Transformation potential of AI - some use cases for you!

Artificial Intelligence (AI) stands out as a transformational technology. Introduction of AI can transform many business processes in companies for efficiency, productivity and customer service excellence. These use cases often will have wide-ranging implications for many stakeholders – management, employees and its customers. In this multipart series, I intend to explore application of AI in industry specific segments and its implications.

Along with Internet of Things (IoT), Artificial Intelligence has immense potential in transforming business operations throughout the supply chain and customer service. Applying Machine Learning (ML) to the machine generated data and combining the data from external sources like logistical providers, distribution channel and ERP systems, companies can accelerate automation, thereby productivity, efficiency and predictability of operations. Likewise, with Natural Language Processing (NLP) capability, companies can drive better customer experience for internal (employees) and external users (customers). Organizations can increase their scale and reach by transform their repetitive business processes leveraging robotics. These AI initiatives while ensuring cost leadership, it opens up additional avenues for value creation and product differentiation.

Supply Chain Transformation:

Due to varying levels of process maturity, home grown tools, different manufacturing equipment and associated applications, multiple region specific ERP systems, etc., a typical company has its supply chain process not tightly integrated. Tightly integrated process flow with a seamless human-machine interaction will reduce overall product delivery times, reduce waste, reduce costs, improve safety in hazardous operations, and productivity. Artificial Intelligence can be applied to several processes leading to supply chain transformation.

Picking and slotting process at the warehouse: Machine Learning with supervised learning can be employed here to analyze highly productive worker profile and the output generated. ML algorithms with robotics can replicate the most productive worker profile and arrange goods in the best order to be picked, thereby enabling everyone in the floor achieve the efficiency of the best productive worker.

• Equipment utilization: Typically there is linear relationship between the output and machine capacity. But Machine learning algorithms can predict outcome combining several nontraditional inputs like employee productivity, environmental data, order forecast etc. and predict equipment capacity needed at the distribution floor.
• Quality control: Visual imaging of good and defective products from different angles and combining with sensor data at the component level, machine learning can improve quality of production. The sources of error can be determined earlier in the process and their corresponding parameters can be altered and continuous engineering is achievable with AI.
• Sterilization Process: Usage of autonomous bots will improve accuracy, eliminate rework and reduce product rejection, by providing end-to-end visibility and remote inspection capability. While freeing up workforce from this typically a hazardous operation, it improves the cycle time, thereby improving the product delivery timelines. 
• Inventory management: ML systems can flexibly adapt to changes in the production or the distribution network due to unforeseen events. Algorithms can incorporate historical sales data, real-time data, advertisement campaigns, prices, or local weather forecasts to maintain just-in-time inventory. Counting by AI enabled drones can further automate inventory management with accurate supply-demand reconciliation, reducing the on-hand inventory.
• Equipment maintenance: The embedded sensors in the manufacturing equipment generates petabytes of data. The individual measuring points can produce a digital image of the current state that can be used to train machine learning models, to detect anomalies. 
• Floor management is typically chaotic with individual processes working in silos. IoT, Data Lake and analytics will facilitate data integration and reduction of bottlenecks. With voice operated controls through NLP application (virtual assistants) at the floor, company can further improve productivity by enhancing human-machine and human-human interaction.

Customer Service Transformation:

• Multilingual support: Companies can save operational costs by employing machine translation along with voice interface. While this will help cut down IT costs, it will improve customer experience.
• Product Support: Companies can build mobile applications with natural language capability, to help product demonstration, and voice activated refills. Voice activated interface through the native assistant in the phones – Siri, Cortana, Alexa, Google Assistant etc., along with analytics to deliver the best customer experience. This will help company to differentiate themselves in the market, provides the feedback loop to its R&D, and at the same time reduces product support cost.
• By adopting NLP at the order fulfillment process, organizations can improve the customer experience, reduce rework and calls to call center. 

The above initiatives will transform individual process areas within supply chain and customer services segments. There are several such processes in a typical industry or business unit that can benefit from Artificial Intelligence led transformation. The above list is just a sample.

With AI, companies can leverage their core assets and deliver better value to its customers. Selective and thoughtful investment in AI maximizes the outcome of Cloud, IoT and Edge Computing. .A well-constructed digital strategy, advances company’s strategic goals.

I will discuss some of the foundational elements needed for AI enabled digital transformation in the next article of this series.