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PMI India
KPIs in Digital Transformation
Ramkumar Arumugam argued that traditional Key Performance Indicators (KPIs) cannot be used to evaluate the outcome of a digital transformation initiative.
He outlined a set of metrics that he had devised for a digital transformation project to automate some of the manual transactions at a retail store in the United States. The project was subsequently scaled up to multiple outlets and functions such as HR and finance. To track the outcomes, the team used a set of business and engineering KPIs.
Some of the business KPIs were zero downtime, usability, business process automation, and responsiveness of the web design. For example, a web business must have zero downtime and provide access to data to users at all times.
For engineering KPIs, he stressed the need to factor in agility, commitment vs delivery ratio, and reduced time in the developmental cycle.
“We built over 50 reusable components like ‘login/log out’ and ‘security’ which were used across the board, and helped increase the productivity of developers by over 15 percent,” he said. The digital solution has also led to 65 percent reduction of paper trail in HR.
He urged project managers to first identify the client’s goals, align the project goals to that, and make the KPIs visible to all the stakeholders so that teams can reflect and adapt as required.
Model for Real Estate Projects
The real estate sector is notorious for construction delays. TV Sesha Sai provided project managers some useful lessons on how to overcome delays during the construction phase.
Drawing from his wide experience, Mr. Sai presented the ‘4M model’ as a simple tool to counter this problem. The four ‘Ms’ are – manpower, method, material, and money. Since these four parameters are the most critical for any construction project, he advised project managers to devise a tool to track them.
He drew an analogy between the model and a car, with each of the four wheels of a car representing one of the 4 ‘Ms.’ “Just like one needs to have all four wheels properly inflated for the car to move at a desired speed, similarly the status of the four ‘Ms’ is crucial for a good pace of construction,” he explained.
He presented a sample dashboard that captured data on the 4 ‘Ms’ and threw up insights on their performance, concern areas, and need for action. He said once a variance is found in the data, they can be sorted into three decision points – pending decisions, open issues, and risks. The 4 ‘Ms’ are plotted on an excel sheet against a work breakdown structure that tells project managers instantly the status of the different parameters. “Sometimes, we feel a project is not moving while it may be on track or vice-aversa. So this dashboard gives a bird’s eye view of the project,” he said.
New Approach to Scheduling
Kartik G. made a presentation on the use of The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) in a rural electrification project in Sambalpur, Odisha. It was a highly complex project, given that it was in a remote location, the threat of the monsoon loomed large, and there was political pressure to electrify the villages quickly.
The schedule of the project, which was being planned from March to December 2018, faced four types of threats – external political factors, availability of workers, customer priorities, and complexity due to the inaccessibility of the location. The team had to work through a wildlife corridor, for which they required special approvals. Huge equipment needed to be transported by animals and people because vehicles could not reach there; and since monsoon floods were common, all of the equipment had to be brought in before the rains started. Sambalpur is also a hotbed for malaria and typhoid, so worker health concerns had to be prioritized as well.
The project team gave weights to each of the criteria that could affect project schedule and uploaded the data on TOPSIS. The system then generated a priority order that helped the team in execution. The Rs. 517 crore project covered nine districts and impacted 25,000 people.
Tool to Predict Material Cost
A nagging problem in the real estate market is cost unpredictability due to fluctuations in commodity prices. With material cost comprising up to 63 percent of a project, can project managers use predictive tools to control project costs?
Deesha Vora and Sundar Rajan have found an answer in the Price Index Prediction (PIP) Wizard, a free predictive analytics tool that helps manage cost escalation and enables better planning and budgeting.
The tool uses historical pricing data for commodities, such as cement and steel, and applies regression analytics to arrive at customized models for each company, commodity, or region. Other forms of data fed into the algorithm are internal factors such as location, quantity, and credit period; and external factors such as government policy, transport costs, and human resource costs.
“We have used this model to predict the price of steel and cement for the next 12 months. The results so far have been 90-92 percent accurate, resulting in savings of 2-5 percent of the project cost toward material,” said Ms. Vora. The other benefits are better ordering and scheduling of orders to meet project demands and higher productivity, since work does not have to stop for material to arrive, which has, in turn, resulted in about 8 percent savings in human resource costs.
Mr. Rajan also talked about risk mitigation through PIP. Project managers can now opt for just-in-time procurement, and thus minimize risks around procurement and prevent cash flow issues. The accuracy rate of cash flow predictions has been up to 95 percent.
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