As manufacturing and supply chain organizations have proven vital to compete in the world marketplace, trillions of dollars have poured into manufacturing, data center expansions, energy, and related supply chain industries such as shipbuilding and ports. These organizations are no longer the stodgy, old world, manual labor intensive ones of the last fifty years. Instead, the successful manufacturing, milling, refining, forging, and other organizations will be high-tech. Artificial intelligence is not only integral to each of these industries, but it will also power productivity and enhanced customer value across every company in the future. Thus, it is an imperative to pay attention to current uses of AI and to think strategically about AI applications to drive productivity, profitability and performance. If you do not lead, you will fall to the wayside.

There are countless uses for artificial intelligence in manufacturing and supply chain. We’ve provided several options and examples.

AI Uses in Supply Chain Planning (Demand & Supply)

• SIOP / S&OP: SIOP (Sales Inventory Operations Planning) utilizes artificial intelligence to reconcile demand and supply with forecasts and “what if” simulations. In today’s disrupted supply chains, predicting issues and recommending optimized alternatives is essential to success.
• Sales insights: By utilizing AI and predictive analytics in analyzing sales data with CRM (Salesforce, Microsoft Dynamics etc.), quoting systems and business intelligence (BI), Sales leaders can gain insights into where to focus attention to grow sales, margins, and assess pricing and mix changes and associated implications.
• Demand planning: One of the classic uses for artificial intelligence is in developing sales forecasts. Forecasting systems detect patterns in historical sales, promotions, seasonality, external signals (like weather or social trends), and even real-time events, resulting in dynamic forecasts that pick up on changes faster than people.
• Advanced production planning: AI is also used in advanced planning/ production planning to analyze constraints like capacity, labor, materials availability, and machine uptime to dynamically recommend the optimal production plan and/or allocate production among facilities to optimize service and cost.
• Advanced logistics planning: AI is utilized in demand driven replenishment to optimize inventory across distribution centers and forward-stocking locations to reduce carrying costs while improving fill rates. It can also utilize real-time point-of-sale (POS), e-commerce, and ERP data to anticipate stockouts and automatically trigger replenishment.
• Capacity & labor planning: AI machine learning forecasts daily or hourly labor needs based on production volumes, inbound/outbound volumes, product mix, and historical trends.
• Supplier & logistics risk modeling: AI can assess supplier risk, lead time variability, geopolitical disruptions, and recommend alternate sourcing or routing options and dynamically reassign orders across multiple suppliers, transportation partners, or 3PLs to mitigate risk.
• Inventory Optimization: A common use of AI is to optimize inventory – balance inventory investment with service levels by predicting optimal safety stock, lead times, and reorder points across the network.
• Transportation Optimization: AI will dynamically adjust routing and load planning based on delivery windows, traffic, weather, and carrier availability to optimize transportation and goods movement.
• Cash flow planning: Another great use of AI is to predict cash flow requirements analyze scenarios to optimize results.
• Predictive analytics, exceptions & alerts: In addition to providing sales insights, there are many uses for predictive analytics to enhance customer value, productivity and profitability. In addition, modern ERP systems such as Oracle and SAP are embedding AI agents and functionality to increase productivity and profitability. There are countless flags to highlight potential shortages, delayed receipts or shipments, errors, potential spikes and/or bottlenecks, etc.

AI Uses in Manufacturing & Distribution Operations

• Predictive maintenance: Instead of following typical preventative maintenance schedules which can negatively impact service and operational performance as production schedules must non-optimally plan around them, predictive maintenance focuses attention on those items that require attention to mitigate machine breakdowns and further costs.
• Predictive quality control: Most successful clients include quality inspection to catch issues sooner in the process. For example, key quality checks are performed after the first operation step instead of waiting to inspect later in the process, thereby minimizing scrap and rework. Moving to predictive quality control allows you to capture quality issues as they occur so that immediate adjustments can be made.
• Process automation: There are countless opportunities for process automation in manufacturing environments. For example, CNC machine tool automation utilizes automated tool changers, part loading/ unloading, and in-process inspection, resulting in the opportunity to continue production without a dedicated resource and/or lights out production. Similarly, in a beverage process manufacturing company, they used automated bottling and filling lines to fill, cap and seal at high speeds. In a distribution
• Robotics: Robotics can be utilized in several areas in manufacturing to reduce variation, improve quality, free up human operators for complex tasks and increase production output. For example, an industrial manufacturer robotic welding cells for repetitive tasks to run around-the-clock without dedicated operators. In a high volume distribution center, they utilized robotic picking systems to improve order accuracy and enable scalable e-commerce fulfillment.
• Autonomous vehicles: Driverless vehicles are used widely in manufacturers, distributors, and is gaining momentum in goods movement. For example, automated guided vehicles (AGVs) and autonomous mobile robots (AMRs) move pallets, cases, totes and other items around the factory and warehouse floor.
• Drones: Drones can monitor inventory and key areas of your operation. For example, an consumer products distribution center used aerial drones to perform inventory checks in high-bay storage. The railroad uses drones to check key areas of track.
• Dynamic slotting: Artificial intelligence will learn product velocity, order frequency, and seasonal patterns to continuously re-slot items in the warehouse, thereby increasing pick accuracy, reducing labor, and, most importantly, quickening the process.
• Energy optimization: Since energy is a key driver behind manufacturing, logistics, and AI, we will need to conserve as much as possible to support growth and mitigate costs. Utilize AI to monitor and adjust energy usage to minimize waste and lower costs.

AI Uses “Across the Board” & for Support Functions

• Worker assistance: This is a critical area that will impact every company. Automation tools will augment your team with AI-driven recommendations, predictive alerts, and smart instructions. For example, a water storage solution manufacturer wanted to make a dramatic upgrade in the use of their MRP system; however, updating data fields to make the transition happen was prohibitive in terms of resource requirements and timing (as they couldn’t perform transactions in the interim). Thus, an innovative team member developed an automation script to address the issue. Thus, the implementation was seamless and quick, resulting in improved service levels, better visibility of requirements and operational performance. In another example, an aerospace manufacturer wanted to run their customer contracts through an AI tool to pick out key pieces of information and speed up and improve a vital process required to grow sales.
• Customer Service automation: Chatbots and AI assistants can provide real-time order updates, ETAs, and issue resolution across multiple channels and proactively notify planners, buyers and/or sales reps about delays, substitutions, and unusual orders.
• Returns automation: AI predicts return likelihood by SKU or customer profile and can automate disposition decisions (restock, refurbish, scrap, or resell) and follow up tasks.
• Product design: Companies can use generative AI models to create and test new product designs rapidly, giving them a leg up on the competition.
• Engineering: AI can provide base engineering designs to speed up engineering and design. It can also suggest improvements that might be overlooked by a person.

Rolling Out AI

There are so many examples and use cases of artificial intelligence that it makes sense to pursue a common sense, forward-thinking strategy to deploy AI.

• Brainstorm: Not all companies and situations are created the same. Get your best talent together, incorporate input from the floor to the technician to leaders, and compile a list of ideas.
• Prioritize: Use a bit of common sense. Determine what’s most important to achieving profitable growth, what will differentiate you from your competition, what’s enables you to go faster, be better, and/or accelerate results. If everything is a priority, nothing is a priority. Choose one of your top ideas and move forward. You can add/ revise when appropriate.
• Encourage success: Invest in resources, develop a culture of innovation, encourage ideas with rewards and recognition (consider recognizing the best idea that didn’t work), and be invested in the day-to-day success.
• Develop cadence: Review on a regular cadence, incorporate into programs such as SIOP to nimbly address and prioritize changing conditions, and make adjustments as needed.

The Bottom Line

No one will succeed in the next decade if not incorporating AI and advanced technologies to spur success. It simply is not feasible to create customer value and profitable growth while navigating more complexity, ambiguity, volatility and uncertainty without utilizing advanced tools and technologies. Look for opportunities to support and bolster your plans with AI, resource them, continually enhance with forward-thinking insights and results will follow.

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Digitization of the Supply Chain Drives Profitable Growth