Most Transportation Management Systems (TMS) give you Business Intelligence (BI) in the form of Key Performance Indicators (KPIs) on your trading partners and your own operation. But the imperative falls on you to determine the value of good performance and implement a resulting decision or reward structure.
Instead, why not embed the BI that you already collect into the operational processes of your TMS and employ it directly as decisions are required?
The Traditional BI Approach
Volume and duration requirements, performance-to-plan, comparative performance information, and historical cost information are readily available in today’s TMS. You can easily determine a carrier’s on-time performance, acceptance/rejection percentage, billing accuracy, unanticipated accessorials, and other metrics. You can also determine the average time required to load and unload at a facility, or to transit between your facility and repetitive destinations (or pick-up points).
The metrics are simple and easy to understand, but it is time consuming for you to implement these KPIs into day-to-day operations. Employing this information is largely a manual process. In addition, most KPIs are backward-facing — the value is only as current as the last “snapshot,” since the metrics gathered over time change constantly. Each rating or metric can improve or degrade over time, requiring you to constantly modify your rules for decision or reward.
BI is delivered to you. To get your metrics, you print a report or view a dashboard, with pre-defined metrics for a given category. When you have analyzed the results, you return to your TMS and set rules to make decisions and to reward good performance and punish bad (making operational changes that influence behavior in the future). This process is separate from, and asynchronous with, your day-to-day transactional management of transportation operations.
This is a flawed way of working. What you really want is integral incorporation of this BI into your day-to-day operational business process.
The Embedded Analytics Approach
Embedded Analytics takes the historical BI that is a byproduct of past shipments and examines it as operational decisions are being made. It automatically applies the most current and relevant KPI to the specific operational decision being made. Because so many of these decisions are impacted by constantly-changing performance metrics in multiple categories, your TMS must become smart enough to make routine, but high-volume and current-data-dependent, decisions for you.
In a more detailed whitepaper, I provide many examples of how companies can use Embedded Analytics to improve transportation management performance and reduce costs. Here are a few examples to consider:
- Give preferential treatment to your best carrier trading partners and reward them with quick payment to influence load acceptance and on-time performance. By setting rules on performance levels that equate to payment speed, your TMS can automatically determine an individual carrier’s payment dwell time — and change that time immediately as performance changes.
- Increase the number of loads offered to carriers based on historical availability, superior acceptance percentage, and on-time performance (and within the limits of your volume incentive agreements). Again, by setting rules that effect the allocation decision employing metrics other than lowest rate, your TMS can determine the best, most effective carrier to offer a load to, and do so without asking for your intervention.
- Allocate premium appointment slots to the carriers that habitually demonstrate the best on-time performance so that your facilities operate smoothly in peak periods. You want to show those peak periods as “unavailable” to carriers that have traditionally been late and caused schedule disruptions. By setting minimum performance standards for peak times, your TMS can automatically determine at appointment request time whether a carrier’s historical on-time performance percentage qualifies for the time slot. The self-service appointment scheduling system can mask peak appointment slots from poor performers, even if those slots are open. Each carrier will see availability based on its historical on-time performance.
- Employ load/unload times and historical transit times to validate appointment duration and current trip time between appointments. Automatically comparing historical load and unload times to current appointment duration can flag those with insufficient duration. Automatically comparing transit time in a lane can determine if sufficient time has been allowed between estimated facility departure time and delivery appointment time and highlight those that are inadequate.
- Develop target rates for quotes, or a “book-it-now” price. Embedded analytics use historical information to calculate a rate by lane. Employing rules, the TMS can automatically keep this rate up to date. For spot rate quotes, the “book-it-now” rate provides an eBay-like function for a carrier to accept the load immediately. It also becomes a target price for bidding. The target rate also provides a benchmark for analyzing rate trends.
In these examples, the BI is embedded directly into your TMS day-to-day operations and processes. The most current rate and performance information is acquired interactively and the process is synchronous. It takes you out of the middle.
You could never manually analyze each transaction and make these decisions as the events arise. There are too many transactions that require examining specific metrics, and then implementing individual operational decisions.
This process must be event-driven and the BI must be examined by your TMS, without human intervention. Further, you do not want to permanently change your carrier, rate, route, and appointment constructs. These embedded analytic decisions are dynamic, made transactionally as events arise, and changed dynamically as each KPI changes.
Your time duration and capacity requirements change as performance dictates. You neither permanently reward a trading partner for historically good behavior nor permanently punish a traditionally bad-performing partner. Decisions and rewards are based on a continuum — and you constantly encourage the best performance that you desire.
Steven Blough is cofounder and President of MercuryGate International, a best-of-breed TMS software provider that delivers transportation solutions for shippers and logistics providers.
 Note: Embedded Analytics is not optimization. Complex decisions are not being made from multiple variables across multiple transactions concurrently. Rather, simple operational decisions are made on a transaction-by-transaction basis from performance history.