How it began
A young engineering graduate (let’s call her Claire) won a position on the Operational Improvement team at a well-established confectionery manufacturing company.
This team’s remit was to identify opportunities that would deliver tangible outcomes such as improved output, enhanced product quality and cost reduction. Claire was allocated the ‘improved output’ brief.
The factory had four separate manufacturing areas, each one designated to a different product set.
Claire’s first act was to requisition the most recent two weeks of performance and run-time data from the Programmable Logic Controller (PLC) hardware operating each of the production lines, so she could set a baseline.
She then used Excel to convert the data into four standard graphs, one for each production zone, with Date & Time as the x-axis and Machine Performance as the y-axis.
Upon examination of the graphs, Claire noticed an interesting anomaly. In Production Area D, there was no machine data at all between 6am (when the shift started) and 7am, after which performance appeared to be normal.
She double-checked, to ensure that her import of PLC data into Excel had been thorough. It had.
Claire needed to check that the initial fortnight’s dataset hadn’t been a bizarre outlier, so she asked for an export of PLC data from the two weeks prior. Same outcome. Every day, no machine data from the first hour of the production shift in Area D.
Intrigued, she had an early night, determined to find the root cause. The following morning, she arrived at the factory at 6.30am – two hours earlier than usual – and walked through the production zones. Areas A, B and C were all operating as expected, which just left Area D. When she got there, she saw that the manufacturing operations staff were all present, drinking tea and chatting, but the production line hadn’t been started.
Claire made her way to the Shift Leader and asked him why the machinery wasn’t running. He yawned, took a swig of his tea and said: “Darren isn’t here yet.”
Claire looked him straight in the eye. “WHO’S DARREN?”
What emerged was that the production line in Area D – which made popcorn – had a complicated start-up sequence which required the presence of a qualified electromechanical engineer. The only technician with the requisite skillset available on a weekday morning was Darren, who diligently went to Area D and started the machine as soon as he arrived on site each day – but for family reasons, his daily shift started at 7am.
The production team in Area D were fully aware of the issue, but were enjoying the free hour every morning and had elected not to highlight it.
Completely unbeknownst to the business, an hour’s popcorn production was being lost every single weekday.
Solving the issue
As a result of Claire’s data analysis and evidence-gathering, two members of the Area D factory-floor crew went on a training course where they were taught the start-up sequence for the popcorn production line. From then on, manufacturing began at 6am every day.
That daily extra hour of production ultimately amounted to a six-figure revenue increase over the course of the next financial year.
Claire subsequently became Head of Continuous Improvement for the company. She accomplished this simply by examining information that the organisation already had available but hadn’t analysed in any detail.
There is hidden treasure buried in your data. This case study shows that analysing that data can uncover underlying issues that may only require a simple solution but can deliver substantial and unexpected financial benefits to your company’s balance sheet.
BCU Advantage offers a variety of products and services designed to help you make the most of your data – find out how a Knowledge Transfer Partnership could help.