Speaking to a full house at the BROWZ Client Summit 2016 Sundance Resort, V.P. of Product Development, Aaron Rudd stated “BROWZ OneView is a significant development in the evolution of supply chain management software that will not only meet our clients needs today, but will meet their supply chain needs as they expand in the future.”
BROWZ OneView is an entirely new interface and user experience for BROWZ clients.
“Our goal was to enhance the way our clients interact with our solutions and their supply chain. From conducting a simple supplier search to in-depth analysis across a global supply chain. BROWZ is empowering our clients with the new OneView platform,” Rudd said.
“The software provides meaningful insight into the entire supply chain using key performance indicators which also provides the flexibility to analyze the performance of individual locations or specified risk level with the click of a button.”
It can be hard to believe that very much happens in a year, but that theory is put to the test when it comes to the parcel express market.
In fact, over the past 12 months we’ve seen major changes in pricing from the parcel duopoly of FedEx and UPS; the accelerated emergence of regional parcel players; and don’t forget we’re all watching the increasing power and reach of e-commerce giant Amazon as it grows its own delivery capabilities globally.
These developments require parcel shippers to do whatever it takes to stay on top of their parcel game from both a financial and operational perspective. To help them along, Logistics Management has gathered Jerry Hempstead, president of Hempstead Consulting, a parcel advisory firm; David Ross, transportation and logistics director at investment firm Stifel; and Rob Martinez, president and CEO at Shipware, an audit and parcel consulting services company.
Over the next few pages, our experts offer their insight into what’s driving parcel market trends and offers some practical advice for how shippers need to re-adjust to ever-changing market conditions.
Logistics Management (LM): How would you describe today’s parcel marketplace?
Jerry Hempstead: All of the parcel carriers are doing well in volume and earnings—even the USPS is making money if you back out the Congressional mandates. And it’s clear that e-commerce is driving the volumes. To top it off, service levels this year are at record levels and are predictable and consistent.
My observation is that there’s no statistical difference between the service performance offered by FedEx and UPS across a year’s worth of activity, although FedEx offers a faster delivery on ground to about 25% more city pairs than UPS. This pressure on speeding up the promise and refining the networks to make the magic happen will only improve the consumer experience in parcel services.
Leave your comments below if you have any opinions and subscribe us to be the first to get our updates.
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.
Share your opinions regarding this topic in the comment box below and subscribe us for more updates.
Business Intelligence Emerges From Decision Support
Although there were some earlier usages, business intelligence (BI) as it’s understood today evolved from the decision support systems (DSS) used in the 1960s through the mid-1980s. Then in 1989, Howard Dresner (a former Gartner analyst) proposed “business intelligence” as an umbrella term to describe “concepts and methods to improve business decision-making by using fact-based support systems.” In fact, Mr. Dresner is often referred to as the “father of BI.” (I’m still trying to identify and locate the “mother of BI” to get the full story.)
The more modern definition provided by Wikipedia describes BI as “a set of techniques and tools for the acquisition and transformation of raw data into meaningful and useful information for business analysis purposes.” To put it more plainly, BI is mainly a set of tools or a platform focused on information delivery and typically driven by the information technology (IT) department. The term “business intelligence” is still used today, although it’s often paired with the term “business analytics,” which I’ll talk about in a minute.
Along Came Enterprise Performance Management
In the early 1990s, the term “business performance management” started to emerge and was strongly associated with the balanced scorecard methodology. The IT industry more readily embraced the concept around 2003, and this eventually morphed into the term “enterprise performance management” (EPM), which according to Gartner “is the process of monitoring performance across the enterprise with the goal of improving business performance.” The term is often used synonymously with corporate performance management (CPM), business performance management (BPM), and financial performance management (FPM).
Contact us if you have any questions or share it in the comment box below.
As with many other areas of the economy, the digital revolution is having a profound effect on delivery logistics.
The combination of mobile computing, analytics, and cloud services, all of which are fueled by the Internet of Things (IoT), is changing how delivery and fulfillment companies are conducting their operations.
One of the most popular methods for fulfilling deliveries today is through third-party logistics, which involves any company that provides outsourced services to move products and resources from one area to another. Third-party logistics, or 3PL, can be one service, such as transportation or a warehouse, or an entire system that maintains the whole supply chain.
But the IoT is going to change how this process operates. Below, we’ve outlined the impact of IoT on supply chain, and how IoT management will transform inventory, logistics, and more.
Internet of Things Supply Chain Management
One of the biggest trends poised to upend supply chain management is asset tracking, which gives companies a way to totally overhaul their supply chain and logistics operations by giving them the tools to make better decisions and save time and money. Delivery company DHL and tech giant Cisco estimated in 2015 that IoT technologies such as asset tracking solutions could have an impact of more than $1.9 trillion in the supply chain and logistics sector.
And this transformation is already underway. A recent survey by GT Nexus and Capgemini found that 70% of retail and manufacturing companies have already started a digital transformation project in their supply chain and logistics operations.
Asset tracking is not new by any means. Freight and shipping companies have used barcode scanners to track and manage their inventory. But new developments are making these scanners obsolete, as they can only collect data on broad types of items, rather than the location or condition of specific items. Newer asset tracking solutions (which we’ll get into shortly in the next section) offer much more vital and usable data, especially when paired with other IoT technologies.
We would like to know what you think about this topic, share your opinions with us in the comment box. If you need assistance, you can contact us via email. You can subscribe us to be the first one to get the latest article.
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.
Imagine the endless possibilities of learning from 2.5 quintillion bytes of data generated every day. Artificial intelligence (AI), which began its journey 60 years ago is well on its course to make this implausible scenario a reality. Artificial Intelligence, is slowly taking over our lives.
From personal assistants like Siri in Apple products to stock trading to medical diagnosis, AI is able to learn from seemingly unstructured data, take decisions and perform actions in a way previously unimagined.
Businesses too are undergoing digitization rapidly. They are using AI – capable of performing tasks normally requiring human intelligence – to create a significant impact in the way businesses operate. In an increasingly dynamic environment comprising demanding customers and the need for speed, it was only a matter of time before the businesses embraced AI to obtain much needed agility. According to Accenture’s Technology Vision 2016 survey spanning 11 countries and 12 industries, 70 percent of corporate executives said they are significantly increasing investments in AI.
Artificial Intelligence in Supply Chain
Organizations are increasingly digitizing their supply chains to differentiate and drive revenue growth. According to Accenture’s digital operations survey 85 percent of organizations have adopted/ will adopt digital technologies in their supply chain within 1 year.
The key implication of this change is that the supply chains are generating massive amounts of data. AI is helping organizations analyze this data, gain a better understanding of the variables in the supply chain and helping them anticipate future scenarios. Thus, the use of AI in supply chains is helping businesses innovate rapidly by reducing the time to market and evolve by establishing an agile supply chain capable of foreseeing and dealing with uncertainties.
Share your opinions with us in the comment box and subscribe to get updates.
It’s been written that a career in supply chain management can be like climbing a mountain.
While there is often a map for the path forward in professions like accounting, medicine and the law, in supply chain management – as with mountaineering – there are any number of paths that can reach the summit.
Those were among the findings from a research series conducted for the Council of Supply Chain Management Professionals (CSCMP) and published in the July/August 2015 issue of Supply Chain Management Review, and reinforced by research conducted by McKinsey & Company and Kuhne Logistics University.
The latter, for instance, found that while many supply chain management executives had experience in logistics, procurement and sales/marketing, “… a surprising number of supply chain executives are appointed without any previous exposure to SCM…in our sample, supply chain executives spent 88% of their previous career span outside the SCM function.”
Are those findings consistent with readers of Supply Chain Management Review and members of APICS Supply Chain Council? And, if so, who is today’s supply chain manager? And, how did he – or she – navigate to their position on the mountain?
Did they start out in the supply chain going back to their college days, or, as in the McKinsey study, did they come into the profession from other parts of the organization?
Moreover, what are their duties today and how do they see the job changing?
Read more at A Portrait of the Supply Chain Manager
Share your opinions with us in the comment box and subscribe us to get updates in your inbox.
While brand damage can be quite costly to the businesses whose sales rely strongly on the customer loyalty they generate from their brand strength, cost volatility and supply disruption is very costly to all manufacturers. In fact, in the latest 2015 study by the Business Continuity Institute, supply chain disruption is double in priority relative to other enterprise disruptions and over three-fourths of respondents cited that they had at least one recent (significant) disruption. The same percentage didn’t have full visibility of their supply chains.
While category management can address and even reduce supply chain risk by ensuring a chosen strategy has the right level of resiliency, prevention and agility, it cannot prevent risk or do much to eliminate the source of risk once something has happened. That can only be done by each party in the supply chain doing everything they can to eliminate the risk. In particular, a supplier needs to do all they can to minimize the risk on their end.
However, not all suppliers are as advanced in supply chain management, and in particular, risk management as the buying organization. That’s why good supplier management combined with SCRM is key. Good risk management is a combination of risk prevention and risk mitigation when a risk is detected. Risk prevention involves selecting suppliers, products and services that are low risk and risk mitigation involves taking action as soon as an indicator is detected.
A supplier is not always good at mitigating or even detecting risk in its supply chain, or may overlook an obvious sign that an observant buyer would not, which is why proper supplier management is key. This begins even when qualifying suppliers. Including risk criteria related to the supplier and supplier location gives a good indication of a supplier’s the risk level. Besides the supplier qualification criteria, supply location-related risks provide an overview on potential threats like natural disasters, political situation, sanctions or economic risk. This gives buyers the chance to take preventive actions.
If you have any questions or opinions, write it at the comment box and subscribe to get updates from us.
Big Data has been one of the most significant and influential aspects of the Information Age as it relates to the enterprise world. Essentially, Big Data is the massive collection, indexing, mining, and implementation of information that emanates from just about any activity that can be monitored and managed electronically. Some of the uses of Big Data include: marketing intelligence, sales automation, strategizing, productivity improvement, and efficient management.
Enhancement of the workforce is one of the exciting and meaningful benefits of Big Data for the business sphere. Recently, human resource managers and analysts have been researching the implementation of Big Data as it relates to employees, and the following trends have emerged:
For many decades, companies and organizations have tried various methods to gain knowledge about what their employees are really like. The productivity that workers can contribute to their employers is based on personal needs as they are balanced against the performance of their duties. With Big Data solutions, both personal needs and performance can be diluted into metrics for efficient analysis.
Modern workplace analytics originates from tracking employee records as well as metrics on their performance, interactions and collaboration. The idea is to focus on the right metrics to create a climate of positive engagement.
Read more at How to Use Big Data to Enhance Employee Performance
Share your opinions about this topic and subscribe to get updates in your inbox.