153. Analytics for Silos to Supply Chains

Invent Your Own Analytical Insights

Analytics software is big news.  But, your richest analytical insights are hiding under foot. To uncover these insights, get curious. Make specific observations about the good and bad activity that you know intuitively exists within your business.  Are these observations symptoms of what underlying root-causes? Write down the questions and theories that arise. Do some customer field research and quick statistical analysis to test and refine your questions and theories. Then, do small, fast, learning-forward experiments to find the golden insights.

This “scientific method” process works best, but with lowest returns, for silo/department bounded opportunities. Service-process and supply-chain opportunities yield increasing returns and resistance.  

Roll Your Own Silo Analytics

You can sense where inefficiencies hide. For example: your inventory manager aspires to best fill-rates with lowest average inventory investment. You both know that there are two sets of cash-traps to minimize: excess stock, and dead items. So, you start a journey towards new measurement, ranking reports, liquidation efforts, and ultimately systems to minimize the creation of new cash-trap SKUs.   

This type of silo improvement uses finer-grain information reports to fine-tune what you already aspire to do. Adoption rates are high, diminishing returns low. A key aspect is that nobody else’s department, ego, power, or incentives – are threatened.    

Process Analytics: Higher Potential, Lower Adoption

Assume for example, you seek to improve a service metric (like zero errors).  The metric is a downstream, emergent total of upstream systems’ effectiveness.

Who is your able process-improvement champion? What do they discover from doing a fishbone diagram to find biggest root-causes for imperfections? Many people in different areas (including some atypical customers) are root causes. The quality is less than imagined. People are embarrassed and defensive. New education, system changes, and cross-training are needed for improvements. Many resist. The program stalls. And, analytics’ reputation suffers.

Supply-Chain Analytics: Highest Potential, Lowest Adoption    

Customer and SKU net-profitability analytics reveal huge opportunities, but…

  • Who believes some big-margin-dollar accounts are big losers? Not the reps earning big commissions from these accounts!
  • Who wants to educate an inefficient-buying customer about how to reduce avoidable small-dollar orders to save both parties big activity costs? 

Conclusions:

Apply the “scientific method” to improve the analytics and insights for all levels of opportunity. But, the bigger challenge will be effective, change management for your analytics-enabled innovations. For more on change management, check blogs 145-148 at www.merrifieldact2.com.

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