How To Apply Automation & AI To Navigate Supply Chains As A Manufacturer

The growing pressures of globalization, product complexity, numerous suppliers, market competition, and customer demands, mean manufacturers must continuously look for ways to better navigate supply chains.

Few things hold greater promise of transforming supply chains than automation and artificial intelligence (AI). A McKinsey survey found that 61% of executives reported reduced costs and 63% experienced increased revenues after introducing artificial intelligence to their supply chains.

But reaping the benefits of supply chain automation and AI takes more than acquiring sophisticated systems and then hoping for the best. Success is contingent on a well-thought-out plan that addresses key pain points. So how do you apply automation and artificial intelligence? We share some ideas.

1: End-To-End Visibility

Dependence on manual monitoring and third-party action can obscure the state of your supply chain. It relegates you to acting on days, weeks, or months-old and perhaps no longer relevant data. Automation and AI enhance the visibility of the supply chain and do so in real-time. You get to eliminate the shortcomings of human intervention. A KPMG survey of supply chain executives found 62% plan to deploy or test AI to increase supply chain visibility.

AI systems can run continuous data crawls of diverse applications and then aggregate it all to form a single, coherent source of truth. The end-to-end visibility can be transformed into contextual signals that would be the trigger for predictive business actions and the mitigation of emerging business risks. Expose hidden bottlenecks and less-than-obvious opportunities for improvement. For example, AI systems could promptly reallocate work when an employee is suddenly unavailable.

2: Business Analytics

Automation and AI can perform the heavy lifting required to run extensive analyses at the organizational and SKU level. With AI, you have the abstract reasoning, creative capabilities, and problem-solving power of humans that’s turbocharged by the precise, large-scale, real-time data collection of automated systems. A Gartner survey of nearly 700 executives reported that more than 80% of respondents believed automation and AI could be applied to any business decision.

Extract data at both macro and micro levels. Sift through large volumes of data and identify patterns. Auto-organize structured and unstructured information originating from multiple applications and platforms. Develop a multi-platform system-wide view that evaluates the strengths and status of supply chain components like workers, robots, and other equipment. Quickly identify the root cause of a problem within a region, facility, or line.

Make fast, timely decisions that get it right the first time. Weigh the implications of various decision scenarios in terms of revenue, cost, and time. For example, increase or decrease prices based on product life cycles, demand trends, and competitor moves.

3: Inventory Management and Demand Forecasting

Fresh, accurate, and accessible inventory data is essential for smart decision-making. Use automation and AI systems to pull all pertinent information into your inventory management system. Keep track of supply levels of raw materials, partially processed products, and end products in real time. Implement sensors and RFID chips to choose automatic record and replenishment points that maximize restocking and visibility.

Apply automation and AI to forecast the demand and develop more accurate predictions. And it’s not just about the seasonality of future sales but also predictions on product decline and/or end-of-life. Automatically track ROI, sales, customer performance, and other financial statistics without having to spend inordinate amounts of time on manual inventory tracking. You can even automate invoice processing to make accounting easier. Send or receive raw materials, input parts, and product orders.

4: Supply Forecasting

Automation and AI can be a means of discovering and predicting future trends which would be the basis for forecasting the inflow of factory inputs. That way, manufacturers can reduce the risk of overproducing low-demand items and underproducing high-demand products.

Structure material bills on time. Scale up supply chain infrastructure on demand with solutions such as robots-as-a-service (RaaS) in tandem with seasonality or forecasted shifts in supply. The past few years have shown a need for dynamic changes to forecasts, especially with disruptions in one location impacting each link in the supply chain. Adapting to actual lead times, instead of those promised in SLAs and other agreements, is a strong hedge against multiple production pressures.

5: Warehouse Management

Automation and AI shrink the amount of manual and/or labor-intensive warehouse processes. That reduces the need for new hires of staff. Cutting down human involvement flattens the productivity curve by minimizing the disruption and fluctuation that characterizes human labor. Automation and AI tools can support worker productivity where manual operations must remain. An Accenture study estimated that AI could boost labor productivity by up to 40% by 2035.

AI-driven warehouse management systems (WMS) facilitate smarter organization and planning. You get better warehouse efficiency as well as a boost in the safety of workers, equipment, raw materials, and products. AI-powered WMS could automatically find the right package type and size for each product. And for fragile or light-sensitive items, it could point out the most appropriate place to store them in the warehouse.

Warehouse robots such as self-driving forklifts can be tasked with diverse activities ranging from picking and sorting, to packing and storing. But most robots are not AI-driven. AI improves the mobility and precision of warehouse robots. The result is intelligent collaborative robots that can work with humans interactively.

6: Transportation

Automation and AI lower unplanned fleet downtime, optimize fuel efficiency, expose bottlenecks, and surmount barriers. You can find the most efficient path to or from a node. Auto-detection of patterns in the departure and arrival of goods helps you automatically identify the most efficient warehouse for the next immediate batch of items.

Automate route optimization to allocate the most efficient routes for transportation. For instance, systems could extract online data on vessel traffic, the shortest route, and the path with the least amount of vessel maintenance stops. Automatically pick up disruption data from internal or external feeds, map out the cascading impact it will have on production or delivery then make transport changes accordingly. Relay real-time updates to shippers, receivers, and other stakeholders to minimize handoff delays.

7: Location Intelligence

Imagine the eCommerce fulfillment logistical planning needed to track thousands of SKUs headed to hundreds of distribution centers and warehouses spread around the globe. Automation and AI can get this done quickly and accurately. At the same time, you reduce reliance on various actors to provide you with information.

Deploy multi-dimensional analytics and real-time tracking to extract location intelligence as well as determine the when, where, and how of each delivery in real time. For example, attach sensors to equipment and shipment containers that transmit data on the status and GPS location of goods being transported.

8: Contingency Planning

The COVID-19 pandemic starkly demonstrated how disruptive employee absence can be to manufacturing supply chains. As social distancing rules came into force, companies that had automated their processes had a lesser headache dealing with employee absence and are likely to have lesser problems maintaining basic operations.

This is especially important when you consider declining workforces and strikes. Large equipment manufacturers are a prime example of where workers are upset about current conditions and unions have power to create change. Manufacturers, regardless of how they respond to employee demands, need to consider contingency planning for workforce issues.

A Deloitte report predicts a growing shortage of staff for manufacturing, warehousing, and distribution companies through this decade. There could be as many as 2.1 million open positions in the US by 2030. Use automation and AI to strengthen contingency plans. Reduce the staff complement and allow the existing workforce to focus on upskilling as well as more strategic tasks.

9: Digital Twins

In a PwC business survey, a near-unanimous 96% of respondents indicated their intention to use digital twins and other AI simulations. AI simulations can provide detailed, risk-mitigating insights into your current and future operations.

Implement digital twins that mirror your supply chain by creating simulations based on reliable, relevant, real-time information. It is a sum-total of your company’s past, present, and future supply chain operations. Tap into digital twins to visualize and produce what-if simulations spanning regions, countries, facilities, product lines, suppliers, third-party logistics providers, customers, and competitors.

Wrapping Up

It is perhaps the hardest time in history to be a manufacturing executive responsible for streamlining supply chains. Manufacturers must work with increasingly complex supply chains that span multiple countries and sometimes continents. With such scale and complexity, failure and inefficiency in one part of the chain cascades across operations with disastrous consequences on customer satisfaction and manufacturer reputation.

Automation and artificial intelligence is no silver bullet but can massively improve supply chain precision, transparency, agility, efficiency, and resilience – all results that can only be good for a manufacturer’s bottom line. An IBM survey of more than 6,000 C-level executives found that companies on average reported 6.3% of revenue gain that could be attributed to AI.