benchmarking Archives

Benchmarking your Freight Rates for Maximum Supply Chain Efficiency

Transportation Management Systems | By Brielle Patterson • 03/30/2023

Why are shippers interested in benchmarking and why are we hearing more about it lately? Benchmarking helps ensure they are getting the most out of their supply chain and also provides a platform for further optimization. In this article, we will discuss why benchmarking is so important, how it works, and how you can use it to maximize your efficiency when shipping goods.

What is Benchmarking?

Benchmarking is the process of comparing current performance against current industry standards or previously established performance levels. This allows shippers to gain insight into how their supply chain compares to others in terms of cost-efficiency, speed, and reliability.

Benefits of Benchmarking

Benchmarking offers numerous benefits for shippers including improved visibility, better cost control, more reliable service providers, and increased transparency throughout the entire supply chain network. It also helps reduce risk by providing insights into potential risks associated with particular processes or operations that could lead to delays or higher costs.

Analyzing Your Data through Analytics

Analyzing data requires an understanding of analytics which can provide insights into which factors are impacting performance negatively or positively so that appropriate changes can be made accordingly. By utilizing analytics tools such as predictive analytics or machine learning algorithms you can gain better visibility into traffic flow trends and identify potential issues before they occur – saving time and money while improving overall efficiency throughout your supply chain network.

Final Thought

Benchmarking your freight rates is an essential part of running a successful business in today’s increasingly competitive environment. It enables shippers to compare their own performance against industry standards and identify areas where improvements can be made in order to maximize cost-efficiency across their entire supply chain network. Additionally, using analytics tools such as predictive analytics or machine learning algorithms provides greater visibility into traffic flow trends so that potential issues can be identified before they occur – ultimately leading to improved efficiency and lower costs over time. By taking advantage of benchmarking techniques combined with advanced analytics tools, businesses can remain competitive while optimizing their freight rate management processes for maximum success!

Brielle Patterson is an experienced marketing professional who has been driving brand and content strategies for various companies. Currently, Brielle serves as the Director of Brand and Content at Emerge in Scottsdale, AZ, where she is responsible for leading a team that supports communication and positioning strategies through content marketing, paid and organic search, public relations (PR), sales enablement, and more. A graduate of Arizona State University with a Bachelor’s degree in Communications and an Associate’s in Graphic Design. Currently residing in Mesa, Arizona.

Benchmarking: Avoid Being The Tallest Pigmy

Business Intelligence | By Mark Sen Gupta • 09/05/2019

Benchmarking is the practice of comparing business processes and performance metrics to industry bests and best practices from other companies.

It is used to measure performance using a specific indicator or indicators resulting in a metric of performance that is then compared to others to assess how well or how poorly a company is performing.

This is sometimes referred to as “best practice benchmarking” or “process benchmarking”. Management uses this process to evaluate various aspects of an organization’s processes in relation to best-practice companies’ processes, usually within a peer group defined for the purposes of comparison. This then allows organizations to develop plans on how to make improvements or adapt specific best practices, usually with the aim of increasing some aspect of performance.

In the process of best practice benchmarking, management identifies the best firms in their industry, or in another industry where similar processes exist, and compares the results and processes of those studied to one’s own results and processes. In this way, they learn how well those studied perform and the business processes that explain why these firms are successful.

The challenge with this approach is if the industry is performing poorly, benchmarking simply indicates whether you stink, really stink, or stink to high heaven. As my colleague Steve Banker has pointed out in some of his writings, in logistics, Class 1 railroads meet their estimated time of arrivals less than 70 percent of the time. And these are not narrow two hour windows like in truckload, but based on arriving the day they promised to arrive. The Class 1 railroads perform so badly, in comparison to truckload, that their very competitiveness as a mode of transportation is in jeopardy once truck platooning technology becomes a main stream technology.

In an analogy of humanity, you could be the tallest in a tribe of pygmies but when compared to an NBA basketball team, you don’t really register.

How to Avoid Being the Tallest Pigmy with Best Practice Benchmarking

When you think about benchmarking, who do you consider as ‘best-practice’ companies and why? Many manufacturing companies are far behind in areas such as digital transformation. It is imperative that manufacturers look outside their microcosms and into industries that are further down the digital transformation path; the medical, marketing, and internet companies are over a decade ahead in many areas. Instead of listing off a litany of reasons why those industries aren’t similar, find why they are. Network outside the manufacturing community and visit other conferences. Digital transformation encompasses a lot of different domains, which many industries share.

The (New) Future of Fulfillment: How Warehouses are Transforming Operations

Uncategorized | By Clint Reiser • 02/06/2019

It was three years ago that Logistics Viewpoints and DC Velocity launched a survey of logistics practitioners to evaluate the future direction fulfillment facilities, technologies, and practices. The survey’s format evaluated past, present state, and expectations for three years into the future. The findings, published on Logistics Viewpoints and on DCVelocity.com, were well received.

Last week Logistics Viewpoints (ARC Advisory Group) launched a new survey (The Future of Warehousing and Fulfillment) that will serve as a sequel to this research. We are evaluating the current state of operations, comparing this state to our past predictions, and evaluating the markets priorities and expectations for three years from today.

What We Learned

Our survey results from 2016 showed that fulfillment accuracy still reigned supreme as the most important warehouse capability. But interestingly, it also showed that adaptability (the ability to handle a wide range of order profiles) and responsiveness (time from order receipt to delivery) were becoming increasingly more important in the world of e-commerce fulfillment.

This year’s research will inquire more extensively into warehouse order fulfillment profiles and processes in use. With respect to order types, three years ago direct-to-consumer and drop-ship were most expected to experience growth (In fact, ARC’s recent research into the warehouse automation market shows that e-commerce fulfillment demands did grow even more quickly than practitioners expected just a couple years ago). With respect to picking operations, piece picking was expected to increase most extensively, followed by cases and then pallets. This trend has certainly occurred. But how have warehouse operations responded to these changing demands? This year’s research will find out.

How are Warehouse Operations Responding?

An important addition to this year’s research is our inquiry into process changes being implemented to meet the high-volume, quick-response fulfillment demands in today’s market. For example, how widespread is the use of intelligent, system directed order batching/waves? And is the use of waves increasing or decreasing in prevalence? Put-walls are viewed as an effective way to consolidate multi line item orders for shipping. But how widespread is this practice, and is it increasingly common or is its use on the decline? In contrast, system directed waveless order processing is viewed as a responsive method for handling high-priority orders that require prompt fulfillment. How widespread is this practice? Is it still used only in limited cases, or is it widespread due to e-commerce demands? And is this practice expected to increase? Finally, what technology and processes are being used today and are expected to be used in the future to manage waveless (or dynamic release) fulfillment?

Past Present, Past Future, Present, and Present Future

Data from the prior survey will be used as an additional frame of reference for the trajectory of change for the common questions with this year’s survey. The survey was designed to provide valuable context for fulfillment operations, including the priorities of one’s peers, current practices in use, and expectations for the future. Of course, this year’s survey will also include a broad-based inquiry about warehouse technology use, demand, and purchasing priorities.

We encourage qualified practitioners to take the survey, and we will provide those participants with a summary of our findings as a sign of our appreciation (please find survey link at end of this article). The full spectrum of responses will provide a comprehensive snap shot of fulfillment capabilities, practices, changes in practices, investment priorities, and expectations for the future.

If you are a qualified practitioner, and you would like to take this survey, please click on the link below:

Survey: The Future of Warehousing and Fulfillment

If you have any questions, feel free to contact me at creiser@arcweb.com.

How Transparent Is Your 3PL Network?

Guest Commentary | By Rob Achtzehn • 08/24/2017

When dealing with the nuances of complex transportation networks, an organization often relies upon multiple transportation-focused 3PL providers, ultimately resulting in a fragmented network that lacks visibility and effective communication. Often this isn’t by design, but organically manifests itself due to different parts of the business being run by different groups. Furthermore, it can be seen frequently in companies that have grown through acquisition or consolidation. In these instances, one business unit used 3PL A and another used 3PL B. Operational status quo continued, and the company never got around to rationalizing its network.

On the other hand, sometimes a company wants to keep a few 3PLs engaged as leverage. That way they can constantly threaten 3PL A with taking away their business and giving it to 3PL B. This can be effective in keeping 3PL’s rates competitive with the market, but this is never considered a best practice or an ideal way to manage a 3PL network, and should ultimately be avoided. All 3PLs are going to the market to secure rates, unless a given 3PL provides direct capacity in the form of trucking assets. Instead of pitting 3PLs against one another to establish rates, a company is better served by evaluating things like procurement practices, carrier management and amount of spend under management at the 3PL level. Many times the difference in rates between 3PLs is really all about the margin anyway. If there is distrust with a 3PL, then companies shouldn’t be doing business with it to begin with.

To gain a better measure of rates, a company should regularly benchmark the rates it is receiving from the 3PL. If it is still unsure about the 3PL’s rates, the company can structure a cost-plus program with its 3PL to see what the 3PL is paying.

Operational Problems That Can Occur With Fragmentation
From an operational standpoint, a lack of visibility across the different networks and different systems can be problematic. Without that visibility, potential opportunities get overlooked, particularly in terms of matching inbound freight with outbound freight. Trucks could be going right by one another on the road that could have been utilized for backhauls within one’s own network.

While this practice is fairly complex, it becomes increasingly difficult when the lanes and data are locked up in different systems, leaving the shipper without access or opportunity to benefit all parties involved. Coordination is tougher as well. Getting multiple 3PLs to work together for the greater good is never easy. Co-opetition is a fun buzzword, but a very difficult strategy to convince partners to adopt and implement.

Options to Consider

When attempting to gain visibility of a fragmented network, consolidation of 3PLs can serve as a potential solution. Recently, our firm partnered with a company for a project that would help it consolidate its network. Ultimately, the company sought a reduction from three major 3PLs to a single 3PL. Even though there were some services that were better from certain 3PLs, there was also some redundant overhead by having three 3PLs working in the network. Many organizations have to evaluate the delicate balance between costs and service levels to determine where the best opportunities lie.

For organizations that want to maintain a balance of 3PLs, there are also some systems that can be implemented that lay across multiple 3PLs to give a shipper visibility across its entire network. These systems obviously take some time, talent, and treasure to implement. However, they are a way to keep numerous 3PLs engaged while giving the shipper the ability to see across the individual businesses that the 3PLs are managing. Shippers truly drive action when they commit resources and time to making use of that platform and engaging with the 3PLs.

Evaluate All Components and Parties Involved
A crucial step to creating greater visibility begins with identifying the organization’s criteria for determining whether consolidation versus overlay proves the best option. This starts with the evaluation of each 3PL to see if multiple 3PLs provide unique services or fit into multiple areas of the business that force the use of more than one 3PL. Furthermore, companies must determine the value they receive from each 3PL by defining the services provided and identifying which services are not easily transferable to another 3PL (i.e., dedicated, cross-docking, load planning, account management, etc.).

Another thing companies should consider is whether the 3PL has additional services not currently being used that align with future company goals. If so, how can the company utilize those services to achieve the desired solution? Furthermore, companies should identify if its business or any segment of its business is highly specialized and limits the number of 3PLs that could service the organization.

Finally, companies should evaluate the people, process and technology provided by each 3PL and how that aligns with its overall business both currently and in the future. Determine which 3PL’s culture best aligns with the company’s corporate culture. This will allow the possibility for a sustained partnership, and at the very least, efficient communication.

Potential Benefits
The benefits associated with consolidation could potentially result in lower costs, but organizations will definitely achieve higher transparency and better communication, which paves the way to better solutions as well. So, just getting visibility to what is happening will not magically allow a shipper company to unlock a ton of value, but it can help create a more efficient supply chain. One of the big benefits of using a single 3PL can be about rate savings, but the overall focus should be more about cost savings as related to efficiency. It can also be a catalyst that enables centralization of the transportation function.

There is no one solution that works for everyone. In some cases, consolidation of 3PL providers may prove the best strategy. In others, it may be an interconnected network of 3PLs. Relying upon an experienced team of professionals to evaluate an organization’s existing network is the first step. From there, they can help figure out what model works best, and if needed, which technology platform best supports the desired model. This remains a key component to implementing the visibility and communication a successful network needs to move towards optimization.

Rob Achtzehn is a Senior Manager in the Transportation Practice at Chainalytics. He has more than 20 years of experience across all areas of transportation and logistics, including sourcing, private fleets, TMS, warehousing operations, and business intelligence.

Benchmarking: A Key Ingredient to Campbell Soup’s Ability to Greatly Improve their Forecasting

Food & Beverage Industry | By Steve Banker • 08/15/2017

Four years ago, Molly Kissell transitioned into the role Director of Integrated Demand Management at Campbell Soup. Campbell Soup’s demand forecast accuracy – as measured at the item/location one month following the forecast – had fallen into the mid-60s.

Demand Management Benchmarking

The initial goal was simply to achieve historical levels of forecast accuracy. For the first couple years, they explored various benchmarking solutions – including participation in Chainalytics’ Demand Planning Intelligence Consortium (DPiC) – and worked to improve planners’ capabilities to use their new demand management solution.

By 2015, Ms. Kissell explains, we had concluded that DPiC “was a great tool set to understand our capabilities and provide realistic aspirations” for improvement. For fiscal year 2017, “lag one forecastability” – the measure described above – was in the mid-70s – a substantial improvement.

But is 76 percent forecast accuracy good or bad? That is one of the benefits of DPiC benchmarking. DPiC focuses on “forecastability.” In other words, not all products and demand scenarios are equally easy to forecast. For some products, 76% accuracy falls well below expectations. For example, a product that possesses steady demand and requires little promotion. For newer, actively promoted products, 76% accuracy would be excellent. This concept is explained in greater detail in the article, “Robust Benchmarking is Still the Exception.”

Ms. Kissell continues “we are getting actionable insights that help us improve accuracy and drive better supply chain capabilities.” Accurate forecasts help companies reduce inventory levels while improving service. Campbell Soup’s primary measure of service includes case fill accuracy for its customers, inventory measured by days of coverage, and the amount of inventory written off for obsolescence. Forecast accuracy remains important, but these metrics, driven by accuracy, ultimately “drive the bottom line.”

Planners use DPiC’s actionable insights to target the top ten items driving the most improvement, where improvement is measured by fast moving SKUs with high forecast errors, inventory levels, or service issues. In terms of assessing raw materials that comprise a finished product, and applying the forecast upstream, “a better forecast may not matter if there are other ways to manage volatility. We are using actionable insights “to Improve the forecast in places where it does matter.”

The DPiC benchmarking has positive effects on the demand management department as well. Ms. Kissell points out that it is “more motivating to have individual targets that are well thought out, with proof that you can achieve them.” In contrast, many organizations’ forecasting goals can feel arbitrary and impossible to achieve. Realistic targets help employees to understand that when their forecast accuracy appears to be low, they may actually be exceling.

But good benchmarking helps organizations “manage expectations both up and down.” Ms. Kissell uses the benchmark data to help the executive leadership “have a better understanding of what is realistic. If the benchmarking demonstrates a target as unachievable, they reevaluate target goals. “The data allows me to go in with rich analytic support I would not have had otherwise. The conversations are moving in the right direction.”

The DPiC data shows the Campbell’s US Retail and Food Service business, the division using this solution, is operating at well above average performance. Consequently, Ms. Kissell explained, “We are looking to expand the use of the Chainalytics solution more broadly with our other divisions. Chainalytics has been a key ingredient in a more holistic strategy to improve our results by strengthening our processes, people, and tools.”

Robust Benchmarking of the Demand Management Process is Possible

Business Intelligence | By Steve Banker • 08/03/2017

Companies love benchmarking data. Its use helps “appropriately” reward managers and line level employees, set tactical improvement targets, and even drive strategic initiatives. Bad benchmarking data gives companies the illusion they are running their business professionally. It is often the opposite; bad benchmarking often leads to poor decisions. Robust benchmarking does exist but is relatively rare. For example, setting labor productivity goals in the warehouse based upon on time studies or predetermined time systems is robust. So is Chainalytics Freight Market Intelligence Consortium (FMiC) which provides for sound lane/rate freight benchmarking.

And Chainalytics has done it again. Their relatively new and less known, Demand Planning Intelligence Consortium (DPiC) provides yet another powerful benchmarking tool. DPiC is built on many of the same principles as FMiC. Chainalytics’ Jaime Reints – Director of DPiC – and Ben YoKell – Vice President of Integrated Planning, graciously gave me a very detailed briefing that allowed me to get into the weeds of what they are doing and how they are doing it. What follows are some highlights from that briefing.

Robust Benchmarking of Demand Forecasts

Here is the basic goal: If, for example, you were looking at a demand planning team that had achieved forecast accuracy of 79 percent for a certain set of products, is that good or bad?

What are the main steps Chainalytics took to get to sound and actionable analytics? First of all, companies must give to get. Their members have contributed over $72 billion in forecast data to the benchmarking exercise. Ms. Reints believes they will have $100 billion in data by the end of the year. Statistically, you need density to have a meaningful analysis.

Secondly, one needs to start with defined benchmark metrics. Different types of metrics are used as if they were measuring the same thing – Mean Absolute Percent Error (MAPE) measured in a monthly bucket will yield different results than a Weighted MAPE in a weekly measure of accuracy. But beyond these standard metrics, Chainalytics has some non-obvious metrics that I had never come across – and I’ve been in this game a long time – which yield quite interesting insights.

Chainalytics has not only defined the core metrics, they calculate these metrics for their members! They’ve mapped the demand master data in their member’s systems, pulled the data out, and made the calculations to ensure that the calculations are accurate. In the process of onboarding new members, they have often uncovered errors in the way companies were calculating their forecast accuracy. Thus, before members even receive benchmarking data, they may have already begun to gain value from this exercise.

Thirdly, there needs to be mathematical rigor in the analysis. And the math needs to have a defendable point of view. Chainalytics’ perspective is that demand benchmarking should be based on an understanding of how hard it is for a company to plan their demand in the first place. Fast moving products that are never promoted and have no seasonality effects are far easier to forecast than extremely slow moving products that consequently have intermittent demand patterns.

Some groups of products may have a promotion that may lead to a doubling of demand; other products can have a promotion that can drive as much as a tenfold increase in demand. The latter is clearly harder to forecast than the former.

The consortium has classified every item along multiple dimensions – stability, trending, seasonality, intermittency, and products that are just being introduced or will soon be retired. This analysis is done at both the item level, and the item/location level. Here, “location” is defined as the company’s internal stocking location. This analysis is done twice a year.

At one point in our conversation Mr. YoKell said, “I remember in 2013 when we started looking at this concept, and we were shocked by the lack of genuine rigor in the few other approaches we found out there. At how non-standard and pseudo-scientific the data out there was. I was aghast at how casual it all was. There was nice window dressing, but no statistical strength.”

I’ve come across the same thing across a wide range of benchmarking tools. Robust benchmarking is still rare. DPiC is an exception.

A complete analysis of logistics trends, technologies, and services