Cognitive Supply Chain Market Size

Cognitive Supply Chain Market Size

Cognitive Supply Chain Market size was valued at USD 7.5 billion in 2022 and is anticipated to register a CAGR of over 16% between 2023 and 2032. The booming e-commerce industry with its increasing supply chain demands is fuelling the market growth. Cognitive technologies optimize operations by processing vast amounts of data in real time, enhancing inventory management, predictive analytics, and demand forecasting. According to the International Trade Association, China is the largest e-commerce market globally, generating almost 50% of the world’s transactions. In 2021, China led the e-commerce market with a revenue of USD 1.5 trillion, placing it ahead of the U.S.

To get key market trends

  Download Free Sample

Companies across industries recognize the need for real-time visibility, data-driven insights, and agile decision-making in volatile environments. The COVID-19 pandemic highlighted the need for investments in predictive analytics, demand forecasting, and adaptive inventory management to enhance resilience and ensure consistent customer service. The pandemic underscored the significance of a connected & digital supply chain.

For instance, in August 2019, Nike's proactive investment in advanced supply chain technology including the acquisition of Celect, bolstered the company's resilience during the pandemic. Celect's predictive analytics allowed Nike to foresee the decline in physical retail sales and swiftly reallocate inventory to e-commerce fulfilment centers. This adaptability enabled Nike to meet the evolving consumer demands, emphasizing the invaluable role of digital supply chain solutions in maintaining operational efficiency and customer satisfaction, particularly in disruptive times such as the pandemic.

The high cost associated with developing and deploying cognitive supply chain solutions presents a significant challenge. Building & implementing advanced technologies, such as Artificial Intelligence (AI), Machine Learning (ML), and IoT, demands substantial financial resources. Moreover, the integration of these technologies into existing supply chain systems requires time and expertise, increasing the overall expenses. Many organizations, especially smaller ones, may find these costs prohibitive, impeding the wide adoption and potentially limiting the market's growth potential.