In the autumn of 1999, Hershey’s was preparing for what they hoped would be a typical Halloween season. By the arrival of the holiday, it would prove to be anything but typical. In fact, the American candy giant would see an almost 10% drop in its stock price over the course of just one day. The reason? A failure to deliver more than $100 million dollars worth of Hershey’s Kisses and Jolly Ranchers candies to stores in time for Halloween. It turns out that Hershey’s had adopted a new order fulfillment system just weeks before their annual Halloween rush, and their IT hadn’t yet been successfully integrated into their value stream. The company would ultimately recover, but the incident still stands as one of history’s biggest supply chain snafus, proving that all supply chains are susceptible to risk and disruptions. Here is a ranking of some of the biggest supply chain disruptions:
In a recent research report, Business Insider found that when it came to machine learning, 53% of the company executives surveyed were interested in the emerging technology, but unclear as to its exact use cases and applications. Similar figures applied to executive attitudes towards other technological advances, such as artificial intelligence and 3D printing. Although machine learning in particular is already driving new Industry 4.0 workflows and fundamentally changing the way that manufacturers do business, it’s no surprise that many have trouble envisioning specific applications for it. The transformative power of new technological advances comes not from generalities, but from specific tools and methods for integration that must be carefully calibrated to specific business functions.
Seasonality, which refers to regular, predictable fluctuations that recur year over year, has traditionally been a major factor in automotive manufacturing. Since car sales often spike in spring and autumn (when new models are traditionally released) and drop off in winter and summer, manufacturers can and do factor seasonal slow-downs and increases in demand (potentially including demand for new parts) into their production processes. With the rise of Industry 4.0 and the emergence of an increasingly global supply chain, however, the nature of seasonality is rapidly changing. Let’s take a look at how seasonalities operates in modern manufacturing.
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?
Although automotive manufacturers have been hearing for years that Big Data is the next big thing, studies often show that executives, not just in automotive but across many different industries, fear that their organizations aren’t ready to take advantage of the new advancements in analytics. Big Data analytics can and will be a huge value-added proposition for companies hoping to stay competitive in the world of Industry 4.0, but it’s true that reaping the benefits of new technological insights often requires significant changes in workflows and IT infrastructure. Luckily, these changes are often not as daunting as they first appear. Here are a few suggestions for getting the most out of your advanced analytics.
Imagine for a moment that you’re an employee at an automotive manufacturing company. Every year of two, the owners create and share a strategic vision for the long-term future with management. Managers, in turn, create shorter-term plans of several months to put the longer-term vision into practice with Sales and Operations Planning (S&OP). As an employee, you manage your day-to-day tasks in accordance with those plans, responding the small crises of the workday with whatever resources and insights are available to you. Perhaps in responding to these situations, you find yourself wishing that there was something to bridge the gap between S&OP and those day-to-day processes. Sales and Operations Execution (S&OE) is that bridge, and it represents the path to the most responsive possible supply chain.
Imagine for a moment that your company is in the businesses of manufacturing parts for both conventional and hybrid vehicles. Based on the demand from your customers over the past year or multiple years, you develop a plan for allocating resources and person-hours to produce the right proportion of hybrid parts to conventional parts based on expected demand. Unexpectedly, a sharp increase in worldwide oil prices triggers a shift in demand away from hybrids and toward conventional automobiles that rely on gasoline. How will your production plans cope with the sudden change? Will your factory floors continue to produce a surplus of hybrid parts while orders for conventional parts go un-filled, or do you have the necessary planning agility to shift production to align with new demand paradigms?
Hopefully, you are in a position to safely assume the latter. But for many complex businesses, rapidly adjusting to demand is a perilously involved tasked, requiring the ability to assess and respond to new circumstances virtually instantaneously. One of the keys to building this level of agility into production processes is the integration of real-time and production planning.
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.