Readiness Is All - Performing 3 Key Business Assessments Before Exploring AI Solutions
The proliferation of new AI-powered technology in the supply chain space often leaves businesses feeling both mystified by the technology itself and vulnerable to missing out on new technology adoption. To be sure, it is a crowded field of providers, and separating wheat from chaff is an essential exercise for any business looking to adopt AI-powered technologies. A key first step in this winnowing process is performing a comprehensive analysis of the current state capabilities of a business through the lens of the proposed technological solution.
A thorough assessment of organizational readiness begins by identifying the change or improvement that the business is seeking within their supply chain. Sometimes, this initial evaluation will uncover cheaper, more straightforward alternatives to AI-powered technology – metrics based operational improvements or improved use of existing technology. However, more complex, data driven improvements to supply chain management are likely to require the adoption of AI-powered technology.
Machine learning algorithms – an essential component of many AI-powered technologies - are structured to accept massive amounts of unrelated data and generate unique business insights that traditional data analysis alone could not produce. A supply chain organization hoping to make more cost effective and resilient decisions on supplier partnerships may leverage these unique insights to improve their strategic decision making processes. Similarly, a supply chain organization hoping to lock in low transportation rates with carriers that meet a high service level threshold may use AI-powered technology to perform this carrier evaluation process regularly and execute limited term digital contracts with the best and most serviceable carriers. In both cases, a clear and well stated goal related to business performance ensures that all departments understand the mandate and are evaluating AI-powered technology with the end goal in mind.
However, a supply chain organization with a clear deliverable identified for adopting AI-powered technology still has additional hurdles to overcome in readying the business for selection and adoption. Developing a positive mindset among employees towards AI-powered technology can be challenging. Leaders within the supply chain space must recognize that many of the insights generated by AI-powered technology may seem outlandish to those employees who have made a career of reading market signals and leading by virtue of their expertise in a particular area of supply chain management. For instance, a North American manufacturer deploys an AI-powered tool to assist in making timely and cost-effective purchases of coal, a key input to their supply chain. The AI-powered tool identifies a correlation between the cost of North American sourced coal and seasonal rainfall rates in the Chinese province of Sichuan. When rainfall rates are higher in the Yangtze Basin and Chinese hydropower is operating at full capacity, the spot price of coal falls globally as Chinese industrial consumption drops. Similarly, drought conditions in the Yangtze Basin correlate with higher global spot market prices for coal. However, basing a strategic purchasing decision on an esoteric leading indicator like this - Chinese rainfall rates - may be anathema to subject matter experts within a company’s global sourcing department. Furthermore, if those subject matter experts are incentivized based on their ability to read markets and make sound purchasing decisions, they are even less likely to accept what may appear to be an especially risky bet.
Companies that are most successful in AI deployment frame the adoption of AI-powered technology as an enhancement of an existing knowledge base, not a replacement for subject matter expertise. Similarly, they frame the technology itself as a tool, not an oracle. It is critical for leadership within a supply chain organization to craft comprehensive change management strategies prior to adopting AI-powered technology to prevent a mistrustful environment from developing within the business. Supply chain leaders within the business should clearly and regularly communicate the goals that the business is seeking through adoption of any new technology. Providing all employees with an overview of the technology that the company is evaluating and regular progress reports as the evaluation process unfolds helps generate employee buy-in. It is especially beneficial if supply chain leadership can identify specific use cases for the technology that are likely to reduce employee workload or streamline complex business processes
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Ultimately, the success or failure of an AI model to deliver meaningful financial results for a supply chain organization depends upon the quality and relevance of the data used to train the model. The most experienced and knowledgeable employees are often the secret ingredient used to build robust training models. Their subject matter expertise and deep understanding of nuanced relationships between inputs within the supply chain make them an invaluable element in selecting and successfully deploying an AI-powered technology. It is essential to communicate their critical role in this process from the outset to prevent these employees from feeling that they are training their replacement. Again, correct framing and thorough communication help circumvent obstinacy and resistance to change.
Lastly, a supply chain organization that is preparing to incorporate an AI-powered technology must conduct a comprehensive evaluation of their existing IT ecosystem and prepare a multi-year roadmap for systems deployment and systems retirement. This evaluation process often reveals how complex and interwoven various systems have become within day-to-day supply chain operations. By preparing a systems roadmap, a company can more easily prioritize and mobilize inter-departmental resources to perform required integrations on-schedule, shift the business to new platforms, and sunset systems that are redundant or no longer relevant to the business. An AI-powered technology must be evaluated for fit within existing systems infrastructure in the near term, and as a potential replacement for existing systems infrastructure in the long term.
AI-powered technology has potential to fundamentally transform the supply chain space in the next five to ten years, promising a new era of efficiency gains and cost control. Supply chain organizations must be deliberate in their approach to this new technology, performing comprehensive business readiness assessments prior to evaluating and selecting the appropriate AI-powered technology. Leverage the industry-leading experience of NTT Data’s supply chain consulting team to assess business readiness for AI-powered technology adoption and to assist with the technology selection and deployment process.