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The Top 3 Challenges in Retail Supply Chain Analytics

Today you can buy anything you want online, from a small rubber stamp to a large house frame to the freshest groceries (handpicked just for you) to a fossil dating back millions of years ago. Virtually anything you could need is now available with the click of a button, with guaranteed shipment times that sometimes have the item to your doorstep the same day as you ordered it.

Retail supply chain analytics is the science that makes this  “practical magic” possible. Retail supply chain analytics relies on algorithms which recommend quantities, dollars and modes to answer questions – ‘what, where, how much, when and how.’

Algorithms: Driving Supply Chain Efficiency

Sophisticated algorithms and software tools enable us to ‘explore, predict and prescribe‘ actions to the businesses. The retail supply chain Software-as-a-Service (a.k.a. SaaS) market, which started as a niche, is big (with a capital B) and is expected to reach about $20 Billion USD by 2021. With the rise of crypto- currencies and blockchain technologies, automated driverless trucking and robotic supply chains, it won’t be too shocking to see this number being surpassed before that.

There is very little doubt about the effectiveness of optimization algorithms in making, versus breaking, the companies today. It becomes tough for big and small businesses alike to accept the bitter trade-off between advancement in analytics, and daily operational struggles of time-to-market and sliding margins.

Challenges in Supply Chain Analytics

Devising a clever analytical strategy that steers the company towards the right mix of insight and operations is vastly important. Here are the top challenges organizations should be aware of when choosing the right strategy for modernizing retail supply chain analytics, whether acquiring a software tool or implementing a managed service.

1. Time, Cost and Effort

Analytical software tools are expensive to acquire and maintain; it takes a long time to realize return on a huge investment in software and resources. There are a lot of uncertainties and unknowns in implementation, common wiht most enterprise software tools.

Enterprise software falls in two major categories:

  • Standalone specialty software – these are tough to maintain and program beyond their UI, and they can be difficult to integrate into the company’s technological ecosystem. This usually puts limitations on how other SORs (System of Records) interact with this standalone specialty tool. As businesses grow in size, these tools are the first ones to get dropped from the portfolio.

Don’t get me wrong, these tools can come in all shapes and sizes – from spreadsheet extensions to single-trick optimization software – and there is a market for them, too. It’s just not in a growing company’s best interest to invest in or maintain them.

  • Software provided as an extra offering within the PLM or ERP systems – This is a great option and will provide the best experience only when used within its own suite of applications. Using this as the only analytical shop within a disparate software portfolio is like herding all zoo animals at once – some will fly, some will run, but some will crawl.

2. Experts Needed

Whether you buy a standalone analytical software or a software provided as one of the ‘bells and whistles’ of a larger software system, you’ll need people with expertise to run the software.

So, if you are a company with realistic budgets, expertise is an age-old dilemma for your ROI, with no simple answer or no simple right mix. For instance, do you train current employees to be experts, or hire trained experts to run the acquired software?

In both cases, there will be a monetary investment and a waiting period with some probability of success. But something to keep in mind when deciding whether the expert training or hiring path is best for your organization: the rest of the business is moving at the speed of light. The longer it takes to get your experts up-to-speed on your analytical software, and the supply chain analytics most important to your business, the greater risk of falling behind.

3. Isolation / Silos

A common but real problem for today’s skilled and sophisticated teams is the ability to deliver compelling disruptions to the enterprise while being isolated from the command center that drives the impetus. All teams within an organization need to work together to showcase, demonstrate and ultimately experiment without risk of failure or without the risk of being shut down. It takes great effort and company-wide cultural changes to break down the silos.

How Digital Technology (and the Right Technology Partner) Can Help

Hitachi Solutions empowers clients by helping them take that next leap with digital technology. With expertise in various disciplines such as Finance, Analytics, BI, CRM, and our complete focus on Microsoft technologies, we turn challenges into opportunities.

Tools like Dynamics 365, and the powerful platform Azure, have the following advantages over the standalone silos and rigid software monoliths of the past:

  • Fast, elastic and scalable: The last half of the decade saw big growth in cloud technology solutions. In today’s world, summoning cloud storage, compute resources or virtual machines is just a few clicks away. And affordable, too – chances are that all options combined will cost you less than a fancy meal!

This power comes from the ability of cloud infrastructure to adjust itself to variable demand (elasticity) and accommodate mass consumption (scalability). Another great advantage of cloud platforms, like Azure, is that they are built as marketplaces.

This means there are several technology options available, and various marketplaces to choose from, , based on the need and commitments already in place. No longer are lengthy justifications needed for ‘one size fits all’ solutions.

  • Analytical readiness: In addition to reliable cloud infrastructure, platforms now have native machine learning libraries, advanced data science routines and even experimentation environments. They support widely regarded open source languages like R, Python and Julia– favorites among the data scientists of the current decade. You can also now easily train algorithms, assess their effectiveness, and implement them for future predictions, scenario building, or simulations. The best part is the super-modular nature of data connections, code repositories, and implementation servers – all managed through simple interfaces (example – Azure Portal).
  • Self- discovery and data stewardship: Digital storytelling and visualization applications, like Power BI, enable the end users to access self-serve analytics. With access to these analytics, end users are able to make more informed, reliable and effective business decisions. Analytics also  help users uncover insights hidden in the data and illustrate them through powerful visual elements and narratives — all while maintaining data governance and ensuring a single source of truth for all analytical and reporting purposes.

To get started with retail supply chain analytics contact us at Hitachi Solutions.