The emergence of a terminology is, at best, a haphazard process. Supply chain is no exception and, in hindsight, a sizeable portion of supply chain terminology is inadequate. Confusing terminology hurts newcomers and seasoned practitioners alike. Newcomers struggle more than they should with accidental complexity. Practitioners may not realize that the premise of their field is more shaky than it appears.
Let’s survey the biggest offenders, terminology-wise, in supply chain and propose suitable alternatives. Even if those alternatives are unlikely to ever be adopted by the community, they should shed some light on overlooked nuances. As a rule of thumb, good terminology should be as neutral and factual as possible. Including positive or “cool” qualifiers is a red flag.
ABC analysis should have been named moving average segmentation. Terminology-wise, the term “ABC” brings nothing to the table, while “analysis” is as vague as it gets. The expression “moving average segmentation” is more specific. It clarifies the inherent flaws associated with this method. Indeed, moving averages not only create instability over time, but also fail at reflecting key patterns, such as cyclicities. Also, segmentation is a crude mechanism which, by design, cannot bring a granular response at the SKU level.
APS (Advanced planning and scheduling) should have been named planning management. First, there is nothing “advanced” about those software products. This term was coined in the 1990s by market analysts to hype a series of software vendors. Most of the software products that fall under the APS umbrella cannot be considered as “advanced” any more by 2020s standards. Second, planning management emphasizes processes characterized by extensive manual data entries. Statistical capabilities only represent a tiny fraction of the software. The bulk of the software capabilities are devoted to the end-user, i.e. the supply and demand planner, who manually manage the plan.
BI (Business Intelligence) should have been named cube reporting. First, this piece of technology has nothing to do with either intelligence as in “artificial intelligence”, nor with intelligence as in “secret intelligence service”. Thus, the term “intelligence” does not belong here. Second, there is nothing inherently “business” specific to this piece of technology. For example, displaying past daily temperatures per zipcode is a fine use case for a cube report. Cube reporting is a user interface overlaid on top of a cube data store, also known as OLAP (online analytical processing) in the database jargon. The cube offers slice and dice operations. While the term “cube” is used, the number of dimensions does not need to be equal to 3. Nevertheless, it remains a single digit number in practice due the combinatorial explosion associated with higher dimensions.
ERP (Enterprise Resource Planning) should have been named ERM standing for Enterprise Resource Management. Their primary goal is, as the ERM name suggests, to track the assets of the enterprise. Those products have little or nothing to do with planning. The core design of ERM, which heavily relies on a relational database, is at odds with any predictive capabilities. The “ERP” terminology was pushed by market analysts in the 1990s to promote a series of software vendors that were trying to differentiate themselves from their competitors. However, there has never been much substance behind the “planning” part of the claims. Software-wise, the transactional realm is more distinct than it ever was from the predictive realm.
MRP (Material Requirements Planning) should have been named MRM (Manufacturing Requirement Management). The reasons are essentially similar to the ones given for the ERP vs. ERM discussion. There is little or no planning involved and, when there is, the design leans heavily towards a manual process. Also, the term requirements is dated as well, as it primarily refers to the management of the BOM (bill of materials) which is nowadays only a small portion of what modern manufacturing management entails. Thus, there is little reason to emphasize this term in particular.
Eaches (EA), a unit of measure, should be better named obvious units (OU). Eaches are used when the relevant unit of measure, while keeping track of inventory, is expected to be self-evident, as is usually the case with packaged goods. Unfortunately, the original intent is lost in the term “eaches”. Furthermore, “eaches” is grammatically odd. The singular form is confusing, i.e. “1 each”, and thus avoided in practice.
EDI (Electronic Data Interchange) originates from the 1970s and dominantly refers to software that transmits purchase orders to suppliers, eliminating clerical interventions from the ordering process. Unfortunately, with the advent of the internet, even surfing the web technically qualifies as an EDI process. The notion of integrated suppliers (conversely integrated clients), hinting at an integration of the respective IT systems, would be a better way to frame the situation.
EOQ should have been named the flat bulk order. Indeed, behind this term, which seems to capture a broad intent, lies a simplistic formula that assumes that the future demand is constant (no seasonality), that the future lead time is constant (no variability), that the ordering cost is constant (no price break), and finally, that the carrying cost is constant (no expiration). The expression flat bulk order properly conveys the actual simplistic nature of the formula.
Order is a good word, but standalone, it’s also profoundly ambiguous. There are client orders, supplier orders, production orders, inventory movement orders, scrap orders, etc. A qualifying prefix is needed to make sense of the expression. The term “level” is fairly similar in this regard and must not be used without a qualifying prefix.
Safety stock should have been named Gaussian buffer. Indeed, there is nothing safe about this method. It relies on having both the future demand and the future lead time distributed against normal distributions (Gaussians), which is never the case, as distributions of interest are not normally distributed in the realm of supply chain. The term buffer clarifies the intent associated with the stock without implying any specific virtue for this arrangement.
Seasonality is a good term, but usually, the term cyclicities would be more appropriate from a supply chain perspective. Indeed, it makes little sense to restrict the demand pattern analysis to the yearly cyclicity, i.e. the seasonality. Day of the week and day of the month are other obvious cyclicities that invariably need to be taken into account. Thus, a supply chain director rarely seeks a seasonality analysis, but rather a cyclicity analysis.
Service level should have been named service rate, which would have been more consistent with fill rate. The term level hints a quantity, as in stock level. However, the service level is a percentage. It’s probably one of the smaller offenders in this list. Yet, it would be nicer to be able to convey the duality service rate vs. fill rate in a more direct manner.
Even (relative) supply chain newcomers would benefit from a better terminology.
DDMRP (demand driven material requirements planning) should have been named sparse prioritized buffering. Indeed, this methodology does provide anything specific to isolate the “true” demand as opposed to the flow: censoring, cannabilizations or substitutions don’t even numerically exist in this framework. Idem, most planning angles are also absent from the numerical framework: range planning, phase-in, phase-out, promotions, etc. The keyword “sparse” aptly qualifies the intent associated with the introduction of “decoupling points”.
Decoupling points should have been named managed SKUs. DDMRP proposes a graph coloring scheme that splits the SKUs into two groups: the decoupling points and the rest. Referring to those “points” as SKUs is clearer. Also, as those SKUs are the only ones intended to actually be inspected by the demand and supply planner, the expression “managed SKUs” is a good fit, and clarifies that all the other SKUs are “unmanaged” from the planner’s perspective.
In certain situations, dramatic simplifications can be achieved.
Artificial intelligence, autonomous system, blockchain, cognitive system, demand sensing, demand shaping, digital brain, knowledge graph, optimal algorithms can all essentially be replaced by the word magic. While there are varying degrees of real engineering to be found outside the supply chain circles for some of those buzzwords, in the context of supply chain enterprise software, those are vaporware of the purest kind.
Finally, some terms remain adequate even if they get flack once in a while.
Value Chain is sometimes proposed as a replacement for Supply Chain. Such a replacement reflects a lack of understanding of Say’s law, named after the work of Jean Baptiste Say, a turn-of-19th century economist. This law can be summarized as supply is the source of demand. The supply comes first, the demand second, and the value last when transactions finally happen. The chain binds to the whole affair. The value chain is primarily touted by consultants trying to sell ROI to their prospects. However, the ‘value’ term happens to be both less specific and more positively biased than its ‘supply’ counterpart.