The Manufacturing & Distribution industry is no stranger to disruption. Arguably, upheavals are what edges the industry forward. 

The evolution from the earliest days of steam power and mechanization through the present age of Industry 4.0 – the smart factory – is a testament to both innovation opportunities and the imperative to advance. To position themselves on the cutting edge and capture a competitive advantage, leading manufacturing and distribution companies have embraced enabling technologies like automation, the cloud, IoT, and augmented reality. 

Now, it’s time to embrace one of the most disruptive yet: AI.

AI’s disruptive strength lies in its ability to not only extract greater efficiencies and elevate quality, but also address today’s M&D challenges, from labor shortages and difficulty retaining talent to machine downtime. 

Leveraged correctly, AI can drive unique value. But, it demands a lot of work on the front end. Build a strong foundation for growing your capabilities by aligning AI with your business strategy, getting the right stakeholders involved, and knowing the unique benefits this landmark technology offers M&D.

Where to start 

Begin with the end in mind

To understand what AI can do for the business, leaders must have a firm understanding of the business, centering on strategy. Clarity around the vision for your organization is paramount. 

Based on that self-awareness, ask how AI fits into that vision – how it can advance your business toward its goals. Consider:

• What are the biggest challenges you are looking to solve for? Increased cost of capital, higher wages, difficulty finding and retaining talent, pressure on the supply chain?
• Are there AI use cases that correlate to your organizational strategy? 
• Can you act on those use cases in your current state?
• What investments are needed to make the most of them?

This ideation and identification should also include prioritization:

1. Identify your biggest opportunities and challenges, sorted into high, medium, and low.

2. Follow this with “issue remediation quantification” – where to start and order based on cost and impact, again sorted into high, medium, and low. Examples may include:

• Numerous manual processes (pick, pack, and ship)
• Automated 3-way match (packing slip, invoice, and payment)
• Tech stack ready for AI

Examine your capabilities

Before you can start on an AI-enhanced trajectory that transforms shop floor automation and eclipses the competition, you need to know exactly what you’re working with.

A proper AI readiness assessment canvases all your organization’s capabilities:

• People – Does your workforce have the skills to accommodate the introduction of AI? Or is training (or additional hiring) necessary?
• Processes – Are your current-state processes standardized and on par with AI’s digital nature – or mired in manual procedures? 
• Technology – Can your tech infrastructure handle the demands of AI without upgrades or replacing legacy components: systems, software and hardware, IT and OT?
• Data – Are you capturing the right data – and capturing it quickly enough – to reap the benefits of real-time analytics enabled by AI?

Who to involve 

Just as AI can benefit to your entire manufacturing business, broad leadership inclusion can extract the most value from adding AI to your operations. 

• Begin with your C-Suite, specifically your CFO, COO, CEO, CTO, and CIO. Consider other C-level leaders based on your organization’s use cases. 
• Departmental leadership from key areas are important, again, based on use cases and affected departments. Operations leaders (e.g., shop and production) should be involved, as well as stakeholders from the back office, HR, and Procurement. 
• IT is not only responsible for the heavy lifting of implementation, but also can ensure that AI is purpose-built.

Getting these stakeholders involved means helping them understand the unique value AI offers. Even if they’re not asking, they’re likely wondering, “How can AI help me execute on my responsibilities?” Help them answer that question, and you’ll have their buy-in. 

Industry-specific benefits 

A definitive move toward AI implementation can yield tangible advantages for organizations throughout the M&D industry.

• Predictive maintenance – Sensors and telemetry data from plant machinery can reveal metrics for identifying optimum times for maintenance. It’s common sense that temperature, friction, vibration, and more all take their toll on equipment. What’s not common knowledge is exactly how much is too much. AI can help identify that precisely and indicate the need for attention before breakdown.
  • Example: Using telemetry to anticipate breakdowns in heavy equipment can save companies millions in working capital – allowing them to address issues at the point of replacing bearings and bushings, not multi-million-dollar engines.

• Quality control – AI can enhance quality by detecting quality issues at a level of detail that’s greater – and exponentially quicker – than humans, enabling assembly lines to run faster. Also, the quality of customer-facing AI such as chatbots can reduce wait times, mitigate talent shortages, and more, all while enhancing customer satisfaction.
  • Example: AI scanner models can be trained to identify product imperfections on assembly lines and remove deficient products before distribution. This cuts down, for example, on warranty claims and customer dissatisfaction. 

• Supply chain management – AI enables real-time routing based on dynamic factors (e.g., traffic, weather). This not only expedites movement, but also saves resources while increasing safety. AI-derived insights elevate decision-making – by humans or AI itself.
  • Example: Embedding GPS locators in mobile storage/transportation units provides real-time tracking and traffic data, which companies can then analyze, using AI, to provide more expeditious routes for the trucks transporting their products. This can also provide tracking info in a customer-friendly format. Beyond decreasing delivery time and gas costs, these capabilities can increase customer (and employee) satisfaction.

• Robotics and automation – Tremendous efficiency gains can come from implementing robotics and automated processes, particularly in picking, packing, and shipping.
  • Example: By adding automated robots onto packaging lines, manufacturing companies can reduce labor costs, packaging errors, and equipment downtime – overall enhancing the shipment process and increasing not only throughput but also shop floor safety.

• Energy management – The need to make operations sustainable and energy-efficient makes energy management increasingly important. While energy savings can be hard to quantify, because they’re dependent on use cases, some organizations like logistics have the greatest potential to benefit.
  • Example: Capturing and analyzing data on delivery truck energy usage can lead to surprising opportunities for savings and efficiencies – such as simply eliminating left-hand turns (and the time spent idling while waiting to make them).

Moving M&D ahead with AI

The benefits of AI are plentiful and readily applicable to M&D. Its value and potential use cases are unquestioned. So instead of “Should we use AI?”, the question should be “When do we start?”

With the right understanding of both AI and your business, you’ll be positioned to make the most of AI and sharpen your competitive edge.