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Business Analytics: Start with Why

Posted on: 09-06-2015 11:04:41786


Last week I made the (very) long trip return to Melbourne, Australia to deliver a keynote presentation at the SMART Supply Chain conference at the Melbourne Exhibition Centre. I also had the pleasure of being asked to sit on a panel of experts and senior business leaders discussing the impact of technology in the supply chain.

Towards the end of the session, one of the audience asked a question around data analytics and whether they should invest now in developing a big data analytical solution or hold off.

My fellow panellists immediately jumped in to urge them to invest – ‘it's the future', ‘don't get left behind' they all proclaimed. I was then asked for my opinion. To the surprise of the other panellists, I urged caution.

We are about to see an exponential increase in the number of potential data collection points, a phenomenon that will explode once the Internet of Things starts to become realised. The world is indeed going to be generating huge amounts of data – so why the caution? Because Big Data is a term, not a solution and too many companies are still struggling with the challenge of managing small data. The hype cycle is often just that – hype. As Lora Cecere puts it a recent blog post: "For me, buzzword bingo on big data is bad. The wrong discussions drive detrimental behaviors.”

If you cannot manage small data, big data will overwhelm you.

One of the best examples of this came about when I led a major project to help a large Aerospace and Defence company to try and realise the value of its investment in SAP.

After a week of investigative digging that started to expose exactly how deep the rabbit hole was, the company was keen to show me something more positive. I was taken to see the head of their new, multi-million investment in a bespoke, e-enabled Collaborative Planning, Forecasting and Replenishment (CPFR) tool.

It was indeed impressive. It was capable of producing a better baseline forecast model than they had ever achieved previously, and could seamlessly pass this down to the main SAP system. It was also ludicrously expensive, had taken years to build and had been developed, as these things often are, in an IT and consultant bubble.

"This is really impressive” I stated, "You've obviously spent man-years worth of effort developing this…I just have one question.”

"Go on”, said the head of the development.

"The people in purchasing can't manage Purchase Orders correctly. Your supplier lead times are all incorrect, the material masters are a joke, the sales orders have dates and quantities that are fictitious and the inventory levels are way off. So what do you think will happen once this clean demand signal hits the system?”

"I'm not with you”, said the project head, "what's all that got to do with this project?”

I replied "Because you've built an IT solution without considering whether it solves the most pressing business problem. You've only improved one part of the total data set needed to manage an end-to-end supply chain. You've done nothing to improve the level of understanding, ownership and accountability for the data required to run a business on SAP. The outputs of your data analysis might be as pure as volcanic spring water, but it's about to enter a sea of effluent. You're not going to want to drink the result.”

I begged him to hold off the go-live and wait instead until my project team had worked with the business users to develop the cultural changes needed to embrace data management. As part of our initiative, we had installed the Operational Analytics tool, Every Angle, to rapidly gain insight into the quality of the transactional and master data. This enabled us to quickly understand what was going on and where the data issues lay. It also allowed us to rapidly develop the reports and metrics needed to allow the newly educated team to understand, control and improve performance. We were starting with one pilot Value Chain and I strongly suggested that the CPFR tool went live with the same subset of products after we had that segment of the business under control.

He didn't listen. He didn't want to. My card was now marked as a Luddite who failed to see the brilliance and technological splendour of this new system. It went live as planned, and my input or opinion as to this project was no longer required.

It passed its 'big data' demand signal down into SAP. Everyone cheered – the go-live was deemed a success.

It was a short-lived celebration.

MRP ran, and converted these new plans into purchase requisitions and production orders. Almost immediately chaos reigned. Thousands of Purchase Requisitions were produced and the vast majority of them were wrong. The lack of control meant that the Purchasing team couldn't tell which were needed and which were not. 48 hours later the situation was beyond repair. By the end of the week, MRP had to be switched off and they tried manually managing the demand. By the end of the month, the CPFR tool itself was turned off. Two months later it was canned altogether at enormous cost and embarrassment.

I took no pleasure in being right, only dismay that so much money and time had been spent on something that made no difference, when there were plenty of less ‘sexier' things that would have transformed the performance of not only the supply chain, but the business as a whole.

We proceeded with the boring task of educating the users, cleaning up the data, and focusing the value chain on strategic outputs.

Did they need better analytics? Of course they did – but they needed analytical tools that helped them to address the real task to be done, not just some mythical ‘magic bullet'. The tools they needed required insight into what was really going on in their business, tools like Every Angle, and then the courage to make hard decisions and the discipline to follow through.

In the same way culture kills strategy, small data can kill big technologies.

A recent post in the Wall Street Journal highlights exactly what value a business can expect if it gets this right. Columbia Sportswear Co. says investments in supply chain analytics provided significant profit growth through being able to sell products at full value rather than discounting, adding $415.6 million in net sales in 2014 over the previous year, a 25% increase to $2.1 billion in worldwide revenue. Also, by being able to track and monitor the supply chain and perform diagnostic analysis they were able to improve their on-time and in-full delivery performance by an extra 50%.

There is enormous business value for those who understand that the key to data analytics is knowing what to analyse. You can't control what you do not understand, let alone improve or innovate it. Therefore, the reverse is true. Using the right analytical tools in order to gain understanding will enable control, control enables improvement, and continual improvement will identify new and exciting innovation opportunities.

Sexy? Not especially. Effective? Hell yes.

Big data is here to stay – but to stop it being a big disappointment you need to acquire the skills, discipline, processes and tools needed to understand, control and improve all the elements of your value chain needed to consistently deliver profitable customer value. Knowledge is not power unless it is both relevant and applied. Data analytics, big or small has little value unless it results in better business models and better value propositions for your customers.

As Simon Sinek famously declared; 'start with why'.

Sean Culey
Value Chain Thought Leader

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