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Transforming Supply Chain Management with Intelligent Software

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Robotic Process Automation (RPA) technology to automate business tasks with AI. Concept with expert setting up automated software on laptop computer. Digital transformation and change management.

Supply chain project management has evolved, shifting from a focus on efficiency to navigating a complex landscape influenced by globalization, technology, and changing consumer preferences. The vulnerabilities exposed during events like the COVID-19 pandemic underscore the need for adaptability. The pandemic posed challenges, disrupting production, leading to closures, and causing delays and increased costs in global transportation.

Additionally, unpredictable shifts in consumer behavior created demand fluctuations, impacting industries differently. Inventory management became more challenging, resulting in shortages or excess inventory. Supplier reliability and labor shortages further strained production capacity.

The crisis highlighted the necessity for digital transformation, remote work, and technology adoption in supply chain management. Regulatory changes and economic downturns added complexity to cross-border supply chains. Financial strain emphasized the importance of robust risk management, leading to a renewed focus on building resilient and agile supply chains. Businesses now invest in technology, diversify suppliers, and reassess inventory strategies.

Intelligent software enhances decision-making and risk management, facilitating collaboration throughout the supply chain. For instance, during sudden demand changes due to lockdowns, the software swiftly analyzes data, enabling real-time adjustments to inventory, production, and distribution. This adaptability ensures a responsive and agile supply chain, surpassing traditional approaches for efficiency and customer satisfaction.

The promise of intelligent software

In the current technological realm, intelligent software signifies more than just automation; it melds advanced algorithms, artificial intelligence, and machine learning to emulate human cognitive abilities. Unlike its conventional counterparts, this software learns, adapts, and autonomously recommends actions, excelling in data analysis and trend prediction. Its continuous adaptation based on feedback refines its performance over time.

How intelligent software could make a difference in specific situations

1. Demand volatility amidst global events.

The COVID-19 pandemic triggered significant demand shifts, straining supply chains. Intelligent software, with real-time analytics, could have monitored consumer behaviors, identified disruptions, and gauged inventory levels. Such insights would have refined demand forecasts, allowing organizations to adjust production and prioritize shipments, mitigating stockout risks and excess inventory costs.

2. Supply chain disruptions due to geopolitical tensions.

Geopolitical uncertainties can disrupt supply chains. Intelligent software could pre-emptively identify vulnerabilities, highlighting dependencies on specific regions or suppliers. Through simulations and alternative sourcing evaluations, it would have enabled organizations to devise resilient strategies, ensuring uninterrupted operations amid external disruptions.

3. Quality control and recall management.

Product recalls pose financial and reputational risks. Intelligent software, with advanced analytics, monitors production for deviations from quality standards. Using predictive analytics, it could anticipate issues, facilitating timely interventions, minimizing recall extents, and preserving brand reputation.

4. Transportation and logistics optimization.

Efficient transportation is crucial for supply chain success. Intelligent software, leveraging predictive analytics, would analyze factors like traffic and weather to optimize transportation strategies. This would reduce delays, enhance resource use, and boost supply chain effectiveness.

5. Inventory management in seasonal industries.

Seasonal industries face inventory challenges due to fluctuating demand and product perishability. Intelligent software, utilizing machine learning, analyzes sales trends and market dynamics to offer precise demand forecasts and inventory recommendations. This ensures optimal inventory levels, reduces holding costs, and capitalizes on market opportunities.

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A Methodology to Quantify the Cost of Supply Chain Risk Management Strategies

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The importance of supply chain risk management has grown exponentially since the onset of COVID-19.

The importance of supply chain risk management has grown exponentially since the onset of COVID-19.

You are the manager of a firm’s large global supply chain. The philosophy that guides your network planning decision-making is to minimize total landed costs subject to meeting defined customer service goals. In recent years, especially since the onset of COVID-19 and the supply chain vulnerabilities exposed and unleashed by this pandemic, you have struggled to find the right balance between minimizing costs and minimizing risks. In particular, how do you quantify the costs of different risk mitigation strategies such as using additional suppliers in disparate geographies, maintaining extra plants and/or capacity, and other similar strategies? How can you view these decisions from a holistic perspective?

In this article, we offer an illustration of a technique to develop a quantitative perspective on the cost of risk management strategies. This quantitative approach can be coupled with other more qualitative factors to facilitate the development of a well-informed supply chain risk management decision-making process and strategy.

We begin with a brief review of the types of risk that firms must assess in creating their risk management strategy. This review provides background context for the methodology we will introduce. Further, recognizing that we cannot explore in detail the topic of risk management strategies in this short article, we also provide additional references at the end of this article for readers interested in exploring this topic in depth. After our brief review of risk types and strategies, we then present our risk management quantitative methodology using a manufacturing network design strategy example for illustrative purposes.

Risks in Developing a Supply Chain Risk Management Strategy

When constructing a supply chain risk management strategy, a firm can assure that it undertakes a holistic view of all potential threats by first evaluating general categories of risk, and then considering specific individual risks. Why take this two-step approach? The danger of immediately focusing on a few specific known risks to a firm before first performing a broad review across all risk types is that immediately diving into specifics may cause some less obvious but important risks to be overlooked. Hence the need for a two-step approach.

Quantitative Methodology for Supply Chain Risk Management Assessment

To illustrate our methodology for quantifying the cost of a supply chain risk management strategy, let’s assume that a firm is developing its global manufacturing and distribution network strategy for the next three to five years. In this example, we will focus on plant locations and capacity plans, and note that a similar process would occur for distribution network locations. For illustrative purposes, we narrow our example to evaluations of supply, operational and natural risks only.

Conclusion

The relative importance of supply chain risk management was increasing rapidly in practice prior to the coronavirus pandemic, and it has grown exponentially since the onset of COVID-19. Making well-informed decisions on the appropriate level of risk mitigation actions to invest in represents a difficult challenge for a firm and its supply chain professionals. Good decision-making requires a careful balancing of both qualitative and quantitative factors.

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5 Ways Analytics Are Disrupting Supply Chain Management

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5 Ways Analytics Are Disrupting Supply Chain Management

5 Ways Analytics Are Disrupting Supply Chain Management

Harmonizing supply chain management analytics will put organizations on a path to automating their operations.

The evolution of infotech increased customer expectations, economic behavior, and other competitive priorities have caused firms to modify themselves in the current business landscape. Supply chains globally are becoming more complex, thanks to globalization and the consistently changing dynamics of demand and supply. As per a forecast by Gartner, the global supply chain management market was valued at USD 15.85 billion in 2020 and is expected to reach almost USD 31 billion by 2026.

Businesses are channeling the power of big data analytics to disrupt transformations at all levels of supply chain management. Data started as a fundamental component of digital transformation and is now a revolutionary concept. It is the key to achieving breakthroughs in supply chain management systems, and more organizations are integrating data analytics to mine data for proactive insights and accelerate intelligent decision-making.

Big data implementation in supply chain management addresses several issues, from strategic to operational to tactical. It includes everything from building efficient communication between suppliers and manufacturers to boosting delivery times. Decision-makers can utilize analytics reports to increase operational efficiency and boost productivity by closely monitoring the system’s performance at each level.

What is Big Supply Chain Analytics, and How Does it Work?

Integrating big data analytics with the supply chain makes big supply chain analytics enable business executives to compute better growth decisions for all possible maneuvers by combining data and quantitative methodologies. Notably, it adds two features.

First, it broadens the dataset for analysis beyond internal data stored in existing SCM and ERP systems. Second, it uses advanced statistical techniques to analyze the new and existing data. This generates new insights that help make better decisions for improving front-line operations and strategic decisions like implementing the best supply chain models.

Here are five ways big data and analytics are disrupting supply chain management:

1) Improved demand forecasting

Demand forecasting is one of the crucial steps in building a successful supply chain strategy. With data science and analytics in play, businesses experience automated demand forecasting. This assists them in quickly responding to fluctuations in the market and streamlining the optimal stock levels every time.

2) Enhanced production efficiency

Data science and analytics play a significant role in gauging organizational performance. Accurate application of big data analytics can help organizations track, analyze, and share employee performance metrics in real time. You can identify excellent employees who are struggling to maintain a consistent performance. This could be quickly done with IoT-enabled work badges, which exchange information with sensors installed in production line units.

3) Better sourcing and supplier management

Supply chain management systems have empowered organizations to collate data on multiple suppliers. Using data science solutions, you can leverage this data to gain insights into the historical record of any supplier. With this, you can gauge based on crucial metrics such as compliance, location, reviews, feedback, services, etc.

4) Better warehouse management

Warehouses are acquiring modern technology and have started installing sensors to collect data on the inventory flow. This helps you build an extensive database containing information based on the weight and dimensions of the packages. With sensors installed in your warehouse, you can identify bottlenecks that obstruct the flow and can be easily resolved at the earliest with the big data-fueled systems.

5) Improved distribution and logistics

Order fulfillment and traceability are essential for business productivity and customer satisfaction. Logistics have traditionally been cost-focused and effectively look for ways that provide them competitive advantages. Data science solutions enable logistic providers to leverage data analytics to improve their operations. For instance, they use fuel consumption analytics to improve driving efficiency. With GPS technology, they can track real-time routing of deliveries and reduce long waiting times by allocating nearby warehouses.

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How business intelligence is helping global businesses succeed

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In today’s always connected and hyper-competitive world, growing market share is a challenge for any organisation. Industry leaders understand that to get an edge over their competitors they need new and better insights into their business to help identify hidden risks and mitigate them and inform their decision making in everything from finding supply chain inefficiencies to uncovering new avenues for sales.

Having actionable intelligence can be the difference between success and failure, but sorting through the wealth of data every modern global business generates is a complex task and it is too easy to miss a critical information point. Business intelligence platforms help companies sift, sort, and process this data into different types of reports with actionable insights that highlight any weaknesses to address and strengths that should be built upon.

Here are four ways business intelligence can help a global business find success.

Help with product pricing

Pricing a product or service can be a difficult question. Business leaders need to find a price point within a specific market that is sufficiently low to attract new customers away from their competitors, but sufficiently high that they can drive profits and create a growing and successful company.

It is difficult enough to try and determine the best price within a single geographic market, such as the UK, but when you look to expand the business abroad, each geographical market has its own unique set of economic and market factors that need to be taken into account. The company will need to set a different price targeted to each local market conditions, and business intelligence can bring together all the relevant data and help stakeholders find the best pricing strategy for wherever they plan to launch.

Identify supply chain efficiencies and weaknesses

Modern supply chains cross continents, and a few days delay at a port in China or a ship that has taken a wrong turn can have roll on impacts throughout a business. The data at each point in this supply chain may be stored in different systems that may be region or sector specific, and trying to combine all that data can be a slow and laborious process.

Business intelligence platforms can help companies integrate these disparate sources of data and create insights such as where in the supply chain is the weakest link and where companies should possibly look for backup solutions should issues arise, and also where stock is left sitting in warehouses for long periods, costing money for storage and creating expensive inefficiencies.

Nurture customer loyalty

The sales and marketing investment required to find new customers is expensive, and so all businesses need to nurture their relationship with the current customers to maintain profitability. These relationships are more challenging to maintain for businesses with overseas operations, as priorities and social niceties vary significantly from region to region, with what some would consider good manners in one country sometimes considered rude in another.

The leader of well established global company understands that standardised customer engagement programmes often do not work well across borders, and local solutions need to be devised. However, maintaining a variety of different programmes around the world is burdensome and it can be a struggle to compare how each performs.

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