A process mining expert suggests that professionals in procurement and supply chain jobs will need to go beyond data analytics and embrace new machine learning and process mining technologies. Writing in Supply Chain Digital, process mining expert, Alexander Rinke, concedes that new data analytics solutions are streamlining and integrating into one place and the previously diverse databases that procurement pros, including supply chain and procurement interims, had to contend with it. It has helped teams develop more reliable risk management and forecasting initiatives, as well as scalable and dynamic pricing models.
Data analytics platforms have helped procurement teams make optimal spend decisions by integrating risk analysis into their output, allowing practitioners to anticipate future difficulties in the supply chain and work proactively to cut or reduce them. But, Rinke points to a shortcoming in using data analytics as a silver bullet solution. Procurement processes often fail to run according to specifications. When this happens, e.g., a delay in an order for materials leading to a standstill in manufacturing and customers waiting for their products, the divergence from the standard can have an immense impact on the efficiency, and even the reputation, of a business. Also, maverick buying can breach the standard: although small on a case-by-case basis, such purchases can rapidly add up to significant sums and may even amount to compliance risks.
Data-led tools also can’t adequately deal with the complexity of global supply chains. Issues could be hidden in logistics, manufacturing or order-handling processes, and may be driven by inner or outer factors.
Rinke believes that the next quantum leap in digital procurement will fuse data analytics with machine learning and process mining technologies. He writes: “By focusing on the specific processes rather than the data produced, businesses can see how efficient (or inefficient) their distribution network is and identify any causes of delays. Larger systemic weaknesses in order processing can be pinpointed, and granular details like vendor data and invoice tracking can be drilled into.”