For most of us words like big data, Internet-of-Things (IoT) and real-time analytics conjure up images of innovative companies and new business models. Whether it is Uber matching taxis to riders, eBay helping sellers set optimal initial bids, NYPD determining gunshot locations, NEST reducing energy use, or airlines applying dynamic pricing, the common denominator is collection and analysis of data in real-time. Though consumer facing applications have got more attention, the impact on intra/cross-enterprise processes has been just as remarkable – examples include Tesco managing shelf inventory based on weather, and Global Brands Group using demand info to prioritize containers to onshore.
Many similar examples have been covered in the media as a sign of things to come. Looking at the organizations covered it is tempting to infer that big data is primarily a big company phenomenon. After all you need a ton of customers and/or transactions to generate copious amounts of data. Even if the data sources are in place, building the IT infrastructure to gather and analyze the data requires time and money. Add the cost of wonks who get the insights from the data, and you are staring at a mammoth budget. All in all, it is easy to conclude that unless you are in the “Fortune N” (or well funded through other means) you don’t have the resources for big-data.
The conclusion does hold up most of the times. For example a smaller e-tailer will not have the data volumes/variety that Amazon can dip into. But with IoT and operational intelligence (OI), this is changing for mid-market companies in manufacturing and/or distribution. For their supply chain operations the playing field can be significantly leveled.
Let us start with manufacturing. Getting equipment connected to the internet is easier than ever before. In most cases PLCs (Programmable Logic Controllers) have been controlling the machines for a long time, and many of them use standard PLC protocol enabling easy and inexpensive connection to a web server. Push this data to OI and complement it with business transactions from ERP/CRM/Supply Chain systems, and the impact can be transformational. At one level you can reduce spare parts inventory because you know of break-downs in advance. More importantly OI can use the information to prescribe juggling of production orders so that priority customers are not denied stock. This level of optimization was hitherto available only to large enterprises. Only they could afford the integration, expensive hardware and developers to write the algorithms. Today the integration with PLCs is easy, and the data can be analyzed by an inexpensive OI system on the cloud. The OI system does the prioritization/re-routing based on easily configurable rules set up by business users. For mid-market companies, a resource frugal short project with minimal upfront costs can deliver levels of optimization that large enterprises consider their advantage.
Coming to inventory management – serialization & bar-codes have been around for decades. But bar-codes need line-of-sight with the reader, and that makes them inefficient in capturing real-time information. RFID used to be expensive, but today the costs have fallen so much that embedding a serialized tag pushes up product cost only marginally. The versatility of Ultra High Frequency (UHF) technology has spawned readers that cover a broad range of distances. Again, this means that the mid-market has an effective means of tracking inventory across locations and even in-transit – a capability that was earlier exclusive to large enterprises.
But what can this new found visibility do? For starters one can check if available inventory is at the locations of demand (open orders). Next you can verify if the in-transit inventory destination is indeed a place of demand. And if misalignments are detected, OI can prescribe the stock-transfers and/or re-routing that can correct the situation at the lowest cost. Sounds simple and obvious – but even with RFID data, this optimization was not easy for companies with limited hardware/software budgets. But today a subscription to a cloud based OI can make all this and much more feasible at a nominal cost.
The improvement in supply chain parity extends beyond the enterprise too. To fight counterfeit/black market issues, large companies used to employ an army of compliance agents. Many mid-market firms find that unaffordable. Now, with serialized tags they can know of a black-market sale the moment a serial-number is reported/registered from an unauthorized location. They can easily track it back to the last channel partner that held the goods and take them to task.
Make no mistake – a large part of the value of IoT is derived when the embedded tag/chip helps optimize the product performance. Tennis rackets with sensors that provide players with tips to improve their game is an example of such sophistication. Monitoring is only a part of the capability IoT provides. But this “Level-1” IoT capability can enable Operational Intelligence systems to provide prescriptions for a mid-market supply chain to match the 800 lb gorilla. Now they can plug revenue leakages and reduce inventory costs using the same techniques as the “leader”.
There is a saying – “God created men, Sam Colt made them equal!” The reference is to Colt’s revolver that enabled the puny guy to stand up to the bully. Aren’t IoT and OI doing the same for mid-market operations?