customer satisfaction, effective demand planning leads to improvements in operational efficiency and increased profitability.

Why is demand planning important?

A key goal of demand planning is balance. The objective is to have the right amount of inventory ready to meet customer demand for products without experiencing shortages or incurring the high cost associated with storing surplus inventory. But finding that balance can be challenging.

Demand planning is an increasingly complex activity that must account for the wide variety of factors that can have an impact on demand. In today’s highly interconnected global business environment, disruption is inevitable, and demand can shift rapidly. Severe weather, natural disasters, labor unrest, geopolitical instability, global health emergencies, even social-media-driven demand spikes – these and other hard-to-predict events have the potential to create uncertainty and volatility.

Consumer behavior also adds to the complexity, especially as consumer expectations continue to grow around every aspect of the purchasing experience. Consumers increasingly want more customization, omni-channel purchasing options, rush delivery, easy returns, and environmentally- and ethically-crafted merchandise, just to name a few.

Companies that do not consider the potential impact of these market- and consumer-driven variables in their demand planning risk stockouts or excess inventory, decreased revenue and loss of market share to the competition.

What are the key elements of demand planning?

Demand planning is a collaborative, cross-functional process that involves input from multiple key stakeholders across the organization, including Sales, Marketing, Finance, Purchasing, and even customers and suppliers. But it’s the demand planner who leads this effort and is ultimately responsible for creating an accurate forecast and building out the demand plan.

The demand planner maintains historical data, monitors demand sensing data, generates the statistical forecast and adjusts the consensus demand plan to create a realistic forecast based on all inputs.

Key demand planning inputs

The demand planner evaluates a wide variety of inputs during the demand planning process that can influence demand for a product or service. These include historical data, input from other functional areas in the organization, such as Sales and Marketing, and external digital signals like weather, holidays and economic indicators.

Demand forecasting

Demand forecasting uses advanced statistical forecasting algorithms to generate a forecast of the future demand for a product or service based on historical sales data. While it is an important initial step in the demand planning process, demand forecasting is just one input to the larger demand planning process.

Demand sensing

Demand sensing is a forecasting method that uses artificial intelligence (AI), machine learning (ML) and real-time data to create an accurate forecast of demand based on short-term trends. Demand sensing taps into external data sources to capture leading indicators, including weather forecasts, consumer buying behavior, point-of-sale inputs, news alerts and social media trends.

What are some key best practices for demand planning?

Success in demand planning depends on the use of proven processes and tools. Current best practices for demand planning make it easier for demand planners to react in real time to new inputs and spend more time on value-added planning. These include concurrent planning, segmentation, collaboration and the adoption of the latest purpose-built technology solutions.

Demand forecasting and concurrent planning

Demand forecasting is an essential part of the demand planning process, but achieving 100 percent accuracy is not feasible, regardless of what methods are used. Many companies employ concurrent planning practices, which involves making and managing synchronized plans across time horizons, business processes and organizational boundaries at the same time.

By combining proven demand forecasting techniques with concurrent planning, demand planners are able to plan, monitor and respond simultaneously and continuously so they are better prepared when changes arise. They can also provide insights in the development of contingency plans, including optimistic and pessimistic views of the forecast. Demand planners can run multiple what-if scenarios, which can determine optimal timing on new product introductions and evaluate impacts of different end-of-life dates, improving the accuracy of the plan.

Supply chain segmentation

Many companies today have tens of thousands of SKUs, hundreds of suppliers and dozens of manufacturing plants and distribution centers dispersed across the globe. With the sheer number of products and distribution channels adding significant complexity, it’s important for demand planners to segment item forecasts so teams can properly prioritize the time they spend analyzing, communicating and modifying their demand forecasts.

Supply chains can be segmented based on a variety of factors, including product complexity, manufacturing process, strategic importance, customer service needs, risk and resiliency and market demand. Segmentation adds value in determining the best forecasting and demand planning strategies.

Supply chain segmentation helps demand planners balance complexity with efficiency and flexibility, which enables them to deliver greater customer value. When it comes to segmentation, there isn’t a one-size-fits-all approach. The demands of the customer base need to be weighed against corporate priorities, key performance indicators (KPIs) and goals, and those will be different for every company.

Cross-functional collaboration

Success in demand planning requires close collaboration among many functional groups in the organization. Part of this process involves gathering the most relevant and up-to-date data for the demand forecast, such as sales forecast information from the Sales team and financial targets from Finance. While getting input from these groups is critical for balancing market and customer needs against supply chain capabilities and risks, effective collaboration goes beyond the tactical exchange of data.

Effective collaboration helps demand planners understand risks and limitations during the planning process, leading to more realistic plans that support common goals across the organization. Without it, making critical decisions can be time consuming and lead to supply chain disruption and the inability to mitigate risks before it’s too late to change direction. Companies need to efficiently bring together data, processes and people to improve supply chain flexibility and, ultimately, the profitability of the enterprise and satisfaction of the customer.

Advanced demand planning technology

As the notion of a digital supply chain gains traction, companies are increasingly taking advantage of new, purpose-built technology solutions that provide specific demand planning capabilities.

With their combination of better information, faster analytics and automation, these demand planning application platforms enable planners to know sooner when demands shift – and respond faster to ensure delivery on their promise to customers. This enables them to streamline the demand planning process, and connect data, processes and people with concurrent planning.

What tools are available to help demand planners?

Spreadsheets and generic software tools

Many companies use generic tools like Excel spreadsheets for demand forecasting and demand planning activities. These tools often don’t meet expectations because they are not specifically designed for performing the range of complex activities that go into the demand planning process. They also do not enable collaboration among groups, which contributes the different contributors to the demand planning process operating in independent silos. As well, the manual effort required with these tools can be a significant drain on the demand planner’s time, leaving less time to review the inputs from the field and do actual demand planning.

Demand planning application platforms

While some demand planners continue to use generic tools and manual processes, many companies are realizing the value of sophisticated software solutions purpose-built for demand planning. These demand planning application platforms include features specifically designed to automate the complex tasks required for demand forecasting, demand sensing and the full range of demand planning tasks.

Modern demand planning application platforms typically offer a variety of benefits that simplify and streamline the demand planning process, including improving short- and long-term forecast accuracy; reducing process cycle times; fostering tighter coordination and collaboration across functions; proactively resolving risks; and making it possible to extract insights from large data sets.

How is emerging technology improving the demand planning process?

With complex global supply chains here to stay, advancements in demand planning technology, especially in the areas of AI and ML, continue to enhance the capabilities of demand planning application platforms, improving accuracy and the efficiency of the demand planning process.

A demand planning platform equipped with supply chain AI and ML capabilities, for example, can predict changes in demand from both internal and external data signals. It can also automate data collection and pre-processing, allowing planners to focus their attention on generating demand insights and forecasting.

Using digital transformation to increase agility and empower planners is especially important when planning for circumstances that don’t exist in historical data, like a social media-driven trend.