Indian organized retail is still developing and currently does not contribute to significant revenue shares of consumer goods companies. The result of this is vendor shortfalls. While most of the optimization software builds its logic on firm vendor commitments, in India such commitments are seldom firm. This poses issues to most supply chain optimization software. The key to utilizing optimization software in such environment lies in using sourcing strategies for reducing shortfills and in using correctional algorithms on top of the optimization software.
Indian retail industry contributes to almost 1/3rdof the country’s GDP. However, this industry is dominated by single-store businesses. Some such businesses occupy as little space as 10 sq. ft (your local PANDABBA!). These businesses are widely spread and usually serviced by wholesale distributors. The wholesale distributors in turn purchase in bulk from branded consumer goods companies. This arrangements essentially dilutes the power of the ‘last mile’ (‘last mile’ is a term more often used in the telecom industry. It basically means the closest value chain to the consumer). The power of the ‘last mile’ lies in consolidating the bargaining power; the dispersed single-store businesses don’t efficiently consolidate this. However, in recent decades, the emergence of organized multi-store businesses is challenging this status-quo. These organized retailers (multi-store businesses) hold the absolute power of the last mile in most developed retail markets like US and Europe. This power translates into heavier pressure on costs, driving down the overall prices to the consumer, and into higher service levels, driving up availability of products for the consumer. This is not happening in India, as yet, mainly because organized retail is not a large part of the Indian retail industry. We still have a seller’s market of sorts. Brands are the main driver of sales. All this creates an environment where short filling on a retailer’s order becomes ‘chalta hai’ (‘short filling’ means providing lesser than the quantity request on an order).
The key reason for this short-filling phenomenon appears to be the relative low contribution to revenue (revenue share) from any single organized retailer to the revenues of a top-brand consumer goods company (see diagram above). However, there are contract manufacturers who enjoy economies of scale (given today’s more efficient production technology). Some retailer’s source private brands (goods which are branded by retailer) from such contract manufacturers. There are chances that a retailer could, in the best case scenario, end up with a significant revenue share of the contract manufacturer. This significant share ensures service discipline (viz. lesser short-fills). However, the path to resolving short-fills is not just hiring a contract manufacturer. It is a path that is part of a roadmap introduced in the 1st article of this series (see diagram below).
The path to lower vendor short-fills is given in steps five thru seven above. It starts from building a base on contract manufacturers. This base would not give immediate benefits but as the relation matures over time and as consumers become familiar with the quality overtime, the retailer might have enough volumes to launch private brands across various products lines and across larger store space. Assuring greater service discipline.
Some retailers in India, even today, handle most of their logistics through a manual or an automated system. Optimization technologies, if configured suitably, can help recommend more optimal solutions than a human can ever produce. These technologies don’t necessarily replace humans. They simply let human’s do more high-end tasks. These technologies, apart from enhancing the process maturity, help build a base infrastructure for the next step in the roadmap.
The next step in the roadmap is an unconventional step. It is about managing vendor short falls by using forecasting software to predict short-fills and late-deliveries. These predications could be later used to alter purchase orders. Though humans can do this too, systems can calculate to a precise level not just across key items but across all sub-items considering the various factors that might affect the consumer goods company – high-sales season, patterns in scheduled and unscheduled maintenance, new product introductions, etc. This step will help handle short-fills from the top-brand services companies too.