The LeanSuite Podcast by Angad Singh

Angad on why most factories are still behind on anything digital, and why the answer is usually "we're able to do it on Excel." He walks through what he sees in discovery, why bigger companies take it on faster than small shops, and the three things a project needs to actually happen. He also lays out where a plant should start — visibility into daily in and out — and the employee count where this starts to pay for itself.

Angad walks through how plants get handed a year over year savings target, how the budget gets built ground up and then cut, and why the target should come from the bottom up instead of top down. He goes through the different types of savings — hard savings, virtual savings, cost avoidance — and how project work gets tracked month over month. He also covers the two questions he asks before building an ROI: what is your savings target for the year, and have you been meeting it.

Angad talks about what he's seen on plant floors where lean work lives on spreadsheets, paper and whiteboards with tags pinned by hand. He covers why paper means there's no data, and no data means no action items to track, why homegrown fixes break down after a few years, and where he tells people to start instead. He ends with the question he asks to see if any of it is worth it: how much admin work is going into managing all this today.

Angad talks about why continuous improvement rollouts stall when a company tries to open up everything at once, and what he tells clients to do instead. He covers the discovery conversation, the train the trainer approach, and staggering a rollout across facilities so the more advanced plants go first.

Angad talks about what happens when problems get logged, bounced around and closed without ever getting to the root cause. He covers capturing every problem in one place, tags that get reassigned instead of resolved, why a one point lesson expires after 60 days, and what it takes to make a fix stick.

Angad walks through why two lines down for the same ten minutes can cost you completely different amounts of money, and why downtime minutes alone hide the resultant losses. He talks about walking the floor hour by hour as a data analyst, how causal and resultant losses and cost buckets get mapped onto MES data, and what a prioritized loss tree changes in a war room. He also covers what happens to that prioritization when the underlying data isn't clean.