The demand-driven supply chain you need now

In the quest to build a responsive and profitable supply chain, companies have long been trying to predict demand accurately, so they can sync production, purchase and inventory with market needs. Yet despite spending millions of dollars in best-in-class planning platforms, forecast inaccuracy is still plaguing many industries.

The image below depicts the three-stage evolution of forecasting methods:

Stage 1 Traditional forecasting took a top-down approach. It forecasted sales—not demand—at an aggregate level, then disaggregated to lower levels, leading to poor results.

Stage 2 Consensus forecasting took a bottom-up approach, using statistical models to forecast demand and collaborating with cross-functional teams to arrive at one plan. This was the first major step towards a demand-driven supply chain.

Stage 3 Demand sensing takes short-term demand data to improve the accuracy of forecasts based on the current realities of the supply chain.

Markets are becoming highly competitive—and that makes demand-driven supply chains vital. Demand variability, low customer loyalty, changing personalization needs across markets, and increasing supply chain complexities are all part of the picture today. Responding to these changing market needs calls for faster planning—from months to weeks to day to hours—and tighter integration between planning and execution.

Integrating demand-sensing analytics with supply chain planning holds the promise of linking planning and execution by sensing real-time demand signals from markets.

The chart below depicts how firms can harness this data to generate insights. A "Sense—> Analyze —> Respond" model, for example, can help you course correct when there are demand-supply gaps, plan promotion timing, and make allocations.

A three-pronged approach

The following actions power a holistic and precision-driven supply chain.

Sensing
At the simplest level, sensing is capturing POS or sell-through data and key demand-shaping drivers, such as trade promotions and marketing campaigns, from internal data sources. Sensing can also include external market data sources, such as social media, weather data, and competitive intelligence.

The Internet of Things (IoT) and big data technologies make it possible to capture and store real-time granular data that analyzes the latest patterns and shifts for effective demand-sensing insights. For example, IoT can capture real-time POS data, analyze the latest rate of sale across channel partners, and track real-time inventory levels across the supply chain. That prevents costly out-of-stock situations and frees you to make smart allocation decisions, deploying inventory to the right distribution centers and markets.

Analyzing
Once you have the data, you can test various hypotheses using statistical analysis to better understand demand drivers. This analysis will help you determine drivers by markets and product portfolios, and by demand shifts over time—all useful for predicting short-term demand for forecast adjustments.

Questions that this can help answer include:

1. Is there a significant misalignment between demand and supply?
2. How can we align/adjust forecasts, production, deployment, allocation, etc., based on the latest demand signals from the market?
3. Is there a correlation between demand and customization or personalization?
4. Is there a significant shift in demand across product portfolios and markets?
5. What are the key drivers of demand in each market and across product portfolios?

Portfolio alerts can monitor key drivers influencing demand or demand-supply gaps, based on the results of statistical analysis. Such analysis would examine the causal relationship between demand and levels of personalization, type of promotion, depth of promotion, promotion placement, pricing, forecast accuracy, forecast bias, and so on.

For example, an alert can monitor stock coverage increases or decreases beyond established control limits. It can check demand variability or shifts, such as whether actual vs. forecasted values for the last three periods fall outside of two times calculated demand standard deviation. The alert can also signify if there is forecast bias—for example, if the tracking signal for the last three periods is either over-forecast or under-forecast (greater or less than 4.5). In addition, alerts let you study daily or weekly changes in demand and supply components to generate demand-sensing insights.

Responding
Different teams can use portfolio alerts, demand-sensing insights, and short-term forecasts to respond faster to demand signals.

• Sales and marketing: By understanding the demand drivers and demand shifts, these groups can run or tweak demand-shaping programs.
• Supply chain: By predicting short-term demand, the supply chain can adjust forecasts, production, deployment, and allocation plans to satisfy demand and push out excess supply.
• R&D and product development: By studying the correlation between product attributes, demand, and consumer behavior, these teams can develop or modify products to fit market needs.

If you're planning a demand-driven supply chain journey, begin with what you have. If you're collecting POS data (e.g., top-channel partners) or end-customer orders, this could be a great starting point for demand sensing. If you're collecting POS/orders data monthly, the next step might be to collect them weekly, then daily, and so on. The more granular the data, the better it is.

To stay on top, you've got no time to lose

Any industry can borrow the concept of demand sensing; but if you're operating in a fast-moving one with a dynamic supply chain, such as in the field of consumer products, consumer electronics, and automotive, you should start your journey now.

Typical benefits that you can realize using demand-sensing analytics are:

• Up to 50% improvement in forecast accuracy
• Up to 70% reduction in excess or unproductive inventory
• Up to 15% improvement in customer service level

It's time to explore demand-sensing analytics to build a responsive, demand-driven, and profitable supply chain.

This blog is co-authored by Sudhanshu Singh, Senior Vice President and Chief Operating Officer, Analytics and Research; Mani Iyer, Assistant Vice President, Analytics and Research; Vikram Sethi, Assistant Vice President, Analytics and Research at Genpact