As we start a new year, we wanted to reflect on the most popular content that resonated with our audience over the last 12 months. From insightful industry analyses to practical how-to guides, our blog posts demonstrate the diverse range of supply chain-related topics we cover and our commitment to providing valuable information to you, our reader.
Here are the most widely read blogs of 2023, as ranked by our LinkedIn metrics, that captured your attention and sparked engagement:
It’s no big secret that many supply chains still run on Excel spreadsheets for forecasting and clipboards for taking inventory counts. However, the adoption of machine learning and advanced analytics is slowly and steadily increasing. Unfortunately, the pace of change hasn’t been quick enough to keep up with the advancements in technology. In this blog, we explain why the time to act is now and recap the areas where artificial intelligence (AI) and machine learning can help manufacturers become more proactive and improve business performance.
Arkieva has been in the supply chain business for close to 30 years. Over this time, we’ve seen many changes in the business landscape and how technology companies respond to customer needs. For nearly 10 years, for example, “digital transformation” has been a prime area of focus across two different goals: Adapting to doing business across digital and traditional channels, and applying automation and predictive analytics to forecasts. This blog explains the difference between these two goals and paints a cautionary tale of how companies that don’t adapt to the “new ways” of doing business may soon be out of business.
We tried something different with this series of blog posts and it went over very well with readers, part 6 in particular. The series featured Jane, who is in the role of inventory planner at her company, and Kate, a consultant who has been helping Jane with safety stock concepts. We used these characters to illustrate the value of safety stocks and highlighted several “real-life” examples that readers can test out and adopt in their own operations.
One change in customer behavior, inaccurate forecast or overly aggressive discount can trigger a wave of disruption that grows deeper as it penetrates the supply chain. This bullwhip effect is talked about a lot these days, and particularly in the aftermath of the pandemic and the related supply chain disruptions. In this blog, we explain the most common causes of the bullwhip effect and show you how to mitigate these issues using improved demand forecasting, enhanced supply chain visibility and collaborative planning, forecasting and replenishment.
Other supply chain planning blogs proved popular attractions this year, too! Here are four more top blogs of 2023, as ranked by Google Analytics site traffic data:
Predicting demand for new products is an ongoing challenge for any company. This is especially true when the product incorporates technology or when it’s replacing an existing product (and the additional function in the new product is limited). This is where the mathematical construction known as the “S curve” comes into play. This blog provides an overview of S curves and why they can be helpful. It also describes a piece-wise linear S curve that we have found particularly helpful and supports the concept of community intelligence.
A process in which historical sales data is used to develop an estimate of an expected forecast of customer demand, demand forecasting gives companies an estimated number of goods and services that customers will purchase in the foreseeable future. This is an important metric because critical business assumptions like turnover, profit margins, cash flow, capital expenditure, risk assessment and mitigation plans, and capacity planning are all dependent on demand forecasting. This blog walks through the basics of demand forecasting, how it’s being used in supply chain management and how to put the concept to work for your own organization.
Let’s face it, business blogs can all start to sound the same after a while, but not this one. Written at a time when the state of Delaware was getting dumped on by snow—with no less than three Nor’easters impacting the region during a short period—this blog explores the sheer amount of planning and resources that went into dealing with and cleaning up all that snow. The author uses the snowstorm example to simplify the concept of statistical forecasting, a method of predicting future demand based on historical data and statistical analysis.
When we ask companies what their forecast accuracy is, the answers generally range from lower than 50% to higher than 95% or somewhere in between. How is this even possible? Imagine a management team being given this range of numbers on the same metric—that would be one unhappy (and probably pretty confused) management team, right? In this blog post, we delve into the variability and suggest more precise ways to report the accuracy in a way that gives management a realistic picture of this important metric.
Our team looks forward to sharing even more valuable insights with you through this supply chain blog in 2024 and beyond. We share our blogs and other insights often on social media, so be sure to follow us for updates (LinkedIn | X, formerly known as Twitter | Facebook | YouTube).