Ten Ways a Parcel TMS Assists Spend Management
Want to get that Parcel TMS initiative you have been planning funded? While features and functions may be appealing, to get the attention of those that approve investments, it is important to understand and articulate the ways that implementation of a Parcel TMS generates savings.
Creating an ROI for a project is almost always required to gain approval for a Parcel TMS investment. While other variables often justify the investment (customer satisfaction, capacity, and security for example), eventually one will be asked, “What is the return on this investment?” While each organization will have a different model for answering this question, following are ten areas you should explore when you create your model:
1) Carrier Agility
This variable is particularly relevant if a shipper has recently negotiated a set of rates with a carrier, but was unable to recognize the potential savings due to delays in activating the carrier in the current TMS. The ROI variable is the amount of lost savings due to the delayed activation.
2) Multi-Carrier Rate Shopping
If your organization is currently using hardcoded rules or a rules engine that acts as a proxy for rates when making carrier and ship method decision making, migrating to a true multi-carrier rate shopping engine is virtually certain to save money. To calculate this ROI contribution, you may want to do a study of your historical data to determine waste due to incorrect carrier and ship method assignment. Typically, this type of analysis will suggest the potential for saving up to 10% of cost.
3) Multi-Ship Point Rate Shopping
Like Multi-Carrier Rate Shopping, most organizations have some rules coded into an Order Management System that allocates orders to various ship points (DCs, stores, or drop-ship vendors). These rules may map zips to DCs (pending inventory availability) or stores based upon customer needs-by-dates (stores fulfilling expedited orders only). Once again, these rules often are acting as a proxy for actual costs to ship that may change over time or not reflect the actual costs at a granular level. Like Multi-Carrier Rate Shopping, an analysis of historical data will likely suggest savings, but in the case of Multi-Ship Point Rate Shopping, the savings can exceed 10% if the shipper has a large set of DCs, stores, and drop-ship vendors.
4) Zone Skipping
Zone skipping is often avoided by shippers because of the perceived required capital cost of complicated material handling equipment to sort cartons to pallets or mailbags. While this investment often does have a positive ROI, should you just wish to “dip your toe in,” you could explore zone skipping to a handful of destinations manually or through a less robust sorting system. If the current parcel technology isn’t able to handle zone skipping then the investment in a new technology will be needed, but the ROI should certainly justify the cost. ROI for this variable will be gained from the savings realized from inducting at the destination hub versus the origin, minus the line haul costs. Often this savings can be over 40% for parcels that are zone skipped.
While a Multi-Ship Point Rate Shopping engine does enable the selection of the best origin (DC, store, or drop-ship vendor), if the stores are not able to rate shop in real time or utilize the robust PTMS features, some of the value is lost. When a store processes an order, a new rate shop should be processed based upon the available carriers and the customer’s needs-by-date (and ideally, a whole host of other variables). Often, orders can be downgraded when shipped from store and still deliver in time due to the proximity to the customer. ROI calculation for this variable would include the set of orders historically that could be downgraded if delivered within zone one.
6) Vendor Drop Shipping
Once again the Multi-Ship Point Rate Shopping engine aids in selecting when a drop-ship vendor should be used. However, if the vendor is not connected to the centralized PTMS, they are often forced to utilize a carrier, ship method, and account number that is preassigned to them in advance or for the particular order. When drop-shippers utilize the same system as the DCs, the value of the final rate shop is realized. ROI calculation for this variable can be difficult since often the drop ship vendor data is distributed (a massive risk in our opinion). One could likely estimate that at a minimum 10% would be saved if a centralized system was used.
7) Corporate and Intercompany Shipping
While the volume of shipments from corporate or within the company are dwarfed by store fulfillment and direct-to-customer shipping, there is often significant waste potential present. The users of these systems are often not professional shippers and therefore don’t understand the magnitude of the cost of expediting, or that when the volume is aggregated the cost can be quite significant. (On the other hand, by centralizing all these shipments, one could potentially take advantage of custom rates.) Requiring users to enter a needs-by-date before receiving options or at least exposing the user to the cost of expediting should also reduce unnecessary expenditure. Finally, since the data is decentralized and often only known by the carrier, there is limited ability to do robust reporting or fraud prevention activities. Like drop ship vendor analysis, analysis of historical data can be difficult since the data is distributed and does not contain order information (customer needs-by-date for example). However, it is safe to assume that 10-20% savings would occur by reducing the number of parcels that were expedited unnecessarily, reduced intentional and unintentional fraud, and access to data on aggregate.
8) Robust Reporting and Audit
While reporting that enables analysis does not yield any savings, the insights analysts gain from the reporting should be quite helpful in identifying cost containment opportunities. Saving is gained through identification of avoidable carrier fees and costs, performing root cause analysis on these costs, and acting to resolve the issues. ROI of data analysis through reporting, dashboards, and other tools can be difficult to quantify as one needs the tools to find the improvements. Potentially looking to other data projects’ ROI analysis may provide guidance on savings that have been realized within your organization in the past.
9) IT Efficiencies
Typically, legacy systems are quite expensive to maintain. More modern technologies and integration methods should result in savings. One challenge will be finding all of the support costs associated with a system – quite often there are more resources than known actually supporting a system. The implementation costs of the new product should be taken into account to validate that the implementation costs are not offsetting the savings for too long. ROI analysis will require identifying the as-is technical support costs and comparing them to the future-state technical support costs.
10) Operational Efficiencies
Finally, take into account the operational costs you feel you could avoid with a new system. Is the User Interface, functional steps required to process an order, or system maintenance resulting in costs that could be avoided? Like the IT Efficiency analysis, one should evaluate as-is costs (including the hidden costs) and compare them to the expected future-state costs.
While there are many reasons to consider upgrading or improving a Parcel TMS beyond cost, preparing oneself for the eventual question of ROI will improve the chances of project funding. The ten examples of ROI inputs are areas you should consider exploring, but every company is different, and some of the variables will not be relevant and there are certainly others that were not mentioned.
We realize this piece is very high-level, and if you’d like to drill down on any of our recommendations, reach out to us with questions or comments. We also welcome opportunities to deliberate or dispute our insights and recommendations. No conversation is off limits.
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