How to Reduce the Bullwhip Effect by Following a Demand-Driven Supply Chain Strategy

  • by Pierre Erasmus, Program Lead and Consultant, SAP
  • June 8, 2016
Learn how to integrate consumer demand signals to achieve a demand-driven supply chain that is used to optimize short-term forecasts and increase demand network visibility. From a demand planning perspective, planners can now compare the traditional consensus demand forecasts with a point-of-sale (POS) statistical forecast and therefore apply corrections based on true consumer demand. From a demand network perspective, demand managers can now visualize the entire demand network, including the retailer distribution center, store stocking, and consumption points, and use this information to increase visibility to reduce the bullwhip effect. Extending their demand network visibility beyond their own supply chains allows organizations to optimize inventory, reduce out-of-stock lost sales opportunities, and increase profitability.
Learning Objectives

After reading this article, you’ll know how to:

  • Adopt a demand-driven supply chain strategy to introduce improved short-term forecasting based on true consumer demand signals as part of your demand planning processes
  • Extend your supply network visibility by including external consumer demand and improve downstream planning and profitability
  • Proactively react to high-value demand fluctuations via alerting your supply chain organization of lost sales and out-of-stock opportunities
Key Concept

The bullwhip effect is a distribution channel phenomenon in which forecasts yield supply chain inefficiencies. It refers to increasing swings in inventory in response to shifts in consumer demand as you move further up the supply chain, closer to the end user referred to as the consumer.

With an increasingly competitive market, manufacturers are competing for market share through brand loyalty, pricing, product innovations, and their ability to streamline their supply chain operations. Within mature markets manufacturers often have to optimize their supply chain operations by minimizing out-of-stock lost sales situations to increase revenue while decreasing waste overstock to reduce cost and ensure profitability. Research has shown that a constant out-of-stock situation affects a manufacturer not just in terms of lost sales opportunities and loss in revenue but also loss in market share as the consumer easily moves to a similar or competing brand. I provide insights on how to reduce the bullwhip effect by increasing demand visibility.

Demand visibility is obtained by extending the traditional view of supply networks beyond the manufacturer’s own supply chain to include its customers’ supply chain consumption and stocking points into its own supply network. This strategy increases extended demand visibility and provides a further opportunity to correct and improve the accuracy of supply chain planning, ideally reducing the bullwhip effect.

Traditionally, demand planners created a consensus demand plan with a forecast based on internal data, such as sales and shipment orders. This forecast focused on the assumed demand based on shipment to the retailer or distributors’ warehouses or distribution centers. If you revisit the traditional approach of forecasting within demand planning, you soon realize the limitations and issues related with this approach, described as the bullwhip effect.

You can reduce this bullwhip effect by increasing demand visibility and correcting or aligning your demand plan based on the current and short-term prediction of “true” consumer demand that could be achieved with an integrated, demand-driven supply chain strategy.

I show you how demand visibility can be achieved by integrating SAP Demand Signal Management with SAP Integrated Business Planning while using SAP Supply Chain Control Tower analytics for visualization.

I focus on how to derive harmonized point-of-sale (POS) data from SAP Demand Signal Management into SAP Integrated Business Planning for demand and the SAP Supply Chain Control Tower. The SAP Supply Chain Control Tower is the analytics module that is part of SAP Integrated Business Planning. I also show you how to use these demand signals in SAP Integrated Business Planning for demand to create a POS statistical forecast and to use this to correct your consensus demand forecast. I demonstrate how to create a demand network to obtain an end-to-end visibility in the Supply Chain Control Tower.

To learn more about optimizing supply chain processes with SAP Integrated Business Planning, attend the SAPinsider IBP Bootcamp, which will be held in Copenhagen April 17-19, 2018. For more information about this bootcamp and to register early, click here.

Problem Statement

The bullwhip effect is causing continuous out of stocks that lead toward a decrease of market share for manufacturers.

With a demand-driven supply chain strategy, you can increase demand visibility and react in time to rapid market fluctuations.

Using SAP Demand Signal Management, you can generate demand signals and integrate the downstream demand data from your consumer demand. Using the Supply Chain Control Tower, you can increase your demand visibility via a demand network. Using SAP Integrated Business Planning alerts, you can react to out-of-stock situations before they happen. Using SAP Integrated Business Planning for demand, you can correct your short-term forecasts using the downstream demand signals.

Integrated Supply Chain Architecture

With an integrated supply chain architecture organizations can leverage synergies across SAP solutions, departments can share data using a single source of truth, and supply chain processes can be optimized. Collectively, this also equips organizations to communicate more effectively while creating transparency between functional and business roles. With an integrated supply chain architecture, business planning becomes less cumbersome. You can reduce efforts for preparing and sharing data across roles and for simulations.

Figure 1 shows what an integrated demand-driven supply chain architecture could look like. SAP Demand Signal Management is used to upload and harmonize POS data from retailers. The harmonized POS data is then used by downstream planning systems, such as Trade Promotion Optimization (TPO), SAP Advanced Planning and Optimization (SAP APO), and SAP Integrated Business Planning, to optimize planning forecasts to align with changing consumer demand. Additionally, the SAP Supply Chain Control Tower is used to create visibility, combining both internal data such as shipping and sales orders with external demand signals coming from SAP Demand Signal Management. 

Pierre Erasmus

Izak Pierre Erasmus is practicing as program lead and consultant at SAP. He specializes in supply chain planning, demand management, solution management, and architecture. As a supply chain consultant, he has led several SAP Demand Signal Management implementations across the United States and Europe. The implementations were often based on the integration of external demand signals derived from data such as point of sale (POS), Nielsen, IRI, social media, weather, and planograms. He also acts as the global program lead for the SAP Model Company, a program designed to move strategic on-premise solutions to the cloud with a subscription price model, whereby the components are preinstalled, associated with best-practice configuration and integrated. The design is focusing on integration between multiple solutions to derive synergies between different functional departments, often hidden in silos of data. Today, there is a hybrid focus of mixing on-premise solutions with cloud solutions. Pierre took an architect and leadership role within SAP around the integration of demand-driven supply chain, advance forecasting, and demand network topics. He has a business intelligence background that includes experience with SAP Business Warehouse (SAP BW), SAP HANA, BusinessObjects Data Services and Analytical (BusinessObjects dashboards, Design Studio, and Lumira). Other areas of interest include research on phantom stock detection, trade promotion optimization, integrated business planning, shelf management, and advance forecasting algorithms.

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