What is Insights?
Infoplus Insights is an add-on module that leverages machine learning, artificial intelligence, and advanced data mining. At the end of the day, it helps our clients answer two general questions:
Given an order, what other orders are similar to it?
Or, in reverse: What sets of similar-looking orders do I have?
Given a set of orders, what kind of labor will it take to fulfill them, considering the various production methodologies (i.e., layout by order, pick to cart, mass distribution) used?
How Does Insights Work?
You're in the right place to find out! There is some unique setup involved before Insights can be properly used. Once that is done, the module will perform a deep analysis on the orders and return its results on the Open Order Insights screen. The information in this screen—accessible with just a few clicks—would take a human a large amount of time and effort to compile.
How to Access Insights
The primary screen for Insights is called "Open Order Insights". This screen can be found by clicking the Insights icon on the left-navigation bar of the dashboard. You can also search for it in the quick actions menu.
NOTE: The primary "Open Order Insights" screen is only available to users with type "3PL Admin". A user's type can be configured in the User table in Infoplus.
In addition to the Open Order Insights screen, there are some additional fields to the Orders table: SLA Date, SLA Status, Production Type, and Similar Orders. These fields are visible on the Viewing Order screen, in the box shown below. SLA Date, SLA Status, and Production Type can also be used on the Orders query screen:
What Data is Needed?
Warehouse Service Types
Insights clients can define their own Warehouse Service Types. Ideas are like "B2C", "Retailer", "Same Day Rush", "LTL".
The table for doing this is simple - just a Name and optional Description.
You can also set, on a LOB and an Order Source, what default Warehouse Service Type should apply to new orders (i.e., "This is my Retailer order source" or "This LOB is always LTL orders").
SLA Days & Times
The Line of Business table has fields for defining the default Service Days and Cutoff Time for the LOB. Note, 0-days means "same day if entered before cutoff time".
This table also has a sub-table where override rules can be setup for an LOB. This allows users to specify by Warehouse Service Type, Warehouse Service Level, Backorder Status, and additional SLA service days & cutoff times.
i.e., "rushes must be entered by noon" or "LTL orders have 5 days of service time".
The warehouse table has a new timezone field, to drive if an order is before or after the cutoff time when its SLA date is being computed.
SLA Date calculations only consider days when the client is working. 2 tables under the Insights Setup app allow us to set these up:
Standard Business Days - defines a client's standard week (true = work that day, false = off that day) - will be pre-populated as M-F.
Non Business Days - define essentially holidays, or other exceptional closed-for-business days. Must be set up every year.
Typical Non Business Days:
New Year's Day
Day after Thanksgiving
Fourth of July
- Production models are used when estimating the steps and touches for every order. With that being said, it requires our clients to provide a detailed list of all of their processes as well as assign a “cost” to each step. An easy way to use the cost feature is to think of the step in terms of seconds. For example, if step 1 is “Hit Execute” in the Insights screen, that might take 20 seconds, so the cost would be 20. Step 2 is to print the documentation and deliver it to the picking team. This step might take 2 minutes, so we assign the cost as 120. Once we have a cost associated with every step of the process, we have a true view of how much labor is involved.
If you plan on setting up a Mass Distribution production model, make sure to specify the corresponding validation function. Currently, Mass Distribution is the only validation available. This function makes sure that all orders being analyzed are identical before recommending a mass distribution production type for an order group. (We’ll explain this in greater detail later on).
Example Production Model: