A information to your information and analytics journey

Having a digitally skilled workforce is at the forefront of every executive’s wishlist, especially in the backdrop of the current labor market. Today’s supply chain workforce actively interacts with technology, be it an enterprise platform, a hand-held or logging device or office productivity software. However, the leap from interacting with technology to a future-ready workforce involves upskilling in areas of digitalization, automation, artificial intelligence and analytics. It’s not as easy as it sounds.

Digital upskilling the workforce doesn’t mandate the need to learn coding or becoming data scientists; instead, it involves learning how to think, act and thrive in a digital world. One trait of digital upskilling is trust in analytics informed outcomes. It involves the adoption of analytics applications akin to technology applications and a healthy balance between gut and analytics insights in decision making.

With significant technological advancements, analytics, prediction and AI are unlocking an incredible array of business opportunities with a potential for transformational impact on organizations. The ability of the workforce to participate in the outcome as an accelerator is a key success factor in the transformation.

With that in mind, here is a 4 point roadmap to guide you as you embark on the journey with data and analytics.

1. Document operational processes and decisions at every step
The flow of product in a supply chain involves numerous events, hand-offs, decisions, systems, people and exceptions. These events tend to be recorded in disparate systems and occasionally in spreadsheets resulting in the absence of an end-to-end view. This augurs well for localized decision-making by a functional operator striving to complete their task without visibility to upstream events or a comprehensive understanding of the downstream impacts.

The first step to achieve a holistic analytics and technology driven optimization involves process mapping every step of operational activities. Special attention has to be paid to ensure that the exception management processes are also documented, not just the happy path. This has to be a cross-functional initiative orchestrated by the centralized business process effectiveness team following the principles of genchi gembutsu (“go and see”).

A comprehensive process map enables the visualization and analysis of a connected product journey to track and interpret the current state of flow, lead times, transit times and decision points.

2. Incentivize data governance at source
We’re all familiar with the adage: “Garbage in, garbage out.” The credibility of the data influences the credibility of the insights, the quality of decisions and the effectiveness of the resulting actions. However, if certain elements of an activity do not get captured (ex: exact time of departure of a truck) or underlying parameters not recorded correctly (exact dimensions of a warehouse rack / bin), the ability to optimize the supply chain will be severely jeopardized.

Operational processes are generally designed to obfuscate complexity and navigate product through a pre-configured network. An unintended consequence of this simplification is the possibility of certain data elements not being captured due to system settings or operator oversight. While this does not impair the operators’ ability to perform the task, it limits the ability to visualize, mine or model the data.

To realize an acceptable level of data maturity, a continuous review of data quality and completeness should be conducted at regular intervals. Completeness refers to capturing data relevant to business goals, not just to address near-term problems but also future strategic initiatives since the quality of analytics delivery generally improves with the volume of historical data available. Quality refers to the credibility of data being captured. Communication of the benefits of the enhanced data capture to the workforce is a critical step in getting all-round buy-in.

Motivating and mandating the capture of events along the supply chain to a deeper level of granularity will set the analytics team up to better provide insights and optimize the supply chain.

3. Identify metrics that matter and align with business goals
Let’s begin with another maxim: “You can’t improve what you don’t measure.” Typically, supply chain performance has been measured on standard metrics like spend versus budget, cost per mile, cost per unit, units shipped, units per hour and transit time.

While these metrics are important, they do not signify the competitive performance of the supply chain nor the alignment with enterprise goals. Also, a load planner or warehouse worker isn’t equipped with the tools to impact the aforementioned metrics, nor do they have the knowledge of the factors that affect them.

Identifying supply chain metrics that matter should follow a top-down approach, starting with the enterprise goals (level 1 metrics) and supply chain’s role in achieving them. High-level supply chain metrics that bolster enterprise goals form the level 2 metrics. The operational metrics that drive the high-level supply chain metrics comprise the level 3 metrics. Diagnostic analytics to perform root cause analysis on level 3 metrics and identify corrective action empowers the operator to understand the problems and positively influence them.

A hierarchical cascade of metrics that links operational metrics with enterprise goals through functional metrics is the key to measuring metrics that matter and ensuring the individual business units are striving towards the same outcomes.

4. Encourage the governance committees to embrace unbiased analytics informed decisions 
Decisions in a supply chain vary between operational, tactical and strategic. To address a business need through a tactical or strategic decision, a functional leader makes a largely experiential recommendation accompanied by a high-level cost-benefit summary for a sign off by a committee. While this is a functionally feasible approach, it projects a recommendation biased towards gut, short on analytical rigor, and devoid of a holistic review of alternate options.

Partnering with the data analytics team can alleviate these limitations. An analytics team with access to additional data sources can leverage its advanced modeling skills to impart an unbiased and comprehensive assessment of the solution options. The role of the business teams in guiding the analytics team through the constraints and rules is paramount to ensuring the quality of the recommendations.

This approach not only improves the decision quality but also encourages collaboration and knowledge sharing, all key tenets for thriving in today’s competitive environment.