Ensuring timely delivery of vital medicines and other materials to primary healthcare facilities is not straightforward even in the most ‘advanced’ economies. In a region such as Yobe State, in north-eastern Nigeria, with vast distances, relatively poorly developed or maintained infrastructure, and the continuing incursions of the ‘Boko Haram’ insurgency, the challenge is even greater.
As the medical materials supply chain in Yobe State was failing – facing multiple and severe stockouts of essential medicines and operating a single warehouse that struggled to meet even minimum requirements for the storage of medical commodities – Yobe’s medical authorities sought advice from the African Resource Center for Excellence in Supply Chain Management (ARC ESM). The ARC ESM seeks to improve the availability of medicines and health products at the last mile by supporting Ministries of Health in building more efficient, effective and resilient supply chain systems around untapped local and regional polls of talent and expertise.
As a result, Yobe has in the last year been implementing a different working model. A new body, the Yobe State Drugs and Medical Consumables Supply Agency, or YODMA, has been created to ensure the availability of health care commodities at the primary level, in part by enlisting the capabilities of private sector firms through a series of public-private partnerships (PPPs) with suppliers. The new system launched in September 2020 and included the signing of an MoU with local pharmaceutical companies to ensure the steady supply of quality and affordable health commodities to YODMA for all health facilities in the State and the enrolment of primary, secondary and tertiary health facilities with YODMA. This model continues to date as additional health facilities are signed with YODMA to access health commodities.
While a centralised health supply chain and public-private partnership reflects best practices, the evidence for such efforts was qualitative rather than quantitative. ARC ESM had reached out to ZLC for assistance, asking us to develop a dynamic supply chain model, calibrated to real data, that would quantify the effects of the YODMA initiative, and act as a tool to support future public health policy decisions around its supply chain, involving stock management, procurement, shipment allocation and so forth. The model would need to strike a balance between total rigour and accuracy, and applicability in achieving significant impact in a timely fashion.
As with any such project, we started by trying to gain a sense of the various players’ understanding and view of the problems through collecting and analysing existing data, from suppliers, from YODMA Procurement, from YODMA’s central warehousing, and the recipient health facilities.
This proved surprisingly difficult. The problem was not so much that the data did not exist (although that was true in some areas), rather, the data was being gathered locally, using different formats and definitions, and collated onto individual, incompatible, spreadsheets. For one player to access the information needed it might take multiple interactions with the data ‘owner’, even assuming this could be identified. Data was inevitably either unavailable, or inaccurate (due for example to errors in re-keying), or just out of date, allowing no opportunity to gain an overview of the actual current situation, let alone to observe and predict current and future trends. At its most basic, the warehouse might, for example, know what stocks it had on hand, but have little idea either of what replenishment was already on order, the order backlog, or what stock existed in primary health facilities and therefore what their future requirements might be.
(Such problems are by no means unique to less developed countries, as the performance of ‘advanced’ economies during the pandemic has starkly revealed. The difference, of course, is that richer countries can throw more money at the problem).
Throughout our engagement, we examined how data could be better gathered, integrated, harmonised and visualised. At almost every point we found problems – identifying what Excel files existed (this was an iterative process), let alone discovering their contents. For example, the central warehouse ‘receipts’ file wasn’t harmonised with the ‘orders placed’ spreadsheet – so if a supplier couldn’t deliver in full, this meant the warehouse was essentially running blind. There are many such examples. To use the jargon, YODMA had no ‘control tower’ capability. We analysed the supply of one particular drug, Artheether, which is an important treatment for chloroquine-resistant malaria. We inferred for example, that there were frequent stockouts, but that while the central warehouse, and therefore procurement, were aware of stockouts at their level they could gain little or no insight into the situation ‘on the ground’.
Nonetheless, and informed by many semi-structured interviews with a wide range of stakeholders, we constructed a system dynamics model, which we believe capture both the physical flows, the required information flows, and the lead times both for decision-making and for execution. (We made two important assumptions that simplified the model but may not be entirely valid – that funds are always available to pay for the materials, and that the suppliers have the required capacity). The model breaks down into seven areas of activity – YODMA’s central warehouse receipts, inventory and shipments; YODMA procurement and resulting shipments from PPP suppliers; YODMA’s demand forecast; YODMA’s order backlog; fulfilment to health facilities; orders from health facilities to YODMA; and the process of signing up new health facilities to the scheme.
That final point introduces a further complication – originally the plan was that the new system would commence by supplying a dozen secondary and tertiary facilities, characterised by relatively few orders in relatively large quantities. In practice, the scheme has been extended rapidly to primary care facilities (46 of a potential 150, and counting), who order more frequently in smaller amounts, and this has threatened to overwhelm the system.
We validated our model by simulating the new system, with a good correlation, and also, as far as the limited data would permit, by simulating how the previous arrangements would have fared, and we demonstrated with a fair degree of confidence that the new way of working is indeed improving the situation in terms of reduced replenishment delays and an improvement in requests satisfied. Importantly, our simulations also tend to verify the understanding we had gained through interviews around how decisions are being made in the absence of comprehensive data: which factors are being ignored, where informal and perhaps quite personal heuristics are being applied. Significant here is the realisation that currently only 10% of desired inventory is being addressed by the new system, suggesting YODMA does not have a clear process to establish its desired level of inventory. The simulations also strongly suggest that the important factors of lead times from PPP suppliers, and YODMA’s order backlog, are effectively being ignored. These are areas for improvement.
Where does Yobe State go from here? In the era of big data, it might be imagined that we would be recommending some overarching, multi-million dollar, ERP system from the likes of SAP. That is not only unrealistic but unnecessary. Much can be achieved through applying simple, regionally available techniques for automated data capture and integration – we have created sample files, bringing together data is that is already available from different sources, to demonstrate what such approaches could look like, and indeed this process would be necessary even if the ultimate aim were for a fully competent ERP system. The system dynamics model can also provide ongoing insight into the overall functioning of YODMA’s health supply chain.
We are meeting with YODMA to discuss how these opportunities can be taken forward. A lot will come down to training people on the ground, not just in how to capture and integrate data, but in how they can use the System Dynamics model, with suitable data input, in future to support future decision-making. The possibility exists to model, predict and improve supply chain performance, not just ‘as-is’, but for example in response to epidemic disease, or to support a vaccination drive, helping achieve the State’s ambitious public health goals.
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