Many manufacturers are hampered by legacy systems and a lack of access to trustworthy data. Despite this, they may be reluctant to invest in a complete overhaul because they believe it would be too costly, too disruptive - or both. Over time, this could be limiting their business performance. In fact, research carried out by Vendigital has revealed that poor quality data could be costing businesses up to 20 per cent of their annual earnings.
In order to address this problem, manufacturers must seek to establish a ‘single source of truth’. While this may not sound straightforward, it can usually be achieved by extracting baseline data feeds from legacy ERP/MRP systems. Once aggregated, and the proper hypothesis and industry knowledge applied, this data will provide the business intelligence needed to inform decision making across the organisation.
One area where such data can drive significant value is across spend categories. By capturing goods invoices and accounts payable information, manufacturers can leverage their supply chain spend across common suppliers or markets; rationalise their supply base and drive operational efficiencies. Independent research suggests that a good spend analytics platform can save a business between 9–18 per cent based on its supply chain spend, just by giving procurement functions the information needed to take action.
Accurate and trustworthy data also provides manufacturers with greater cost visibility. Take, for example, the troubled print media sector, with such organisations often maintaining a portfolio of print publications. Without access to reliable data around direct and indirect costs at a product level, it can be difficult to understand the true profitability of each product and make decisions about where to focus future investment.
In order to establish a single source of truth, manufacturers should start by consolidating all available cost data and moving it into a centralised data hub. It is important that a holistic, cross-organisational approach is adopted. If individual functions have their own version of the truth, or if data is difficult to decipher due to it being stored across multiple spreadsheets or systems, there may be risk of misinformation.
In order to ensure data insights are interpreted accurately, integration through a centralised platform may not be enough; data-based assumptions will require validation. For example, if a manufacturer is purchasing two, near-identical parts of a particular product, internal cost data may suggest that the business can switch to a single part, in order to drive efficiencies. However, there may be a good reason why this has not been done before, which is not immediately obvious from data sets alone. For example, one part may have a much longer life span than required compared to the other.
In an age of automation and the growing use of artificial intelligence and machine learning, the importance of validating data insights becomes even more important. Without supervision, there’s a risk that such technology could begin generating incorrect assumptions, leading to poor decisions. Securing expert support to oversee such systems and to confirm the validity of key business data can help manufacturers to avoid being misled by false assumptions, which could cost them heavily in the long term.
In order to keep pace with their competitors, it’s essential that decision makers are not held back by inaccurate information. By creating a single source of truth within their organisation and by validating data-based insights with care, manufacturers can boost their business intelligence and significantly improve enterprise value.