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Supply Chain and Operations Management Glossary (E)

e: The mathematical constant 2.71828182846 useful in computing continuous compounding of interest. e = limit as n goes to infinity of (1 + 1/n)n. For example, if continuous compounding is used at interest rate r per year, then after n years, one dollar will have grown to ern.

eaches: In warehouse picking, a single unit to be picked, as opposed to a case. EBITDA (Earnings Before Interest, Taxes, Depreciation, and Amortization): A measure of the profit due to operations, excluding cost items related to how financing, tax deferral, and other non-operations activities are performed.

Echelon inventory: assuming product is measured in same units throughout the supply chain, the echelon nventory at a given level in a multi-echelon system is the inventory at that level plus all downstream (i.e., towards the consumer, levels). For contrast, see: pipeline inventory. ECR (Efficient Consumer Response) erminology used in the grocery industry for JIT, short lead time distribution.

EDI (Electronic Data Interchange): A method of electronic interchange for business-to-business
transactions developed before the internet. It specifies electronic format standards for about 150 different data fields for common business documents such as PO’s, invoices, etc. The standard is sometimes called EDIFACT or X12. Information about a wide variety of international standards is available at http://www.nssn.org. See also X12.

EDLP (Every Day Low Prices): The seller (e.g., a grocer) maintains a constant low price, as opposed to usually high prices with an occasional short promotion period of very low price. See also: forward
buying.

Efficient frontier: Given a multi-dimensional measure of goodness (e.g., product price, delivery time, and product quality), a point is said to be on the efficient frontier if there is no other point, which is at least as good on every measure and strictly better on at least one measure of goodness. See also, tradeoff curve.

Elasticity of demand: (% change in quantity demanded)/(% change in price). If the elasticity = 1, then a small change in price will not change the seller’s sales revenue.

EOQ (Economic Order Quantity): An order size that minimizes the sum of fixed cost of ordering plus inventory costs. If K = fixed cost of placing an order, h = holding cost, and D = demand rate, then the order quantity is given by Q = (2*K*D/h)^.5.

EPC (Electronic Product Code): A proposed 96 bit code to be used to identify individual product items (not just SKU”s) in the same manner as a serial number. The code is stored in RFID tags on the product. This would allow a firm to easily track items through the supply chain. See http://www.autoidcenter.org, UPC.

Erlang B formula: See Erlang Loss.

Erlang C formula: A formula for the expected fraction of calls that must wait, in a system with S lines or servers, where demand has a Poisson distribution with rate D, call processing times have an exponential distribution with mean L, and calls that find all lines busy will wait. The formula was originally developed by A. K. Erlang for the Copenhagen telephone system. If we define the arriving load as r = D * L, then in the LINGO modeling language, the expected fraction of calls that find all S lines busy (and thus wait) is given by @PEB( r, S). If we denote this probability by C(r,S), then it can also be computed from the Erlang Loss formula, B(r,S), by the relation: C(r,S) = r*B(r,S)/(S-r + r*B(r,S));

Erlang Loss Formula: A formula for the expected fraction of sales lost, when a base stock policy is followed, demand has a Poisson distribution, and any demand that finds the system out of stock is lost. The formula was originally developed by A. K. Erlang in the telephone industry to predict the expected fraction of calls lost, given a fixed number of lines. If D is the demand rate, L is the expected lead time (or call processing time), S is the stock level (or number of phone lines) and r = D*L, then the expected fraction demand lost, B(r, S), can be calculated recursively as follows: B(r,0) = 1; B(r,S) = r*B(r,S-1)/(S + r*B(r,S-1)). It is available in the LINGO modeling language as @PEL( r, S). Also known as the Erlang B formula.

ERP (Enterprise Resource Planning, see also MRP and DRP):
 Comprehensive software system for the firm that, at least in theory, has coordinated modules to perform all the standard business data processing functions such as: General ledger (GL), Accounts receivable (AR), Accounts payable (AP), Asset management (e.g., depreciation), Human resources/payroll (HR), Forecasting, Purchasing, Inventory, Materials equirements Planning (MRP), Production Planning, Warehouse Management (WMS), Sales, Order management, and distribution .

Experience curve: term used by Boston Consulting Group for learning curve.

Exponential distribution: If D is the arrival rate of retail demands, and the number of demands per unit time has the Poisson distribution. Then, the time between successive demands has the exponential
distribution. Specifically, if t is the time between successive demands, then the p.d.f. is D*e-Dt, and the
c.d.f. is 1- e-Dt. An interesting feature of the exponential distribution is the memory less property (i.e., if
the time between demands is exponential), then at any instant, the distribution of the time to the next event has the exponential distribution, regardless of the time since the previous event.

Exponential smoothing: a weighted moving average forecasting method in which the weight applied to
old data decreases exponentially with age. In its simplest form, if D(t) is the demand observed in period t, S(t) is the forecast computed after observing D(t) and alpha is the smoothing constant (e.g., 0.2, then S(t) = alpha*D(t) + (1-alpha)*S(t-1) ). It is a special case of the ARIMA class of forecasting models. There are extensions to include trend, seasonality, and estimation of demand variability. See
also, Croston’s method.

Extranet: a communication network setup among a set of firms that do business together. It is typically based upon IP, but restricted in some way to only a limited set of firms

About the Author: AJ Amjad Khanmohamed

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