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October 30, 2016
By ZAP BI
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Data Intelligence,Tips & Tricks,Trends
One of the nicest things about living in a city where rent is frighteningly out of proportion to average salaries is that most people share a roof with strangers, who later become friends. This circumstance has provided me insight into three very different professions to my own, and as a BI consultant where I rely on sector specific insight as currency, this means that every conversation we have about our respective workplace challenges is treated as gold dust. On one particular day, a random conversation yielded important lessons about manufacturing analytics.
One of my housemates works for a high street food retailer, in logistics and supply chain management. Her primary concern, particularly at this time of the year, is to make sure the goods from the suppliers reach the stores within time constraints which prevent a) deterioration during transit and b) gaps on the shelves. The data from previous years confirms for her that between now and Christmas her most unpredictable obstacle will be the weather (unpredictable in the sense that some years it doesn’t factor; in others we have 2 inches of snow and the country grinds to a halt), while the most predictable and costly challenges will emanate from the suppliers themselves.
Her company will spend the next few months planning around and managing for each possible eventuality where their key suppliers do not meet their obligations. This strikes me as an incredibly pressurized and expensive activity on either side. While her company will have to resource this problem around the clock, the suppliers, in turn, risk fines and damaged relationships, possibly even the discontinuation of their contracts ahead of next Christmas.
One of the things we often talk about as BI practitioners is the benefit of data-driven decision making. This Christmas logistics scenario made me wonder how many of the conversations which will take place between now and the New Year will be conducted on an equal footing in terms of data availability and analysis?
The BI Problem Revisited
I think what we are really talking about is data-driven interactions. How many of the manufacturers supplying to this retailer, who face the same seasonal pressures on their own supply chain, will have invested comparable in data collection and analysis infrastructures which enable them to explain where and why the breaking points have arisen, and how they will go about resolving them? Plus, with all the noise of data which occurs around the thousands of processes in production, packaging, storage, and distribution any manufacturer will have responsibility for, how will the suppliers begin to hone in on the key variables which must be controlled in order to ensure they can get the goods out in time? From my experience in BI, my guess would be, very few.
What often stands in place of robust BI is a good understanding on a macro level of how much money the company has made in prior periods, and how many new sales have occurred. On the micro level, operational reporting will tell a manager whether his or her process is meeting internal demand. This creates a gap, however, in the middle, where the company must find some way of managing risks in critical external relationships, forecasting against future demand, and responding to or picking up problems as they arise in any given point in the supply chain.
By joining the data from multiple operational processes together and highlighting the customer as a common thread of analysis, trending breaking points over time, a demand-driven BI platform will facilitate the proactive management of risk throughout the manufacturing system. Resolving this central gap between macro results and micro-transactional reporting becomes the differentiating factor in retaining a valuable contract next year.
The benefit of BI in its purest sense is that it should provide insight into what’s happening around you. A relationship manager in any given manufacturer will have absolutely no control over the factors which contribute to problems in production, mistakes in inventory management or delays in distribution. These processes could be happening on sites thousands of miles from where the individual is sat. Each process is likely to rely on a wealth of other suppliers and components.
The inter-dependency can be ugly:
However, the expectation of the customer will always be that they should understand why it has happened, and make promises to the effect that it won’t reoccur. Putting a practical, self-service BI tool, with near real-time data, into the hands of a relationship manager becomes an immediate enablement factor for meeting this expectation.
The savvy customer will expect to see evidence over time that improvements are being made in minimizing the risk of this process failing; as a result, they are able to reassign their own resources away from troubleshooting this supplier, which saves them money too. If the manufacturer can prove it is a reliable partner over a couple of Christmases, next time the retailer is tendering a contract they will find themselves on top of the short list. Over time the manufacturer can push data out to the customer, analyzing trends and spotting weaknesses before any flags are raised or irate meetings are held. And then, should the worst happen and we have a fairy tale white Christmas, the supplier not only has a wealth of analysis to fall back on but years of positive relationship building.
The manufacturing industry needs BI because it needs to keep customers, just as much as it needs to deliver Christmas puddings. Firstly, by sorting through the noise of data produced by operational systems to identify the critical breaking points in any process in order to apply controls and monitor them over time, manufacturers with their own robust BI infrastructure will be able to respond to queries when things go wrong, plan ahead to ensure that problems happen with less regularity and surpass customer expectations by sharing their results. Secondly, by insuring their ability to respond to changing pressures on the supply chain, allowing for the future increase in demand and variety, within their BI infrastructure, manufacturers can maintain a competitive advantage over other suppliers and their own limits.
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