Generative AI has taken the world by storm, and the supply chain industry is a perfect candidate to benefit, with opportunities for integration of generative AI from strategic planning to execution processes. Yet, the question remains: Is your supply chain mature enough to integrate Generative AI effectively?
Visualize the following supply chain scenario: data on forecasting are confined to spreadsheets, updates on transportation are trapped in emails, and inventory levels are locked in databases. Each department is working in isolation, optimizing for localized goals that may not align with broader company objectives.
Generative AI can help transform these siloed workflows and fragmented data into a single, cohesive entity. By integrating both structured and unstructured data—from databases to emails to online data and even voicemails—generative AI can facilitate enhanced operational visibility, deep insights and prescriptive recommendations to reach corporate goals.
As a Principal Data Scientist on Logility’s research and development team, I’ve worked on Logility’s pioneering initiatives developing generative AI solutions for our customers. My job is to design technology that lets customers capture these benefits.
Assessing and Understanding Generative AI Readiness
When building new partnerships to introduce generative AI solutions, we can use the following framework to consider how to evaluate and ensure generative AI readiness in supply chains, and how software can enable successful generative AI integration.
1 Align Your Business Goals
Generative AI should complement clear business objectives. As you define specific goals for your generative AI implementation, make sure these goals fall within broader supply chain and company objectives. One you’ve identified key performance indicators (KPIs) to measure success, ensure understanding of the different aspects of the data across the company and how they link together.
2 Assess Data Integrity and Accessibility
Successful generative AI deployment hinges on high-quality, accessible, and interpretable data. The data strategy should be built around ensuring both data access and an implementation team who understand the significance and importance of all key variables.
Two early steps are 1) auditing existing data across all supply chain functions to identify important data elements, how they are interconnected, and what kinds of questions they answer and 2) implementing data governance policies to ensure ongoing data quality.
Now is a great time to start data governance processes if they aren’t already in place! Remember, perfection isn’t necessary to start. In fact, this can be a great first use case for generative AI —integrating data across discrete systems.
3 Foster Cross-functional Knowledge and Collaboration
Breaking down business silos is critical for effective generative AI implementation. You can foster high levels of understanding of business requirements and goals by encouraging collaboration between IT, planners, and leadership.
Creating cross-functional teams as you move into the planning and implementation processes for generative AI ensures buy-in and leverages diverse expertise. Including stakeholders from across your business ensures that your generative AI implementation will be designed to gain insights from across silos, creating a more integrated and effective system.
4 Prepare the Team and Managing Change
Preparing your team is as important as setting up technology systems. Make sure that the team is aligned with the purpose and goals of the project, and that there is a clear understanding of expected features, capabilities and results. Based on assessments of skill sets and knowledge gaps, develop comprehensive training and education programs.
As with any new technology or implementation, address skepticism and resistance through clear communication. In the case of generative AI readiness, it is important to address concerns and provide education to employees who are uncomfortable with the technology.
Your workforce should understand that generative AI enhances, not replaces, human expertise. This approach leverages the strengths of both AI and human judgment, optimizing operational efficiency without compromising the unique insights provided by your team. Transparency encourages adaptation and acceptance across your organization.
5 Ensure Technical Infrastructure
The right technical setup is crucial. Work with both your internal team and generative AI technical partners to ensure that the proper infrastructure is in place. Things to consider for generative AI readiness might include:
- Evaluating cloud-based vs. on-premise solutions based on your needs
- Checking integration capabilities with existing systems
- Planning for scalability to accommodate future growth
Make it a priority for external partners to have a full understanding of your data, business goals, and requirements so that they can help make sure the infrastructure is built to meet your unique needs.
6 Discern Data Security and Integrity
Choose trusted expert technology partners who uphold the highest data security standards and contribute to maintaining the integrity of your data.
7 Ensure Transparency and Expectation Management
Dismantle the ‘black box’ myth! Your generative AI systems can and should be explainable and transparent. For example, answers to generative AI queries can provide context such as the source of the data to answer the question or the reasoning behind the answer. This helps manage expectations and provides your users with confidence in the system results.
8 Consider and Plan Phased-In Requirements
When determining requirements for your generative AI application, you may want to consider implementing your project in a phased approach. One way to do this is to start with a small area of business and scale as your supply chain maturity level increases.
Another way to think about this is to phase in features based on the complexity of the types of questions/answers you build into the system. For example, the initial features that you implement may include questions that provide insights from data connected from multiple business processes. Follow up questions may support highlighting risks and issues in your supply chain, executing scenarios and mitigation recommendations. You can then build in functionality which aligns generative AI with some of your execution processes.
Ready, Set, Go!
Successfully integrating generative AI into your supply chain operations involves a structured approach. To ensure a smooth transition, you may consider starting small and scaling gradually. Make sure you conduct a comprehensive data audit, train staff and set realistic expectations. establish continuous improvement, and finally, choose the right partner and technology.
Utilizing Generative AI in your supply chain sets the stage for a more connected, intelligent, and responsive system. The benefits are substantial, ranging from improved forecasting accuracy and optimized inventory management to more efficient supply plans.
As you consider your next steps, I encourage you to take a critical look at your current operations. Where could generative AI drive the most value? What areas of your supply chain are most ready for this technology? And importantly, how can you prepare your organization for this transformative journey to generative AI readiness?
The future of supply chain management is proactive and data-driven. With Logility’s support, you can lead this transformation, optimizing your operations and setting new industry standards. Are you ready to take the next step and revolutionize your supply chain with Generative AI? Contact us today to get started.
Written by
Lynne Goldsman
VP, Research & Development
Short bio
Lynne Goldsman works on developing innovative generative AI solutions at Logility. Lynne previously helped lead Logility’s innovation team to research and create state of the art outcomes for clients. Her career spans over 25 years of serving in many roles as research analyst, data scientist, developer, and supply chain consultant.
