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Data and the Scheduling Machine – Why Bad data leads to poor decision making

Updated: Oct 3, 2019

By: - Ian Shirley


Data - The DNA of good decision making

Decision making on the back of poor data is a recipe for disaster – For example, how many times have you seen poor schedule information being passed throughout the supply chain leading to confusion, over-production, high supply chain volatility and frequent changes, all leading to a lack of trust over the information that is available?

This schedule volatility often causes the wider supply chain to go into “Start-Stop” mode – a bit like when a car brakes suddenly on a busy motorway, resulting is a significant tail back (for no apparent reason) further down the motorway – I think that we have all been there! The academics call this the “Forrester” or “Bull Whip” effect and is often caused by a lack of control and integrity within the customer’s Scheduling Machine – A term we will come on to later


Data quality has a huge impact on businesses, and it is the effectiveness of the business processes that often drives data quality. Business systems are supposed to be designed to help us make good decisions, if the data is poor, the quality of those decisions suffer and as the data gets worse, confused operational priorities impact business performance and effective planning becomes impossible.

In an attempt to do things better, many companies take their data offline and try to simplify things by using “sticking plaster” solutions such as planning boards or by extracting sales priorities to spreadsheets or manual lists. The drawback is that the off-line solution then needs to be kept up to date which is another unnecessary, labour intensive activity. Initially the offline solution may give the illusion of better order, but managers may not realise that they have reduced the effectiveness of functions that rely on accurate system data, such as managing purchased materials, planning future load and capacity and giving customers reliable delivery dates. It not only creates more work for less return, but also causes the issues that good systems and data were designed to help us prevent in the first place.


Every manufacturing company has a “Scheduling Machine” which is not just the manufacturing planning system but a combination of system, data, business processes and the way in which people in the business operate. Well maintained, it maximises the effectiveness of our businesses whilst keeping cash employed and lead times to a minimum. Businesses often employ complex capital equipment to make products and we wouldn’t hesitate to ask how well they are performing - Our scheduling machines also need to be in good order so that we can confidently understand how well this part of the business is performing. Keeping any machine in good condition requires preventative maintenance, but how do you know if your data is in good condition or requires attention? - The quality of the data generated may hold the key.


Your Scheduling Machine will need maintenance. It is the beating heart of the factory and, poorly maintained, will degrade business performance. A poorly maintained Scheduling Machine can go unnoticed but the effects are inevitable. Performance will degrade while cash employed and lead times will both rise putting a strain on the finances as well as the relationships with your customers.

How well your scheduling machine is working is written into the DNA of the data and analysing it critically should point to the root causes of the underlying issues. As a result, detailed and practical steps can be taken to improve the business and monitor the effectiveness of those improvements on an ongoing basis.


In summary, data quality has a huge impact on our businesses, and it is the effectiveness of our business processes that drives data quality. Good data drives the quality of our decision making, and good data is the result of an effective Scheduling Machine within the enterprise. The Scheduling Machine should be seen in the same way as other capital equipment and employ a degree of planned maintenance and ongoing verification of data integrity – Over time, a lack of maintenance leads to business inefficiency, poor productivity and will often result in supply chain volatility.


Ian Shirley is a manufacturing supply chain specialist with over 35 years of delivering operational improvements in the aerospace, automotive and electrical engineering sectors.









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