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    4 Simple Steps to Begin Capitalizing on AI Applications in Supply Chain

    Posted by: Vasco Kollokian | July 12, 2022

    AI Applications in Supply Chain

    In an increasingly competitive global economy, warehouse managers are under pressure to digitally transform their supply chain operations with AI applications. “Today’s warehouse must be smart, and that can only happen by working with the data that sits behind the moving of things,” says Forbes. “Smart warehousing integrates new physical and analytical technologies to realize a host of benefits, including faster problem resolution, improved labor efficiency … [and] the ability to predict and better adapt to business demands,” among others.

    Artificial intelligence (AI) must play a key role in modern warehouse management operations as a result. Fortunately, growing warehouse AI applications in the supply chain enable warehouse operators to become more competitive than ever.

    Functionally, AI can help solve logistics, workforce, safety and optimization issues quickly — even as the dynamics of the warehouse change in real time. But while “companies are already using AI in their warehouses and distribution/fulfillment operations … operators view cost, complexity and lack of understanding of how to use AI as key impediments to further investments,” Supply Chain Dive reports.

    This article explores what it takes to capitalize on AI applications in the supply chain using an ecosystem of technologies for warehouse use cases. It focuses on four key steps that will put warehouse operators on the right path in their AI journey toward greater operational value as well.

    4 Steps to Capitalize on AI Applications in Supply Chain

    1. Identify Potential Warehouse Functions for AI Applications

    AI can help warehouse operators mitigate the multidimensional requirements of running a truly modern warehouse. Specifically, AI can augment human decision-making, enabling personnel to focus on their core competencies rather than juggling complexities of logistics or adjusting to real-time operational challenges in the warehouse.

    To begin, warehouse operators must identify the most impending warehouse functions that can benefit from AI applications in the supply chain. Questions they should ask include:

    • What business processes or areas currently consume the most time and resources?
    • How can their improvements be measured (which KPIs would track such improvements)?
    • What processes or areas are most likely to be disrupted by changes in warehouse conditions (e.g., seasonality, product mix, increased demand)?
    • In which functions do employees currently spend the least time on value-added activities?

    After warehouse operators identify the right warehouse functions for AI applications in the supply chain, they can begin to explore the different types of vendors who leverage AI technologies in their solutions and how those technologies can provide quick time-to-value, when applied to their specific warehouse needs.

    2. The Applicability of AI Technologies for the Warehouse Use Cases

    Whereas AI technology spans numerous areas of specialization, many of its elements are readily applicable to warehouse use cases. Analytics often plays a key role in this and it typically spans into several areas:

    • Descriptive: What happened?
    • Diagnostic: Why did it happened?
    • Predictive: Could it happen again and how frequently?
    • Prescriptive: What can be done to either circumvent or promote it?

    Although all play key roles in any analytics systems, the former two is where traditional business intelligence systems usually operate with heavy reliance on human input for interpretation. However, the latter two is where AI distinguishes itself.

    Many warehouse management systems have sizable rules-based functionality that are typically configured by human experts. Once set, they are hardly touched. AI can easily and automatically adjust the parameters of such rules, to accommodate changing business conditions without any human intervention. It can easily predict events and prescribe courses of action. For example, the AI in the warehouse can predict the demand of a particular item and in response, can easily prescribe an adjustment of allocation of inventory space and frequency of replenishment to meet pick demand accordingly.

    The AI can also prescribe efficient putaway, storing, picking and replenishment strategies to ensure the most continually optimal warehouse operations.

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    3. Align Warehouse Personnel with AI Applications in Supply Chain

    Picking typically makes up the lion’s share of warehouse activities (~70%). Any increase in picking efficiency will translate to more productivity. However, warehouse operators must demonstrate the benefits of AI applications in the supply chain (e.g., in picking) to other stakeholders to secure “buy-in.” That includes warehouse personnel, whose work AI will impact. Indeed, personnel may distrust AI-powered warehouse management systems (WMS) because they are unfamiliar with how the technology works or they are concerned about relinquishing decision-making responsibilities to AI.

    Warehouse operators can overcome these concerns by involving warehouse personnel in the AI adoption process and providing them with training on how to interact with AI functions. Furthermore, warehouse operators can communicate the value AI brings to the warehouse, such as improved time management and reduced stress.

    For example, AI can maximize workers’ pick productivity during regular shifts to cut down on late nights, overtime and exhaustion. Personnel can also spend less time struggling with logistics complexities and more time focusing on their core responsibilities (e.g., operating a forklift safely and effectively). AI-driven decision-making can help absorb the mental overload during unexpected or rush events such as multiple trucks arriving or leaving simultaneously and some unannounced, where personnel would otherwise struggle to identify the best course of action in a timely way.

    Initial impressions of AI-based instructions may be negative — for instance the pathways AI prescribes for a pick path travel may be counterintuitive to the forklift driver who is used to S-shape traversal in parallel aisles warehouse setup. But warehouse operators can begin with small-scale applications of AI to demonstrate its viability quickly and prove to stakeholders and personnel AI delivers results. Incorporating the feedback of warehouse personnel to optimize AI-driven decision-making will help with their adoption.

    4. Identify an Opportunity for Initial Adoption

    Begin with the “minimizing pick travel distance” problem, where AI can demonstrate successes quickly before you move on to other applications. Early use cases may include:

    • Picking: Automate clustering and real-time route guidance (picking sequence) for warehouse pickers using AI-enabled warehouse management systems.
    • Storage: Automate the storage process by using warehouse management systems to track inventory levels and reorder or replenish stock when needed.
    • Order Release: Automate the order release process using AI to identify when orders are ready for release to accommodate transport service schedule, while ensuring warehouse efficiency.

    As personnel begin working with AI applications in the supply chain, they will build trust in its decisions and its unique ability to improve its own functionality based on real results. In time and after seeing positive results themselves, their perception of AI as a “black box” may give way to a true sense of increased control.

    5. Begin Your Journey Toward an AI-Enhanced Warehouse

    At Tecsys, we have found that most warehouse stakeholders are open to the idea of using AI applications in their supply chain warehouse. They simply don’t know where to begin. Indeed, 80% of warehouse operators claim “their organizations need a better understanding of how AI can be used,” Supply Chain Dive reports. Fortunately, you can take these common-sense steps now to begin your own warehouse transformation. A partner whose core competency is leading AI solutions and integrating them with warehouse management systems can help.

    Next Steps

    Tecsys is a leading developer and implementation partner for practical supply chain AI technologies. We start with your specific problems and develop solutions that seamlessly integrate with your warehouse management system (WMS) and other digital infrastructure. We consult warehouse operators on how to maximize the value of their digital investments as well. Contact one of our AI experts today to learn more about AI-driven opportunities in your warehouse.

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