Earlier this year, the Bank of Canada raised concerns about the country’s productivity problem. The report highlighted the need to equip workers with better tools, new technologies, and improved skills and training.
Small and medium-sized enterprises (SMEs) represent over 90% of all businesses in Canada and overall are the largest employers. So, how can these companies boost productivity while reducing operational costs? The answer is automation.
In our experience working with large entities of 5,000 to 30,000 employees, these organizations have the resources and capacity to incorporate automation. They easily operate within modern platforms and rely on machines to make decisions and execute transactions.
But what about traditional businesses that manufacture products or deliver services in physical space? Brick and mortar firms often don’t realize which processes may need a redesign to take them further in the productivity game. Their leaders don’t necessarily want to invest effort in such projects and technology may not be everyone’s cup of tea.
How do they cross that bridge? The first step is to find out which data is readily available and if there’s additional data that can be tracked.
For example, financial statements are commonly used to evaluate commercial success. Positive trends in sales, profits, and cost are desirable. Inventory records are also a part of standard bookkeeping used in basic planning. While businesses do consider those numbers individually, combining data sources can be a completely different management level.
Cross-reference between systems opens up opportunities not only to better understand performance, but also to automate many processes. In the above example such a process can break down profit and loss statements by product without needing a whole finance team doing that analysis. This saves weeks of work on quarterly basis.
Imagine adding third party data that is not generated internally, which can answer questions on how much to spend, when, where to advertise, or which offer to make to a particular customer. Those initiatives can go even further by introducing predictive modeling into decision-making.
Understandably the level of complexity scares people. However, tasks can be set up as a one-time exercise and then left alone to collect, integrate, transform data, run results through required logic, drop off electronic order forms to suppliers, and contact prospective customers with targeted offers. No ongoing human intervention required.
It’s not an argument to get rid of people. But think about putting machines in place for repetitive work while allowing people to create and explore new projects. In today’s tough business environment can you afford not to adopt that strategy?
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