Strategic Roadmap – Sangeeta Jaiswal

Analytics Strategic Roadmap for Manufacturing Excellence

Client Ask: To create a strategic analytics roadmap for manufacturing excellence, that incorporates existing initiatives.

Process: Qualitative Research, Quantitative Research, Outside in perspective, Synthesis, Insights, Ideation, Concept Directions, Feedback and Iteration, Road mapping.

Solution: We gave the clients strategy and recommendations on existing initiatives, new solutions, control tower, data governance, cloud architecture, the vision of manufacturing analytics engine & success plan.

Learnings: Challenge the brief, especially in strategy projects where outcomes are unclear, and periodically reflect on the directions and decisions taken.

Time: 12 weeks

Role: Design Strategist & User Researcher.

Client’s Ask

To create a strategic analytics roadmap for manufacturing excellence, that incorporates existing initiatives. We tried to understand their motivation:

01

The client is looking to create a cohesive company-level analytics strategy.

02

All the sites globally use different systems to manage operations and are not using the full potential of each system.

03

Currently, analytics are driven by multiple teams across different zones and functions in a fragmented system landscape.

04

The clients have invested in multiple solutions, and they need an expert’s view on best ways to utilize them.

05

There is no governance system in place to drive advanced analytics cases within one platform.

Research Plan

Assimilate & Inspire:

Qualitative Discussion & Survey

We conducted 30 interviews across global leadership teams to get an overview of the entire manufacturing ecosystem, the digital transformation strategy, levels in the manufacturing process, and their capability model. We also conducted 12 interviews over 3 sites to identify use cases and define an implementation plan for the 3 plants in a bottom-up manner to bring the value of the global analytics roadmap to the shop floor. We also floated 3 surveys and received 30 responses.

What we heard from global leaders during the interviews

~60%

respondents felt that the key challenges in analytics implementation are disparate systems, multiple versions of the truth, and insufficient skillset

67%

see predictive analytics play a big role in the future

78%

say first data automation & integration is 1st step

What we heard from the surveys

~72%

respondents believe human error, complex & nonstandard KPI definitions are the leading cause of reduced data quality

~61%

respondents believe that currently data is not consumable for insight generation & requires manual intervention

~72%

respondents believe that lack of right skill set, lack of longterm planning & lack of infrastructure are the key barriers to leveraging analytics

Outside-in view, Analogous inspiration

We complemented our research findings with an outside-in perspective, to borrow inspiration from industry best-in-class examples.

Understanding the Role of Analytics in Manufacturing Excellence

We gathered all the insights from the interviews and mapped areas where analytics could have the most impact in the manufacturing ecosystem.

Problem Frame

After the research phase, we identified the following ways to drive value for Manufacturing Excellence.

01

There are 45+ existing initiatives across the Client’s Manufacturing Organisation globally.

However, the scaled value has not been realized.

Value derivation can leapfrog with consolidation, focus on adoption, and cloud tech.

02

Manufacturing org. needs to create a single source of truth for all, having quality & reliable data

03

Programmatic value within Manufacturing analytics will be driven by:

User-persona-driven product features
Robust data governance
Enhanced processes (through KHMS)
Embedding augmented intelligence
Hand-holding during hyper-care phase

04

$500Mn+ potential savings realized across Fractal’s clients by implementing some of the bestin-class analytical solutions like predictive quality, predictive maintenance, spare parts optimization, etc.

7% – 11% improvement in asset utilization through predictive maintenance
10% improvement in product off spec management through predictive quality
5% reduction through improved demand forecasting accuracy and
Inventory optimization of spare parts
15% reduction in capex through long-term capacity planning

Global need states identified

Site-level need states identified, were a subset of global needs

User Personas

Solution Frame

Demystifying a Control Tower:

Air traffic control towers are critical to the functioning of airports, and they do this by coordinating takeoffs, landing, ground traffic, and aircrafts in flight within a range of 5 miles of the airport.

Understanding what is a control tower?

A control tower is an elevated entity in the architecture that provides an unobstructed view into all key information required to control and make decisions on the various operations of the manufacturing

Why do you need a Control tower?

A control tower enables organizations to connect events across manufacturing with key business metrics to improve and accelerate decision-making, resolve risks and prioritize outcomes in real-time.

Success criteria: A PoV on an ideal control tower

The Vision

Holistic 4-pronged solution framework to digitize the manufacturing decisions:

01 Leveraging existing solutions

Exploring existing initiatives to map with need states
Enhancing and scaling the solutions
Assess the life cycle of solution to recommend next steps

02 Developing analytical solutions

Identifying the new analytical solutions in form of visibility, predictive, and diagnostics as per need states
Recommending must-have functionality and features in visibility solutions

03 Setting up architecture for scale

Setting up a global architecture to support analytical needs
Leveraging existing cloud infrastructure to build a scalable and robust platform
Setting up governance structure and team

04 Driving adoption strategies

Identifying training needs and upskilling resources
Creating an adoption framework for success
Assessing the change management activities to support and enable a smooth transition

Out of key 14 existing solutions:

6 to continue, 7 to be explored, while 1 needs to be phased out

User Experience

Key Features

Value Created:

Challenging the status quo

Best Practices set for Kraft- Heinz

Innovation or enhancements

Decision Enablement

Decisions being taken due to the initiative which were not taken before

Data foundation / Frameworks built to integrate sources

Speed of Insights

Reduced data to report creation time

Reduced overall data to decisions time

Cost Savings

Decommissioned periodic reports created by Kraft-Heinz Associates, or by vendors

Time to integrate data sources, or generate insights, rework due to follow-ups

Revenue Increase

Direct revenue increase by decisions suggested by Fractal

Indirect revenue increase by decisions suggested by Fractal

Reflection:

Set the expectations right from the beginning and manage it throughout the project to avoid scope creep and last-minute changes.

Challenge the brief, especially in strategy projects where outcomes are unclear, and periodically reflect on the directions and decisions taken.

Drive a pilot phase and produce tangible results to provide proof of value for our recommendations and increase our credibility and foster longer client relationships.