Effective big data analytics solutions can bring many benefits to an organisation, with a newRead More
How big data helps firms reduce losses
Effective big data analytics solutions can bring many benefits to an organisation, with a new report highlighting how the technology can help firms such as retailers, restaurants and manufacturers reduce their shrinkage rates.
A study by PwC observed these sectors can have different early warning indicators of this issue, but they can often be difficult to spot because the relevant data is rarely kept in one place.
However, the ability to gather this information in one place and analyse it effectively can help firms get ahead of problems and address them early – before they become expensive issues, CSO Online reported.
Bill Titus, managing director of PwC's loss prevention strategy and analytics, explained that traditionally, companies use signals such as inventory levels, shoplifting rates and accidents to measure their shinkage costs. This means risks need to be measured later on in the cycle, so by the time businesses have the information needed to react, the damage is already done.
But there are other indicators that firms can use to keep an eye on their operations, which when measured using predictive analytics, can serve as vital early warning signs.
For instance, inventory integrity is one metric that could give businesses better insight into what is going on if it can be tracked accurately. Even though the total volume of inventory may still be in line with what's expected, if the individual SKUs don't match up to what's in the system, that's a sign that something is going wrong.
"I might have run something up incorrectly, or entered something incorrectly," Mr Titus said. "That's an indicator of how efficiently a store is being operated."
Other indicators that can provide early warnings of problems include cash variances, employee turnover, price change volumes and unfilled management positions. Being alerted to any of these can ensure firms can take action to preempt any issues.
Mr Titus said, for example, that if data analysis shows a deterioration in performance after a store management position has been vacant for 90 days, this can help businesses take action by ensuring the position gets filled well before this.
"At 60 days, I can have someone pick up the phone and talk to human resources about filling the position, rather than five or six months down the line having to send someone in because you have a huge amount of turnover, customer satisfaction is down, and so on."
The impact of such a proactive approach can be significant, as PwC highlighted one example of a major US retailer that was able to reduce its shrinkage costs from $1 billion to $250 million through the use of a data-driven loss prevention programme.
However, it will not be a simple process for firms to implement, so companies will need a clear idea of what they hope to achieve and the skills and tools they will need to see success.
Key information that is needed for this analysis comes from areas such as point of sale, finance, human resources, store operation, and supply chain departments. External sources of data can also be added to this mix, including crime rates, financial and economic indicators, and industry benchmarks.
"It is hard to pull this data together," Mr Titus said. "It lives in ten different places in the company, some isn't accurate, some lives in spreadsheets."