Use Cases for AI in Distribution: Demystifying AI for Distributors

May 2, 2024
  • The first step in harnessing AI within distribution involves thorough preparation of data. This includes data consolidation, where all relevant data sources such as transaction data and market trends are identified and integrated using APIs to connect systems like ERP and CRM to a centralized data warehouse. 
  • AI customization is essential, involving the cleaning and preprocessing of data to ensure quality and the detailed description of data features to the AI. 
  • Ensuring data privacy through vectorization converts sensitive data into numerical representations that maintain data relationships while ensuring anonymity.
  • AI applications in distribution are diverse and impactful. In sales analytics, AI leverages predictive analytics for demand forecasting and customer segmentation, enhancing personalized marketing strategies and customer retention. 
  • AI automates manual processes, notably in order entry, significantly reducing manual errors and processing time. In operations, AI automates routine tasks such as inventory management and vendor management, enhances predictive maintenance, and ensures consistent product quality, overall boosting efficiency and reducing human error. 
  • AI-driven demand forecasting uses historical sales data and external factors to accurately forecast market demands, dynamically adjusting to market changes. 
  • Contract digitization through AI automates the review and management of contracts, improving efficiency and reducing errors, while AI in code generation automates repetitive coding tasks, enhancing code quality and speeding up the development process.
  • Initiating the integration of AI in distribution should focus on achieving quick wins to overcome inertia and secure stakeholder buy-in. 
  • It’s crucial to view AI as an enabler rather than a standalone solution, focusing on areas where AI can immediately enhance business operations and customer relations. 
  • This approach helps build confidence and lays a foundation for more extensive AI capabilities over time, supporting a strategic and phased adoption of AI technologies in the distribution sector.

Most distributors see the potential value of artificial intelligence (AI) and know they need to get their arms around it. In fact, EY says 99% of CEOs across every industry recognize the significant impact AI may have on their businesses.

But despite this recognition, many distributors are already falling behind in building a plan for the technology.

When I have been at conferences, around 50% of distribution leaders are still “thinking about AI,” or “thinking about starting a roadmap for AI.”

I empathize with these leaders: Running a distribution business is admirable. It takes a team of well-trained, disciplined, and smart leaders.

But I’m here to say: AI is changing the game. And it’s happening faster than you might realize.

You’re not alone if you feel ill-equipped for AI and the push toward automation. The options can feel overwhelming, and it’s easy to fall into analysis paralysis. It can also be difficult to separate hype from reality. 

So, where should distributors start? Let’s take a look at real examples of where distributors are already leveraging AI in operations, sales, and more. 

My hope is that ideas like this may provide inspiration for your own organization. 

Before You Get Started with AI

Throughout each of the AI use cases noted below, we would like to call out some general rules to apply before you can make these come to life. These are essential actions to prepare, customize, and secure data for AI implementation. ProfitOptics can help with this prep work.

Prep Step 1: Data Consolidation and Preparation

  • Data Sources Identification: Identify all relevant data sources including transaction data, customer interaction data, market trends, and external factors like economic indicators.
  • Data Integration: Use APIs to integrate these data sources with the LLM. This involves connecting your ERP, CRM, and any other relevant systems to a centralized data warehouse or directly to the LLM if it supports direct data fetching.

Prep Step 2: AI Customization Through Contextual Understanding

  • Data Cleaning and Preprocessing: Ensure data quality by cleaning and preprocessing the data. This includes handling missing values, removing duplicates, and standardizing formats.
  • Feature Explanation: Input detailed descriptions of your data features into the LLM. Explain what each column in your dataset represents, especially those that are unique to your industry or company.
  • Contextual Training: Provide the LLM with context on how certain data points influence your business operations. For example, if certain customer behaviors or seasonal trends significantly impact sales, this information should be made clear to the LLM.

Prep Step 3: Ensuring Data Privacy Through Vectorization

  • Manual Analysis Insights: Input insights from manual analyses conducted by your data analysts. This might include patterns or trends they have observed over time that are not immediately obvious from the raw data.
  • Apply Vectorization: Convert sensitive data into vectors. This process involves creating numerical representations that preserve the relationship between data points without revealing the actual data.
  • Verify Anonymization: Ensure that the vectorization process adequately anonymizes data without compromising the ability to gain valuable insights from it.

 6 Use Cases for AI in Distribution

AI Use Case 1: Sales Analytics

AI has no problem eating an elephant — when that elephant is data. And distributors have a lot of that. 

Through predictive analytics, AI analyzes historical sales data and external factors to forecast demand, enabling distributors to optimize inventory and streamline supply chains accurately. 

Customer segmentation and targeting also benefit from AI; the technology sifts through vast datasets to identify distinct customer segments based on behavior and demographics, facilitating personalized marketing strategies.

Another example of where AI is used in sales: AI-driven recommendation engines generate personalized product suggestions, while predictive analytics aids in customer churn prediction, allowing for targeted retention strategies. If you’ve ever wanted an easier way to analyze customer sentiment and predict churn to move from reactive to proactive in your markets, AI can help you get there.

AI Use Case 2: Automate Manual Processes, Such as Order Entry 

AI brings transformative benefits to sales reps in distribution by offering tools and insights that streamline workflows and optimize efficiency. 

One example is AI-powered solutions that personalize reordering, cross-selling, and upselling recommendations online and in person. Here, implementing personalized sales recommendations reduces administrative burdens, freeing up time for sales reps to concentrate on cultivating relationships and closing deals.

Additionally, through AI-driven lead scoring and prioritization, sales reps can focus on high-potential opportunities, improving conversion rates. Predictive analytics further enhances sales by providing accurate forecasts, allowing reps to anticipate market demand and align their strategies accordingly. 

AI's contribution extends to route optimization tools that streamline field sales activities, maximizing the efficiency of customer visits. On the digital front, chatbots powered by AI handle routine inquiries and extract valuable insights for more personalized and effective communication.

AI Use Case 3: Operations

Automation powered by AI can take on your routine, time-consuming tasks so that your people can focus on more value-added activities, all while reducing human error and speeding up workflows.

Ways to leverage AI for distribution operations include:

  • Inventory Management: Automated inventory management, facilitated by AI algorithms, ensures optimal stock levels, minimizes excess inventory, and identifies slow-moving items.
  • Vendor Management: AI can analyze vendor performance, assess reliability, and identify cost-saving opportunities. This helps distributors make informed decisions when selecting and managing suppliers.
  • Predictive Maintenance: Predictive maintenance minimizes downtime by forecasting equipment maintenance needs based on performance data.
  • Quality Control: AI extends its impact to quality control, automating inspections, and ensuring consistent product quality.
  • Supply Chain Visibility: Real-time supply chain visibility empowers distributors to proactively manage shipments, monitor inventory movements, and identify bottlenecks.
  • Route Optimization: AI algorithms can optimize delivery routes and consider factors such as traffic patterns, delivery schedules, and vehicle capacities. This reduces transportation costs, enhances delivery speed, and improves overall logistics planning.

Collectively, AI transforms distribution operations, fostering agility, informed decision-making, and heightened efficiency.

AI Use Case 4: Demand Forecasting

AI-driven demand-generation strategies ensure that products and services align with market needs, minimizing the risk of overstock or shortages.

AI can analyze vast historical sales data to uncover subtle trends and seasonality. Through predictive analytics, distributors can use this to forecast demand by considering historical data, market trends, and external factors, allowing distributors to anticipate shifts in consumer behavior.

What sets AI apart is its ability for dynamic adjustments, adapting in real-time to changes in market conditions and unforeseen events. AI provides a comprehensive view by integrating a diverse range of external data, such as economic indicators, further improving forecast accuracy.

AI also facilitates customer segmentation and tailors demand forecasts to diverse purchasing patterns. It analyzes the lifecycle of products, assesses the impact of promotions and marketing efforts on demand, and continuously learns from new data, refining forecasts over time.

AI Use Case 5: Contract Digitization

AI revolutionizes contract digitization in distribution by automating and optimizing legal processes, making them more efficient and less prone to errors. In a nutshell, the analysis of clauses, risk assessment, and workflow automation enhance the overall contract review process.

For distributors looking to dip their toes into artificial intelligence for contract digitization, using AI tools to standardize contract templates is a great place to start. 

Additionally, summarization tools can condense lengthy contracts for quick comprehension. Free platforms like ChatGPT can digest hundreds of words in seconds and output a simple and concise summary — but be cognizant of inputting propriety or otherwise confidential information into these free tools.

AI algorithms can also categorize and organize contracts based on predefined criteria and facilitate the efficient retrieval and management of digital repositories. AI empowers distribution companies to achieve greater efficiency, accuracy, and transparency in contract management through these advancements.

AI Use Case 6: Code Generation

As more and more distributors find themselves building digital products, software engineering is a growing expense. Code generation powered by AI reduces the time and resources required for software development and documentation while improving quality.

For example, automated scripting and programming tools streamline repetitive coding tasks, saving developers time and effort. AI also contributes to code completion, providing developers with context-aware suggestions and reducing errors.

Not only that, but AI excels in pattern recognition, optimizing code for efficiency, detecting and correcting bugs, and suggesting improvements or refactoring. Customizable code templates, continuous integration and deployment (CI/CD) automation, and code documentation tools further accelerate development processes.

By automating routine tasks and enhancing coding efficiency, AI empowers developers to focus on high-level design and innovation, accelerating software development within the distribution industry.

Get Started on Your AI Journey

The use cases above don’t even scratch the surface of how distributors can leverage AI to optimize operations, enhance customer relationships, and gain a competitive edge.

If it sounds like going all in with AI is biting off more than you can chew, know it doesn’t have to be that way. Distributors can strategically navigate AI implementation by first targeting areas of quick wins to combat inertia, gain stakeholder buy-in, and build confidence. 

The key is to view AI as an enabler rather than a standalone solution. 

Getting off the ground can be the most challenging part, but we’re your partner on your AI journey. We’ll get you started with quick wins and the proper foundation to build capabilities over time. Our team offers deep domain experience and the data and technology fluency to solve complex business challenges. Let’s find that AI fit for your organization — get started now.

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