#7: Where Is My Thing? Part 1

The fascinating world of Supply Chain is full of interesting challenges, some of which have traditionally proven too difficult to address consistently, efficiently, and in a cost-effective manner. And, perhaps, none more so than the challenge of providing effective end-to-end traceability throughout the complex and ever evolving logistics network.
Be it providing real-time feedback about orders to customers, dynamic updates to 3^rd parties and other partners, retracing an order back in its entire journey to identify potential bottlenecks, or any other useful logistics scenario you can think of, an enterprise’s level of capability in providing end-to-end traceability is an integral part of its competitive advantage.
In what follows, we define end-to-end traceability and provide an overview of some of the most significant challenges in implementing it effectively.
In a subsequent article, we will concentrate on some of the cutting-edge technologies that can help overcome the above challenges.

What Is End-to-End Traceability

In the context of Supply Chain, End-to-End Traceability encompasses the ability to trace an item of interest from the start of its journey within the Supply Chain Network, along the path to its every point of use, or point of transformation, all the way to the end of its journey, where it officially ceases to exist or is considered out of scope.

The above is a rather overloaded definition so, let’s take a moment to break this down a little and look more closely at its most important components (the words in italic within the definition):

Item of Interest

In the context of modern Supply Chain an item of interest may refer to material goods, services, finances, or information.

Start of Journey

This refers to the first moment an item of interest, or any constituent part of it, becomes part of a Supply Chain Network. While effective tracking of any item of interest in its entirety (such as a physical end product) is in itself a tough challenge to overcome, an organization’s ability to track any item of interest to its every constituent part, all the way to their respective origins, is a real measure of the organization’s capability to provide effective end-to-end traceability.

Point of Use

This typically covers every part of the Supply Chain Network where an item of interest is “handed over” to a 3^rd party, be it a partner, a distributer, or the end customer. One might equally refer to this as a Point of Exchange.

Point of Transformation

This is where an item of interest is enhanced, enriched, broken down or otherwise transformed into another form (or multiple items). Transformation usually involves a change in how an item of interest is referred to within the network (for example, a change of Unique ID), which is the main reason why traceability fails, especially when it relates to backward traceability for identifying source of problems.

End of Journey

This is where an item of interest officially goes out of scope and is no longer “of interest”. It may involve physical removal of the item from the network (for example, removal of customer data from every database/data warehouse, etc.) or an item’s complete depreciation (something the accountants appreciate well!).

In short, if an item of interest spends any time at all in a specific step/location within a Supply Chain Network, its presence at the location, when it got there, and when it leaves the said location must be known in order to enable an efficient end-to-end traceability within the Supply Chain.

Why Effective End-to-End Traceability Is So Hard

Let’s face it: One look at the definition of end-to-end traceability provided earlier should be enough to give the reader a hint of how challenging it can be to get this right, especially in the highly complex context of Supply Chain. But let’s take a closer look at some of the most obvious reasons why this might be the case:

Network Complexity

They say it takes a village to raise a child. Well, in the complex world of Supply Chain it takes a complex network of diverse players to get an item of interest, be it a physical object, a service, or a data product from its point of creation all the way to a paying customer. This includes a host of 3^rd parties, subcontractors, regulators, partners and the like, each of which add a layer of complexity to the end-to-end process that makes effective step by step tracking of an item that much more challenging. This is, primarily, due to the lack of an overall set of standards that every participating entity can follow without fail. But whether one blames this lack of standards on modern capitalism, globalization, regulatory requirements, or any other reason, it still requires tremendous effort from organizations to truly overcome its negative impact.

Fragmented Technology Stack

This challenge manifests itself at two distinct levels: At the network level, affecting Supply Chain as a whole, and at the enterprise level, which is unique to each individual player within Supply Chain.

At the network level, is maybe expected that various players within a Supply Chain Network have different levels of technology maturity, leading to utilization of a variety of technology solutions that may not all sit in well together. And this will inevitably result in critical information getting lost while an item of interest is moved from one part of network to the next. This is partly due to the lack of standards that we touched on earlier. But the fact remains that some of the technologies used by different Supply Chain players are just not suitable or good enough for the complexity of the data or fluidity of the scenarios a modern Supply Chain participant should be able to cope with.

At the level of individual enterprises, there are 3 main reasons diverse technology stacks may come to life: Mergers, acquisitions, and internal company structure, including restructuring efforts. While ending up with different technology stacks in the aftermath of a merger or an acquisition is expected in most situations, there are many cases of internal restructuring or general company culture that promotes separation of different departments or units of activity leading to a siloed approach to handling every aspect of operations and analytics. And with siloed approach comes the pain of having to deal with different data formats, local standards of communications and diverse sets of assumptions that eventually result in a situation where most critical pieces of information are lost or misrepresented.

Compliance with Regulatory Requirements

While this may be regarded as part of the Supply Chain Network Complexity issue discussed earlier, it does require its own section to highlight a few specific characteristics of this challenge in more details.

To start with, it is not possible to delay meeting such requirements indefinitely without accepting to pay a high price for it. And the high price is not just direct financial fines that need to be paid when a company falls short of expectations. Continuously missing regulatory deadline and the “bad boy image” that comes with it is not good for a company’s reputation and ultimately, not good for business and will eventually hit the bottom line hard.

While some aspects of regulatory requirements may be open to interpretation by individual companies, giving these companies a sense of control over what to implement and how, this apparent “freedom of information” does not always translate into speedy delivery of robust solutions. This is mostly due to the fact that such requirements normally entail detailed revision of how a company captures, enriches, and handles relevant information across various internal systems and how well they integrate with 3rd party (including regulators’) products. Such activities require the right of set of skills, capable resource management and clear prioritization, whose collective cost can quickly spiral out of control.

And let’s not forget that like Supply Chain as a whole, regulatory requirements are dynamic and forever evolving which means their influence on end-to-end traceability is not a one-off activity that can be fixed once and for all.

Challenges of Data Quality

Whether an item of interest whose end-to-end traceability and continuous visibility is our goal happens to be a physical object, a service, or a pure data product, what is essentially used to enable traceability is data. And as you would expect, one of the biggest problems with data in any context is its overall quality.

Data quality shows its face in different ways:

1. The source of data may be questionable, which is likely to occur if you are relying on 3rd parties to provide you with critical information (although, this can happen even inside an enterprise that does not have regular measures in place for controlling data quality).

2. Data may be incomplete, which could be a result of utilizing technology solutions that do not capture all required details. Or perhaps data undergoes pre-defined processes, which may remove some critical details, before it can be shared with another system/component.

3. Data may not have the right level of granularity within it. Note that this is not exactly the same as having incomplete data at hand. On the contrary, the available data may be quite detailed and accurate but not quite suitable for the purpose because you expect data and a higher or lower granularity (think of having highly accurate data at the country level where what you need is data at the level of individuals towns and cities).

4. Data may be available but inconsistent. This is typically the case when the same data appears in multiple sources (all internal, all external or a combination of internal and external) but somehow, they do not tell the same story. In such cases it may not always possible to clearly favour one source over the other, specially if each source offers unique and essential perspectives that cannot be ignored. This is where tremendous amount of reconciliation work may be required to identify data inconsistencies and to work around them which inevitably leads to overall loss of accuracy which negatively impact traceability.

5. And then there is a small matter of accuracy. In other words, available data may not be accurate enough for the purpose (think of inventory levels not accurately reflecting the latest status, perhaps due to system failure or inadequate processes for maintaining inventories).

6. Finally, there is the issue of data timeliness. Perhaps, more than any other data characteristic, traceability relies on timely system updates that capture when an item of interest appears at a particular stage within the Supply Chain network. Be it due to systems’ inherent inadequacies, operational or process failures not having real-time, or most up-to-data data makes traceability an impossible objective to achieve.

As critical as all the above data quality issues are for a successful implementation of end-to-end traceability, one important, and oft-neglected, aspect of data relates to the effective modelling of enterprise data. This is the model that clearly captures how real-world objects, related to the item of interest, are modelled inside various internal system and how external influences are captured within that model.

Many companies simply forget about the importance of having a data model that captures the entire network of activities. This means as an item of interest moves from one part of the network (be it internal or external) to another, some aspect of its existence may not even be captured at all. In such cases one can only fill the emerging gaps in visibility with assumptions which may or may not be remotely accurate.

A well-designed enterprise data model can also respond well and quickly to dynamic changes to the Supply Chain Network and adopt appropriately to short-long term disruptions. All things considered equal, lack of a comprehensive and flexible enterprise model is the most important reason effective end-to-end traceability is hard to achieve in a sustainable and repeatable manner.

Conclusion

Successful end-to-end traceability in Supply Chains requires addressing multiple challenges such as network complexity, presence of fragmented technology stack (both internal and external), compliance with regulatory requirements, and data quality.

In the part 2 of this article, we will delve into some of the most promising technology solutions that may be utilized to address these challenges.

At Tetrixx, we are obsessed with effective utilization of modern technologies to transform and improve how our clients do business. We would love to hear your take on this topic and, of course, would be happy to discuss with you many ways in which we can help you become a more competitive Supply Chain player in your specific domain. Find us at www.tetrixx.io for more details.