Over the last 2 decades warehouses have changed dramatically to meet the needs of the “I need it now” consumer (full transparency, this is me). If you think about it, it is pretty insane that I can log onto nearly any website, click buy, and in two days that item arrives at my doorstep.
Prior to starting Takt, I never appreciated the systems and processes required to make this happen:
- The consumer clicks buy on the website (this is referred to e-commerce).
- That order is sent from the e-commerce platform to the organization’s Enterprise Resource Planning System (ERP) such as SAP or Oracle.
- That order is then sent to the WMS and routed to one or more warehouses based on inventory volume and availability.
- The items in that order are then put into a wave by the WMS or sometimes another system purpose built for waving (Waves organize orders into groups based on order date, warehouse zone, etc).
- The WMS then assigns that task to a picker who retrieves the task on their handheld device. The picker walks or drives to the location, grabs the item off the shelf.
- In some warehouses, the picker places the order on a robot such as 6 River Chuck’s or Locus Robotics, which then drives it to the staging area.
- From there, the WMS communicates with the shipping vendor like UPS, Fedex, or DHL to create a shipping label that is printed and placed on the box by the packer.
- Finally, that box is placed on to a waiting UPS, Fedex, or DHL trailer to be sent to their depot for sorting and delivery to your house.
Pretty crazy that this works millions to hundreds of millions of times a day across the world.
As the need for performance and speed has increased, so have the demands of the systems in the warehouse that make these processes possible. In order to run an effective operation, operations teams have been forced to become data experts, typically leaning on the power of spreadsheets to make sense of data across systems.
The problem is that this spreadsheet-based insight is anything but real-time. These systems have different data, schemas, and use different naming conventions. Because of this, operations teams often get data days if not weeks after the day is complete, leaving little room for performance adjustments.
This is especially painful for Third-Party Logistics providers (3PLs), who often have to work across multiple systems (WMS, Time Clock, etc), some of which are provided by their customers. This makes it impossible to track your facility performance or P&L – and you can forget about any strategic decision making or Continuous Improvement.
Retail customers aren’t immune to this problem either. Retail customers usually have their core WMS, but oftentimes have a separate returns system (RMS), and receiving systems. Being able to identify processes, coach employees, and forecast for the future is critical to meeting customer goals and warehouse goals.
From the outside this may seem like a simple problem. “Why not find a system that can do it all?” The truth is, each one of these systems solves a specific problem very well which equates to real savings in that area. Not to mention, the various automation and robotics vendors that all solve different problems from box making to goods-to-person.
In absence of a cohesive way to look across all these systems some companies have made big investments into data teams and tools who’s sole responsibility is to get the data out of the various systems and into a data warehouse like Snowflake or BigQuery.
“Focus on the things that make your beer taste better.” - Jeff Bezos
This quote from Jeff Bezos has always stuck with me. He gave this talk at Startup School in 2008 about Amazon and what would become AWS (Amazon’s cloud business) as we know it today. In this example, he talks about how German Beer Brewers outsourced electricity generation to another company because it didn't make their beer taste better. Generating electricity is “undifferentiated heavy lifting” that beer brewers could do but doesn’t make their product different.
Bringing data together across systems within the warehouse requires building a top-notch data pipeline and technical team to deliver insights in real time operations. Not to mention the cost to host, maintain, and secure the data from the various systems. For a mid-size operation this can be years of development and hundreds of thousands of dollars a year. All of this work doesn’t mean you ship units faster, more efficiently, or for less cost. What helps you ship quickly, efficiently, and for less cost are the insights sitting within the various systems. Delivering these insights to your team in real time will have a direct impact on your business.
At Takt, we saw this problem and immediately realized we could make this easier and more cost effective. We bring together data across your systems into a schema that fits your business, so that supervisors and operations teams can quickly access the insights they need to run day to day operations, and analyst teams can run custom queries to do things like analyze workmix, processes, and facilities with ease.
Need to go deeper? No problem, Takt’s API enables these teams to pull the data into a custom BI tool like Tableau, PowerBI, or Google Data Studio for custom analysis. With all of these paths, operations teams and analysts focus on insights and not data heavy lifting.
The result? Your business can focus your resources on strategic decisions – whether that’s identifying cost savings, selecting and implementing automation, or even just evaluating if your process changes are driving the right ROI for the business.
Takt consolidates your data, delivers insights, and provides tools for your operations team to win, and focus on what makes your beer taste better.