Tag Archives: analytics

Overcoming 5 Major Supply Chain Challenges with Big Data Analytics

Posted on by

Big data analytics can help increase visibility and provide deeper insights into the supply chain. Leveraging big data, supply chain organizations can improve the way they respond to volatile demand or supply chain risk–and reduce concerns related to the issues.

Sixty-four percent of supply chain executives consider big data analytics a disruptive and important technology, setting the foundation for long-term change management in their organizations (Source: SCM World). Ninety-seven percent of supply chain executives report having an understanding of how big data analytics can benefit their supply chain. But, only 17 percent report having already implemented analytics in one or more supply chain functions (Source: Accenture).

Even if your organization is among the 83 percent who have yet to leverage big data analytics for supply chain management, you’re probably at least aware that mastering big data analytics will be a key enabler for supply chain and procurement executives in the years to come.

Big data enables you to quickly model massive volumes of structured and unstructured data from multiple sources. For supply chain management, this can help increase visibility and provide deeper insights into the entire supply chain. Leveraging big data, your supply chain organizations can improve your response to volatile demand or supply chain risk, for example, and reduce the concerns related to the issue at hand. It will also be crucial for you to evolve your role from transactional facilitator to trusted business advisor.

Read more at Overcoming 5 Major Supply Chain Challenges with Big Data Analytics

What do you think about this article? Share your opinions with us in the comment box.

Related articles

How Does Big Data Analytics Help in Decision Making

Posted on by

Staying ahead in the game is paramount for any business organization to survive in this competitive world. The future poses challenges that need tackling in the present. Every decision made today has a significant impact on the future of that organization. The rate at which a company responds to challenges in the present and the future is what determines their rate of success. Data Science and Big Data analytics can help organizations in decision making and drive the company to a realistic future.

The Deciding Factor

It is paramount for businesses to understand the big data concept and how it impacts the organization activities. Discussed below are ways in which Big Data facilitates faster and better decision making;

Accelerating Time-to-Answer

The time cycle for decision making is decreasing rapidly. Companies have to make decisions more quickly in this period than in the past. Accelerating decision-making time is crucial for the success of any organization. The use of Big Data doesn’t change the urgency of decision making. Big Data analytics mitigates.

Customer reaction to a product is an important factor to consider when making a decision. Using data resources to understand the preferences of customers is one way of pointing out gaps existing in the market. However, the problem is how do you integrate and act in real time? The key is to know how to combine Big Data with your traditional Business Intelligence to create a more convenient data ecosystem that allows for the generation of new insights while executing your present plans.

Accelerating your time-to-answer is crucial for customer satisfaction. For example, if your answer time is usually in minutes, Big Data can reduce it to seconds. If it takes weeks for a client to have their problem tackled, then reducing it to days is more convenient for your customers. Customer retention is critical to the success of your organization.

Read more at How Does Big Data Analytics Help in Decision Making

Write your opinions below and subscribe us to get more updates in your inbox.

Related articles

How Big Data And Analytics Are Transforming Supply Chain Management

Posted on by

Supply chain management is a field where Big Data and analytics have obvious applications. Until recently, however, businesses have been less quick to implement big data analytics in supply chain management than in other areas of operation such as marketing or manufacturing.

Of course supply chains have for a long time now been driven by statistics and quantifiable performance indicators. But the sort of analytics which are really revolutionizing industry today – real time analytics of huge, rapidly growing and very messy unstructured datasets – were largely absent.

This was clearly a situation that couldn’t last. Many factors can clearly impact on supply chain management – from weather to the condition of vehicles and machinery, and so recently executives in the field have thought long and hard about how this could be harnessed to drive efficiencies.

In 2013 the Journal of Business Logistics published a white paper calling for “crucial” research into the possible applications of Big Data within supply chain management. Since then, significant steps have been taken, and it now appears many of the concepts are being embraced wholeheartedly.

Applications for analysis of unstructured data has already been found in inventory management, forecasting, and transportation logistics. In warehouses, digital cameras are routinely used to monitor stock levels and the messy, unstructured data provides alerts when restocking is needed.

Read more at How Big Data And Analytics Are Transforming Supply Chain Management

Related articles

The Key to Analytics: Ask the Right Questions

Posted on by

People think analytics is about getting the right answers. In truth, it’s about asking the right questions.

Analysts can find the answer to just about any question. So, the difference between a good analyst and a mediocre one is the questions they choose to ask. The best questions test long-held assumptions about what makes the business tick. The answers to these questions drive concrete changes to processes, resulting in lower costs, higher revenue, or better customer service.

Often, the obvious metrics don’t correlate with sought-after results, so it’s a waste of time focusing on them, says Ken Rudin, general manager of analytics at Zynga and a keynote speaker at TDWI’s upcoming BI Executive Summit in San Diego on August 16-18.

Challenge Assumptions

For instance, many companies evaluate the effectiveness of their Web sites by calculating the number of page hits. Although a standard Web metric, total page hits often doesn’t correlate with higher profits, revenues, registrations, or other business objectives. So, it’s important to dig deeper, to challenge assumptions rather than take them at face value. For example, a better Web metric might be the number of hits that come from referral sites (versus search engines) or time spent on the Web site or time spent on specific pages.

TDWI Example. Here’s another example closer to home. TDWI always mails conference brochures 12 weeks before an event. Why? No one really knows; that’s how it’s always been done. Ideally, we should conduct periodic experiments. Before one event, we should send a small set of brochures 11 weeks beforehand and another small set 13 weeks prior. And while we’re at it, we should test the impact of direct mail versus electronic delivery on response rates.

Read more at The Key to Analytics: Ask the Right Questions

Related articles

10 ways big data is revolutionising supply chain management

Posted on by

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.

Read more at 10 ways big data is revolutionising supply chain management

Hope this article could help you. Subscribe to get updates in your inbox.

Related articles

Supply Chain Analytics: How Manufacturers Can Get The Most Value Out Of Their Automated Data Capture Technology

Posted on by

There are a prolific amount of data sets and data sources available that can help overcome the aforementioned challenges. The problem is that “big data” is coming in from so many varied sources, and manufacturers simply do not know what to do with it or how to use it. The answer lies in supply chain analytics. With the right analytical tools, manufacturers can obtain actionable, meaningful, and supported insight from available data in order to make better business decisions. When it comes to AIDC, supply chain analytics are most useful in two chief areas: device optimization (the technology itself) and labor management (those who are using the technology).

AIDC Device Optimization

With supply chain analytics, manufacturers can receive timely and relevant feedback about their AIDC platform to determine how the technology is performing – feedback beyond what is provided by a typical Mobile Device Management solution. Through this insight, users can better understand the underlying causes of inefficiencies, identify areas for continuous improvement, perform predictive analysis, and more. For example, through dashboard and reporting tools, manufacturers can easily see device utilization data to determine user adoption rates. They can monitor battery performance of their devices in the field to prevent downtime. Or, they can even make sure that the right tools are available at the right time. As a result, manufacturers can optimize their mobile deployments to attain additional ROI.

Labor Management

The second component to this equation involves labor management. Using supply chain analytics, it is possible to match the right tools with the right people, and the right people with the work. Analytics platforms accomplish this by gauging and managing the labor resources that use the technology in terms of measurement of activity benchmarking, engineered labor standards, and dashboard reporting. These tools take into consideration production data (volume), integrated with labor, cost, customers, and time data.

Achieving Analytics Success

Supply chain analytics tools can provide practical and fully actionable (fact-based decision making) information to help optimize the supply chain from an AIDC and human capital standpoint. Along with the right AIDC tools and support organization behind those tools, supply chain analytics can assist in driving more revenue, reducing your cost structure and improving the experience of your customers and your workforce. Yet, this is only a piece of the supply chain analytics puzzle. Looking forward, manufacturers will continue to extend the capabilities of analytics tools to gain insight into the overall performance of the manufacturing facility. With the influx of the Internet of Things (IoT), more data points are available than ever before, which allows manufacturers to gauge the efficiency of a particular production line or overall equipment effectiveness (OEE).

Read more at Supply Chain Analytics: How Manufacturers Can Get The Most Value Out Of Their Automated Data Capture Technology

Do you have any opinions about this topic? Please share with us in the comment box. Thank you.

Related articles

7 Reasons to Merge Revenue Cycle and Supply Chain Management

Posted on by

Using technology to merge supply chain management and revenue cycle departments may help advance cost-to-charge transparency and increase accuracy in terms of managing reimbursement costs. “In most provider organizations[,] supply chain management (SCM) and revenue cycle operations function in silos, occasionally responding to anecdotal evidence to make improvements in the processes linking the two areas,” confirms HSRC-ASU. “Hospitals and health care systems that become proficient in managing the revenue environment achieve strategic advantage by reaching their financial goals and assuring a stream of revenues to support their clinical efforts,” the researchers explain.

According to HealthITAnalytics, supply chain management should be considered as a marathon endeavor, not a short-lived sprint. Successful supply chain involves connecting costs with analytics to enact substantial long term change. Additionally, hospital executives claim non-EHR health IT acquisitions strengthen the supply chain, states HealthITAnalytics.

Consistency is an essential key to ensuring accurate coding and pricing efforts. “Linking the traditional aspects of supply chain management (e.g., strategic sourcing, logistics, and inventory management) to margin management decreases the probability of lost charges occurring,” the researchers state. “Prices should be strategically set to optimize maximum allowable reimbursement. Charge capture processes should be incorporated in pricing strategies in each of the targeted areas,” they add.

HSRC-ACU confirms seven reasons to combine revenue cycle management and supply chain management:

  1. Increased and more accurate reimbursements
  2. Strengthened contract negotiations and enhanced contract compliance
  3. Improved transparency
  4. Streamlined cross-check utilization of supplies and ease of monitoring supply revenue
  5. Capturing cost-to-charge data visibility will be smoother
  6. Billing will be more accurate
  7. Labor will be wisely utilized and not wasted

Read more at 7 Reasons to Merge Revenue Cycle and Supply Chain Management

Thank you for reading, please do share your opinions about this article in the comment box. Subscribe to get the latest updates in your inbox.

Related articles

Resilinc to Unveil the Top 10 Supply Chain Risk Management Insights of All Time

Posted on by

The Top 10 Supply Chain Risk Management insights of all time are the most impactful conclusions, lessons learned, and heuristics that all SCRM practitioners should be acutely aware of in order to maximize their chance of success in achieving risk management and resilience performance excellence. Bindiya Vakil, founder and CEO of Resilinc, and Ann Grackin, CEO of ChainLink Research, will lead the discussion.

“These are the insights born from real-life experience in the trenches, battle scars, and “ah hah” moments,” said Vakil. “They are based on Resilinc and ChainLink Research company experience—working with the most complex supply chains in the world as solution providers, consultants, and practitioners in previous lives—as well as crowd-sourced contributions from risk thought leaders and luminaries in industry and academia.”

The top 10 insights will each be presented as important threads in an overall strategic-framework fabric. When implemented in their totality, the top 10 insights may form the backbone of a successful best-practice-driven SCRM program.

Participants in this Webcast will have the opportunity to:

1. Gain insights and best practices to improve SCRM and resilience program performance.

2. Apply insights as part of a strategic framework for success.

3. Benchmark their organization’s resilience program best practice adoption against the top 10 insight list.

Read more at Resilinc to Unveil the Top 10 Supply Chain Risk Management Insights of All Time

Will you take part in the event? What are your expectations ? Share with us in the comment box.

Related articles

FusionOps Unveils LiveAnalytics for Supply Chain Data

Posted on by

FusionOps Unveils LiveAnalytics for Supply Chain Data

FusionOps, a supply chain analytics company that provides a cloud-based business intelligence (BI) application, has launched LiveAnalytics for supply chain data. LiveAnalytics uses images and live metrics to create infographics for supply chain processes and workflows.

The FusionOps application allows businesses to create new analytics from scratch. In addition, the application offers thousands of configurable analytics, metrics and tickers, FusionOps said.

LiveAnalytics leverages FusionOps’ interactive, root-cause analysis across the supply chain. FusionOps said LiveAnalytics users can visualize changes in their supply chains in real-time and evaluate data from all functional areas to become more efficient.

Some of LiveAnalytics’ features include:

  1. Alerts – When alerts are triggered, users are notified via email about supply chain events in real-time.
  2. Key performance indicator (KPI) dictionary – The new KPI dictionary explains pre-built and company-specific metrics.
  3. Personalized navigation – Users can access thousands of dashboards, KPIs and reports directly from LiveAnalytics’ main navigation and “Favorites” menus.

Thanks for reading this article. Welcome to leave a comment below or send us a message if you have any opinions.

 

Related articles

New Approaches to Analytics to Revolutionize Logistics

Posted on by

New Approaches to Analytics to Revolutionize Logistics

Three stages are commonly used to categorize an organizations maturity in their use of business intelligence and analytics technologies:

  1. Descriptive: What happened in the past?
  2. Predictive: What will (probably) happen in the future?
  3. Prescriptive: What should we do to change the future?

Descriptive analytics typically means good old fashioned business intelligence (BI) – reports and dashboards.  But, there is a newish technology in the Descriptive category – one that I might argue is worthy of a category in its own right.  That technology is visual data discovery.  The visual data discovery approach has a rapidly growing fan base for many reasons, but one stands out:  It increases the probability that business managers will find the information they need in time to influence their decisions.

Visual data discovery tools typically provide:

  1. Unrestricted navigation through, and exploration of, data.
  2. Rich data visualization so that information can be comprehended rapidly.
  3. The ability to introduce new data sources into an analysis to expand it further.

By helping to answer a different class of question – the unanticipated one – visual data discovery tools increase the probability that managers will find the information they need in time to influence their decisions.  And that, after all, is the only valid reason for investing in business intelligence solutions.

If you have any opinions, you are welcome to leave a comment or send us message.

Related articles