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5 Reasons Why Your Logistics Cost-Cutting Initiatives Fail

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5 Reasons Why Your Logistics Cost-Cutting Initiatives Fail

5 Reasons Why Your Logistics Cost-Cutting Initiatives Fail

The True Cost of Shipping Understanding, regulating, and reliably evaluating the performance, and the real cost of freight, in today's world of high-velocity, down-to-the-minute logistics, necessitates collecting and correlating data from a half-dozen distinct systems.

Effective cost-cutting programs face ongoing hurdles in terms of accuracy, speed, and thoroughness.

If you leave a little error in a transaction record untreated, it will progressively skew your spending and service analysis accuracy.

Shippers' teams require confidence in the data that guides them as they attempt to make better judgments.

Furthermore, enhancing the shipper-carrier connection is critical in a hyper-competitive industry with soaring consumer demands.

As a result, shippers must understand how their insights influence their decisions.

"It doesn't matter what system or technology you use or how much money you spend on technology if you're justifying judgments based on faulty data." You won't receive the outcomes you need to efficiently manage the business and limit costs."

Cost-cutting initiatives fail for five common reasons:

1. Obtaining the relevant information takes far too long

It shouldn't take long, but gathering all of your data and making sure you're getting and combining all of the right sources takes time. 

Your TMS (with contracted rates and accessorials), your carrier's system (for bills of lading, tracking the shipment's transit history, a delivery receipt indicating any potential exceptions, and the freight invoice), and your financial accounting or payables system (how/when/how much was paid) are all possible sources.

Delays cost time and money when it comes to making confident and accurate decisions. A week can make a huge difference in the outcome of your decisions. Sure, based on the greatest info available, you may have made the best option. But, in the meantime, what has changed? What impact might that new information have on your decision and the efficacy of your cost-cutting efforts?

2. You don't believe the data

If you agree, you should investigate why you don't have faith in it. Yes, the master data used to be rather good. However, master data must be meticulously preserved beginning with its creation. 

Things shift: Locations, vendors, item masters, SKUs, and packaging requirements are all subject to change. Inventory changes hands, as stores open and close. Maintaining master data is not a one-time task; it is a continual and objective process.

Let's clear up a misunderstanding: data cleansing refers to resolving the root cause of a mistake rather than removing it.

If an error is introduced (or data becomes stale, eroding timeliness), the process employing the data will continue to make the same mistake, again and over again. It only takes one piece of erroneous data to have a detrimental impact on judgments.

Trust in the data will be a problem unless the information around each element are thorough and accurate. Cost assumptions and spend analysis will be off the mark due to improperly managed master data, but pinpointing the core cause of the problem will be nearly hard without all of the necessary details.

You lack confidence and doubt the reliability of your outcomes if you don't believe the insights you're offered. As the risk level rises, so does the likelihood of success. While making a decision is preferable to making no decision at all, could your next decision cost you your job?

3. You have to "repair" your datasets on a regular basis

There's a lot of shady behavior going on here. When completing analysis, it's common to "clean" the data, which includes removing any data that appears to be incorrect. 

These data points, on the other hand, provide more context by detailing the exceptions that occur. Because it no longer represents the real-world it was describing, removing this data invalidates the entire dataset.

Let's clear up a misunderstanding: data cleansing refers to resolving the root cause of a mistake rather than removing it.

One example is seasonality. Your Chicago to Sacramento route planner provides miles based on the shortest path. In the summer, that's fine. But now that it's winter, vehicles can't make it across the Rocky Mountains on I-70.

Instead, they'll have to travel south on I-40, and your TMS will flag extra miles as "out of route" and "over budget." Valid scenarios are removed from your study when these "outliers" are removed. What appears to be an error could actually be vital information needed to make the best option possible for your cost-cutting goals.

4. Staffing levels with high rate-tolerances must be balanced

Exceptional management is a battleground for many businesses. It's a problem with business culture that typical freight-audit firms exacerbate. These companies exist to create a rivalry between the shipper and the carrier. 

That is, they are constantly hunting for a flaw in the carrier's billing. Their job is to find errors, and their very life depends on it. They never try to prevent the fault from happening again. They believe that the more mistakes they find, the more value they are delivering.

Rate tolerances are established to help both parties manage the volume of exceptions. The higher the tolerance, the fewer the exceptions, and thus the fewer bodies needed at the shipper to deal with them. But it's just a trick of the light. 

The shipper must pay the audit firm extra money in order for the audit firm to uncover and remedy the same recurring errors. Fixing the source of the problem is a deterrent for the freight audit firm.

Furthermore, the shipper pays the carrier late or insufficiently, which irritates the carrier. The carrier relegates the shipper to a lower priority, diverting capacity away from the shipper and towards a rival. Eventually, the shipper is compelled to settle for less-than-ideal options or pay more to convey the same freight.

It's understandable that a carrier will make an error while applying a rate now and then. However, when looking at the data, the vast majority of errors occur because a regulation or contract term is inadequately defined and left open to interpretation, or because data is simply missing. To ensure that no one is guessing, it is vital to clarify norms and words to remove subjectivity and ambiguity.

Your supply chain doesn't have to be slowed down by exception handling. Artificial intelligence and machine learning techniques may be used in your processes. 

In a manner that our human teams can't, technology can detect and remedy the cause of exceptions, as well as resolve them. Systems can automate much of what our people are now performing, allowing you to allocate them to more value-added work if implemented early in the process.

The total cost of insufficient or erroneous data, lack of data quality and data integrity governance systems, and improper spend analysis and management can easily exceed millions of dollars.

5. Despite technological advancements, costs continue to rise

Shippers' outdated TMS platforms require monthly updates and patches to be operational. These items require additional payments to be paid to the software provider. If it's an internal system, the implementation and testing will take time and money from internal IT resources. 

Additional system plug-ins or add-ons are frequently required as the profile of an organization's supply chain and freight cost evolves, and requests for more velocity and flexibility develop.

It all boils down to the data, and how comprehensive, accurate, and timely it is, as well as the governance process's success in ensuring proper quality maintenance and validation.

"Am I using data to make decisions or to justify my judgments?" you might wonder. It's remarkable how many people choose the latter option. It makes no difference what system or technology you use or how much money you spend on technology if you are justifying judgments based on faulty data. You won't receive the results you need to efficiently manage the business and limit costs.

It's Amazing How Small Amounts Add Up

The total cost of insufficient or erroneous data, lack of data quality and data integrity governance systems, and improper spend analysis and management can easily exceed millions of dollars.

Consider a national store transporting 1,200 truckloads every day at a cost of $1,850 per load. Every day, if your freight audit and pay tolerance is 1%, you're possibly inflating your actual freight spend by $22,200! 

When you compare accurately rated shipments to a potentially inflated historical rate to calculate how much your cost-savings initiatives will save, you'll be comparing accurately rated shipments to a potentially inflated historical rate. You saved $500,000 instead of $1,000,000 because the rate tolerance caused you to pay 1% extra; it wasn't because you made a poor decision.

Providers of technology and services

The freight data picture is confusing at the end of the day. It can be difficult to maintain the quality, trust, and confidence in your freight insights. 

Each shipper and carrier has their own take on the issue and what caused it. Focusing on the three pillars of data quality becomes a game-changer as a result of these challenges:

  • Accurate - the data has the correct value and format.
  • Complete - the data has all of the qualities; nothing has been "cleaned" out of it.
  • Data is available and accessible in a timely manner.

Detailed insights and results that can be quantified and monitored are required for successful cost-cutting projects. We believe that having complete, accurate, and timely logistics data allows shippers to make better decisions. 

An effective cost-cutting strategy is impossible to achieve without thorough, timely, and accurate logistical data. Shippers may improve their insights and eventually achieve the long-term cost savings they require thanks to today's cloud-based supply chain technology innovations.

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