Imagine for a moment that you’re on a flight from London to New York. You probably take It for granted that someone has charted an appropriate route at an appropriate altitude based on weather and air traffic patterns, and that departure, arrival, and flight time have all been carefully calculated based on past flights and current conditions. At the same time, no matter how much planning has gone into a flight, you probably also take it for granted that there is a pilot in the cockpit, measuring real-time information with her instruments and communicating with air traffic control to make necessary adjustments and course corrections as new scenarios emerge.
Plenty has been written on the right way to choose the supply chain management technology that best fits your company’s needs, much of it focusing on broad organizational points like defining specific needs and long term business goals. Coming into the IT procurement process with intra-operational buy-in and a well-founded idea of how new technology should integrate into your workflows and key performance indicators (KPIs) is, of course, crucial to finding the right solution, but that’s not the end of the discussion. Once you’ve assessed your specific needs and your short- and long-term goals, how do you evaluate the technology itself?
In a recent piece offering predictions for the state of supply chain management (SCM) in 2018, Gartner asserted that SCM was going to become increasingly technology-centric, with market forces putting tremendous pressure on manufacturers to adopt new and emerging technologies. When companies search for areas where their IT might be modernized, many may look to transport as a business process that can be further optimized through the adoption of modern logistics software. Once a new solution is adopted, however, it’s crucial that companies understand their technology well enough to get the most out of it, so that they can improve workflows and add value. To that end, here is a quick guide to putting transport logistics to work within your organization.
Imagine you’re playing musical chairs. The music starts and stops and your instinct is to rush to the nearest seat before your competitors beat to you to it—but instead of a circle or a row of chairs, the chairs are scattered and hidden around the building at random. No one knows how many chairs there are, and no one is sure how to reset them before the next round begins. Surely this would be a confusing way to play the game, just as it would be a confusing way to run a business. And yet, many companies do just that, keeping real resource allocation hidden within planning siloes and mission critical data obscured by layers of disconnected IT infrastructure. The result is that long term cross-operational planning becomes impossible, with planners stuck in a reactive loop of constantly responding to roadblocks without the ability to be proactive. Integrated planning has long been touted as cost saving solution for complex businesses, one that specifically addresses the break-fix mentality that mires companies in minute-to-minute logistical snafus, but what is it, exactly, and how does it work?
As the world of manufacturing becomes ever more competitive, many are trying to stay ahead of the competition by driving toward a lean supply chain. While this approach is no doubt a boon for identifying potential cost savings and supply chain optimizations, it can often leave companies more vulnerable to supply chain risk. As inefficiencies and redundancies are pared down, manufacturers can become less insulated against possible uncertainty and disruptions. As a result, supply chain managers are now more than ever searching for ways to combat risk and preserve the value-added improvements of their lean supply chains. Even in a market filled with unpredictable externalities, risk management can have a tremendous impact on the bottom line, but for variant-rich industries managing risk is often easier said than done. Let’s take a look at some of the biggest hurdles companies face in combating supply chain risk.
Ask any manufacturing company planner or manager about the most valuable aspect in effective production planning processes and you’ll hear one thing over and over again: data and reporting. In today’s increasingly digitized supply stream, the ability for companies to quickly, accurately, and efficiently gather data and reporting about any number of production elements from material procurement to job allocation is paramount in helping companies manage a streamlined, productive value stream. As the cliche goes: The devil is in the details, and in modern manufacturing those details are the data and reporting.
This is where the development and proliferation of advanced analytics in the manufacturing landscape has given planners and managers a critical value proposition in making best practice decision-making based on the viability of quality reporting. Because advanced analytics relies in large part on the adoption of intelligent technologies or solutions like Industry 4.0, Big Data, and The Internet of Things (IOT), planners and managers have the ability to put large amounts of data and reporting to work to leverage lean manufacturing principles for greater derived value. The addition of Cloud technology and other centralized data storage platforms further makes advanced analytics a critical element of an integrated reporting solution.
We discussed in a recent blog post the industry-wide debate on the long-term, disruptive power of Big Data in today’s manufacturing landscape. Essentially, the debate boiled down as to whether Big Data was actually a value proposition manufacturing companies could depend on into the future as opposed to a flash-in-the-pan phenomenon that may simply fade as new supply chain management theories and philosophies come into view. While it was clear from our examination Big Data is here to stay, there’s still much to understand about how exactly Big Data fits into an integrated supply stream.
For example, how should manufacturing companies leverage the information, data, analytics, and predictive insights created via Big Data into a competitive advantage? Or, what benefits does Big Data provide planners and managers in terms of reducing supply chain management risks? Lastly, how does Big Data impact the entire production cycle from planning and procurement to transport management and customer relations?
While Big Data has certainly permeated nearly every aspect of today’s manufacturing and supply pipeline, some industry analysts still question the validity, value proposition, and staying power of Big Data for companies as they strive to streamline their operational platforms and leverage lean manufacturing principles for optimal productivity and profitability.
Competition. More so than ever before, today’s manufacturing companies are facing increasingly intense competition in an ever-expanding global marketplace. With new and emerging markets coming online in disparate parts of the globe, today’s manufacturing and supply network is growing more diverse, complex, and intertwined, and manufacturing companies need more powerful, integrated, and intelligent solutions to cut this complexity and maintain the efficacy of their planning and production programs.
Enter advanced analytics and its ability to give manufacturing companies the insight and visibility into their overall supply and production platforms. With an endgame of helping manufacturing companies analyze and sort data, streamline processes, and increase the efficiency of planning and production programs, advanced analytics is a critical value proposition in a variant-rich industry where network partners operate at numerous disparate points across the globe. Because advanced analytics relies in large part on the adoption of intelligent technologies or solutions like Industry 4.0, Big Data, and The Internet of Things (IOT), planners and managers have the ability to put large amounts of data and reporting to work to leverage lean manufacturing principles for greater derived value.