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TechHub - Texas A&M's Technology Store

Data and Methodology

Purpose

This page provides insight into the methodologies, data sources, and calculations used to develop the TechHub Dashboard. By detailing our processes, we aim to:

  • Build trust with stakeholders through establishing transparent practices.
  • Enable informed decision-making by outlining the logic and formulas behind our metrics.

Please reach out to techhub@tamu.edu with any questions or feedback regarding our dashboard or this Data and Methodology resource page.

 

Methodology

The following methodologies and formulas were used to derive many of the metrics and graphs seen on the TechHub dashboard.

Operational Metrics

Average Business Days to Approval

A timestamp is captured at the time an order is placed and also when the order has been approved in Aggiebuy. By comparing the two timestamps (and accounting for weekends and holidays) we are able to determine the amount of business days approval took for each order and then calculate the average across all standard orders.

  • Formula:  ∑(T2 - T1) / N

T1 = Order Creation Timestamp

T2 = Order Approved Timestamp

N = Number of Standard Orders

  • Notes: Excludes holidays and weekends, rounds the timestamp difference to the nearest full day. Only calculated for standard orders, bulk and custom orders are not included in this metric.
  • Sources: Timestamps are captured via AggieBuy and the TechHub eCommerce Platform, the total number of standard orders is captured in the TechHub eCommerce Platform.

 

Average Business Days to Fulfill

A timestamp is captured at the time an order has been approved in Aggiebuy and also when the order has been completed by the TechHub team. By comparing the two timestamps (and accounting for weekends and holidays) we are able to determine the amount of business days the fulfillment process took for each order and then calculate the average across all standard orders.

  • Formula:  ∑(T3 - T2) / N

T2 = Order Approved Timestamp

T3 = Order Fulfilled Timestamp

N = Number of Standard Orders

  • Notes: Excludes holidays and weekends, rounds the timestamp difference to the nearest full day. Only calculated for standard orders, bulk and custom orders are not included in this metric.
  • Sources: Timestamps are captured via AggieBuy and the TechHub eCommerce Platform, the total number of standard orders is captured in the TechHub eCommerce Platform.

 

Labor Hours Saved

With input from fellow Technology Services groups, an estimate was established for the average time spent acquiring and provisioning a single computer by an IT Professional, both pre and post TechHub involvement (170 minutes and 55 minutes respectively). This yielded an estimated 115 minutes of labor saved for these IT Professionals per computer. Additionally, accessory purchases were estimated to save 10 minutes per item. 

  • Formula:  ∑[(C ⋅ (115 if C ≤ 9 else 57.5)) + (A ⋅ (10 if A ≤ 9 else 5))] / 60

C = Number of Computers Purchased on an Order

A = Number of Accessories Purchased on an Order

  • Notes: Bulk purchases (10 or more of the same product) count for the half the amount of time saved, as the acquisition and provisioning process becomes more efficient at higher volume. The 115 minutes saved per computer and 10 minutes per accessory are estimated based on many time-saving steps handled partly or fully by TechHub, including product testing and vetting, vendor negotiation, asset tagging, and more.
  • Sources: TechHub eCommerce Platform.

 

On-Time Delivery Rate

The total time to fulfill an order is calculated using the timestamps captured at the time of approval and time of completion. An order fulfilled within 5 business days is considered "On Time", and the percentage of all orders considered "On Time" gives the TechHub "On-Time Delivery Rate".

  • Formula:  ∑O / N

O = Standard Orders Fulfilled within 5 Business Days

N = Number of Standard Orders

  • Notes: Excludes holidays and weekends, rounds timestamp differences to the nearest full day. Excludes bulk and custom orders.
  • Sources: Timestamps are captured via AggieBuy and the TechHub eCommerce Platform, the total number of orders is captured in the TechHub eCommerce Platform.

 

Sales Metrics

Revenue

Revenue is calculated by summing the total of all orders, sourced directly from sales data.

  • Formula:  ∑R

R = Total Revenue of an Order

  • Source: TechHub eCommerce Platform

 

Savings Metrics

Standard TechHub Savings

This metric represents all savings to colleges, schools, and units made through orders placed on the TechHub website (custom bulk purchases from before the website launch are not included). Savings are calculated per item at time of purchase. TechHub tracks competitor pricing (i.e. online retail pricing and pricing found on AggieBuy) for all products provided on the TechHub website. The competitor pricing is updated monthly to reflect pricing changes. If a product exists in AggieBuy, the savings will be calculated against the lowest price found in AggieBuy, otherwise the lowest retail price will be used in the calculation.

  • Formula:  ∑[max((A if A ≠ ∅ else R) - T, 0)]

A = AggieBuy Pricing

R = Online Retail Pricing

T = TechHub Pricing

A ≠ ∅ means AggieBuy Pricing exists (is not empty)

  • Notes: If the competitor pricing is lower than the TechHub pricing, this is not counted as negative savings, but instead counted as $0 saved.
  • Sources: TechHub eCommerce Platform, AggieBuy, online retailers.

 

All-Time University Savings

This metric includes all of our Standard TechHub Savings as well as all Custom Bulk Purchase Savings that occurred before the full launch of the TechHub eCommerce Platform. The Custom Bulk Purchase Savings is calculated by comparing the TechHub pricing of sold products against competitors pricing.

  • Formula:  B + ∑[max((A if A ≠ ∅ else R) - T, 0)]

B = Custom Bulk Purchase Savings

A = AggieBuy Pricing

R = Online Retail Pricing

T = TechHub Pricing

A ≠ ∅ means AggieBuy Pricing exists (is not empty)

  • Notes: If the competitor pricing is lower than the TechHub pricing, this is not counted as negative savings, but instead counted as $0 saved.
  • Sources: TechHub eCommerce Platform, AggieBuy, online retailers.

 

Data Sources

The following data sources were utilized to power this dashboard. The dashboard updates any changes from these data sources twice daily:

TechHub eCommerce Platform

  • Sales - All information regarding what has been purchased, when, by whom, etc. is captured directly on our website.
  • Products - Product information including category, commodity code, and purchasing history is captured here.

 

inFlow Inventory Software (internal)

  • Sales - Additional sales information is captured within our sales processes on this internal tool, including Requisition number, AggieBuy PO#, etc.
  • Vendors - We use inFlow to capture and store vendor related information such as HUB status, associated products, and inventory purchases.
  • Products - Additional information on products including cost history, current inventory levels, and order history are captured here.
  • Purchasing - All inventory purchasing is recorded here, including cost, vendor, dates, etc.
  • Savings - In addition to capturing our cost and sale prices for our products, we also use inFlow to record competitor pricing from AggieBuy and online retailers, allowing us to calculate how much we save our customers.

 

AggieBuy

  • Approval and Fulfillment Time - As a TechHub order moves within AggieBuy from a Requisition to a P.O. and finally into Invoicing, we capture timestamps along the way. These timestamps give us insight into how long each process (order approval, order fulfillment, and payment) takes.
  • Savings - On a monthly basis we look in AggieBuy for pricing on every product we sell, and we then record the best price we find within our inFlow Inventory system to compare against our own pricing.

 

External Sources

  •  Savings - For products that can't be found in AggieBuy, we will search for pricing data online and find the best retail price available for each product to compare our pricing against for savings data.

 

Contributors

 

Data Analysts:

  • Hank deVilleneuve

 

Data Engineers:

  • Jeff Phillips
  • Hank deVilleneuve

 

Project Owners:

  • Tom Wood
  • Isaac Podolefsky

 

This page will be updated as new data sources and methodologies are incorporated into the dashboard.

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