As companies struggle to remain competitive with never-ending disruptions and pressures in today’s volatile marketplace, artificial intelligence provides a vital tool to drive productivity, profitability and performance. Artificial intelligence (AI) enables computers and software to perform tasks that typically require human intelligence. AI covers a large range of systems to perform tasks such as reasoning, learning, problem-solving, and decision-making in addition to predictive tasks which are prevalent throughout manufacturing and supply chain and embedded in forward-thinking strategic processes such as SIOP (Sales Inventory Operations Planning).

Machine learning is a subset of AI that is prevalent in manufacturing and supply chain. It focuses specifically on enabling systems to learn from data instead of being programmed and is used in many applications such as sales forecasting and inventory optimization. In thinking about what’s trending and is integral to the future of supply chain, agentic AI comes to mind. Agentic AI is another subset of AI that simulates a human agent and focuses on specific goals. These agents don’t just predict, they act. Thus, they can take over basic supply chain planning tasks to free up planners, buyers, and analysts for higher-level tasks.

We will discuss what you need to know about artificial intelligence to start utilizing it to improve customer value and drive profitable growth. Here’s what we’ll cover:

The Value of Artificial Intelligence

Artificial intelligence can provide dramatic value for manufacturers, distributors, and supply chain organizations. In today’s volatile, uncertain, complex and ambiguous (VUCA) business environment, organizations must develop strategies to stand out from the crowd and deliver powerful value to differentiate from the competition. The core benefits of AI include the following:

  • Faster, smarter decisions (speed): More quickly responding to demand and supply shifts will keep your organization ahead of the competition in a profitable and productive way.
  • Efficiency & automation: Increasing efficiency and automating tasks not only drives productivity and profitability but will also allow executives to refocus resources away from mundane tasks to high-value ones, thereby also increasing engagement. 
  • Improved forecasting & planning: Trends, seasonality, and real-time inputs are utilized by AI to develop predictive forecasts and optimized plans.   
  • Agility in disruption: Disruptions have become the “new normal”. Whether it is a strike, hurricane, or geopolitical event such as Houthi rebels attacking ships in the Suez Canal, AI can quickly respond and adjust plans, find alternate routes, etc.
  • Scalability: AI enables scalability without adding headcount. What could be more important to profitable growth than scalability as it will increase output while minimizing the need for additional resources, facilities, etc.
  • Customer experience: AI can personalize service, provide 24-7 support, and enable smarter order fulfillment resulting in improved OTIF (on-time-in-full), OTD (on-time delivery), lead times, etc.

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.

How Does AI Relate to ERP?

Many executives lament that AI is simply a fad and there are no real-world uses. On the other hand, AI is also seen driving the future success of organizations with large organizations pouring trillions into AI infrastructure. Forward-thinking ERP system leaders are embedding AI at a breakneck pace while every new software claims their platform enables AI functionality. So, what is really going on? 

What is clear is that the ERP, software, and IoT system executives believe AI is the future. On the other hand, companies are more skeptical. Here is our take on AI and what this means vs ERP:

  • ERP systems: ERP provides the backbone for manufacturers and distributors. These transaction-heavy systems will not be replaced by AI; however, the ERP systems of the future will be those that embed AI throughout their system. Predictive demand planning, cash flow planning, production planning, distribution planning, maintenance planning, automated processes, and immediate quality inspections will become expected. AI agents will be incorporated into ERP functionality to accelerate success and lessen the workload. Generally, the larger companies such as SAP and Oracle are investing heavily. Refer to the example of how SAP is embedding AI in our article, Takaways and Insights from SAP Sapphire Event. Old, stodgy ERP systems will no longer suffice. 
  • AI software: The best companies will have top talent that can distinguish between what is a software touting AI that is largely marketing oriented vs a software that can provide a quick competitive advantage with AI functionality. It will not be easy; however, the name of the game will be speed. Thus, forward-thinking executives will invest in the appropriate software tools AND process expertise to stay ahead of the curve.  For example, in a recent SIOP opportunity, the executives were reviewing SIOP process consultants as well as demand planning and AI software options. Although it is tempting to pursue software with the idea that it will achieve scalability objectives, it must be accompanied with the appropriate process expertise to bring results to fruition. 
  • ERP vs AI: Manufacturers and distributors will require a forward-thinking ERP software that will invest and stay ahead of the curve with AI; however, with the quick pace of AI, they will also require add-on, peripheral, and stand-alone tools to meet specific needs. Instead of either or, it will be an “and” equation in most cases. According to tech experts, AI software might take over cloud systems as it can be easier to develop new than re-write the software. On the other hand, AI is not meant to be a transaction-based system. Thus, evaluating ERP and related systems including AI platforms will become a task requiring a higher level of expertise and broad knowledge to ensure success yet speed will be of the essence. For example, a client with a simple order fulfillment process had several best of breed systems (CRM, e-commerce, finance, BI) yet needed ERP-like functionality to pull the order fulfillment cycle together successfully and automate several Excel spreadsheets to support growth plans. In the “old days”, there would have been no choice but to pursue an ERP upgrade. It still might be the best solution as it depends on the long-term business requirements, but an add-on software and/or AI software might just fill the gap for the next several years. 
  • Chat GPT & its competitors: These types of tools are accelerating progress when used proficiently. They still make up inaccurate information from time to time, and so the person utilizing the tool must be “better”. In essence, they must know when to revise/ edit, know which prompts to use, know enough about the topic to know if the responses don’t “add up”, etc. On the other hand, as you master these tools, your efficiency will increase ten-fold. 

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.

AI & the Future of Work

Although AI can offer dramatic improvement opportunities for your business, you must be careful not to mortgage the future. Some experts think AI will spur layoffs as companies automate tasks. Others see opportunities to scale up and utilize additional resources to manage, build, and maintain AI systems. Creativity, strategic thinking, and empathy will continue to require human judgement. All of these positions will be true. Companies will require less low-skilled resources yet will want more high-skilled resources. 

What is required to serve customers and prepare for profitable growth? We agree with John Naisbitt, a futurist and best-selling author of a 1982 book Megatrends: Ten New Directions Transforming Our Lives: “The more high tech we become, the more high touch we must be.” Thus, the best companies will be forward-thinking. They will utilize AI to automate mundane tasks, accelerate results, and predict the future. As they pursue these strategies, they will invest heavily and upgrade their job positions and workforce to what’s needed to succeed in the future. These strategies will embrace alternative forms of education and training that focus on action and entrepreneurship. In fact, AI is likely to flip the script on traditional education. Companies must embrace concepts such as apprenticeship, entrepreneurship, boot camps, and fellowships and build the workforce of the future.  

The Bottom Line & Resources

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. If you are interested in a complimentary discussion on AI readiness / SIOP readiness (key use of AI), fill out a quick form here. To gain insights on AI and advanced technologies and uses of AI such as advanced planning, inventory optimization, and SIOP, refer to our blog for tips, strategies, case studies and examples. 

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