Today’s CPG, manufacturing, and retail enterprises are facing their Netflix ‘point of inflection.’ A media company that started as a DVD-by-mail rental service, was faced with the winds of change when the digital streaming era emerged on the horizon. Much like the industries in the CPG and manufacturing sectors, Netflix faced the challenges of changing consumer preferences and the rise of digital technology.

Netflix provides a textbook case study on successfully embracing change. Enterprises for whom the supply chain is the lifeline of their business will do well to draw a leaf out of Netflix’s story that predominantly revolves around ‘The Data First Approach.’ Supply chain disruptions, such as those caused by the COVID-19 pandemic, natural disasters, or geopolitical events, have exposed vulnerabilities in global supply chains. Omni-channel retailing has introduced a new layer of sophistication. All these changes in the landscape have prompted a revisit to make the supply chain more agile and resilient.

The increasing need to sense demand Real-Time

One, ‘customer demand’ has always been at the crux of how a supply chain works. Supply chains commonly operate in the de facto mode of using last year’s demand for demand forecasting. However, disruptions caused by COVID-19, and turmoil across key geographies have played havoc with the supply chains. It has exemplified the over-reliance on demand forecasting and subsequent lack of resilience and agility in supply chains to bear the brunt of disruptive events.

Two, the increasing need to cater to both in-store and online customers and the whole gamut of new fulfilment methods like BIS, BO, BOPIS, ROPIS, BORIS, and BOSFS, have stretched the prediction models in new directions. Demand forecasting and inventory placements just got more complex!

The case for demand-sensing

Demand sensing, unlike demand forecasting, uses data acquired, days and even just hours ago to make spot-on short-term predictions about the demand for goods and services. This is imperative for customers and brand owners in the fast-moving CPG, Manufacturing and Retail space.

Getting demand sensing to work for you

Only a data-first approach powered by end-to-end digitalization allows swift and seamless data sharing with all stakeholders in a product’s ecosystem. Suppliers upstream can track stock, plan, and expedite inventory replenishment across stores by gaining near-instant access to ‘customer demand’ data.

For instance, large retailers like Amazon and Walmart (with its Luminate program) are moving away from portal download to sharing the data with brands in near real-time through API-driven digital integrations. Enterprises like Carrefour and Instakart are leveraging cloud platforms and big data stores for the same.

The number one challenge faced by enterprises is the heterogeneity of data and data sources across the stakeholders. Procuring data from hundreds of channel partners across geographies, each of whom uses disparate systems and software, is a mammoth task. Having obtained the data, next comes the task of scrubbing the data and transforming it to make it consumption-ready to enable upstream suppliers to extract insights from it.

The second challenge is the new need for enterprises and brands to run large, heavily staffed, complex IT programs at scale to continue to reap the benefits of demand sensing.

You can address both these challenges by using off-the-shelf AI-powered demand-sensing solutions. These customizable solutions come with inbuilt capabilities, domain expertise and enable automation, thereby reducing the time and effort needed to acquire and process data. They provide brands and enterprises with rich insights that can be translated into action.

Improving Outcomes with Real-Time Data

Real-time data is the real deal! Real-time data is like the lubricant that does away with the friction points in your supply chain. It drives home at least five distinct advantages.