Written by 3:55 pm analytics, Big data supply chains, Digital Supply Chain

Lifting The Gray Curtain

Recently, I hosted the Supply Chain Insights Global Summit. Executing an event in the middle of the Covid-19 pandemic is challenging.

One of the goals of the conference was to challenge the audience to redefine work. To accomplish this goal, I invited people doing the work (planners and truck drivers) to share their perspectives. Allyson Hatfield, demand planner at MARS Petcare presented.

As Allyson presented her story of working for multiple consumer products companies, with very advanced technologies (demand sensing, advanced automation of forecasting, data lakes and descriptive analytics), she spoke of why at the end of the day, the most important technology that she uses is Excel. In our research, we find that 72% of planners primarily depend on Excel and desktop analysis despite the rollout of advanced platforms for planning in 92% of manufacturers with greater than 5B$ in revenue. The question is, “Why?” The answer lies in the need to redefine work to improve the demand planner’s ability to model. Net/net, the current architectures are hard-wired into back-office Information Technology deployments with layers of security and integration, and batch processing. As such, they serve the business leaders as a system of record for planning information, but they do not serve the core user very well.

At the end of the presentation, I asked Allyson her perspective on open source analytics. My question was, ” Would the world of Python, R, and NoSQL offer for an improved modeling environment with a digital twin deployment.” Her answer was telling. She said, “I have no idea what those terms mean.” “Oops!,” I said internally to myself and quickly rebounded to thank her for a great presentation. Allyson’s presentation was just that. A super wake-up call for all to stimulate thinking to rethink work.

Similarly, after the conference, I started working with a chemical company to formulate a supply chain strategy. After a decade of focus on IT standardization and hard-wiring planning into tight integration with SAP ERP, they are underperforming on growth and margin. This is despite the purchase of Terra Technology Demand Sensing and Multi-tier Inventory Management (MEIO) in 2014 for 12-14M$ (including implementation). When I asked about the use, the response was:

We don’t use it. The solution was too black box.

After implementation, in 2016, no business or product owners were appointed. The Company went through many reorganizations. As a result, the ownership and knowledge got lost on the way. The solution couldn’t be much maintained and the error reduction level deteriorated due to network changes, order pattern evolution which normally requires base remodeling. Hence the solution couldn’t be much maintained. The Supply Chain Executives were not interested in the project, and as a result, did not see the value.”

My take? We talk analytics but struggle to embrace the capabilities of the Art of the Possible. The struggle is twofold: helping people to imagine the future using new forms of analytics and using technology to improve work. As shown in Figure 1, most companies struggle with business and IT alignment. The focus of IT is focused on standardization while the business is struggling to use technologies deployed. Leadership support and understandings are also a challenge, and issues of employee skill levels to understand the potential of new technologies abound.

Figure 1. Top Challenges in the Adoption of New Forms of Analytics

Using Analytics In the World of Gray

We are managing the supply chain in the Pandemic in phases. Each is new and unprecedented. Based on research with scientists and looking at the Global Vaccination data, I think that we are about 40-50% through the Pandemic response. My view is that we are in phase 3 of the pandemic.

The supply chain investments of the last decade focused on improving black & white processes–order-to-cash and procure-to-pay. The investments in transactional automation (ERP) did not help as the world became grayer with increased demand and supply variability.

In the next months, shortages and disruption loom. In the coming phases of the pandemic, Companies will learn that their current processes are not equal to the challenges in the world of increasing grayness.

I also forecast dismal retail performance for the holidays. (Remember the song, All I am Getting for Christmas is My Two Front Teeth? I think that these lyrics echo the looming reality of the coming holiday. The reason? Teeth are 3-D printed while most goods will struggle to get through the ports amid the disruptions of global trade.)

Let’s look at the past to forecast the future:

Phase 1. Lockdowns and Disbelief. Spring 2020-February 2021. We started the pandemic with twenty-one more days of inventory than we had in 2007. As consumer buying behavior patterns shifted, the focus was on the design and execution of the middle and last mile. In this period, 56% of global manufacturers felt that they performed well or very well. However, based on industry and experience, 21% of manufacturers reported struggling during the first part of the pandemic. What drove the difference? The use of descriptive analytics across functional teams.

Phase 2. Optimism and Reconnection. March 2021-August 2021. As inventories dwindled, labor issues surfaced and shipping issues proliferated, supply chain teams began to accept that the only new normal was disruption. Companies invested in improving visibility capabilities to improve functional efficiency of logistics, manufacturing, and supply. Separate and different projects, few companies leveraged new technologies to improve visibility for cross-functional decision-making across source, make and deliver. Most companies continue to invest by following their in-house standardization compliance directives.

Phase 3. Massive Disruption. September 2021-March 2022. The lack of supplier visibility and sensing catch the supply chain on the backfoot. My prediction? Facing massive shortages in building supplies, technology, retail, and automotive, companies begin to redesign supply chains and take analytics more seriously. (This is my hope.) They realize that the investments of the last decade are not equal to the business requirements and relax the IT standardization dictates.

Phase 4. Rebuilding. March 2022-Onward. As Company, after Company, reports earnings shortfalls, the supply chain takes on even greater importance. Business leaders force the CFO and IT organization to drive innovation to accelerate business results. Investments in visibility and supplier collaboration sky-rocket and procurement innovators like Coupa are forced to build successful direct material networks. Consultants scramble to reskill to build schema on read applications in NoSQL and rules-based ontologies to drive learning systems. As they face the variability that follows, it becomes clear that just applying machine learning to improve optimization in yesterday’s applications is fools play.

Going Forward By Going Forward

Only 4% of manufacturing companies are innovators and 17% are early adopters. The adoption curve is skewed with 81% of manufacturers following the leaders.

Innovators need to invest now in building cognitive computing systems that can sense market data and drive bi-directional orchestration. Start with demand processes and map the trends from the customer’s customer to the suppliers’ supplier. Focus on bi-directional orchestration based on unified decision models to drive better outcomes. (For more on outside-in process definition, consider joining the Project Zebra discussion on October 14th to gain insights on the outside-in process testing on graph-based architectures.)

Software robots need adult supervision through learning systems. Start by identifying the business problem and then applying new approaches to drive value. In the journey, realize that it usually requires multiple analytic techniques and innovation not available from traditional sources.

Figure 2. Current State of Analytics

Forward progress, also means learning a new language. As shown in Figure 3, the gaps in familiarity of terms and techniques is high. Spend times educating employees on use cases and sponsor pilots to explore new areas of analytics.

Figure 3. Terminology Gaps

As the world becomes grayer and grayer–with increases in variability of all types–analytics offer promise. However, the benefits cannot be achieved without education, exploration, and leadership.

Global Summit Update

Thanks to all that helped with the Supply Chain Insights Global Summit. The video, presentations and pictures are now posted. At this event, we challenge teams to think differently and drive new outcomes.

If you follow our work and are interested in attending, mark your calendar for September 6-9th 2022. We will hold the event at the Westin, Dulles in Washington, DC. We will continue with the theme “Imagine” and ask attendees to allow their supply chain innovations to take flight. Like this year, we will have both an online hosted presence and a in-person experience.

Summary

Good luck with your analytics journey. This data and a more complete sharing of the data collected in our recent report, will be shared in a follow-up report in our newsletter.

Here I present the current facts, but I always look forward to hearing other’s thoughts. Please let me know your opinions.

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