Updated: Apr 30, 2022
The global productivity management system market size was valued at USD 47.33 billion in 2021 and is expected to expand at a compound annual growth rate (CAGR) of 13.8% from 2022 to 2030.The growing requirement to manage tasks and workflow among the business to amplify the growth and swiftly growing advancements in Machine Learning (ML) and Artificial Intelligence (AI) are the key factors driving the market. Furthermore, the growing adoption of cloud computing in the business processes and the increasing adoption of enterprise mobility, smartphones, and Bring Your Own Device(BYOD) to expand the mobile workforce will surge the demand for productivity management software, contributing to the growth of the market.
What is a Super Killer Productivity App?
A Super, "killer" Productivity App compels your customer to buy it, even at an elevated price point, because the killer product makes life/business so much faster, easier and cheaper; a killer product sells itself. I spent the first 24 years of my software development career designing, selling, building, delivering and supporting some not so deadly, but mostly killer product solutions. I tried over and over again to turn what I saw as killer products into a business success. The tipping point came together for me after 24 years of experience learning software architectures, designs, algorithms and underlying software products. In 1989 I took my 24 year education and made what would be my last attempt to make money. This last company produces about $.6B in wealth for investors and employees.
Some people make a really good grilled steak. They have a really good mental model for preparing and grilling a steak. Others bake a real popular cake; they have a mental model that results in a really good cake. In both cases they start with ingredients, component products, and a recipe, a mental model; so it is with a killer software solutions to high value customer problems.
What Do We Look For the Killer Product To Do
The Supper Productivity App sits 50,000 feet above the many computer applications that a customer and customer's employees interact with to aggregate information and perform rather complex pedestrian actions in order to solve a problem or accomplish some valuable goal. Look for a process that can consume hours of time for customers sitting at their computers just to do repetitive things that could be automated and packaged into one screen, dashboard, zoom map or diagram, hiding all the apps and automated processing being done by the Super Productivity App. We are looking for an order of magnitude improvement at least. The more the improvement the more the value; the more the profit margin and the lower the sales cost for the startup.
The Super Killer Productivity App (SKPA) is a Mental Model Too!
It is the customer's recipe for the steps required to successfully complete huge chunks of a complicated multistep critical task. To be wildly successful the Super "Killer" Productivity App must not be customized for each customer mental model, recipe. The Startup must design a general purpose product that represents 90%+ of the programming to automate the customer's mental model, recipe. If the SKPA needs enhancements, the customer price for software development cost is reduced so that both startup and customer get a win-win deal and the startup ends up with ownership of the new software. The evolving general software application base defines the startup's advantage and value.
The Customer Partnership that started the SKPA in 1985
American Express set up a network across the US to support point of sale transactions for their American Express credit card. The network was built with many connected network devices obtained from diverse network equipment manufacturers that offered a proprietary management system that only worked with their device. American Express had a centralized network control center in Phoenix, Arizona, with a network problem resolution war room. Designed like an oversized conference room, on the walls around the room were input/output devices from each network equipment manufacturer; thus access to every communications device in the AmEx network. When the network failed somewhere in the US the printers, teletypes, CRT systems around the walls would be used to collect data about the failure. A team of network engineers would collect information bringing it to the center of the big conference room and placing it on the conference table in the middle so they could collaborate and determine the exact component and it location that caused the failure.
This fault management process, mental model, took at least a couple of hours and up to 8 hours or more for the more difficult ones. The cost to AmEx was estimated to be $50,000 per hour in 1985, a significant cost to revenue and customer satisfaction. The worst network failures were limited but went on for days especially when there was more than one failed component in the network. After delivering the first release of SKPA AmEx took care of simple problems in minutes and complex problems in less than an hour.
Super Killer Productivity Architecture Used 1989 to 2000
The platform architecture available to my startup, Objective Systems Integrators (OSI) 33 years ago was limited. There was a relational database, we chose Oracle. There was an Artificial Intelligence app, we chose AIExpert. There was a graphical user interface dashboard app, we chose SL Corp. There were many many device specific management applications that SKPA would have to sit on top of collect info and issue commands to. The platform on which the SKPA, NetExpert, performed was a unix OS based Sun Microsystems network of machines. The architecture supported multiprocess parallel processing. There was no Interprocess Message Handler (IPMH) at the time to pass requests and responses between specialized functional modules of the SKPA architecture spread across a network of processing nodes. Think of it as an early version of the Cloud with Software as a Service (SaaS) architecture, in 1989.
For the many diverse devices that must be intelligently managed to gather information and execute management commands there is a type of multiprocessing module called a Gateway, a translator, that would handle all interaction by translating data from diverse devices to a format normalized to data meaningful to SKPA and vise versa SKPA commands that were meaningful to an individual diverse system.
AI Inference Engine - Rule Engine
In 1989 the state of AI software was more like an inference engine/rule language than todays Machine Learning (ML) and Artificial Intelligence (AI), Data Science. The product we first included in SKPA, AIExpert, was a general purpose inference engine. A year later we developed our own inference engine, Ideas, and told our customers it was their rule engine; calling it a simple rule engine intimidated the customer experts less. One or more rule engine modules were configured in the networked multiprocess configuration.
The User Dashboard Interface
Each Sun Workstation runs an instance of a Dashboard multiprocess module to control one terminal interface for one network engineer.
I am publishing a series of articles analyzing my one big startup success searching for the reasons, the mental model, that explains my success. My belief is that the mental model that worked in 1989 would work in 2022. The purpose of this series of articles is to test my belief. I invite you to follow if you are interested.