Separate Demand Signals from ‘Market Noise’

Bring Precision to your Forecasting

Multivariate Demand Signal Management isolates actual demand signals from inconsistent market activity and improves forecast quality.

Multivariate Demand Signal Management (MDSM) brings order to the chaotic flood of structured and unstructured market data a business generates and consumes, revealing complex patterns and helping planners separate actionable demand signals from “market noise and nonsense”.

Perhaps your response to this is a shrug, followed by, “Sounds great, but it’s not applicable. My company’s planning function isn’t disrupted by irrelevant inputs because we base replenishment on shipments, some seasonal adjustments and a dash of gut feel, nothing more.”

If so, then you should know this: the race is on to deploy the capabilities required to convert large, complex and disparate sets of downstream market data into insights that can improve execution across the organization. You can be proactive and use MDSM to explore and analyze data you already own, model additional data sources that could help explain demand variability… or sit by and watch.

What exactly is Multivariate Demand Signal Management?

Let’s start at the beginning. What Multivariate Demand Signal Management is not is a replacement for demand forecasting. In fact, it augments core demand forecasting solutions by providing more insight to improve forecast quality and demand response. MDSM actually sits between mid- and long-range planning (often accomplished using aggregate time-series techniques) and extremely short-term Demand Sensing technology (“What should I ship today?”).

The Logility Multivariate Demand Signal Management solution is an enterprise application for integrating all relevant demand signals into a single source of truth and performing predictive and visual analytics that improve forecast quality and uncover insights to make decisions ahead of the demand curve. As such, MDSM is a more proactive approach than basing inventory positions and replenishment schedules on shipment data – it gives better access to downstream data, analysis and the insight to make more informed decisions.

More than forecasting trends and seasonality, it is about identifying and measuring market signals, then using those signals to shape future demand. Imagine understanding market dynamics well enough to move beyond simply reacting to actual influencing outcomes!

The benefits offered by Logility Multivariate Demand Signal Management

In short, forecast quality and consensus planning will greatly improve as a result of clearly understanding how market forces correlate with shifts in true demand. Multivariate Demand Signal Management delivers visibility and collaboration tools to create a direct view of real demand from customers as it happens, using this as intelligence to be fed back up through the supply chain.

Let’s consider a real-world application. You discover that during the period in question, your company’s sales into the retail channel are significantly higher than sales reported using POS data. Ultimately, the problem is a combination of an overly optimistic forecast and the wrong product mix. Inventory increased as overall customer service levels fell, and margins are set to erode as product is put on clearance.

For the planner to avoid this situation in the future, a deeper understanding of causal factors is required.

For example, a planner using Logility Multivariate Demand Signal Management to explore and analyze data related to products, stores, territories, inventory, promotions, pricing, raw material price fluctuations, competitive activity and consumer sentiment, would discover that several brand extension launches were hampered by aggressive competitive activity, negative reviews in social media, raw material shortages and shipping delays. The planner could then immediately model adjustments and commit an improved forecast back to the master demand planning system.

Some things to consider

Take note that MDSM works best in certain environments and does present some challenges. Industries such as Fast-Moving Consumer Goods, Retail, Pharmaceuticals, High Tech and Chemical are good fits because they tend to share the same characteristics: inventory-driven, consumer-centric, price sensitive, with a reliance on global supply chains, and highly influenced by external factors like commodity prices. And it follows that they often share similar business and operational goals: cycle-time reduction, improved fill rates, improved customer service, better cash flow, elimination of over/under stocks, and waste reduction.

As for challenges, the primary consideration isn’t access to the right signals for your business; it’s defining data storage and management strategies to ensure all relevant data can be gathered, housed, structured and blended appropriately. From there, the Logility MDSM engine allows users to explore causal models and discover patterns in the data. Powerful visualization tools allow users to understand the impact of what-if simulations. The final step is integration back to your master demand planning systems.

In summary, blend the data you have with the data you need and improve the quality of your forecasts with Logility Multivariate Demand Signal Management.

Daniel Bachar

Written by

Daniel Bachar

Short bio

Daniel Bachar is a Product Marketing Director for Advanced Analytics for Logility. Daniel brings more than 10 years of experience in sales, marketing, supply chain planning, and advanced analytics. He provides a unique blend of business and industry knowledge, leading successful efforts to integrate new technologies into effective supply chain solutions. His experience includes development, design and go-to-market strategy of supply chain and advanced analytics products, helping clients with complex business problems to achieve complete visibility into their supply chain operations. Supply Chain Brief

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