Mitigating demand volatility to improve forecasting: an intelligent workflow from IBM and SAP

Organizations are continuing to emerge from the lingering effects of the pandemic and ongoing supply chain disruptions. They are focused on reviving their strained supply chains and trying to understand their vulnerabilities and risk areas. What most are finding is that volatility remains in full force and continues to have detrimental impacts on planning and executing their supply chains.

One of the ways the SAP and IBM partnership are helping clients is through the joint creation of a new supply chain solution for demand planning. This unique fusion of SAP Integrated Business Planning (IBP) and IBM machine learning algorithms and proprietary data sets can help companies better manage volatility in the supply chain, even in the case of unforeseen disruptions like a pandemic.

The IBM Institute for Business Value’s Smarter Supply Chain Study details why this intelligent workflow solution is needed. When asked how challenging the pandemic had been for planning demand, 62.7% of supply chain executives answered, “Extremely Challenging.” And 64% of the same group said “Demand Volatility” was an extreme challenge to deal with in current conditions. The results from this study paint an all-too-familiar picture of how disruptive events in the supply chain lead to pronounced volatility, which has a bullwhip effect throughout the remaining areas of a supply chain. All this volatility comes at a steep price: for example, out-of-stock conditions alone amount to nearly USD 1 trillion in lost sales every year.

This is why IBM and SAP teamed up to develop helpful intelligent workflows as part of our evolution partnership. The first workflow centers on this demand volatility challenge and provides key demand-sensing capabilities to help companies better forecast for short-term planning. Here’s how it works:

SAP IBP generates a traditional, time-series-based forecast that accurately projects demand across the mid- to long-term planning horizons to help clients gain visibility into future needs.
IBM’s Demand Sensing Intelligent Workflow focuses on the short-term horizon and “senses” recent, actual demand signals and their potential influence on the forecast to limit the chance of surprises. It analyzes both internal data sets (such as point-of-sale (PoS) and warehouse withdrawals) and external data (such as weather forecasts, IBM’s COVID-19 Risk Index and social sentiment) and draws correlations between these factors and the forecast.
It then quantifies these correlations into short-term forecasts and passes them over to IBP through a custom integration layer. Our approach produces a comprehensive demand plan that is accurate across all planning horizons and helps to better account for volatility by incorporating a much larger collection of data sets than typically used.

By using this intelligent workflow, companies can expect benefits including:

Improved forecast accuracy by 20–30%
Reduction of inventory levels by 5–10%
Detection of early signs of disruption and shifts, and a better understanding of their interdependencies and impacts
Reduced stock-outs, leading to higher customer satisfaction

There are more supply chain disruptions on the way and the resulting volatility that accompanies them. Is your supply chain ready? Is your organization struggling with demand volatility and mitigating it in your demand plans? Is supply chain volatility impacting your ability to meet customer needs? Make your digital transformation a reality by bringing intelligence to your entire enterprise.

The post Mitigating demand volatility to improve forecasting: an intelligent workflow from IBM and SAP appeared first on IBM Business Operations Blog.

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