This Supply Chain Matters blog is the third in a market education series addressing the digital transformation of manufacturing capacity planning and specifically overcoming change management barriers.



During 2020, industry supply chain and manufacturing teams faced significant challenges in having to deal with and overcome the effects of the COVID-19 pandemic. That caused what we described in our 2021 Predictions as a need for rethinking existing processes and decision making in the form of either New Definitions, Thinking and Directions related to needs for added agility, efficiency, as well as resilience for supply chain and manufacturing capabilities.

Thus far in 2021, cascading and more pronounced material, transportation and production disruptions have provided multi-industry supply chain management teams a considerable amount of added learning and discovery related to what is needed in supply chain and production management in the “next normal”.

In prior Supply Chain Matters blog commentaries that included: The Digital Transformation of Capacity Planning Management and Supply Chain Digital Transformation- The Need for a Phased Approach, our reader takeaways were that maintaining the status quo in managing an inside-out focused planning and production capacity management processes are not a recipe for success. We have advocated that business needs for added agility, efficiency and resiliency of product supply networks now require more dynamic production capacity management for either batch process or discrete manufacturing process environments.

The need is for managing and more dynamically adjusting production capacity to key customer or market needs, along with the ability to context operational or tactical capacity planning decisions to specific financial or customer service outcomes. Indeed, manufacturing and production capacity is a key element of overall synchronization of product demand and supply network processes.

We advised that in this existing dynamic environment of constant change, teams should address supply chain transformation in a time-phased manner rather than a big-bang wholesale approach. Each phase of transformation needs to support specific objectives, expected and clearly measured business benefits, which in-turn, provides the funding and resources to move to subsequent phases.


Change Management Factors Related to Manufacturing Capacity Planning

Today’s far more dynamic business environments now mandate an outside-in orientation in supply chain and manufacturing planning. Transformation involves capability in more timely sensing of market or customer needs and an overall integrated and synchronized response that includes required production changes to daily, weekly or monthly plans in a much more managed process.

Yet, with added complexity and more data, learning and organizational change management remains a constant need. On the one hand, continual surveys point to an overwhelming consensus among business and supply chain management executives that digital transformation is essential to be able to effectively compete in the next normal of business.  On the other, conversations related to timetable or readiness reflect added change management obstacles.

It is important to consider that new thinking and direction related to outside-in sensing of customer needs cannot be readily achieved by planning processes that remain predicated on historical data extracted from transactional based systems or placing the burden on decades old legacy systems.

Our client sources and our own discussions with multi-industry participants indicate that some organizations remain averse to either revised business process approaches that foster more analytical driven processes enabled by advanced technology. The reasons involve some common themes:

  • Being wedded to internal generated spreadsheets to augment the lack of flexibility and information timeliness of existing legacy ERP, manufacturing or supply chain software applications. Spreadsheets serve as a default mechanism of control in the lieu of integrated analytical information. However, spreadsheets are time-consuming to maintain, restrictive to broad access and themselves cannot keep-up with today’s more accelerated clock speed of business.
  • A lack of understanding in realizing that some existing ERP systems plan at the family level for tactical supply chain planning, while production planning and scheduling systems require planning at the detailed item-level, usually accomplished by MRP or MPS The latter are derived from transactional data, not analytical or machine learning patterned data.
  • Claims for not having the right data or internal analytical of advanced technology skillsets to extract insights from the data.
  • Senior IT leadership not being receptive to bringing on a new technology provider which would add to data integration needs and added IT maintenance and analysis costs. This form of argument tends to have a built-in bias toward the existing dominant ERP backbone system, where legacy knowledge currently exists. Needs for transformational support towards more probabilistic or stochastic based planning usually end-up in the legacy ERP timetable of upgrade, likely with the risk of considerably added costs and disruption.


Our go-to expert in articulating the mathematical and data science limitations of today’s predominant ERP planning systems landscape is Joannes Vermorel, Founder and CEO of Lokad, a specialty software company with a strong technological, data science and mathematical core. We once coined this technology provider as “mathematicians on-demand”. In a 2012 blog commentary,

Vermorel educated our readers to the differences in traditional deterministic vs. probabilistic or quantile forecasting and planning approaches in areas where demand volatility exists. In recent conversation I asked Joannes why some many companies continue to struggle with integrating information from ERP applications, especially for production capacity planning and management. His observation was succinct and direct: legacy ERP planning systems are by definition a transactional architecture vs. today’s more Cloud based advanced analytical and data science-based analytics technology. To perform detailed analysis of data requires significant database calls or the construction of data cubes that need to access by read access (RAM) memory. The challenge, from a data science lens is that RAM technology has for the most part not undergone improvements in over a decade. You cannot prioritize access to certain parts of the data cube. That is why today’s newer Cloud and purpose based analytical systems are becoming more preferred as a layering approach to mirror and enhance transactional data.


Overcoming Change Management Barriers- Takeaway Messages

In previous market education blogs in this series, we have advocated that setting new direction and overcoming change management barriers requires:

Sorting out the realities of fragmented or outdated data and eventually viewing more integrated data management as the fuel for enhanced analytics and more predictive decision-making capabilities. The goal is supported by unifying data in the context of cause and effect linking and determining alternative means toward more what-if decision making capabilities without a classic full optimization of all planning data. Planners are just as frustrated with the constant need to have to revert to spreadsheets to support required decision-making. Educating planners that their jobs can be more meaningful and more value-added with less reconciliation of data and more actual analysis is the path toward broader job satisfaction.

Setting a declared goal for more connected supply chain planning processes with an eventual goal toward establishing autonomous planning capabilities enabled by artificial intelligence and machine-learning technology.

A specialty technology or services provider like Expero, can help your organization to define the various phases toward an ultimate set of autonomous planning capabilities. It would initially include a short time-to-benefit effort for unlocking the data that already exists, flagging and cleaning-up data that would not lend itself to more predictive and prescriptive decision support needs, while linking upstream and downstream factors of data management. Subsequent phases would address streamlining the data into heuristic based cause and effect relationships and exception-based management related to areas such as capacity, inventory, resource or geo-spatial data. In essence, these series of pilots, incremental learning and new benefits are effective milestones in a change management process that demonstrates future state decision-making processes and capabilities. It allows participants to experience the change.


This three-part market education series has been developed with the collaboration of Radiant Path Supply Chain Planning, an Expero solution.


Bob Ferrari

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