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Learn the basics of effective demand planning to improve the accuracy of your forecasts, align inventory levels, and enhance profitability.

Demand planning is the supply chain management process of forecasting demand so products can be reliably delivered and customers remain satisfied. Effective demand planning can improve the accuracy of revenue forecasts, align inventory levels with peaks and troughs in demand, and enhance profitability for a particular channel or product.

Demand planners keep an eye on internal and external factors that could impact demand, such as labor force issues, natural disasters, weather patterns, and news events or other influences. Gathering information from all possible sources is the best way to generate an accurate forecast and ensure integration with the supply forecast to efficiently meet customer demand.

The importance of demand planning

The market can shift on a dime and demand plans needs to move at the speed of the changing market. If demand plans can’t be adjusted with agility, companies could end up with stock-outs and unhappy customers, or warehouses full of unused inventory, unhappy finance managers, and millions of dollars in wasted capital.

In an ideal world, demand planners must stay ahead of the market instead of merely reacting to it, and make decisions based on near real-time market data, rather than solely on historical data. That’s not always possible, but with the advent of cloud-based planning platforms, it’s closer to reality than ever before.

Check out our on-demand demo to see how a cloud-based demand planning solution will enable you to sense, shape, and orchestrate demand with a holistic view of data and trends.

Elements of the demand planning processes

Let’s take a look at a few of the processes involved in demand planning.

Trade promotion management:

Trade promotions are marketing tactics (most often in retail companies) that focus on generating in-store demand through discounts, giveaways, in-store promotions, and other similar techniques. Trade promotion management is designed to help brands stand out from their competition through highly coordinated promotion activities and builds stronger connections with retailers.

Trade promotion planners seek to plan collaboratively at detailed and aggregated levels so they can adjust products, campaigns, and promotions without long delays, aligning an optional trade promotion spending plan that incorporates end signals from distributors and customers across all time periods, products, and geography.

Top-down and bottom-up trade promotion management and analysis includes profit and loss data, creating insights around promotion spending. It also tracks and identifies which promotions don’t optimize margins due to ineffective trade promotion spending and poor brand growth from trade promotions and creates maximum ROI using a broad range of data.

Product portfolio management:

Product portfolio management is the process of managing every facet of the product lifecycle, from new product introduction to end-of-life planning. The goal of product portfolio management is to maintain a high-level view of the entire portfolio and reveal where product lines are interconnected and interdependent.

Product portfolio management includes planning for fitting new products into the existing product portfolio, understanding how introducing those new products will affect other products (cannibalization), and the analysis of attachment rates (how the sales of one product affects the sales of another). Planners involved in product portfolio management are heavily involved in scenario planning to ensure that they’re aware of each product line’s effect on the other product lines to optimize the product mix, maximize profitability across product lines, and increase global market share.

When new products are launched, it’s important to know how the new products will affect the global planning strategy, the cost of introducing that new product, and the revenue and profits that will be generated by the new products. Using intelligent product portfolio management techniques, feasibility models are connected to ideation processes, scenario-based profitability models are generated, and the process of taking a product from idea to commercialization is accelerated.

When this process is collaborative, a real-time granular forecast model can identify how different market segments across geographies might purchase this new product and at what price. In a collaborative system, new product introduction links sales and supply chain, resulting in key connections to sales and operations planning (S&OP) processes, production planning, and allocation planning.

Statistical forecasting:

Statistical forecasting in demand planning leverages historical data to generate supply chain forecasts using various advanced statistical algorithms. In demand planning, it’s essential to have data-backed forecasts to avoid stock-outs or overstocks and ensure that customers are satisfied.

There are multiple aspects to how statistical forecasting makes demand planning more effective. Demand planners can analyze many algorithms and decide which forecast is most accurate by reviewing each model’s accuracy and bias measures. Then they can choose from the best model for each product and product family.

And when a forecasting dashboard is part of the equation, it becomes easier to customize forecast algorithm assumptions and measure accuracy with techniques like mean absolute percentage error. With statistical forecasting, demand planners can quickly identify outliers and exclusions based on user-defined parameters, including standard deviation or the inter-quartile range.

Seasonality has a major impact on demand planning. Retailers have many factors to sort through to ensure that they’re prepared for various seasonal events. Will they be ready for the holiday shopping rush? What if weather patterns shift and all those winter coats they’ve stocked aren’t purchased? With statistical forecasting in demand planning, these questions are easy to answer because multiple statistical simulations can be run, including models to forecast the impact of intermittent demand, multi-linear regression forecast quantity, price, attach rates, and discounts.

The skills demand planners need

Demand planning is undergoing large-scale radical change with an emphasis on digital transformation. Artificial intelligence (AI) and machine learning are already beginning to make an impact on how demand planners operate.

Algorithmic “touchless” supply chains that weave in the power of big data, blockchain, robotics, and 3D printing may soon be the rule rather than the exception. Demand planning of the future will be always on, dynamic, and non-linear. The power to react quickly and make value-based decisions is essential to staying ahead of the market.

To lead the way into a transformative future, demand planners need to combine technical and business knowledge with collaboration and communication skills. The ability to influence department leaders that partner with demand planning is key, as well as the skills to interact intelligently with leaders across the organization because supply chain initiatives often reach across business units. Strong business acumen is a must-have — you’ll be more effective working with your counterparts in finance, sales, and marketing if you can speak their language.

The effective demand planning leader of tomorrow is tech-savvy and comfortable working alongside the world of machines. Some have said AI won’t replace managers, but managers who work with AI will replace managers who don’t. This highlights the transformation taking place in supply chain management: Humanity is essential but so is technology. It’s not a paradox — it’s the new normal. The new demand planning leader is digitally dexterous and also skilled with people.

The already many-faceted role of a demand planning leader is changing. To thrive in this new world, demand planning professionals must grow their capacities in collaboration, communication, and leadership, and pair those skills with in-depth technical knowledge if they want to become and stay a powerful force for the future of demand planning.

Digital demand management 

The future of demand planning is what Supply Chain Brain calls “digital demand management” (DDM). It’s centered around implementing demand-driven structures, frameworks, and digital enterprise architectures. Multiple groups connect to facilitate a seamless exchange of information, ideas, and solutions that are synchronized with the omnichannel buying habits of consumers.

Even in our personal buying habits, the competitive demand landscape has changed radically in the past few years. There are rapidly emerging digital markets, new competitors, and faster market changes that can threaten the extinction of any enterprise that doesn’t adapt. DDM makes complex data comprehensible, actionable, predictive, and prescriptive because it provides a real-time synchronized knowledge base that permits improved customer focus.

The development of DDM starts by challenging traditional linear thinking across supply chains. The old linear thinking accepted the inevitability of forecast cycles that are weeks or months old, poor visibility into SKU locations, and the inability to address ongoing variability that disturbs traditional network and inventory optimization systems. Linear decision-making adds unnecessary time and causes potentially false demand signal amplification.

The essential change is to replace traditional functional metrics with DDM, fostering collaborative execution across the entire supply chain. The resulting demand management organization enables a business to be simultaneously planned dynamically in real time, both horizontally and vertically, enabling true digital collaboration.

DDM requires a new planning model that enables causal and external factor analysis, along with a process-control approach to smart digital network management. The beauty of DDM is that it works within a range of pre-defined acceptable variability. Using real-time DDM forecasts — based on standard deviations along with the range of consumer behaviors that are likely to occur — participants can agree to ranges of performance, commitment horizons (periods of risk), and exception conditions. The result is a beneficial digital collaboration that enables next-generation demand planning.

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