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    Using artificial intelligence for supply chain planning Featured

    January 17, 2019

    Supply chain planning and optimization, including demand forecasting, are among the key areas where AI is already beginning to be deployed. Experts say that global supply chains have become so complex and are affected by so many variables, that AI may be essential to help identify and predict problems and potential solutions.3

    “Supply chain managers must take into account more data than any person can possibly process,” or “Most companies simply do not understand the full depth and breadth of their supply chain risks, and are therefore not prepared to respond efficiently or effectively to the many potential disruptions.” The inherent complexity of global supply chains, along with the dramatically increased volume of data, make it almost impossible to extract all the necessary insights and make informed business decisions. And the volume of data continues to increase, in part due to the trend to connect supply chain management devices to the Internet.
    Accordingly, companies are already applying AI-based machine learning to automatically analyze vast amounts of supply-chain management data, identify trends, and generate predictive analytics the ability to predict problems and outcomes, that the benefits in global supply chain management include reductions in forecasting errors. “Software solutions are beginning to apply machine learning capabilities that can automatically detect errors and make course corrections, while processing real-time data streams,” with companies collecting mountains of data that can be used to train algorithms to learn where things went wrong, we are at the tip of the iceberg of how much companies will leverage these capabilities. For example, some supply chain management solutions use AI to gather and correlate external data from many sources, including social media, newsfeeds, weather forecasts and historical data.
    As an example, one major food manufacturer used an AI-based demand forecasting solution to tackle a common problem: meeting customer demand while minimizing inventory. The challenge was complex, involving around 10,000 different products, each subject to variation in demand. By applying predictive analytics, the company was able to more accurately anticipate customer behavior by integrating the impact of promotions and other special offers into its statistical models.
    A restaurant can be a complex business to run. Anticipating demand to order the right amount of ingredients at the right time, and handling it all manually for a business with notoriously thin margins to begin with typically constitutes a significant challenge even in the best of times. One restaurant chain decided to take advantage of advanced technology to gain a deeper line of sight into demand, and learn to plan better. The restaurant chain used machine learning and artificial intelligence (AI) tools to analyze point-of-sale data.While this process started out largely manual, the system was able to recognize patterns in the data quickly and learn from them, enabling the restaurant to move toward a fully automated planning process.

    Rather than forcing planners to predict based on the information they had on hand at the moment, the restaurant chain used a seamless flow of current and historical data to begin to sense, anticipate, and even forecast demand and plan accordingly. The restaurant’s staff enjoyed reduced workloads, but the results also cascaded backward through the supply chain. Suppliers were able to plan more accurately, resulting in less waste, greater efficiency, and improved flexibility throughout the network.
    Supply chain planning has always been a data-rich, analytical process. But as linear supply chains evolve into interconnected digital supply networks (DSNs), powered by advanced technologies and interconnected systems, the way we think about supply chain planning could fundamentally shift.
    What is synchronized planning?
    Synchronized planning generally describes a state in which a constant flow of data from throughout the supply network enables organizations to accurately plan production to match actual demand. In an interconnected DSN, this filters across to other nodes, enabling suppliers, logistics, and fulfillment to more accurately plan and, ultimately, take action to provide the resources when and where they are needed. The result is a more dynamic, flexible, and efficient capability that combines traditional planning and execution.
    Organizations have typically used historical data to forecast future demand; however, in the absence of a broad pool of information, planning often was somewhat based on conjecture and could not account for unexpected shifts, from demand fluctuations to weather. The dynamic and integrated nature of the DSN, however, can lead to even more complex planning demands and what we think of today as “planning” could fundamentally change: Different business functions should be fully integrated with each other as well as with an ecosystem of suppliers, customers, inventory, and production to drive the strategic initiatives of the organization. In short, planning synchronization is important to the success of the network.
    Fragmentation of production. Global manufacturing does not affect just the smart factory but also customers. As production has become more interconnected and global, planners should account for the procurement and inventory demands of multiple physical locations each specific to local fluctuations in demand and supply, as well as the local availability of inputs. This fragmentation can bring new complexities and ever more data points to consider and adapt to, and can strain traditional planning to the breaking point.
    Shifting consumer expectations. Across all industries, customers seem to increasingly expect more individualized products and services, whether it be a personalized soda can, a drug formulated to each patient’s individual biochemistry, or large equipment built specifically fit to purpose. Most customers also expect these customized goods at a faster pace, and engage with companies across multiple channels on their path to purchase.
    Greater cost pressures. Higher pressures all around on margins, from investors, along with higher levels of industry consolidation appear to be driving the need to deliver lower-cost planning capabilities and optimize costs across the DSN. Companies can use financial and operations data from across their various locations and suppliers, cost fluctuations of various inputs, and other sources to develop a more holistic picture of their financial positions throughout the network. This can enable organizations to find opportunities to leverage lower-cost options or prepare for unexpected market fluctuations where possible.
    New technologies and data sources. Advanced technologies, more powerful computing capabilities, and a wealth of data from connected systems and external data sources allow organizations to streamline operations, anticipate market shifts, improve service, and encourage growth. Shift from sequential to concurrent planning. The first major shift to synchronous planning is typically simple multialgorithmic forecasting. In an interconnected, always-on environment, demand planning can generally no longer function as an isolated business activity conducted monthly. The speed of decision-making is simply too fast to allow for that. Rather, it needs to become a living process with constant inputs to allow for faster operational decision-making.
    Forecast causal factors and demand drivers. Organizations can perform near-term forecasting, a still-emerging but established approach, using structured and unstructured data available throughout their networks. The value of leveraging structured and unstructured data from legacy systems to get started on the journey to DSN adoption; data can similarly be used to help optimize margins and gain a better understanding of demand drivers.
    Pivot from deterministic to optimized supply. Because DSNs are “living” systems, planning itself begins to shift from a static to a dynamic approach, finding opportunities to minimize cost for inputs while still maximizing the ability to meet demands. Utilizing real-time demand, cost, and capacity information, organizations can optimize their supply plans, transportation, and inventory. This can result in improved service with fewer inefficiencies in terms of logistics and distribution: minimized transportation, storage, procurement, and production costs; improved fleet utilization; rebalanced distribution to demand centers; reduced freight expediting costs; and optimized inventory levels.
    Automate processes with AI. Still often considered an emerging area, this represents the objective for many organizations who wish to make the move to a “lights-out” planning function in which advanced technologies power autonomous planning capabilities. Organizations can enable a machine to replicate human actions and judgment by leveraging cognitive technologies on top of existing current assets and applications. Use of AI to automate planning processes can make them more scalable and flexible, with analyses performed with a high degree of accuracy, in real or near-real time.
    Create synchronous planning ecosystems. Beyond using advanced technologies to drive planning within their own “four walls” of the company, organizations can expand these capabilities throughout their ecosystem, among suppliers, sellers, and logistics partners. Put simply, a synchronous planning ecosystem connects the end to end supply chain to a consumer driven supply network, using automated decisions and sharing a single version of consumer demand in order to synchronize demand and supply across every node in the network in near real time.

    Redefined business value. Not only can synchronized planning reduce costs and improve asset efficiency, it can enable growth in strategic markets and speed up the order-to-cash process. Smart products connected to a DSN offer new opportunities as they capture data and insights that can be monetized across the network. Synchronous planning can help improve the cost basis in three areas: reduced overhead, cheaper raw materials and inventory holding cost, and continuous optimization to reduce disruptions.
    With respect to reduced overhead, the use of AI and cognitive analysis engines to perform and execute most of the analytical “thinking” in many supply chain planning activities can reduce the need for many heavily focused supply chain professionals to a few supply chain generalists who are more competent in analytics. Further, digital advances can help identify substitute materials or connect the purchasing engine to alternate lower-cost sources across the entire network in near-real time. Increased visibility and monitoring can decrease the holding cost of inventory to the network as forecasts are improved and service levels increase, decreasing the need for safety stock.
    Additionally, continuous monitoring and optimizing of the network flow can help to reduce disruptions and, therefore, cost. For example, sensors on trucks and pallets can identify when materials have been exposed to damaging shocks or out-of-tolerance temperatures. With this information, deliveries can be rerouted and substitute material planned immediately avoiding downtime and the need for lengthy root cause analysis.
    Improved asset efficiencies. Greater asset efficiency is also typically a byproduct of synchronous planning. The sharing economy can make better use of high-cost and under-capacity assets. For example, a company that only operates two shifts per day could sell its third shift to another company. Furthermore, automated inventory management can dramatically increase supply chain efficiency.

    Streamlined payment processes. A potential outcome and benefit of synchronous planning is the ability to speed up the order-to-cash process. With the ability to track and trace products in near-real time, delivery completion can be confirmed immediately. This allows for instantaneous invoice issuing and faster payment collection. Today, many companies have hundreds of millions of dollars tied up in clunky invoice processes; synchronous planning can help to unlock and put that money to work.

    Growing universal applicability. Synchronous planning can become more beneficial as DSNs grow more complex and extend across the globe. Specifically, it can assist in fast-moving or complex networks, networks that are fragmented or have many partners, and emerging economies with low digitization. Indeed, multinational corporations can have a distinct need for coordinated information flow and decision-making globally; synchronized planning can enable them to work more effectively with supply chain partners. In emerging economies in particular, companies often find themselves with immature supply chain synchronization capabilities. Yet they may be reluctant to invest heavily to build that capability for many reasons. As AI takes hold in the planning space, the need to develop in-market talent could be dramatically reduced, paving the way to accelerated sophistication in emerging markets. The digital revolution and the emergence of advanced AI, block chain, and other technological capabilities create incredible opportunities for the optimization and synchronization of business processes, dramatically improving business planning speed and effectiveness.

    Start with manageable challenges. Not all problems are created equal. Organizations may be able to see the end state of truly synchronous planning they’d like to achieve but may be unsure how to get started in a manageable way. Focusing on discrete tasks such as deployment planning can be a good first step, moving goods to satisfy a target in a much easier way.

    Grow from there as the results ripple outward. Moving to a fully autonomous, AI-driven planning function across the entire ecosystem of suppliers and stakeholders will not happen overnight. Rather than starting at the endpoint, organizations can look to scale upwards in terms of sophistication, which, in turn, can be enabled by the data they continue to pull in from across their networks.

    Artificial Intelligence is impacting every industry, and supply chain Management is no deferent. While companies now have access to nearly unlimited data, I believe just having data is not enough to improve supply chain performance. Instead, we can leverage artificial intelligence to use this data to make the right decision to move forward faster, smarter and cheaper. Without the good data, however, AI may just as well be making the wrong decision.



    Last modified on Thursday, 17 January 2019 13:23

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