Demand Planning is a process by which supply and demand in a manufacturing chain is managed according to forecasted and real-time fluctuations in customer demand. Implementing a demand planning solution is no easy task, but it has a high return due to achieved cost efficiencies, increased sales revenues from accurate customer demand plans, and higher profits from decisions such as inventory replenishment that depend on accurate forecasts of demand.

Demand fluctuations in the supply chain can leave your business flat-footed at the very time it needs to respond to customers or prospects. There are many intricacies in today’s marketplace: globalization pressures, rising customer demands, supply chain complexities, the need to increase revenues across global markets, and the need to lower costs and improve productivity (do more with less).

Better demand planning creates consistency in the process all the way from ordering materials to the final sale. Improved demand planning introduces agility into the supply chain, manufacturing, marketing, operations, logistics and sales, allowing you to adjust quickly as peaks and valleys are encountered.

Assumptions Aren’t for Forecasting.

When organizations bring a new product to market, they make many assumptions as to how much profit it will generate. One of the assumptions is that the right product will be delivered to the right customer in the right place at the right time without maintaining inventory that will become excessive or obsolete. This is the simple goal and if it can be achieved, profit and revenue will follow.

But this is also where the rubber meets the road: If the demand plan is right, potentially everything else in the supply chain will follow suit. But if the forecasting and demand planning is wrong, the wasted cost can be quite high, and revenue and profits will be lower. Over-forecasting leads to excess inventory and margin erosion; under-forecasting results in lost sales, expedited products, manufacturing snafu’s and many disappointed customers. Accurate demand planning helps you overcome the negative impact on downstream inventory and production plans; loss of sales; stock-outs; inaccurate capacity and resourcing; low service levels; excess inventory, and more.

CT Global’s SAS Demand Planning solution consists of Forecast Analyst Workbench, Inventory Optimization Workbench, Collaboration Planning Workbench, and Demand Signal Analytics (reporting). With the ability to create a highly accurate baseline forecast, demand and supply can be shaped at all levels of the product hierarchy.

Features of CT Global’s Demand Planning Solution Powered by SAS:

  • Create an accurate, scalable, and sustainable planning process
  • Model and simulate the financial impact of forecasts
  • Consensus forecasting balancing opinion with Forecast Value Add and data science
  • Optimize planning and efficiency with collaborative planning using advanced analytics. Collaborating with stakeholders allows you to combine statistical forecasting with domain knowledge to create an accurate consensus plan.
  • Accurately predict the demand and performance of new products using a combination of data mining, segmentation and clustering, statistical forecasting, and domain knowledge.
  • Across a multi-echelon distribution network, combine your demand forecast with inventory optimization and Capacity Planning to achieve lower inventories and increased customer satisfaction.
  • Override forecasts at any level to instantly see the impact changes will make across geographies, markets, and channels and product hierarchies.
  • Shape supply across the supply chain network with what if
  • Sense demand signals faster as marketplace demand changes. These demand signals can be downstream sales (such as POS data), price and promotion activities or even macro-economic factors such as unemployment or housing starts
  • Align demand and supply to improve customer satisfaction and retention
  • Include advanced features such as price/promotion evaluation and optimization, financial management, profit management, and optimization

SAS Demand Planning and Optimization (DDPO) is best suited for demand planners, forecast analysts, inventory analysts, buyers, and business users such as sales account managers, business analysts, finance analysts, and demand planning and supply chain management.

CT Global Solutions can deliver a demand planning solution for your company to create a foundation for accurate downstream plans and decisions. Our solutions deliver:

  • planning certainty and more accurate demand forecasts
  • a structured process
  • increased revenue
  • collaborative planning
  • visualization
  • improved service levels
  • lower inventory levels
  • more accurate capacity and resourcing plans
  • a higher quality of data
  • an automated data load
  • and advanced analytics and optimization.

We use the advanced predictive models from SAS that incorporate artificial intelligence (AI) and machine learning (ML) to create the most robust solution for your organization.

The Road Map to Success.

The implementation road map for demand planning creates a logical development of capability, business impact, and financial return that systematically builds the analytical maturity of the company. This typically happens in 4 phases:

Phase 1: While priorities may shift depending on the starting point, it is common practice to start with increasing the accuracy of the demand forecast to an acceptable level. This involves not just using robust forecasting models but automating the data feeds into the forecasting models. Forecasting, after all, is data-driven.

Phase 2: The second phase introduces consensus planning to streamline the communications between stakeholders and create the final demand plan that is sent to supply planning. Consensus planning builds the collaborative process and analytical support needed to sustain and scale the demand forecast to keep up with the growth of the business. This is where decision science and opinion are partners in creating the final demand.

Phase 3: Once the process is established for building the forecast and finalizing the demand plan, phase three adds advanced analytical capabilities to the demand planning solution. These additional capabilities include new product forecasting to increase completeness and accuracy and enable better downstream decisions.

Phase 4: The fourth and final phase is to optimize downstream decisions, including inventory replenishment, that depend on accurate forecasts. This is where the financial and business benefits of optimal decisions are realized.

There are financial and business benefits at each phase, with the highest return on investment (ROI) achieved at the highest level of maturity. The order of the four phases is recommended to build capability and maturity for successful demand planning. In some cases, the order may vary depending on specific client circumstances. For example, basic forecasting or data automation, may already be in place and impact the phasing of capabilities.



Demand Planning and Decision Optimization Maturity Model in Detail.

Phase 1: Data Driven Forecasts. Companies often start this journey with the recognition their forecasts are inaccurate. This may be inevitable given the prevalent use of intuition and spreadsheets and legacy systems lacking more advanced causal models that take into account price and promotion activity. It leads to a highly negative impact on demand fulfillment, supply chain cost, and loss of profit from inaccurate decisions.

It makes sense to start the demand planning journey to create a basic forecasting capability that builds confidence in the quality of the forecast and the demand and supply plans that are based on the forecast. When the baseline statistical forecast is more accurate, demand planners can pay more attention to exceptions, rather than touch and examine every forecast. Forecast Value Add Analytics guide the demand planner into the changes that may be needed.

Forecasting is a data driven process, and attention must be paid to accessing needed internal and external data and automating the data feeds needed to build accurate forecasts. Research confirms planners spend as much as 80% of their time accessing and moving data and jockeying spreadsheets. This doesn’t leave much time for analysis for the thousands of products and locations they are responsible for. This manual and inefficient planning process also negatively impacts management who may spend as much as 50% or more of their time reviewing and responding to the forecast results. How can demand planning be a strategic business partner with an inefficient process like this? This is the time to fix this problem and enable both accurate forecasting and shift work away from manual effort to value-added analysis.

Phase 2: Consensus Planning. Once the forecasts are built, they are shared with other stakeholder teams including sales, marketing, supply planning, and operations. A barrier to successful demand planning may be the lack of process for efficient and effective collaboration. This lack of process can be quite significant given that all the teams must meet in person to discuss the forecasts, share their plans, communicate changes, absorb changes proposed by other teams, and collectively agree on the final demand plan. This is never easy, but in companies where there are many SKU’s, distribution centers, plants, customers etc. there may be thousands or millions of forecasts needed which is an impossibility with an informal and manual collaboration process. In these situations, the planning process is not scalable or sustainable.

The SAS Demand Planning uniquely includes consensus planning. This is a process, user interface, and powerful analytical tool that replaces the difficult, time intensive, and drawn out collaboration process. Consensus planning automatically distributes the output of the statistical forecast to the different teams. It provides an intuitive way for each team to use the forecast to work on their plan and share their work in a standard format that can be easily absorbed by the other teams. For example, suggestions or overwrites can be proposed to the base forecast or planned promotions and reviewed by stakeholders. It is easy to convert these changes into a financial outcome to enable financial as well as business decisions. Importantly, it helps tie the demand plan to financial goals. This process supports the emergence of new consensus planning and financial scenarios with proven impact for review and approval by the teams.

Phase 3: Advanced Forecasting. Phase three builds on the more accurate forecasting of phase one, and the scalable infrastructure of phase two. It is now time to add capabilities that build on the knowledge gained in the first two phases, as well as leverage the new automated process to deploy advanced capabilities. Examples include new product and location forecasting and includes price and promotion activity as well as macro-economic data.

New product forecasting is a challenge because there is no data available to develop the forecast. Using analogous data from other products and locations and applied using ML embedded in the SAS software, New Product and Location Forecasts can be applied in a trustworthy fashion. The ML embedded in the SAS software selects the most analogous products and automatically updates the model as new data emerges

Promotions can also be a challenge because the data will reflect previous promotions which may not be repeated and fail to reflect planned promotions and price changes. Often promotions occur to the side and are not included in the demand forecast. This challenge is solved in this phase by including the forecast impact of promotions in the demand plan. This allows planning and marketing to perform shaping and simulation exercises to match demand with supply. It also allows marketing to understand the effects of marketing promotions and pricing elasticity.

Phase 4: Optimal Decisions. Demand and supply decisions that fail to use the robust forecasts and processes developed in prior phases will result in lower customer satisfaction, lower market share, lower revenues, lower prices, inaccurate production plans, more stockouts, higher levels of inventory and the wrong mix of product. Phase four is the time to fix these problems and grab the business and financial dividends that accrue from the investment in forecasting and infrastructure. This combines the power of advanced analytics to perform scenarios for both supply and demand so that alignment within organizations is a single version of the truth.

If done correctly, demand decisions will reflect market potential, and also yield optimal inventory levels and mix to profitably fulfill the market potential. The goal is to create certainty on the supply side and successful business execution on the demand side with the highest possible ROI. Examples of optimal decisions include:

  • Price, Promotion and Revenue Management Optimization
  • True Multi-Echelon Inventory Optimization to optimize inventory across the end to end supply chain including instant what if simulations and Supply Management
  • Managing expiry dates to increase inventory turns and reduce obsolescence.
  • Managing Interchanges, including Phase In/Out, Serial numbers, Substitutes. Etc.
  • And much more… just ask!


SAS is the leader in analytics and demand planning. Through innovative software, SAS empowers and inspires customers around the world to transform data into intelligence. SAS gives you THE POWER TO KNOW®.

CT Global Solutions is a strategic SAS partner that helps turn your data into profits. CT Global amplifies the value of SAS using its expertise in financial modeling, demand planning, and decision optimization. CT Global puts SAS to work to MAKE EVERY DECISION COUNT.

For more information, contact CT Global at

Download a version of this article, The Demand Planning and Supply Chain Optimization Roadmap.


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