Data Analytics: Fuelling Supply Chain Agility
Deterministic models ─ modelling the future based on the past ─ don’t handle changes very well. The past is a great predictor of the future, but only until it’s not. The great toilet paper debacle of 2020 is a shining example. If you’re lucky enough to be in an industry that sees very little change in either supply or demand and faces no threats to its supply chain, then perhaps luck is all you need.
If you’re not, I suggest you keep reading.
For many, S&Op planning is a heterogeneous process, consisting of a mashup of information derived from disparate systems. Organisational data often lives in silos. Although you may have an ERP, or WMS in place, more often than not when it comes to S&Op planning, including procurement, more is needed.
Hence, each department often has its own additional set of spreadsheets, apps, and other information gathering and analysis techniques that are all then pieced together to arrive at an S&Op plan. To say it’s a complex thing is putting it lightly. It’s a beast. Now let’s add to the confusion.
Rest assured, once you’ve mashed up your S&Op plan and have secured all of your resources, it will be time to do it all over again, because the landscape never remains static.
The Wall Street Journal describes the bullwhip effect as: “This phenomenon occurs when companies significantly cut or add inventories. Economists call it a bullwhip because even small increases in demand can cause a big snap in the need for parts and materials further down the supply chain.”
I, however, prefer Wikipedia’s description, “The bullwhip effect is a distribution channel phenomenon in which demand forecasts yield supply chain inefficiencies. It refers to increasing swings in inventory in response to shifts in consumer demand as one moves further up the supply chain.”
Avoiding the costly downfalls of the bullwhip effect and maintaining efficiencies requires a highly responsive agile supply chain and a robust procurement and S&Op process. Without it, companies are left where many of them find themselves today, in reactive mode, spending their days putting out fires and piecing back a broken plan.
Which brings us to big data and data analytics.
With new tech and big data comes a bevy of big buzzwords. Let’s decipher.
From Gartner, Big data is high-volume, high-velocity and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation.
From IBM, data science is a multidisciplinary approach to extracting actionable insights from the large and ever-increasing volumes of data collected and created by today’s organisations.
