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Speed in decision-making, speed in reducing cycle-times, speed in operations, and speed in continuous improvement.

According to Gartner, supply chain organizations expect the level of machine automation in their supply chain processes to double in the next five years. At the same time, according to a recent study, annual Industrial IoT (IIOT) spending by growing companies is estimated to be a whopping $500 billion by the year 2020.

As global supply chains are increasing in complexity, the margin for error is rapidly shrinking. With increasing competition in a connected digital world, it becomes even more critical to maximize productivity by reducing uncertainties of any and all kinds. Mounting expectations of supersonic speed and efficiencies between suppliers and business partners of all types further underscores the need for the industry to leverage the prowess of the Artificial Intelligence (AI) in supply chains and logistics

Artificial Intelligence (AI) in Supply Chain & Logistics

Artificial Intelligence and Machine Learning (ML) are already beginning to change the face of the supply chain industry, which will further exacerbate the divide between the winners and the losers. By culling out deep-rooted inefficiencies and uncertainties, Artificial Intelligence and Machine Learning drive enterprise-wide visibility into all aspects of the supply chain and with granularity and methodologies that humans simply can’t mimic at scale. Ai in supply chains is helping to deliver the powerful optimization capabilities required for more accurate capacity planning, improved productivity, high quality, lower costs, and greater output, all while fostering safer working conditions.

When faced with a pandemic like COVID-19, establishing a good understanding of the impact on supply chains and contingency plans can help manufacturing companies deal with uncertainties in the right way.

The COVID-19 epidemic has resulted in severely affecting thousands of supply chains globally, the economic impact of which will linger for months to come. 

According to The Organisation for Economic Co-operation and Development (OECD), the Coronavirus could cut global economic growth in half, with several industries across the board facing a major fall off. China, the world’s second-largest economy and several internal supply chains plunged as the Coronavirus spread from here to other countries in Asia, Australia, Europe, the Americas, and the Middle East. As a result, preventive actions that intended to block the further spread of the virus, including travel restrictions and large scale quarantine, have only resulted in further plummeting and disrupting global food, retails and medical supply chains, halting critical business operations, and freezing revenues.

A recent survey by the Institute For Supply Chain Management, nearly 75% of companies reported some sort of supply chain disruptions owing to coronavirus-related transportation restrictions, and the figure is expected to rise further over the next few weeks. This, in fact, is just one of the many facets of the global COVID-19 impact but a significant one. 

Further, a study from Dun & Bradstreet suggests on a global level, 51,000 companies have “one or more direct or Tier 1 supplier,” from China and an additional five million companies have Tier 2 suppliers there, with 938 of those being Fortune 1000 companies.

Scouting for the Needle in the Supply Chain Haystack 

While smart investments in technology like Artificial Intelligence (AI) are helping to capture huge amounts of previously disaggregated data, there arises a bigger question:  Is there a way to detect business bottlenecks sooner, given this ever-exploding pile of mashed-up data?

To guarantee results, supply chain managers need to be able to cut through this noise with a powerful tool to make use of this vast amount of data with targeted operational analytics to detect, quantify and rank the bottleneck operations building-up in business processes early on.

The Impact on Supply chains is twofold – 

First, companies must closely monitor short-term and long-term demand and inventory to account for any production loss in case of factory closures and economic slowdown. 

Second, retailers who are faced with inventory depletion as consumers stock up for potential quarantine or extended stays at home. This has severely affected the smooth supply chain functioning across the globe due to the “panic buying” ripple effect. 

Few questions that arise as a result of the above:

  1. How can food and retail supply chains use the power of Artificial Intelligence (AI)-lead advanced analytics in supply chains to plan, prepare and manage this crisis?
  2. How can manufacturers effectively leverage AI to manage demand volatility and mitigate this supply shock?
  3. How can AI-lead intelligent automation and analytics help create contingency plans and create safe working environments? 

Benefits of AI-Powered Supply Chains

Studies suggest that AI and Machine Learning (ML) can deliver unprecedented value to supply chain and logistics operations. From cost savings through reduced operational redundancies and risk mitigation, to enhanced supply chain forecasting and speedy deliveries through more optimized routes to improved customer service, Ai in Supply Chain is being preferred by several manufacturers globally. 

According to McKinsey, 61% of manufacturing executives report decreased costs, and 53% report increased revenues as a direct result of introducing Ai in supply chain. Further, more than one-third suggested a total revenue bounce of more than 5%. Some of the high impact areas in supply chain management include planning and scheduling, forecasting, spend analytics, logistics network optimization and more.

1) Bolstering Planning & Scheduling Activities 

Most often, supply chain managers struggle to establish an end-to-end process to plan for profitable supply network accounting, especially while being faced daily with increasing globalization, expanding product portfolios, greater complexity, and fluctuating customer demand. Lack of complete visibility into existing product portfolios due to unplanned events, plant shutdowns, or transportation problems makes this task even more convoluted. A typical smart supply chain framework includes multiple products, spare parts, and critical components, which are responsible for accurate outcomes. In many supply chain industries, these products or parts can be defined using multiple characteristics that take a range of values. This can result in a high number of product configurations and applications. Also, in many cases, products and parts are also phased-in and phased-out regularly, which can cause proliferation leading to uncertainties and the bullwhip-effects up and down the supply chain.

By implementing AI in supply chain and logistics, supply chain managers can enhance their decision making by predicting building-up bottlenecks, unforeseen abnormalities, and solutions in order to streamline production scheduling that otherwise tends to be highly variable due to dependencies on manufacturing operations management. Furthermore, AI in supply chain also led to accurate predictions and quantification of expected outcomes across different stages of the schedule enable the scheduling of more optimal alternatives as and when such interruptions occur during execution.

2) Intelligent Decision-Making:

AI-lead supply chain optimization software amplifies important decisions by using cognitive predictions and recommendations on optimal actions. This can help enhance overall supply chain performance. It also helps manufacturers with possible implications across various scenarios in terms of time, cost, and revenue. Also, by constantly learning over time, it continuously improves on these recommendations as relative conditions change. 

3) End-End Visibility:

With the complex network of supply chains that exist today, it is critical for manufacturers to get complete visibility of the entire supply value chain, with minimal effort. Having a cognitive AI-driven automated platform offers a single virtualized data layer to reveal the cause and effect, to eliminate bottleneck operations, and pick opportunities for improvement. All of this using real-time data instead of redundant historical data. 

4) Actionable Analytical Insights:

Several companies today, lack key actionable insights to drive timely decisions that meet expectations with speed and agility. Cognitive automation that uses the power of AI has the ability to sift through large amounts of scattered information to detect patterns and quantify tradeoffs at a scale, much better than what’s possible with conventional systems.

5) Inventory and Demand Management

One of the biggest challenges faced by supply chain companies is maintaining optimum stock levels to avoid ‘stock-out’ issues. At the same time overstocking can lead to high storage costs, which on the contra, don’t lead to revenue generation either.

Bringing in the perfect balance here is mastering the art of inventory and warehouse management.

When applied to demand forecasting, AI & ML principles create highly accurate predictions of future demand based. For example, forecasting the decline and end-of-life of a product accurately on a sales channel, along with the growth of the market introduction of a new product, is easily achievable.

Similarly, ML & AI in supply chain forecasting ensures material bills and PO data are structured and accurate predictions that are made on time. This empowers field operators with data-driven operations to approach in maintaining the optimum levels required to meet current (and near-term) demand.

6) Boosting Operational Efficiencies

Besides the treasures still largely trapped in disaggregated data system silos at most corporations, IoT-enabled physical sensors across supply chains now also provide a goldmine of information to monitor and manipulate supply chain planning processes too. With billions of sensors and devices, analyzing this pot of gold manually can create huge operational resource wastage and delayed production cycles. This is where intelligent analytics powered by AI in supply chain and logistics delivers immense value. When supply chain components become the critical nodes to tap data and power the machine learning algorithms, radical efficiencies can be achieved. The value is realized through the application of machine learning in price planning. The increase or decrease in the price is governed by on-demand trends, product lifecycles, and stacking-the-product against the competition. This data is priceless and can be used to optimize the supply chain planning process for event greater efficiencies.

7) Unlocking Fleet Management Efficiencies

One of the most underrated aspects of the supply chain is the fleet management process. Fleet managers orchestrate the vital link between the supplier and the consumer and are responsible for the uninterrupted flow of commerce. Along with rising fuel costs and labor shortages, fleet managers constantly face data overload issues. Managing a large fleet can easily seem like a daunting task more akin to an air traffic controller. If you can’t find the information you need quickly, or properly utilize the data you collect, you may find your data pool quickly turning into an unproductive swamp. AI in supply chain and logistics provides real-time tracking mechanisms to gain timely insights including the optimal times by where, when, and how deliveries must and should be made. Such powerful multi-dimensional data analytics further aides in reducing unplanned fleet downtime, optimizing fuel efficiencies, detecting and avoiding bottlenecks. It provides fleet managers with the intelligent armor to battle against the otherwise unrelenting fleet management issues that occur on a daily basis.

8) Streamlining Enterprise Resource Planning (ERP)

A study by Panorama Consulting determined that “63% of manufacturing companies exceed their ERP budgets with average implementation costs overrun rising to $3 million.” Because supply chain managers deal with heterogeneous purchasing, procurement, and logistics across global supply chains, they tend to have more complex business processes than out-of-the-box traditional software can handle. AI in supply chain and logistics helps streamline the ERP framework to make it future-ready and connect people, processes, and data in an intelligent way. Finally AI correctly implemented on ERP and related data systems data becomes more receptive and event-driven over time, while processing greater amounts of data, to intelligently learn, quantify, rank, and prescribe remedies proactively and more frequently over time. For the true operations and supply chain geeks, the true Kaizen AI leveraging Yokoten ML gold at the end of the efficiency rainbow. 

9) The AI-powered Supply Chain is Here to Stay

Gartner predicts that “The rise of IIoT will allow supply chains to provide more differentiated services to customers, more efficiently”.

As supply chain companies shift their focus from products to outcomes, traditional business models will become dated and then obsolete altogether, with the bodies and brands of the laggards and losers scattered along the way. With global supply chains strengthening their roots, competitive pressures will force firms to extract every possible ounce of cost from their respective operations. This is even more pronounced for local, regional, and national firms that are limited in their economies of scale, currency hedge capabilities, market concentration, and limited technology and operational budgets. In such cases, looking at and embracing the winning SaaS and cloud solutions is a strategy for keeping up, and getting ahead of, the international conglomerates with massive IT and OT budgets, and greater margins of error in the near-term for making poor and expensive supply chain optimization technology mistakes with expensive consultants. 

With all these influences coming to bear simultaneously, we are about to see a paradigm shift from simple reactive intelligence to predictive, adaptive and continuous learning systems that drive better decisions for continuous improvements using ML and AI in supply chain and ML on your existing data sources.

AI in Supply Chain can help in Optimization

According to PwC, AI applications have the power to transform the way business is done and contribute up to $15.7 trillion to the global economy by 2030.  Today, AI can seed in the much needed exceptional agility and precision in supply chain optimization. It can also trigger a transformational increase in operational and supply chain efficiencies and a decrease in costs where repetitive manual tasks can be automated.

Click here to know how choosing a good AI-driven Supply Chain Optimization Software can help manufacturers leverage AI-enabled efficiencies for optimum results.

Readying Your Supply Chain for Artificial Intelligence

Before investing heavily in new technologies, You must first assess your state of digital readiness. This assessment involves three steps:

1) Set realistic expectations. 

Every organization must conduct a self-awareness test before committing to AI implementation. Gather key internal stakeholders and ask thoughtful questions that scrutinize the targets and goals of a proposed implementation.

If you haven’t yet had formal discussions about new technology integrations, decide what these integrations might help you achieve. Quantify your broad expectations for the short and long term. Weigh those against the hypothetical costs of implementation — including technology-acquisition expenses; the effects of temporary productivity disruption; and the labor costs of installation, setup and training.

At this stage, it can be useful to establish new KPIs to measure the impact of integrating AI in supply chain management. These should be related to the company’s traditional high-level goals. At a more granular level, professionals should understand what AI and automation would contribute to specific company operations. Digital transformation doesn’t occur in a vacuum —existing personnel and processes across the organization will be impacted, even if the implementation is on a relatively small scale.

Once you have (1) an idea of the expected ROI of AI, (2) the potential impacts of digital transformation and (3) an estimate of costs, start thinking about your project timeline. Here, your focus should be on long-term efficiency gains, rather than immediate fixes. Your investment is not going to pay off right away. The benefits of AI supply chain management are cumulative in nature, and you’ll likely have to make near-term sacrifices to achieve significant future advantages.

2) Know how the company currently uses technology. 

After understanding what you hope to gain from AI in supply chain from a broader operational standpoint, assess your organization’s technology readiness. That assessment should be focused on three components: people, skills and tools.

Start by consulting with human resources staff to gain an understanding of the potential personnel impacts of technological transformation. Chances are good that you’ll need to bring in personnel to fill new roles in your organization, so you’ll need a plan for identifying and recruiting those people. You may also need to train existing employees and ensure they understand how their responsibilities and workflows will change during and after implementation.

Examine your existing technology stack and discuss its advantages and limitations with relevant stakeholders. Interoperability is a critical measure of tech readiness, so try to get a sense of how well your various technologies are working together now.

Do this by asking questions: Why is a language used for this application, and is it used for any others? How efficient are the data collection and storage tools, and how easy is it to retrieve data on demand? To what extent are we leveraging open-source technologies? Are our critical applications closed and dependent on vendor services and customization, or are they interoperable and application programming interface (API)-ready?

Looking ahead, you’ll also want to think about where your new tech stack will be located —on-site; in a data warehouse; in a private, hybrid or public cloud; or some combination of those. Who will need access to it (and from where) to keep operations running smoothly and KPI benchmarks met? In sum, this assessment requires a combination of meticulous planning at the personnel and application levels, and big-picture thinking about the state of the entire enterprise.

3) Dive into your data. 

Data is the fuel that feeds AI, and you’ll need a lot of it to maximize your returns. Most business leaders know this, and they assume that they don’t have enough data to make an AI investment worthwhile. This is a common misconception.

Within most organizations, there is usually an abundance of data being generated, stored and forgotten. For these companies, the challenge isn’t collecting new data — it’s locating, consolidating and analyzing existing data. Often, most of a company’s data is collected for compliance purposes or use during audits.

Companies will want to consolidate their business and operations data — regardless of the amount — to assess overall data readiness. And your organization probably has more data than you think. When stakeholders claim there isn’t enough data, that it isn’t clean, or that they’re unsure which data is relevant, they are succumbing to a common fallacy. They assume scarcity when availability is the real issue. Siloed data isn’t helpful to most operations, so it might as well not even exist.

Before implementing AI in supply chain management, organizations might have to spend considerable time and effort breaking down silos, which often are intertwined with company culture and deeply embedded business processes.

A lack of commonality between different personnel types, such as information technology, operations technology, and operations and business, is also a culprit. Each of these teams has a different core objective and looks at data differently. What may be immensely valuable to one department is often just noise to another, and in many organizations, a lack of regular interaction among teams leads to a lack of communication about important things like data.

Digital transformations can force internal teams to overcome silos and even restructure to facilitate increased collaboration. Ideally, however, a company should remove silos before beginning a digital transformation. Doing so will not only make the transition process easier and more effective, but provide insight on if the business is ready for such a transformation. If you can’t compel teams to work together and share important business information as a matter of course, you might not be ready.

AI is already beginning to transform the manufacturing landscape and revolutionize supply management. If your organization hasn’t yet begun a digital transformation, and you’re feeling left behind, don’t worry: Machine learning is still in its nascent stage, and AI won’t disappear. That’s not to say you should wait for AI technologies to fully mature before exploring their usefulness to your organization. Instead, follow the steps above to determine your company’s digital readiness. That exercise should inform your next steps.

If you’re not ready for transformation, prepare a plan to implement artificial intelligence in supply chain. If you are, start creating and executing your implementation plan. The manufacturing sector is changing fast, and you can’t afford to sit still.

Implementing AI integration

Today’s supply chain executives are short on time, and having multiple meetings to discuss solution implementation is a burden they can’t afford. Integrated AI tools provide actionable insights that eliminate bottlenecks and unlock real-time value. That’s important because supply chain companies need more execution — not more analysis.

Implementing a full AI solution might seem daunting and cost-prohibitive, and it’s true that costs can range from millions to tens of millions of dollars, depending on the size of the organisation. Businesses must first undergo a full digitisation process and then implement an analytics program before they can integrate AI tools. Oftentimes, companies waste significant resources in this process because they don’t incorporate the end user feedback and end up having to backtrack to address unanticipated problems.

But there is an alternative. An agile approach enables organisations to begin implementing AI in cost-effective ways. By integrating third-party vendors, they can start where they are, learn what works for their businesses, and scale up as needed. This tactic allows for much faster AI integration than building a new platform from the ground up or building on top of legacy solutions.

Here are some of the benefits associated with agile AI strategies:

1. Maximised data

Supply chain companies excel at managing the flow of goods and services, and legacy platforms were designed to handle the data associated with these processes. But because they were built before AI and machine learning, they’re not equipped for the demands of today’s supply chain industries. Newer platforms are built with technology stacks that can handle data capture, storage, processing, analysis, and visualisation, and they’re designed for quick integration. Rather than wait for legacy vendors to build machine learning algorithms into their platforms, supply chain companies can take advantage of new tools immediately.

2. Automated critical analyses

Supply chain operations are complex, and it’s difficult for a human to recognise patterns in inefficiencies, even with the aid of traditional business intelligence solutions. Operations teams can reduce the amount of time it takes to analyse data by leveraging AI tools. AI works 24/7, and its sole job is to analyse inputs and highlight trends. Analysts can use those insights to identify potential areas of improvement, forecast demand and inventory levels, schedule maintenance and downtime activities, and predict potential equipment failures.

As an example of how this is working in another industry, consider AI’s role in agriculture. Weather forecasting and smart image processing enable growers to identify pests, weeds, and disease early on so they can protect healthy crops. Predictive analytics enable them to gauge how environmental factors will influence their crop yields, and real-time soil monitoring helps them adjust water levels to optimise growth. Supply chain companies can enjoy similar real-time and predictive benefits through AI solutions.

3. Enhanced competitiveness

AI is not just a nice-to-have; it’s an imperative to stay competitive. These tools reduce processing time and facilitate smarter, faster decision-making. AI provides a view into market trends and even weather patterns that might impact operations, and that data can make all the difference in maintaining strong customer relationships and industry credibility. Having a view into when, where, and why bottlenecks occur can transform a company’s workflows and radically improve a supply chain company’s profitability.

By partnering with third-party AI vendors, supply chain businesses can move away from the cumbersome old model of waiting for legacy platforms to catch up with new technologies. The most successful businesses will be those that apply scalable, easily integrated solutions to their existing processes.

Guest Post by Anita Raj, AI & Industry 4.0 Evangelist

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