“Build robust digital supply networks instead of linear models”

By Vikas Kumar

The manufacturing industry is currently facing multi-pronged challenges. The pandemic has caused a severe disruption in supply chains. In fact, the disruption in the supply chain is the stress test that caught most organizations by surprise and offered very little time to prepare.

Overall there has been a decline in business and organizations have had to contend with sharp spikes and declines in consumer demand, production downtime, and supply and transportation delays. This, coupled with the threat of global trade wars, is keeping large organizations on their toes. The sub-sectors that have experienced a major impact include consumer durables, pharmaceuticals, telecom, power, chemicals, and agro-chemicals sectors.

Rising fuel and energy prices are also impacting the sector in multiple ways – increasing the manufacturing and logistics costs and impacting sales in sectors such as automotive. This is in turn is leading to a ripple effect on the components industry as well.

The pandemic-induced repatriation of the workforce from industrial hubs to the hometowns in the interiors of India is also impacting manufacturing productivity and output. For specific industries such as electronics appliances and automotive, the ongoing global shortages of products such as semiconductors are further impacting production output and p roduct prices. Worse, traditional sales and distribution models are limiting the reach and market addressability.

Use of advanced technologies like IoT and sensors, AI/ML, edge computing, and 5G network infrastructure enables factory operations to become demand oriented.

From a different perspective, however, ‘revenge buying’ or ‘revenge spending’ in some aspirational product segments like appliances, electronics, and automobiles are likely to bolster sales in the near to medium term.

The solution

It is important that organizations de-risk the supply chain through decentralization. It also needs to identify alternate sourcing channels and build robust digital supply networks instead of the traditional linear models. A Capgemini research reveals that 68% of organizations are actively investing in diversifying their supplier base and 62% are diversifying their manufacturing base.

There is also a need for organizations to focus on new-age technologies, as well as rebuild and revamp the focus on centralized decision making and process automation along with technologies such as cloud, internet of things (IoT), big data and analytics (BDA), artificial intelligence (AI), and cybersecurity that are pivotal to enabling value chain visibility.

Philip Morris International (PMI), for example, has developed a digital twin of its entire global manufacturing footprint. This allows the company to assess the impact of changes in regulation, changes in the product portfolio, or even business disruption on a monthly basis versus only on a yearly basis

Manufacturing organizations will also need to embed sustainability in the DNA rather than considering it as an option – prudent energy monitoring and management mechanisms and increased usage of alternate sources of fuel and energy. Unilever has identified that the seven brands with the highest turnover in the company are all Sustainable Living Brands and they grew 69% faster than the rest of the business

Companies need to invest in omnichannel sales and distribution to maximize their reach and minimize the impact of disruptions due to situations like the ongoing pandemic.

Companies need to invest in omnichannel sales and distribution models to maximize their reach and to minimize the impact of disruptions caused due to situations like the ongoing pandemic. PepsiCo launched two direct-to-consumer websites – PantryShop.com and snacks.com – to meet customer demand. The websites, built from the ground up in less than a month, offer consumers direct access to some of Pepsi’s top-selling SKUs.

The technology trends

• Advanced analytics and AI-enabled supplier risk forecasting: Predicting risks in advance and use machine intelligence to identify and implement mitigation plans like new supplier recommendations, alternative material recommendations, etc.
• Digital transformation on the factory floor to become more resilient: Wide prevalence of advanced technologies like IoT and sensors, AI, machine learning, computer vision, robotics, advanced analytics, augmented and virtual reality, cloud computing, edge computing, and 5G network infrastructure, etc. This enables factory operations to become demand-oriented, data-driven, and digitally executed.
• Focus on smarter products to provide alternate revenue models: Use of IoT, cloud computing, and advanced analytics to gather and process large volumes of data and information from products. This provides ample opportunity for manufacturers to devise new subscription models that deliver recurring revenue (and data). For example, the use of sensor data from appliances for designing customized warranty packages.
• Digital (Amazon-like) brand experience for customers: Seamless experience across digital and real touchpoints like a digital catalog with 3D DMUs (Digital MockUps), chatbot, AI-enabled product guide and recommendation, AR/VR virtual experience with infinitely many customizations, etc. This is especially applicable in industries like automotive, electrical appliances, machinery, and equipment, etc.

Kumar is Vice-President, Capgemini Invent India