Tag Archives: supply

Big data analytics technology: disruptive and important?

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Of all the disruptive technologies we track, big data analytics is the biggest. It’s also among the haziest in terms of what it really means to supply chain. In fact, its importance seems more to reflect the assumed convergence of trends for massively increasing amounts of data and ever faster analytical methods for crunching that data. In other words, the 81percent of all supply chain executives surveyed who say big data analytics is ‘disruptive and important’ are likely just assuming it’s big rather than knowing first-hand.

Does this mean we’re all being fooled? Not at all. In fact, the analogy of eating an elephant is probably fair since there are at least two things we can count on: we can’t swallow it all in one bite, and no matter where we start, we’ll be eating for a long time.

So, dig in!

Getting better at everything

Searching SCM World’s content library for ‘big data analytics’ turns up more than 1,200 citations. The first screen alone includes examples for spend analytics, customer service performance, manufacturing variability, logistics optimisation, consumer demand forecasting and supply chain risk management.

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Why Supply Chains Need Business Intelligence

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Companies that want to effectively manage their supply chain must invest in business intelligence (BI) software, according to a recent Aberdeen Group survey of supply chain professionals. Survey respondents reported the main issues that drive BI initiatives include increased global operations complexity; lack of visibility into the supply chain; a need to improve top-line revenue; and increased exposure to risk in the supply chain. Fluctuating fuel costs, import/export restrictions and challenges, and thin profit margins are driving the need for businesses to clearly understand all the factors that affect their bottom line.

Business Intelligence essentially means converting the sea of data into knowledge for effective business use. Organizations have huge operational data that can be used for trend analysis and business strategies. To operate more efficiently, increase revenues, and foster collaboration among trading partners companies should implement BI software that illuminates the meaning behind the data.

There is a vast amount of data to collect and track within a supply chain, such as transportation costs, repair costs, key performance indicators on suppliers and carriers, and maintenance trends. Being able to drill down into this information to perform analysis and observe historical trends gives companies the game-changing information they need to transform their business.

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Walmart and Target are refusing to surrender to Amazon

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While many public companies focus their attention on embellishing their quarterly results, Amazon has always taken the long view.

The online retailer leader has invested heavily in infrastructure including a nationwide network of warehouses, robots which help ship orders, and even predictive technology that helps the company know what a customer plans to buy before he or she orders it.

Amazon even has a pioneering deal with the United States Postal Service which allows for Sunday delivery in some markets.

All of this has not come cheap, and it has hurt Amazon’s short-term profitability in some quarters, but it has helped the company build a strong competitive advantage over its chief rivals Wal-Mart and Target.

Those two physical retailers are struggling to change their supply chains to meet the needs of individual digital customers rather than stores. That’s a radical switch that requires major changes to how both brick-and-mortar chains operate.

But if either Wal-Mart or Target can hope to compete with Amazon, they have to recreate the digital leader’s ability to ship millions of products in a two-day window efficiently. Both companies seem to at least understand the problem and are taking steps to catch up.

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One step ahead: How data science and supply chain management are driving the predictive enterprise

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DHL, the world’s leading logistics company, today launched its latest white paper highlighting the untapped power of data-driven insight for the supply chain. The white paper has revealed that most companies are sitting upon a goldmine of untapped supply chain data that has the ability to give organizations a competitive edge. While this wealth of supply chain data already runs the day-to-day flow of goods around the world, the white paper has revealed a small group of trailblazing companies are utilizing this data as a predictive tool for accurate forecasting.

“The predictive enterprise: Where data science meets supply chain” is a white paper by Lisa Harrington, President of the lharrington group LLC that was commissioned by DHL to identify the opportunities available to companies to anticipate and even predict the future. It encourages companies to get ahead of their business and direct their global operations accordingly.

Data mining, pattern recognition, business analytics, business intelligence and other tools are coalescing into an emerging field of supply chain data science. These new intelligent analytic capabilities are changing supply chains – from reactive operations, to proactive and ultimately predictive operating models. The implications extend far beyond just reinventing the supply chain. They will help map the blueprint for the next-generation global company – the insight-driven enterprise.

Jesse Laver, Vice President, Global Sector Development, Technology, DHL Supply Chain, said, “At DHL, we’re helping our customers get ahead of the competition by working with them to harness the wealth of data information from across their businesses, allowing us to develop smarter supply chain solutions that factor in their wider business operations. For our technology customers, we use data analytics to predict what’s going on in the supply chain, such as what products are in high demand, so we can tailor our solutions accordingly.”

While supply chain analytics technologies and tools have come a long way in the last few years, integrating them into the enterprise is still far from easy. Companies typically progress through several stages of maturity as they adopt these technologies. The descriptive supply chain stage uses information and analytics systems to capture and present data in a way that helps managers understand what is happening.

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Global supply chain threatened by terror and flow of migrants

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Supply chains are suffering a rise in costs and multiple disruptions due to the reintroduction of border controls in Europe and the rise of radical Islam in the Middle East.

The Charted Institute of Procurement and Supply (CIPS) – with a presence in 150 different countries – confirms that ISIS activity and Russia’s rigid attitude in world politics have contributed to the heightened risk.

Meanwhile, the migrant crisis is making some European countries close their borders, as is happening in Hungary, Croatia and Slovenia. Crossing the border in these countries can take up to 90 minutes, while other activities such as the transport of livestock have stopped entirely for several days in the past month.

This supply chain issue has caused the delivery prices for some German companies to rise by as much as 10 per cent and has increased the risk of the supply chains in other several countries of the Middle East and North Africa, such as Kuwait, Bahrain, Turkey and Tunisia.

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10 ways big data is revolutionising supply chain management

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Big data is providing supplier networks with greater data accuracy, clarity, and insights, leading to more contextual intelligence shared across supply chains.

Forward-thinking manufacturers are orchestrating 80% or more of their supplier network activity outside their four walls, using big data and cloud-based technologies to get beyond the constraints of legacy enterprise resource planning (ERP) and supply chain management (SCM) systems. For manufacturers whose business models are based on rapid product lifecycles and speed, legacy ERP systems are a bottleneck. Designed for delivering order, shipment and transactional data, these systems aren’t capable of scaling to meet the challenges supply chains face today.

Choosing to compete on accuracy, speed and quality forces supplier networks to get to a level of contextual intelligence not possible with legacy ERP and SCM systems. While many companies today haven’t yet adopted big data into their supply chain operations, these ten factors taken together will be the catalyst that get many moving on their journey.

The ten ways big data is revolutionising supply chain management include:

  1. The scale, scope and depth of data supply chains are generating today is accelerating, providing ample data sets to drive contextual intelligence.
  2. Enabling more complex supplier networks that focus on knowledge sharing and collaboration as the value-add over just completing transactions.
  3. Big data and advanced analytics are being integrated into optimisation tools, demand forecasting, integrated business planning and supplier collaboration & risk analytics at a quickening pace.
  4. Big data and advanced analytics are being integrated into optimisation tools, demand forecasting, integrated business planning and supplier collaboration & risk analytics at a quickening pace.
  5. Using geoanalytics based on big data to merge and optimise delivery networks.
  6. Big data is having an impact on organizations’ reaction time to supply chain issues (41%), increased supply chain efficiency of 10% or greater (36%), and greater integration across the supply chain (36%).
  7. Embedding big data analytics in operations leads to a 4.25x improvement in order-to-cycle delivery times, and a 2.6x improvement in supply chain efficiency of 10% or greater.
  8. Greater contextual intelligence of how supply chain tactics, strategies and operations are influencing financial objectives.
  9. Traceability and recalls are by nature data-intensive, making big data’s contribution potentially significant.
  10. Increasing supplier quality from supplier audit to inbound inspection and final assembly with big data.

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Visibility Is Key when Driving Supply Chain Performance

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At its heart, supply chain management requires a balancing of operational efficiency, customer satisfaction and quality. Managing the true cost to serve for each and every order is the aspiration to allow better negotiation and value creation across the supply chain. Customer- and consumer-centricity helps anticipate product and service requirements. Supply chains are becoming more extended and complex with a consequent increase in risk and the need for resilience. There are multiple data sources making it difficult to manage and measure end-to-end processes and metrics. Aligning priorities through integrated planning remains pivotal, but there is an explosion of data available that needs to be incorporated and the value extracted to understand how supply and demand issues impact profit and revenue targets.

New technology provides greater supply chain transparency. Strategic supplier engagement continues to be important as a way of reducing costs and mitigating risk. Effective supply chain management can be either a compelling competitive differentiator or, conversely, a source of risk, cost and poor customer service.

Organizations are looking to enable better and more consistent decision-making across complex processes with diverse systems and data. Many are leveraging business intelligence (BI) platforms to give them the capability to make decisions across the organization, including environments in which mobility and access to decision-critical information on the go is crucial. Putting the information in the hands of the people on the front line—those managing supply chain processes—is key to enabling decision-making at the point of decision. But this requires synchronizing an enormous amount of data that comes from many systems and sources in a way that it can be easily consumed by people who need to act on the insights.

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