As supply chain consultants, we work with lots of data.
Having good quality, accurate data, for the most part, is a minimum requirement for this industry.
The type of data we use depends on the problem to be solved, but suffice to say, at the risk of ‘over-analysis,’ we rely on fact based assessments for many of the recommendations we provide to our clients. And as I once learned the hard way (but we won’t go into that now), the risk of getting it wrong is not a risk worth taking.
In fact, one could argue, data quality is important for everyone who works in logistics/supply chain. Give us good quality data to analyse and we are happy… poor quality data and the converse is true!
So what makes data quality such an important issue?
A typical supply chain challenge, which is a hot topic with many organisations today, relates to long term planning or strategic network design.
Questions posed in this type of planning work are typically very complex, periodically evaluated, and deal with capital investment decisions (such as building, extensions, or closing of plants and warehouses etc.). Design software used to model a supply chain is also very data intensive since complex models of the supply chain, and its possible behavior over time, has to be built.
Anyone who has been involved in supply chain modeling understands that over 60% of the process/project time is typically spent on “identifying, collecting, and validating data” that is used to model and create a view of the supply chain, while a further 20% is spent actually analysing the output.
Now it may be questionable as to why users would spend 3 times the amount of time on “data” and so little on the part that appears to yield the greatest value to the business.
As you would expect, the quality of the output (of the model) is highly dependent on the quality of the inputs – the data. Remember that old adage, “garbage in, garbage out”?
So what kind of data are we talking about?
- Product, relationships/structures, rules (used on quantity, popularity, etc.)
- Locations, and all the relevant constraints (limits, boundaries, throughput limiters, costs etc)
- Lanes, or a representation of how products move from “a” to “b”, and all the relevant constraints (alternative modes, costs, other constraints, availability etc.)
- Demand (actual orders short term and forecasted demand for many months, and typically years)
- Resource capacity (suppliers, plants, key bottleneck resources)
- Other data such as calendar, units of measure, currency, tax, duty draw back, etc.
Much of this data is master data: products, parts, and locations etc. Much is reference data (similar to master data, but not a core entity, such as units of measure, currency conversions etc. And there are other types of data that do not fall into either definition easily (such as calendar).
With such key business investment decisions potentially at stake, the impact of poor data quality on the strategic planning and design process at best forces users to come up with ‘workarounds’ based on data aggregations that may only help in reinforcing the adage that ‘all models are wrong, some are more useful than others…’
Another example of the importance of data quality can be found in warehouse management systems. The primary purpose of a WMS is to control the movement and storage of materials within an operation and process the associated transactions. Directed picking, directed replenishment, and directed putaway are the key to WMS.
The detailed setup and processing within a WMS applies a basic logic, or a combination of item, location, quantity, unit of measure, and order information to determine where to stock, where to pick, and in what sequence to perform these operations. The characteristics of each item and location must be maintained either at the detail level or by grouping similar items and locations into categories.
An example of item characteristics at the detail level would include exact dimensions and weight of each item in each unit of measure the item is stocked (eaches, cases, pallets, etc) as well as information such as whether it can be mixed with other items in a location, whether it is rackable, max stack height, max quantity per location, hazard classifications, finished goods or raw material, fast versus slow mover, etc.
Unfortunately, a common problem experienced by many companies who have lived through ‘less than successful’ WMS implementations lies more often than not with poor quality data. Without correct data, the WMS cannot effectively manage the warehouse operation, as it relies on logic (i.e. correct data) to ‘direct’ workers to the various tasks involved. And, as many companies have unfortunately discovered, it is much more difficult to resolve data issues following an implementation, so it makes a lot of sense to get it right before hand.
Put simply data is as much a key ingredient of the supply chain as trucks and warehouses!
The modern supply chain actually consists of three different chains running in parallel:
- Physical supply chain along which the products move;
- Financial supply chain because we all like to get paid for the products we make;
- Information supply chain.
The latter is growing in importance as trading partners, governments and regulatory authorities require increased levels of data to be assigned to products – and this is likely to grow in importance in the coming years.
Improving data quality is a critical step in improving the effectiveness of the supply chain.
Measuring it and sharing the results is the first step to improving data quality – in fact data quality should be one of the key indicators that suppliers and their customers discuss at their quarterly performance reviews. To date in many cases the report card might read “could do better”.
Research firm Aberdeen Group conducted a survey recently and discovered that only 16 percent of supply chain visibility implementations have data quality that could be considered adequate. That leaves 84 percent with data quality that is below par – supply chain dashboards need to shine a light on the problem with proper metrics, to make sure it is dealt with to the benefit of supply chain efficiency.
I’ve touched on two examples of how data quality impacts supply chain performance; there are obviously plenty of others and I will provide you with more examples in future communications.
I suggest you go take a look at what is happening in your business. It can be overwhelming but I hope I’ve convinced you – enormously important.