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Supply Chain News: Highlights from the 22nd Annual Third-Party Logistics Study Part 2

For the 22nd consecutive year, Dr. John Langley of Penn State University again led the annual Third Party Logistics Study, released as usual this year at the CSCMP conference in Atlanta in late September, a collaboration between Langley, again this year Penske Logistics, and new sponsors recruiting firm Korn Ferry and consultant Infosys, basically replacing Capgemini, which was a sponsor for many years.

In part 1 of our review, we focused on the core data from the survey of shippers and 3PLs, including the dreaded “IT gap” relative to the perception of 3PL IT capabilities (see Highlights from the 22nd Annual Third-Party Logistics Study.)

Again this year, the report augments that core data with commentary and other survey data on what might be called special topics, which this year included use of blockchain technology in the supply chain, automation and digitization, and risk and resiliency in shipper-3PL relationships.

We provide highlights of each of these sections below.

Use of Blockchain in the Supply Chain

Blockchain, the technology that first broke into prominence as the foundation of the digital currency Bitcoin, has much promise in the supply chain by its potential ablity to make it easier and less expensive to share data across the supply chain versus traditional IT approaches.

As the 3PL study notes, the technology also “improves security because each transaction is validated and recorded by an independent third party. No one party can modify, delete or append any record without a validation of the edit from others in the network. The goal is to create one version of the truth, link information and create transparency.”

For every each movement of goods across parties, blockchain could identify the parties involved, price, date, location, quality, state of the product, and other information relevant to managing shipments and the products on those shipments.

The report says “The public availability of the ledger makes it possible to trace back every product to the very origin of the raw material used.” But this isn’t exactly correct, as often a controlling party – say a Walmart – will restrict which parties have permission to see what information – and it seems highly unlikely to SCDigest that Walmart would open up all its supply chain data to its supplier and carrier base.

That noted, the report says “The information shared would increase visibility and minimize the potential for human error. It could also dramatically reduce time delays, eliminate added costs, minimize human error and decrease corruption,” – and could even conceivably provide insight into real-time demand for goods and logistics services.

Nevertheless, the technology is very new, with conflicting standards – and is still little known or understood by most. The study found the majority of respondents – 67% of shippers and 62% of 3PL providers – said they don’t know enough about blockchain to rate it at this time.

However, the report encourages 3PLs to consider adopting blockchain as a way of differentiating their offerings.

This section of the report concludes with the observation that “Costs to collect information have decreased, but it isn’t free, and the value proposition [of blockchain] is yet to be defined. There is a balance of mitigating risk and improving security and the costs associated with doing so. More time is needed to determine which companies are willing to invest in the technology.”

 

Automation and Digitization in the Supply Chain

This section covers a broad range of technologies that are impacting the supply chain, from AI and big data to autonomous trucks, with a lot of space devoted to various levels of truck driving automation.

More traditionally, the report says that there is often a lot of data in core Warehouse Management and Transportation Management systems that could be mined for the mutual benefit of shippers and 3PLs.

From a 3PL perspective, an executive from Penske Logistics notes that “Granular data related to the handling of an order and the handoff to the warehouse or the fleet, as well as the visibility out on the road and time records of deliveries, makes you much more efficient and provides insight into the costs to serve individual customers or individual stops. Then you can make better decisions on how you price your product in market.”

Moving to newer technologies, the report noted the rising interest in and availability of on-line freight matching services, as the Uberization of load matching – from companies including Uber itself but a growing number of others – is already having an impact on shippers and 3PLs.

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Q&A On Distributors’ Annual Pricing Changes

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HEC Q&A on Distributors’ Annual Pricing Changes for FY2017

Q: More and more distributors appreciate the importance of pricing.  In your experience, how robust are the price increase management practices used by distributors today?

A: It varies greatly. Many bigger distributors have made investments in this area. Smaller distributors may not have formal price management processes and tools. They manage prices on a day-to-day basis through approvals, controls, and incentives – and they may rarely take time to address pricing in a strategic way. They may look at the price lever strategically at the time of budgeting. At that point, they may prepare a financial forecast to show where budgeted price lifts should come from, reflecting some data and the judgment of the individuals involved.

Q: Is this approach problematic?

A: If budgeted price moves do not generally materialize or stick, then this shows that the approaches failed to deliver. One major risk is that these distributors may get caught in a vicious cycle of chasing volume to make up for less-than-planned margins. Chasing volume then just puts even more pressure on price. More robust analytics can make the difference, so pricing can actually drive profitability, rather than being a problem area.

Q: What are some ways that more robust analytics can help, beyond what’s in management’s heads?

A: The industry knowledge of management can be very helpful. They can help identify key drivers of price sensitivity. A robust price segmentation model should leverage those insights and build on them. Analytics can help refine basic models and drive to higher levels of accuracy. For instance, after management identifies known drivers of price sensitivity, analysis of data can often tell which of those attributes may be more important in particular parts of the business. So, the segmentation model becomes more refined, as it is now driven by a combination of insights and also data. As another example, going beyond segmentation: it is also typically possible to align folks on relative areas of price risk. For example, raising prices at small accounts is typically viewed as less risky than making moves at large customers. This is interesting, but for many distributors this is not a new insight – they may already be pricing significantly higher at smaller customers. Analytics can tell you: according to the data, where have you already pushed the envelope so far that further price increases are unlikely to stick, and where do you potentially have some room left still?

Q: Even distributors with strong analytics resources can find it challenging to translate pricing recommendations from their analytics into actual results. What are some ways that help with this translation, so recommendations are not “overridden” by judgment of those managing day-to-day operations?

A: Better recommendations stand a better chance of being implemented. Still, for pricing recommendations to stick, there must also be buy-in, beyond just mathematical sophistication and accuracy. This can mean involving key folks in developing the mathematical models, so they also feel some ownership. The pricing recommendations should also be validated and tweaked before implementation. Stakeholders, particularly the front line, should get some education on why the pricing recommendations make sense. In our experience, if the math is strong, if it is developed in a transparent and collaborative manner, if it is its rolled out in a way that fosters buy-in, and if the right set of incentives exist, then it is generally possible to get folks to follow the guidance, even without relying heavily on controls.

Q: All this sounds hard. Pricing across large product and customer portfolios gets complex for B2B distributors, and getting sales folks on board can be a change management challenge. How can distributors do a better without major investments, like buying pricing software or hiring a full-time pricing analyst?

A: Pricing software implementations can be expensive, and too often they do not live up to their promises. For various reasons, price optimization technology has failed to deliver at many distributors. Analytics software is only effective if there is a qualified analyst to use it. As an alternative to relying solely on internal tools or hiring an analyst, some businesses opt to hire specialists like ourselves to use on an as needed basis. This gives them a way to surgically insert a highly specialized asset to augment their internal resources. In a high-impact area such as pricing, this approach can make sense in a lower mid-market business.

Lee Nyari
Managing Partner of The Innovative Pricing Group, LLC