‘Data’ is important and ‘data-driven decision’ is even more important. That’s what Moneyball – one of the brilliant sports movies ever made on a true story – is all about and Brad Pitt nailed his role as ‘Billy Beane’. But, why are we talking about a sports movie to discuss apparel shop floor data?

It’s a sure thing that many of us can connect with what we’re trying to say here. Those who have watched the movie exactly know how Billy, as the General Manager of the Oakland A’s club, reinvented and assembled his uncompetitive and losing baseball team as a competitive and winning one that too in the lowest possible budget.

To get the job done for his team, Billy meets Peter Brand who is a young Yale Economics graduate with a vision of a reformist and skills of a data scientist. He knows how to assess a player’s value by studying his past performance and this he does by analysing the data, by examining the available statistics. By taking data-driven decisions, he is also able to foresee the positive changes his analysis can bring to the team and to its reinforcement.

Most importantly, he explains his data-driven insights to the most adamant team management members in the simplest way possible so that, without having to require the involvement of technical systems or expertise, everyone understands what he says. However, they deliberately try to bring him down which is a common practice everywhere!

In one of the famous scenes, Billy asks Peter to do valuation of three players but Peter gets engrossed in his job of ‘evaluating data’ and ends up valuating 51 players. Playing with data is so much fun, isn’t it?

So, what data does Peter evaluate to make a team of 11 players for Billy’s Oakland A’s club? ‘Using statistics the way we read them will find value in players that nobody else will see,” emphasises Peter during his conversation with Billy, adding, “Of the twenty thousand notable players for us to consider, I believe there is a championship team of 25 people that we can afford because everyone else in baseball undervalues them like an island of misfit toys…”

By evaluating the data, Peter came up with certain conclusions that proved to be fruitful decisions for Oakland A’s in the tournament… He analysed that the club needed to win at least 99 games in order to make it to the post-season, needed to score at least 84 runs in order to win those many games and needed to keep runs below 645 collectively in these matches.

He also mentioned while writing year-over-year projection codes for the players that they are overlooked for variety of biased reasons and perceived flaws – age, appearance and personality! One of the 20,000 players for them to consider is Bill James and he, according to available data, is overlooked again and again despite having potential.

Peter pointed out another aspect that a player, Chad Bradford, is overlooked because he throws the ball in a bizarre manner. “He is one of the most undervalued players and nobody in the big leagues care about him because he looks funny. This guy could not be just the best pitcher in our bullpen but one of the most effective relief pitchers in baseball as the delivery rate is phenomenal in whatever way he throws,” asserted Peter.

See these two clips of the brilliant conversation yourself to know the power of data-driven insights and decisions –



What’s the take for apparel factories?

Now, just replicate this entire scenario on an apparel manufacturing shopfloor and think what’s common in nature? The need for collecting data and taking decision out of it was always there because it has been observed since decades that organisations are used to taking decisions which are based on their intuition but not on facts.

Most of these shopfloors are uncompetitive just like Oakland A’s. They are in dire need of reinvention/revamp just like Oakland A’s. Most of these shopfloors never considered taking decisions based on data and are run by adamant management.

But, it is also true that most of these factories have people like Billy Beane and Peter Brand who know the significance of data-driven decisions and data democratisation to eliminate roadblocks in order to boost visibility in processes and improve performance of team members which results in better productivity and efficiency – that too in the lowest cost possible .

All the apparel factories, irrespective of size, have abundance of ‘unidentified’ data that’s everywhere on their premises which, if studied well, can help prioritise processes, track production and report against production orders and schedules. Not just this, with this data, they can build a team of ‘intelligent analysts’ who can sense what’s going wrong and where improvisation can be done without investing a lot of money!

For example, the shopfloor data, which needs to be collected and evaluated further, can help factories extensively in follow up of KPIs online and this can be done through shopfloor tools that are available in the market easily. These tools can collect and display any information related to production, performance, quality in real time and can greatly help in calculating factory efficiency percentage, on-time delivery rate, average style change-over time for different SKUs, RFT Quality rate and downtime percentage.

Since this data tracking helps in real-time monitoring, as all the sewing machines are interconnected with each other, the tools – with the help of digital boards on shopfloor – display the efficiency of a single operator, entire line and entire shopfloor; quality defects, real time stock/inventory update, shipment information of products and group productivity. This data collection can then help factories identify where the bottlenecks are and what steps/decisions teams at shopfloor can take to improvise the processes. Most importantly, this data is available for everyone to see on the shopfloor, that’s why the concept is called ‘data democratisation’.

Another example of taking a data-driven decision is predicting the ‘Quality Defect’ through data-analytics and AI. The factories should be able to study the historical data of their SKUs and predict possible defect in operations and products. The factories can deploy the data-collection tools that can scan all the defects that have ever occurred in the past in all SKUs they have worked on, and can potentially identify who (man of machine) was responsible for those defects, in which production lines these defects occurred and when these occurred… This process can help factories take a call way in advance in the ongoing projects/orders.

If we talk about the pre-production process, data-driven decisions are as important and relevant as they are on production floor. Today cutting technology industry has come up with hundreds of sensors which are able enough to crack when failure such as knife breakage or defect in bearings is going to happen inside the fabric cutting machine. Until a few years back, when such sensors were unavailable in auto-cutting machines, the fabric wastage rate and downtime of machine were too high!

The only thing one needs is to prepare a Peter Brand with the help of technology in order to take control of these shopfloors – one who doesn’t hesitate explaining and upskilling his team members by making data available to them as well as providing precise integration of the same in the processes such as pre-production, production, post-production and quality.

So, this article is just to explain you all the importance of data democratisation! If you believe your company offers a technology/software that fundamentally works on improving data-driven decisions on apparel shopfloor, do not hesitate to get in touch with us at – nvarshney@stitchworld.net and we will share your technology with our audience by following due process of an editorial coverage.

Maryan Barbara
Maryan Barbara

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